Dynamical Modeling and Simulation of Multi-body Systems by Using Udwadia-Kalaba Theory

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人工智能英文参考文献(最新120个)

人工智能英文参考文献(最新120个)

人工智能是一门新兴的具有挑战力的学科。

自人工智能诞生以来,发展迅速,产生了许多分支。

诸如强化学习、模拟环境、智能硬件、机器学习等。

但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。

下面是搜索整理的人工智能英文参考文献的分享,供大家借鉴参考。

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物理学名词

物理学名词

1/4波片quarter-wave plateCG矢量耦合系数Clebsch-Gordan vector coupling coefficient; 简称“CG[矢耦]系数”。

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Computers & Electrical Engineering样文

Computers & Electrical Engineering样文

Adaptive control of dynamic mobile robots withnonholonomic constraintsFarzad Pourboghrat *,Mattias P.KarlssonDepartment of Electrical and Computer Engineering,Southern Illinois University,Carbondale,IL 62901-6603,USAReceived 16November 1999;accepted 22August 2000AbstractThis paper presents adaptive control rules,at the dynamics level,for the nonholonomic mobile robots with unknown dynamic parameters.Adaptive controls are derived for mobile robots,using backstepping technique,for tracking of a reference trajectory and stabilization to a fixed posture.For the tracking problem,the controller guarantees the asymptotic convergence of the tracking error to zero.For stabili-zation,the problem is converted to an equivalent tracking problem,using a time varying error feedback,before the tracking control is applied.The designed controller ensures the asymptotic zeroing of the sta-bilization error.The proposed control laws include a velocity/acceleration limiter that prevents the robot Õs wheels from slipping.Ó2002Elsevier Science Ltd.All rights reserved.Keywords:Mobile robot;Nonholonomic constraint;Dynamics level motion control;Stabilization and tracking;Adaptive control;Backstepping technique;Asymptotic stability1.IntroductionMotion control of mobile robots has found considerable attention over the past few years.Most of these reports have focused on the steering or trajectory generation problem at the ki-nematics level i.e.,considering the system velocities as control inputs and ignoring the mechanical system dynamics [1–3].Very few reports have been published on control design in the presence of uncertainties in the dynamic model [4].Some preliminary results on control of nonholonomic systems with uncertainties are given in Refs.[4–6].Two of the most important control problems concerning mobile robots are tracking of a refer-ence trajectory and stabilization to a fixed posture.The tracking problem has received solutions *Corresponding author.Tel.:+1-618-453-7026.E-mail address:pour@ (F.Pourboghrat).0045-7906/02/$-see front matter Ó2002Elsevier Science Ltd.All rights reserved.PII:S0045-7906(00)00053-7242 F.Pourboghrat,M.P.Karlsson/Computers and Electrical Engineering28(2002)241–253including classical nonlinear control techniques[1,2,7].The basic idea is to have a reference car that generates a trajectory for the mobile robot to follow.In Refs.[1,2],nonlinear velocity control inputs were defined that made the tracking error go to zero as long as the reference car was moving.In Ref.[7],they used input–output linearization to make a mobile platform follow a desired trajec-tory.The problem of stabilization about afixed posture has been shown to be rather complicated. This is due to violating the BrockettÕs condition[8],which states that for nonholonomic systems a single equilibrium solution cannot be asymptotically stabilized using continuous static state feedback[9,10].The BrockettÕs condition essentially states that for nonholonomic systems an equilibrium solution can be asymptotically stabilized only by either a time varying,a discontin-uous,or a dynamic state feedback.In addressing the above problem,in Ref.[10]a smooth feedback control was presented for the kinematics control problem resulting in a globally marginally stable closed loop system.They also designed a smooth feedback control for a dynamical state-space model resulting in a Lagrange stable closed loop system,as defined in their paper.A two dimensional Lyapunov function was utilized in Ref.[3]to prescribe a set of desired trajectories to navigate a mobile robot to a specified configuration.The desired trajectory was then tracked using sliding mode control,resulting in discontinuous control signals.The mobile robot was shown to be exponentially stable for a class of quadratic Lyapunov functions.In Ref.[9],they formulated a reduced order state equation for a class of nonholonomic systems.Several other researchers have later used this reduced order state equation in their studies.In Ref.[4],the problem of controlling nonholonomic mechanical sys-tems with uncertainties,at the dynamics level,was ing the reduced state equation in Ref.[9],they proposed an adaptive controller for a number of important nonholonomic control problems,including stabilization of general systems to an equilibrium manifold and stabilization of differentiallyflat and Caplygin systems to an equilibrium point.In Ref.[2],they gave several examples on how the stabilization problem can be solved for a mobile robot at the kinematics level.Their solutions included time-varying control,piecewise continuous control,and time-varying piecewise continuous control.They also showed how a solution to the tracking problem could be extended to work even for the stabilization problem.Here,we present adaptive control schemes for the tracking problem and for the problem of stabilization to afixed posture when the dynamic model of the mobile robot contains unknown parameters.Our work is based on,and can be seen as an extension of,the work presented in Refs. [1,2].Using backstepping technique we derive adaptive control laws that work even when the model of the dynamical system contains uncertainties in the form of unknown constants.The assumption for the uncertainty in robotÕs parameters,particularly the mass,and hence the inertia, can be justified in real applications such as in automotive manufacturing industry and warehouses, where the robots are to move a variety of parts with different shapes and masses.In these cases,the robotÕs mass and inertia may vary up to10%or20%,justifying an adaptive control approach.2.Dynamic model of mobile robotHere,we consider a three-wheeled mobile robot moving on a horizontal plane(Fig.1).The mobile robot features two differentially driven rear wheels and a castor front wheel.The radius ofthe wheels is denoted r and the length of the rear wheel axis is 2l .Inputs to the system are two torques T 1and T 2,provided by two motors attached to the rear wheels.The dynamic model for the above wheeled-mobile robot is given by Refs.[10,11].€x ¼k m sin /þb 1u 1cos /€y ¼Àk cos /þb 1u 1sin /€/¼b 2u 28><>:ð1Þ_x sin /À_y cos /¼0ð2Þwhere b 1¼1=ðrm Þ,b 2¼l =ðrI Þ,and that m and I denote the mass and the moment of inertia of the mobile robot,respectively.Also,u 1¼T 1þT 2and u 2¼T 1ÀT 2are the control inputs,and k is the Lagrange multiplier,given by k ¼Àm _/_x cos /þ_y sin /ðÞ.Here,it is assumed that b 1and b 2are unknown constants with known signs.The assumption that the signs of b 1and b 2are known is practical since b 1and b 2represent combinations of the robot Õs mass,moment of inertia,wheel radius,and distance between the rear wheels.Eq.(2)is the nonholonomic constraint,coming from the assumption that the wheels do not slip.The triplet vector function q t ðÞ¼x t ðÞ;y t ðÞ;/t ðÞ½ T denotes the trajectory (position and orientation)of the robot with respect to a fixed workspace frame.That is,at any given time,q ¼½x ;y ;/ T describes the robot Õs configuration (posture)at that time.We assume that,at any time,the robot Õs posture,q ¼½x ;y ;/ T ,as well as its derivative,_q¼½_x ;_y ;_/ T ,are available for feedback.3.Tracking problem definitionThe tracking problem consists of making the trajectory q of the mobile robot follow a reference trajectory q r .The reference trajectory q r t ðÞ¼x r t ðÞ;y r t ðÞ;/r t ðÞ½ T is generated by a reference ve-hicle/robot whose equations are_xr ¼v r cos /r _y r¼v r sin /_/r ¼x r8<:ð3ÞThe subscript ‘‘r’’stands for reference,and v r and x r are the reference translational (linear)velocity and the reference rotational (angular)velocity,respectively.We assume that v r and x r ,as well as their derivatives are available and that they all are bounded.Assumption A 1.For the tracking problem it is assumed that the reference velocities v r and x r do not both go to zero simultaneously.That is,it is assumed that at any time either lim t !1v r t ðÞ90and/or lim t !1x r t ðÞ90.The tracking problem,under the Assumption A 1,is to find a feedback control law u 1u 2 ¼u q ;_q ;q r ;v r ;x r ;_v r ;_x r ðÞsuch that lim t !1~q t ðÞ¼0,where ~q t ðÞ¼q r t ðÞÀq t ðÞis defined as the trajectory tracking error.As in Ref.[1],we define the equivalent trajectory tracking error ase ¼T ~qð4Þwhere e ¼½e 1;e 2;e 3 T ,and T ¼cos /sin /0Àsin /cos /00010@1A .Note that since T matrix is nonsingular,e is nonzero as long as ~q¼0.Assuming that the angles /r and /are given in the range ½Àp ;p ,we have the equivalent trajectory tracking error e ¼0only if q ¼q r .The purpose of the tracking controller is to force the equivalent trajectory tracking error e to 0.In the sequel we refer to e as the trajectory tracking error.Using the nonholonomic constraint (2),the derivative of the trajectory tracking error given in Eq.(4)can be written as,[1],_e1¼e 2x Àv þv r cos e 3_e 2¼Àe 1x þv r sin e 3_e3¼x r Àx 8<:ð5Þwhere v and x are the translational and rotational velocities of the mobile robot,respectively,and are expressed asv ¼_xcos /þ_y sin /x ¼_/ð6Þ4.Tracking controller designHere,the goal is to design a controller to force the tracking error e ¼½e 1;e 2;e 3 T to ing backstepping technique,since the actual control variables u 1and u 2do not appear in Eq.(5),we consider variables v and x as virtual controls.Let v d and x d denote the desired virtual controls for the mobile robot.That is,with v d and x d the trajectory tracking error e converges tozero asymptotically.Also let us define ~vand ~x as virtual control errors.Then,v and x can be written asv ¼v d þ~vx ¼x d þ~x ð7Þ244 F.Pourboghrat,M.P.Karlsson /Computers and Electrical Engineering 28(2002)241–253Let us choose the virtual controls v d and x d ,asv d v r ;x r ;e 1;e 3ðÞ¼v r cos e 3þk 1v r ;x r ðÞe 1x d v r ;x r ;e 2;e 3ðÞ¼x r þk 2v r e 2þk 3v r ;x r ðÞsin e 3ð8Þwhere k 2is a positive constant and k 1ðÁÞand k 3ðÁÞare bounded continuous functions with bounded first derivatives,strictly positive on R ÂR -ð0;0Þ.Observe that our approach from here on is general for any v d and x d (with well defined first derivatives),i.e.any differentiable control law that makes the kinematics model of the mobile robot track a desired trajectory can be used instead of Eq.(8).Eq.(8)is similar to the control law proposed by Ref.[1],but with the advantage,as we are going to prove later,that it can be used to track any reference trajectory as long as As-sumption A 1holds.Now,consider the following adaptive control scheme:u 1¼^b 1ðÀc 1~v þe 1þ_v d Þu 2¼^b 2 Àc 2~x þ1k 2sin e 3þ_x d _^b 1¼Àc 1sign b 1ðÞ~v ðÀc 1~v þe 1þ_v d Þ_^b 2¼Àc 2sign b 2ðÞ~x Àc 2~x þ1k 2sin e 3þ_x d ð9Þwhere c 1,c 2,c 1,and c 2are positive constants and ^b 1is an estimate of b 1¼1=b 1and ^b 2is an estimate of b 2¼1=b 2.Result 1.If Assumption A 1holds ,then the adaptive control scheme (9)makes the origin e ¼0uniformly asymptotically stable.Proof .Consider the following Lyapunov function candidateV 1¼12e 21Àþe 22Áþ1k 21ðÀcos e 3Þð10Þwhere k 2is a positive constant.Clearly V 1is positive definite and V 1¼0only if e ¼0.Taking the time derivative of V 1,we obtain_V 1¼e 1ðÀv þv r cos e 3Þþe 2v r sin e 3þ1k 2sin e 3x r ðÀx Þð11ÞFurthermore,using Eqs.(7)and (8),we have_V 1¼Àk 1e 21Àk 3k 2sin 2e 3À~v e 1À~x 1k 2sin e 3ð12ÞIn view of Eqs.(1),(2)and (6),we find the time derivatives of ~vand ~x ,as _~v¼_v À_v d ¼€x cos /À_x sin /_/þ€y sin /þ_y cos /_/À_v d ¼b 1u 1À_v d _~x ¼_x À_x d ¼€/À_x d ¼b 2u 2À_x d ð13ÞF.Pourboghrat,M.P.Karlsson /Computers and Electrical Engineering 28(2002)241–253245Consider the Lyapunov function candidateV2¼V1þ12ð~v2þ~x2Þþb1j j2c1~b21þb2j j2c2~b22ð14Þwhere~b1¼b1À^b1¼1=b1À^b1and~b2¼b2À^b2¼1=b2À^b2.Considering Eq.(9)we get:_V 2¼Àk1e21Àk3k2sin2e3Àc1~v2Àc2~x260ð15ÞSince V2is bounded from below and_V2is negative semi-definite,V2converges to afinite limit. Also,V2,as well as,e1,e2,e3,~v,~x,^b1,and^b2are all bounded.Furthermore,using Eqs.(5),(7)–(9)and(13),the second derivative of V2can be written as€V 2¼À2k1e1e2ðx rþk2v r e2þk3sin e3þ~xÞþ2k1e1ðk1e1þ~vÞÀ_k1e21þ2k3k2cos e3sin e3ðk2v r e2þk3sin e3þ~xÞÀ_k3k2sin2e3À2c1~vðb1^b1ðÀc1~vþe1þ_v dÞÀ_v dÞÀ2c2~x b2^b2Àc2~xþ1k2sin e3þ_x dÀ_x dð16Þwhich from the properties of k1,k2,and k3,the assumption that v r and x r and their derivatives are bounded,and from the above results,can be shown to be bounded,i.e.,_V2is uniformly contin-uous.Since V2ðtÞis differentiable and converges to some constant value and that€V2is bounded,by BarbalatÕs lemma,_V2tðÞ!0as t!1.This in turn implies that e1,e3,~v,and~x converge to zero [12,13].To show that e2also goes to zero,note that,using the above results,thefirst error equation can be written as_e1¼e2x rÀk1e1ð17ÞThe second derivative of e1is€e1¼_x r e2þx rðÀe1xþv r sin e3ÞÀk1e2x rðÀk1e1ÞÀ_k1e1ð18Þwhich can be shown to be bounded by once again using the properties of k1,the assumptions on v r and x r,and Eqs.(7)and(8).Since e1is differentiable and converges to zero and€e1is bounded,by BarbalatÕs lemma,_e1,and hence,e2x r tend to zero.Proceeding in the same manner,the third error equation can be written as_e3¼Àk2v r e2Àk3sin e3ð19Þand its second derivative can be shown to be bounded.Since e3is differentiable and converges to zero and€e3is bounded,again by BarbalatÕs lemma,_e3!0as t!1.Hence,k2v r e2and thus v r e2 tend to zero as t!1.Clearly,both v r e2and x r e2converge to zero.However,since v r and x r do not both tend to zero(by Assumption A1),e2must converge to zero.That is,e1,e2,e3,~v,and~x must all converge to zero.hIn Section3,we demonstrated that the system is stable if k2is a positive constant,and that k1ðÁÞand k3ðÁÞare bounded continuous functions with boundedfirst derivatives and are strictly positive on RÂR-ð0;0Þ.To get a better understanding on how the control gains affect the response of the system,we write the equations for the closed loop system when~v and~x are equal to zero as[1] 246 F.Pourboghrat,M.P.Karlsson/Computers and Electrical Engineering28(2002)241–253_e¼Àk 1e 1þx r þk 2v r e 2þk 3sin e 3ðÞe 2Àx r þk 2v r e 2þk 3sin e 3ðÞe 1þv r sin e 3Àk 2v r e 2Àk 3sin e 30@1A ð20ÞBy linearizing the differential equation (20)around e ¼0,we get_e¼Ae ð21Þwhere A ¼Àk 1x r 0Àx r 0v r 0Àk 2v r Àk 30@1A ð22ÞTo simplify the analysis,we assume that v r and x r are constants.The system Õs closed loop poles are now equal to the roots of the following characteristic polynomial equation:s ðþ2nx 0Þs 2Àþ2nx 0s þx 20Áð23Þwhere n and x 0are positive real numbers.The corresponding control gains arek 1¼2nx 0k 2¼x 20Àx 2r v 2r k 3¼2nx 0ð24ÞWith a fixed pole placement strategy (n and x 0are constant),the control gain k 2increaseswithout bound when v r tends to zero.One way to avoid this is by letting the closed loop polesdepend on the values of v r and x r .As in Ref.[2],we choose x 0¼x 2r þbv 2r ÀÁð1=2Þwith b >0.The control gains then becomek 1¼2n x 2r Àþbv 2rÁ1=2k 2¼b k 3¼2n x 2r Àþbv 2r Á1=2ð25Þand the resulting control is now defined for any values of v r and x r .In the above,it is shown that the proposed algorithm works for any desired velocities,ðv d ;x d Þ.However,in practice,if the tracking errors initially are large or if the reference trajectory does not have a continuous curvature (e.g.,if the reference trajectory is a straight line connected to a circle segment),either or both of the virtual reference velocities in Eq.(8)might become too large for a real robot to attain in practice.Hence,the translational/rotational acceleration might become too large causing the robot to slip [1].In order to prevent the mobile robot from slipping,in a real application,a simple velocity/acceleration limiter may be implemented [1],as shown in Fig.2.This limits the virtual reference velocities ðv d ;x d Þby constants ðv max ;x max Þand the virtual referenceaccelerations ða ;a Þby constants ða max ;a max Þ,where a ¼_vd and a ¼_x d are the virtual reference accelerations.In practice,these parameters must be determined experimentally as the largest values with which the mobile robot never slips.F.Pourboghrat,M.P.Karlsson /Computers and Electrical Engineering 28(2002)241–253247An important advantage of adding the limiter is that it lowers the control gains indirectly only when the tracking errors are large,i.e.,when too high a gain could cause the robot to slip,while for small tracking errors it does not affect the performance at all.Thus,by using the limiter one can have higher control gains for small tracking errors to allow for better tracking,while letting the limiter to‘‘scale down’’the gains,indirectly,for large tracking errors,to prevent the robot from slipping.5.Simulation results for tracking control problemHere,the results of computer simulation,using MATLAB/SIMULINK,are presented for a mobile robot with the proposed tracking control and with the velocity/acceleration limiter.The computer simulations for the above controller without the limiter,although not shown here,produce similar results,but with somewhat different transient characteristics.All simulations have the common parameters of c1¼c2¼100,c1¼c2¼10and b¼250.Also selected are,the damping factor n¼1,v max¼1:5m/s,x max¼3rad/s,a max¼5m/s2and a max¼25rad/s2.Moreover,the robotÕs dy-namic parameters are chosen as b1¼b2¼0:5,which are assumed to be unknown to the con-troller,but with known signs.Simulation results for the case where the reference trajectory is a straight line are shown in Figs. 3and4for t2½0;10 .The reference trajectory is given by x rðtÞ¼0:5t,y rðtÞ¼0:5t and/rðtÞ¼p=4, defining a straight line,starting from q rð0Þ¼½x rð0Þ;y rð0Þ;/rð0Þ T¼½0;0;p=4 T.The mobile robot, however,is initially at qð0Þ¼½xð0Þ;yð0Þ;/ð0Þ T¼½1;0;0 T,where/¼0indicates that the robot is heading toward positive direction of x.As it can be seen from thesefigures,first the robot backs up and then heads toward the virtual reference robot moving on the straight line.Figs.5and6show the simulation results for tracking a circular trajectory.The reference trajectory is a point moving counter clockwise on a circle of radius1,starting at q rð0Þ¼½x rð0Þ;y rð0Þ;/rð0Þ T¼½1;0;p=2 T.The reference velocity is kept constant at v rðtÞ¼0:5m/s.The initial conditions for the mobile robot,however,is taken as qð0Þ¼½xð0Þ;yð0Þ;/ð0Þ T¼½0;0;0 T.Again,as it is seen from thesefigures,the robot immediately heads toward the reference robot,which is moving on the circle.It then reaches it quickly and continues to track it.6.Stabilization problem definitionThe stabilization problem,given an arbitrary desired posture q d ,is to find a feedback control law,u 1u 2¼u q Àq d ;_q ;t ðÞ,such that lim t !1q t ðÞÀq d ðÞ¼0,for any arbitrary initial robot posture q ð0Þ.Without loss of generality,we may take q d ¼½0;0;0 T.6.1.Stabilization controller designRecall that there is no continuous static state feedback that can asymptotically stabilize a nonholonomic system about a fixed posture [8–10].The approach to the problem taken here is the dynamic extension of that in Ref.[2]where a kinematics model of the mobile robot is used.In-stead of designing a new controller for the stabilization problem the same controller as for the tracking problem is used.The idea is to let the reference vehicle move along a path that passes through the point ðx d ;y d Þwith heading angle /d .The stabilization to a fixed posture problem isnow equivalent to,and can be treated as,a tracking problem(convergence of the tracking errors to zero)with the additional requirement that the reference vehicle should itself be asymptotically stabilized about the desired posture.As in Ref.[2],we let the reference vehicle move along the x-axis,i.e.y rðtÞ¼0and/rðtÞ¼0,for all values on t.The design method is the same as derived for the tracking case.However,in this casev r¼_x r¼Àk4x rþgðe;tÞ;ð26Þwithgðe;tÞ¼k e k sin tð27Þwhere k4>0.Different time-varying functions gðe;tÞhave also been suggested in the literature,see Refs.[2,11]and the references therein.Since,from the Section5,the tracking errors e1,e2,and e3are bounded,the time-varying function gðe;tÞis bounded.Therefore v r and the state x r also remain bounded.By taking the time derivative of Eq.(26),it can be shown in the same way that_v r is bounded.Since v r and_v r are bounded,the assumptions made in Section3concerning the reference velocity are fulfilled.If v r is not equal to zero,then e must converge to zero.When e tends to zero,gðe;tÞalso tends to zero. Therefore,the robotÕs position x must track x r,which converges to zero and hence lead the mobile robot to the desired posture.6.2.Simulation results for stabilization control problemHere,the simulation results for the stabilization problem are shown in Figs.7and8.The control parameters and system parameters are the same as for the simulations shown for the tracking problem and k4¼1.The mobile robot is initially at qð0Þ¼½xð0Þ;yð0Þ;/ð0Þ T¼½0;1;0 T.As it is seen from thefigures,the stabilization about thefinal posture at the origin is achieved quite satisfactorily.Note,in this case,that the robot actually turns around and backs up into the final posture.7.ConclusionsTwo important control problems concerning mobile robots with unknown dynamic parameters have been considered,namely,tracking of a reference trajectory and stabilization to afixed posture.An adaptive control law has been proposed for the tracking problem and has been ex-tended for the stabilization problem.A simple velocity/acceleration limiter was added to the controller,for practical applications,to avoid any slippage of the robotÕs wheels,and to improve the tracking performance.Several simulation results have been included to demonstrate the performance of the proposed adaptive control law.References[1]Kanayama Y,Kimura Y,Miyazaki F,Noguchi T.A stable tracking control method for an autonomous mobilerobot.vol.1.Proceedings of IEEE International Conference on Robotics and Automation,Cincinnati,Ohio,1990, p.384–9.[2]Canudas de Wit C,Khennouf H,Samson C,Sordalen OJ.Nonlinear control design for mobile robots.In:ZhengYF,editor.Recent trends in Mobile robots,World Scientific,1993.p.121–56.[3]Guldner J,Utkin VI.Stabilization of nonholonomic mobile robot using Lyapunov functions for navigation andsliding mode control.Control-Theory Adv Technol1994;10(4):635–47.[4]Colbaugh R,Barany E,Glass K.Adaptive Control of Nonholonomic Mechanical Systems.Proceedings of35thConference on Decision and Control,Kobe,Japan,1996.p.1428–34.[5]Fierro R,Lewis FL.Control of nonholonomic mobile robot:backstepping kinematics into dynamics.J Robot Sys1997;14(3)149-163.[6]Jiang ZP,Pomet bining backstepping and time-varying techniques for a new set of adaptive controllers.Proceedings of33rd IEEE Conf on Decision and Control,Lake Buena Vista,FL,1994.p.2207–12.[7]Sarkar N,Yun X,Kumar V.Control of mechanical systems with rolling constraints:application to dynamiccontrol of mobile robots.Int J Robot Res1994;13(1):55–69.[8]Brockett RW.Asymptotic stability and feedback stabilization.In:Brockett RW,Millman RS,Sussmann HJ,editors.Differential Geometric Control Theory,Boston,MA:Birkhauser;1983.p.181–91.[9]Bloch AM,Reyhanoglu MR,McClamroch NH.Control and stabilization of nonholonomic dynamic systems.IEEE Trans Automat Contr1992;37(11):1746–56.[10]Campion G,d’Andrea-Novel B,Bastin G.Controllability and state feedback stabilization of nonholonomicmechanical systems.Canudas de Wit C,editor.Advanced Robot Control,Berlin:Springer;1991.p.106–24. [11]Kolmanovsky I,McClamroch NH.Developments in nonholonomic control problems.IEEE Contr Sys Magaz1995;15(6):20–36.[12]Krstic M,Kanellakopoulos I,Kokotovic P.Nonlinear and Adaptive Control Design,New York:Wiley;1995.[13]Karlsson MP.Control of nonholonomic systems with applications to mobile robots.Master Thesis,SouthernIllinois University,Carbondale,IL62901,USA,1997.Farzad Pourboghrat received his Ph.D.degree in Electrical Engineering from the University of Iowa in1984. He is now with the Department of Electrical and Computer Engineering at Southern Illinois University at Carbondale(SIU-C)where he is an Associate Professor.His research interests are in adaptive and slidingcontrol with applications to DSP embedded systems,mechatronics,flexible structures andMEMS.Mattias Karlsson received the B.S.E.E.degree from the University of Bor a s,Sweden and the M.S.E.E.degree from Southern Illinois University,Carbondale,IL,in1995and1997,respectively.He has been employed at Orian Technology since1997.He is currently an on-site consultant at Caterpillar Inc.Õs Technical Center, Mossville,IL.His current interests include control algorithm development for mechanical and electrical systems and software development for embedded systems.F.Pourboghrat,M.P.Karlsson/Computers and Electrical Engineering28(2002)241–253253。

IAU2000岁差

IAU2000岁差

(Resolution B1.6). The model, designated IAU 2000A, includes a nutation series for a non-rigid Earth and corrections for the precession rates in longitude and obliquity. The model also specifies numerical values for the pole offsets at J2000.0 between the mean equatorial frame and the Geocentric Celestial Reference System (GCRS). In this paper, we discuss precession models consistent with IAU 2000A precession-nutation (i.e. MHB 2000, provided by Mathews et al. 2002) and we provide a range of expressions that implement them. The final precession model, designated P03, is a possible replacement for the precession component of IAU 2000A, offering improved dynamical consistency and a better basis for future improvement. As a preliminary step, we present our expressions for the currently used precession quantities ζA , θA , zA , in agreement with the MHB corrections to the precession rates, that appear in the IERS Conventions 2000. We then discuss a more sophisticated method for improving the precession model of the equator in order that it be compliant with the IAU 2000A model. In contrast to the first method, which isห้องสมุดไป่ตู้based on corrections to the t terms of the developments for the precession quantities in longitude and obliquity, this method also uses corrections to their higher degree terms. It is essential that this be used in conjunction with an improved model for the ecliptic precession, which is expected, given the known discrepancies in the IAU 1976 expressions, to contribute in a significant way to these higher degree terms. With this aim in view, we have developed new expressions for the motion of the ecliptic with respect to the fixed ecliptic using the developments from Simon et al. (1994) and Williams (1994) and with improved constants fitted to the most recent numerical planetary ephemerides. We have then used these new expressions for the ecliptic together with the MHB corrections to precession rates to solve the precession equations for providing new solution for the precession of the equator that is dynamically consistent and compliant with IAU 2000. A number of perturbing effects have first been removed from the MHB estimates in order to get the physical quantities needed in the equations as integration constants. The equations have then been solved in a similar way to Lieske et al. (1977) and Williams (1994), based on similar theoretical expressions for the contributions to precession rates, revised by using MHB values. Once improved expressions have been obtained for the precession of the ecliptic and the equator, we discuss the most suitable precession quantities to be considered in order to be based on the minimum number of variables and to be the best adapted to the most recent models and observations. Finally we provide developments for these quantities, denoted the P03 solution, including a revised Sidereal Time expression.

动静载作用下煤岩多场耦合冲击危险性动态评价技术

动静载作用下煤岩多场耦合冲击危险性动态评价技术

㊀第49卷第4期煤炭科学技术Vol 49㊀No 4㊀㊀2021年4月CoalScienceandTechnology㊀Apr.2021㊀移动扫码阅读邓志刚.动静载作用下煤岩多场耦合冲击危险性动态评价技术[J].煤炭科学技术,2021,49(4):121-132 doi:10 13199/j cnki cst 2021 04 015DENGZhigang.Multi-fieldcouplingdynamicevaluationmethodofrockbursthazardconsideringdynamicandstaticload[J].CoalScienceandTechnology,2021,49(4):121-132 doi:10 13199/j cnki cst 2021 04 015动静载作用下煤岩多场耦合冲击危险性动态评价技术邓㊀志㊀刚1,2(1.煤炭科学技术研究院有限公司安全分院,北京㊀100013;2.煤炭资源高效开采与洁净利用国家重点实验室,北京㊀100013)摘㊀要:深部开采冲击地压灾害孕灾过程中既有静态基础量又有动态变化量,剧增的原岩应力与覆岩断裂㊁井下爆破等引起的动载扰动是诱发冲击地压灾害的源头,因此实现冲击危险性快速㊁高精度评价必须综合考虑动静载作用㊂笔者开展了典型煤岩霍普金森压杆试验及数值模拟,分析了动载对煤岩体破坏作用以及对应力场的影响,针对应力变化可以直接引起介质中震动波波速变化,且波速变化前的幅值与变化幅度均受应力场影响这一特性,掌握了震动场与应力场的耦合关系,建立了多场耦合冲击危险性动态评价技术:以原岩应力场表示煤岩孕灾过程的静态基础量,以采动应力场和震动场表示煤岩孕灾过程的动态变化量,以波速异常指数㊁波速梯度指数㊁应力异常指数㊁应力梯度指数为评价指标可实现煤岩冲击危险性动态评价㊂研究结果表明:动载作用下能量以震动波形式传递,造成应力场的重新分布,应力呈现分区传递特点,并且在能量达到某一阈值后引起煤岩损伤破坏,但无论动载直接作用在岩石上还是煤体上,岩石是能量传递路径,煤层是能量耗散㊁释放主体,破坏主要发生在煤体中㊂多场耦合冲击危险性评价技术在某工作面经现场应用,在工作面逐渐揭露断层过程中冲击危险性由强冲击危险性降低到中等冲击危险性,现场监测数据表明评价结果与现场实际相符㊂关键词:动静载荷;冲击危险性;震动场;多场耦合;动态评价中图分类号:TD324㊀㊀㊀文献标志码:A㊀㊀㊀文章编号:0253-2336(2021)04-0121-12Multi-fieldcouplingdynamicevaluationmethodofrockbursthazardconsideringdynamicandstaticloadDENGZhigang1,2(1.MineSafetyTechnologyBranch,ChinaCoalResearchInstitute,Beijing㊀100013,China;2.StateKeyLaboratoryofCoalMiningandCleanUtilization,Beijing㊀100013,China)收稿日期:2020-12-02;责任编辑:朱恩光基金项目:国家科技重大专项资助项目(2016ZX05045003-006-002);国家自然科学基金面上资助项目(51674143)作者简介:邓志刚(1981 ),男,吉林长春人,研究员,博士,中国煤炭科工集团三级首席科学家㊂Tel:010-84261581,E-mail:dengzhigang2004@163.comAbstract:Staticbasicquantityanddynamicvariationquantityexistintheprocessofrockburstindeepmining.Dynamicloaddisturbanceandtheincreasingofin-situstressfieldarethesourceofrockburst.Therefore,thedynamicandstaticloadmustbeconsideredcomprehensivelyinthefastandhigh-precisionevaluationofrockbursthazard.Hopkinsonpressurebarexperimentsandnumericalsimulationswerecarriedouttoanalyzetheinfluencesofdynamicloadonthedamageandstressfieldofthecoalrock.Inviewofthefactthatthechangeofstresscoulddi⁃rectlycausethechangeofvibrationwavevelocityandtheamplitudebeforeandafterthechangeofwavevelocitywereaffectedbythestressfield,thecouplingrelationshipbetweenvibrationfieldandstressfieldwasmasteredandthemulti-fieldcouplingdynamicevaluationmethodofrockbursthazardwasestablished.Intheprocessofcatastrophe,thein-situstressfieldrepresentsthestaticfoundationquantity,andtheminingstressfieldandthevibrationfieldrepresentthedynamicchangequantity.Thewavevelocityanomalyindex,wavevelocitygradientin⁃dex,stressanomalyindexandstressgradientindexareusedasevaluationindexestorealizedynamicevaluationofrockbursthazard.There⁃sultsshowthattheenergyistransmittedintheformofvibrationwaveunderdynamicload,resultingintheredistributionofstressfield.Thestresspresentsthecharacteristicsofzonaltransmission,andcausesthedamageofcoalandrockwhentheenergyreachesacertainthreshold.However,nomatterthedynamicloaddirectlyactsontherockorthecoal,therockistheenergytransferpath,thecoalseamisthemainbodyofenergydissipationandrelease,andthefailuremainlyoccursinthecoal.Themulti-fieldcouplingdynamicevaluationmethodofrockburst1212021年第4期煤炭科学技术第49卷hazardwasappliedonacertainworkingface.Therockbursthazardwasreducedfromstrongtomediumintheprocessofgraduallyexposingfaults.Thefieldmonitoringdatashowedthattheevaluationresultswereconsistentwiththeactualsituation.Keywords:dynamicandstaticloads;rockbursthazard;vibrationfield;multi-fieldcoupling;dynamicevaluation0㊀引㊀㊀言我国多数矿井进入深部开采阶段,冲击地压灾害频度㊁强度显著增加[1],冲击地压防治工作任重道远㊂2018年8月1日,国家煤矿安全监察局印发的‘防治煤矿冲击地压细则“开始实施,规定: 开采具有冲击倾向性的煤层必须进行冲击危险性评价 , 开采冲击地压煤层必须进行采区㊁采掘工作面冲击危险性评价 , 当评估煤层有冲击倾向性时,应当进行冲击危险性评价 ,并且以冲击危险性评价结果作为冲击地压监测㊁卸压等工作开展的依据㊂目前冲击危险性评价方法较多㊂一类是以冲击地压主要诱因为切入点的冲击危险性静态评价技术,如窦林名等[2]提出的综合指数法,综合考虑了岩体结构㊁力学特性㊁地质因素等条件㊂姜福兴等[3]采用模糊数学的方法,用垂直应力与煤体单轴抗压强度的比值㊁弹性能量指数2个指标评价煤体的冲击危险性,且根据应力叠加原理建立了冲击危险性评价模型,后又在此基础上提出了冲击地压分类评价技术手段㊂张科学等[6]综合考虑开采深度㊁冲击倾向性㊁煤层顶底板性质㊁地质构造㊁开采技术提出了基于层次分析法的煤层冲击危险性模糊综合评价模型㊂张宏伟等[7]应用地质动力区划方法对煤矿冲击危险进行评价㊂邓志刚[10]基于三维地应力场反演技术开展了相关研究,综合考虑构造应力㊁采动影响等因素,实现了对采区宏观区域的冲击危险评价㊂欧阳振华等[11]考虑瓦斯作用,将煤层气属性㊁抽采效果分析作为一类地质因素㊁开采技术条件,提出一种含瓦斯煤冲击危险性改进型综合指数评价方法㊂但是由于冲击地压致灾机理不清,灾害孕育㊁发展㊁发生的过程中影响因素繁杂,以及复杂多变的采掘及地质条件,致使静态评价方法主要是宏观上为煤层开采前的防冲工作提供一定参考,缺少对于采掘过程中因局部区域地质及开采条件变化㊁卸压措施等因素引起的冲击危险性动态变化的量化能力,因此,另一类基于现场监测数据的冲击危险性动态评价技术是当前研究工作的重点,如刘少虹等[12]基于地音与电磁波CT探测数据提出的冲击危险性层次化评价方法;李宏艳等[14]基于微震监测数据建立的考虑响应能量和无响应时间的冲击危险性动态评价技术㊂姜福兴等[15]应用矿压观测法观测冲击地压工作面支架压力㊁立柱压缩量,判断工作面顶板来压规律,结合巷道的变形及其围岩应力分布进行观测,评价及预测冲击危险性㊂何学秋等[17]采用电磁辐射法评价冲击危险性,主要参数为电磁辐射强度和脉冲数㊂曹民远等[19]采用数值模拟和理论计算的方法分析了采掘工作面应力扰动叠加的影响,提出了近直立煤层动态权重评价法的计算体系㊂但是冲击地压的孕灾过程中既有静态基础量,又有动态变化量,因此目前仅依靠单一理论或方法快速㊁高精度的进行冲击危险性评价难度较大㊂我国煤矿进入深部开采后,剧增的原岩应力场成为冲击地压灾害发生的必要条件㊂覆岩断裂㊁井下爆破等带来的强动载扰动易成为诱发冲击灾变的充分条件,但目前冲击危险性评价的研究工作中少有兼顾动静载综合作用的理论或方法㊂为此,笔者以震动场㊁采动应力场表示孕灾过程中动态变化量,以原岩应力场表示孕灾过程中静态基础量㊂提出了波速异常指数㊁波速梯度指数㊁应力异常指数㊁应力梯度指数4个冲击危险性评价指标,并在此基础上建立了多场耦合冲击危险性动态评价技术以实现井下高精度冲击危险性动态评价㊂1㊀煤岩动载破坏试验分析1.1㊀典型煤岩动载破坏霍普金森压杆试验分离式霍普金森压杆(SHPB)试验系统(图1)由压杆系统㊁测量系统和数据采集处理系统3个部分组成㊂图1㊀SHPB试验装置Fig.1㊀SHPBexperimentaldevice当动载试块受到不同气压后获得不同初速度撞击入射杆,在杆内产生入射脉冲εi,试件在该应力作用下产生高速变形,同时产生反射脉冲εr和透射脉冲εt㊂如图2所示㊂选取强冲击倾向性煤样试件4221邓志刚等:动静载作用下煤岩多场耦合冲击危险性动态评价技术2021年第4期个,中砂岩试件4个,尺寸均为ø50mmˑ100mm㊂本次试验煤岩样取样点分别为某典型冲击地压矿井3-1煤回风大巷HF6导点处顶板和311102工作面煤层㊂煤岩物理力学参数见表1㊂分别采用气压0.2㊁0.4㊁0.6㊁0.8MPa发射子弹,撞击入射杆,记录其入射㊁反射和透射波曲线㊂图2㊀SHPB试验原理Fig.2㊀PrincipleofSHPBexperimental㊀㊀煤样㊁岩样入射波㊁反射波和透射波曲线如图3㊁图4所示,仅出示驱动应力为0.2㊁0.4㊁0.8MPa时的结果㊂对比分析可知,随着撞击杆驱动应力增加,入射波波速幅值㊁入射波波速变化率均有所增加,反射波和透射波波峰和波谷增高,透射波持续时间缩短,这也和冲击地压发生的突然㊁猛烈性质一致㊂1.2㊀典型煤岩动载破坏数值模拟采取有限元方法对煤岩霍普金森压杆试验进行模拟,进一步分析动载作用下煤岩体损伤破坏机理㊂数值模型如图5所示㊂模拟试件分为煤样㊁岩样㊁煤-岩组合样,岩-煤组合样,其中煤-岩组合样是指震动波入射端在煤上,岩-煤组合样是指震动波入射端在岩石上㊂煤样㊁岩样尺寸为ø50mmˑ100mm,煤岩组合样中煤㊁岩样尺寸均为ø50mmˑ50mm㊂入射杆㊁透射杆材料参数按钢材设定[20],密度为7794kg/m3,弹性模量为211GPa,泊松比为0.285㊂表1㊀煤岩物理力学参数Table1㊀Physicalandmechanicalparametersofcoalandrock试样密度/(kg㊃m-3)单轴抗压强度/MPa弹性模量/GPa泊松比抗拉强度/MPa内摩擦角/(ʎ)黏聚力/MPa煤样1325.4038.7623.4740.2822.49318.5213.894岩样2111.9840.4347.3950.2222.83935.6015.525图3㊀煤样不同气压下的波形Fig.3㊀Waveformsofcoalunderdifferentairpressure图4㊀岩样不同气压下的波形Fig.4㊀Waveformsofrockunderdifferentairpressure3212021年第4期煤炭科学技术第49卷图5㊀霍普金森试验数值模型Fig.5㊀SHPBexperimentnumericalmodel煤岩物理力学参数见表2㊂加载在入射杆端部的震动波信号为SHPB试验中不同气压驱动子弹记录的入射杆应变波信号㊂不同震动波作用下煤岩体应力㊁损伤分布如图6 图9所示,限于篇幅煤样㊁岩样仅出示驱动应力为0.2㊁0.4㊁0.8MPa时的结果,煤岩组合样仅出示驱动应力为0.2MPa和0.8MPa时的结果㊂分析可知,震动波作用引起煤岩应力重新分布,应力传递呈现分区传递特点,即存在应力传递优势面㊂在震动波波速峰值㊁波速变化率较低时,震动波对煤岩介质表2㊀数值模拟参数Table2㊀Numericalsimulationparameters试样弹性模量/GPa泊松比密度/(kg㊃m-3)屈服强度/MPa单轴抗压强度/MPa内摩擦角/(ʎ)黏聚力/MPa煤样3.4740.32132017.2524.6018.5213.890岩样7.6830.23251944.9750.2743.1010.656图6㊀煤样应力与损伤分布情况Fig.6㊀Stressanddamagedistributionofcoalspecimen没有破坏作用,即震动波对煤岩介质的破坏与损伤存在阈值㊂煤岩体发生破坏的位置同时是单元受拉损伤㊁受压损伤极值位置,因此震动波作用下煤岩体破坏模式为拉压复合破坏㊂无论震动波直接作用在岩石上还是煤上,煤岩组合试件的破坏主要发生在煤体上,说明岩石是能量传播的路径,煤体是能量耗421邓志刚等:动静载作用下煤岩多场耦合冲击危险性动态评价技术2021年第4期图7㊀岩样应力与损伤分布情况Fig.7㊀Stressanddamagedistributionofrockspecimen图8㊀煤-岩样应力与损伤分布情况Fig.8㊀Stressanddamagedistributionofcoal-rockspecimen5212021年第4期煤炭科学技术第49卷图9㊀岩-煤样应力与损伤分布情况Fig.9㊀Stressanddamagedistributionofrock-coalspecimen散㊁释放的主体,这也符合冲击地压主要发生在煤层中的事实㊂1.3㊀震动场与煤岩冲击危险性的关联依据采煤工作面和掘进工作面煤岩体破坏失稳主要形式,结合SHPB试验和数值模拟研究结果,煤岩体震动场与冲击危险性的关系总结如下:①震动波是能量传递的载体,震动波所具有的能量超过一定阈值时可引起煤岩破坏,易诱发冲击地压灾害㊂②震动波传递引起应力分布变化,应力传递沿优势面进行㊂随着震动波能量增加,优势面周围易出现煤岩损伤破坏,引起煤岩冲击灾变㊂③当震源位于岩层时,能量传递速度较快,在煤岩界面发生衰减,煤体在震动波作用下发生破坏;当震源位于煤层时,煤体对震动波传递速度相对较慢,能量多耗散在煤层中,主要诱发煤体破坏,对岩层造成的破坏较小㊂2㊀煤岩动㊁静载冲击危险性评价指标考虑动静载作用煤岩冲击危险性评价指标包括应力场相关指标和震动场相关指标,其中静载作用主要表现为应力场的变化,动载作用主要引起震动场的变化㊂2.1㊀应力场冲击危险性评价指标基于煤矿冲击地压应力控制理论[21],煤岩体冲击破坏是应力作用的结果,一是取决于应力绝对值大小,二是应力梯度变化㊂因此,建立应力异常指数和应力梯度指数㊂应力异常指数表征一定区域内不同位置应力差异的指标,计算公式为γσ=σr-σminσmax-σminˑ10(1)式中:γσ为应力异常指数;σr为监测区域某点应力,MPa;σmax㊁σmin分别为监测区域内实时应力最大值和最小值,MPa㊂应力梯度指数是表征一定区域内不同位置应力变化速度差异的指标,计算公式为gσ=gσr-gσmingσmax-gσminˑ10(2)式中:gσ为应力梯度异常指数;gσr为监测区域内某一点的应力场梯度;gσmax㊁gσmin分别为监测区域内应力最大㊁最小梯度㊂2.2㊀震动场冲击危险性评价指标综上,震动场波速绝对值㊁变化速率对煤岩破坏有显著影响㊂因此,提出表征震动波波速的波速异常指数和表征震动波波速变化速率的波速梯度指数,作为2个基于震动场的冲击危险性动态评价指数㊂波速异常指数表征一定区域内不同位置震动波波速的差异,计算公式为γθ=θr-θminθmax-θminˑ10(3)式中:γθ为波速异常指数;θr为监测区域某点震动波621邓志刚等:动静载作用下煤岩多场耦合冲击危险性动态评价技术2021年第4期波速,m/s;θmax㊁θmin分别为监测区域内震动波波速最大值和最小值,m/s㊂波速梯度指数gθ是通过震动场波速变化速率表征煤岩体发生冲击地压的危险程度,计算公式为gθ=gθr-gθmingθmax-gθminˑ10(4)式中:gθ为波速梯度异常指数;gθr为监测区域内某一点的震动波波速梯度;gθmax㊁gθmin为监测区域内震动波波速最大㊁最小梯度㊂3㊀煤岩多场耦合冲击危险性动态评价技术结合笔者以往研究[22]和上述研究成果可知,一方面煤岩应力场改变可以直接引起介质中震动波波速变化,且波速变化前的幅值与变化幅度均与应力场大小相关;另一方面,震动场传递会造成煤岩应力场的重新分布㊂因此,考虑动㊁静载作用开展煤岩冲击危险性动态评价关键在于分析震动场-应力场的耦合作用㊂煤炭开采之前,煤岩体处于重力和构造应力组成的原岩应力场之中;开采过程中,煤岩体形成采动应力场;原岩应力场和采动应力场相互作用,煤岩体损伤变形,震动产生,以弹性波的形式向外传播形成震动场㊂冲击地压是原岩应力场㊁采动应力场和震动场综合作用的结果,煤岩体中多场耦合关系如图10所示㊂图10㊀煤岩体中多场耦合关系Fig.10㊀Fieldincoalrockmassanditscouplingrelationship为了准确描述煤岩体中各种场的关系,从冲击危险性评价角度建立统一数学模型R(ti,s;mj)=0㊀㊀(i,j=1,2,3, )(5)式中:ti为场的变量,一般情况下有多个,既可以是标量也可以是矢量;s为场的源或者汇,通常只有一个;mj为煤岩体的物理性质变量,如弹性模量㊁泊松比㊁剪切模量㊁波速等多个变量㊂基于该函数煤岩体中的3种场的冲击危险性评价具体表达式如下:1)原岩应力场为Y(h,c,f;ρ,μ)=0(6)式中:h为采深;c为地应力;f为体积力;ρ为煤岩体密度;μ为泊松比㊂2)震动场为S(x,y,z,t,E,f;ρ,μ)=0(7)式中:x㊁y㊁z为震源的位置坐标;t为发震时间;E为震源能量㊂3)采动应力场为F(u,f;ρ,μ)=0(8)式中:u为位移㊂3.1㊀原岩应力场与采动应力场(RM)耦合冲击危险性评价模型㊀㊀原岩应力场冲击危险性评价指标见表3㊂原岩应力场冲击危险性指数定义为R=(R1+R2+R3+R4)/4(9)其中,R1㊁R2㊁R3㊁R4为不同评价指标得分㊂原岩应力冲击危险性反映煤岩体自身发生冲击地压的固有属性,其数值大小反映了煤岩体采动后,发生自发型冲击地压的可能性和危险性㊂原岩应力场冲击危险性指数取值与冲击危险等级关系见表4㊂表3㊀原岩应力场冲击危险性评价指标Table3㊀Rockbursthazardevaluationindexsofin-situstressfield变量影响因素阈值分值R1开采深度hhɤ400m1400m<hɤ600m2600m<hɤ800m3h>800m4R2向落差大于3m的断层推进的工作面或巷道,工作面或掘进工作面至断层的距离LdLdȡ100m150mɤLd<100m220mɤLd<50m3Ld<20m4R3向背斜或向斜推进的工作面或巷道,工作面或掘进工作面与之距离LzLzȡ50m120mɤLz<50m210mɤLz<20m3Lz<10m4R4同一水平煤层冲击地压发生次数nn=01n=122ɤn<33nȡ34㊀㊀采动应力冲击危险指标包括:应力异常指数和应力梯度指数㊂二者取值与冲击危险等级之间的关系见表5㊁表6㊂7212021年第4期煤炭科学技术第49卷表4㊀原岩应力场冲击危险性等级划分标准Table4㊀Rockbursthazardclassificationcriteriabasedonin-situstressfield阈值冲击危险性评价指数冲击危险等级Rɤ11无1<R<22弱2ɤR<33中等Rȡ34强表5㊀应力异常指数冲击危险性等级划分标准Table5㊀Rockbursthazardclassificationcriteriabasedonstressanomalyindex阈值冲击危险性评价指数冲击危险等级γσɤ11无1<γσ<32弱3ɤγσ<53中等γσȡ54强表6㊀应力梯度指数冲击危险性等级划分标准Table6㊀Rockbursthazardclassificationcriteriabasedonstressgradientindex阈值冲击危险性评价指数冲击危险等级gθɤ11无1<gθ<32弱3ɤgθ<53中等gθȡ54强㊀㊀基于原岩应力场与采动应力场耦合的冲击危险性评价模型为DRM=a1R+b1γσ+c1gσ(10)㊀㊀其中:DRM是原岩应力场与采动应力场耦合的冲击危险性评价指数;a1,b1,c1分别为原岩应力场和采动应力场耦合冲击危险性评价权重系数,不同矿井取值不同㊂原岩应力场与采动应力场耦合的冲击危险性指数取值与冲击危险等级之间的关系见表7㊂表7㊀原岩应力场与采动应力场耦合冲击危险性等级划分标准Table7㊀Rockbursthazardclassificationcriteriabasedoncouplingofin-situstressfieldandminingstressfield阈值冲击危险性评价指数冲击危险等级DRMɤ11无1<DRM<32弱3ɤDRM<53中等DRMȡ54强3.2㊀原岩应力场与震动场(RS)耦合冲击危险性评价模型㊀㊀震动场冲击危险性指标包括:波速异常指数和波速梯度指数㊂二者取值与冲击危险等级之间的关系见表8㊁表9㊂原岩应力场与震动场耦合的冲击危险性评价模型为DRS=a2R+b2γθ+c2gθ(11)㊀㊀其中:DRS为原岩应力场和震动场耦合的冲击危险性评价指数;a2,b2,c2为原岩应力场和震动场耦合冲击危险性评价权重系数,不同矿井取值不同㊂原岩应力场与震动场耦合的冲击危险性指数取值与冲击危险等级之间的关系见表10㊂表8㊀波速异常指数冲击危险性等级划分标准Table8㊀Rockbursthazardclassificationcriteriabasedonwavevelocityanomalyindex阈值冲击危险性评价指数冲击危险等级γθɤ11无1<γθ<32弱3ɤγθ<53中等γθȡ54强表9㊀波速梯度指数冲击危险性等级划分标准Table9㊀Rockbursthazardclassificationcriteriabasedonwavevelocitygradientindex阈值冲击危险性评价指数冲击危险等级gθɤ11无1<gθ<32弱3ɤgθ<53中等gθȡ54强表10㊀原岩应力场与震动场耦合冲击危险性等级划分标准Table10㊀Rockbursthazardclassificationcriteriabasedoncouplingofin-situstressfieldandvibrationfield阈值冲击危险性评价指数冲击危险等级DRSɤ11无1<DRS<32弱3ɤDRS<53中等DRSȡ54强3.3㊀采动应力场与震动场(MS)耦合冲击危险性评价模型㊀㊀采动应力场与震动场耦合冲击危险性评价模型为DMS=a3γσ+b3gσ+c3γθ+d3gθ(12)㊀㊀其中:DMS为采动应力场与震动场耦合冲击危险性评价指数;a3,b3,c3,d3分别为应力异常指数,应力梯度指数,波速异常指数,波速梯度指数的权重系数,不同矿井取值不同㊂采动应力场与震动场耦合的冲击危险性指数取值与冲击危险等级之间的关系见表11㊂3.4㊀多场耦合(RMS)冲击危险性动态评价模型冲击地压发生的本质是煤岩体具有的冲击能量821邓志刚等:动静载作用下煤岩多场耦合冲击危险性动态评价技术2021年第4期超过围岩吸收能量的极限㊂应力场可以表现煤岩体表11㊀采动应力场与震动场耦合冲击危险性等级划分标准Table11㊀Rockbursthazardclassificationcriteriabasedoncouplingofminingstressfieldandvibrationfield阈值冲击危险性评价指数冲击危险等级DMSɤ11无1<DMS<32弱3ɤDMS<53中等DMSȡ54强未受扰动的地应力场和受采动影响而形成的采动应力场,是煤岩体承受应力的状态量㊂震动场主要表现煤岩体无法承受外部高应力差作用发生损伤破坏,在此过程中以震动形式释放出能量的时空域,可以表现煤岩体积蓄能量的过程㊂冲击地压的不仅发生在高应力区,也发生在煤岩体由低应力区向高应力区转化的过程中,采用煤岩体多场耦合的方法可以充分全面评价监测区域的冲击危险性㊂基于上述对RM耦合㊁RS耦合和MS耦合的冲击危险性评价模型,构建煤岩体多场耦合(RMS)冲击危险性动态评价模型㊂冲击危险性指数算法如下D=DRM+DRS+DMS(13)多场耦合冲击危险性评价指数D与冲击危险性等级的对应关系见表12㊂表12㊀多场耦合(RMS)冲击危险性等级划分标准Table12㊀Rockbursthazardclassificationcriteriabasedonmulti-fieldcoupling阈值冲击危险性评价指数冲击危险等级Dɤ51无5<D<102弱10ɤD<153中等Dȡ154强4㊀工程应用选取典型冲击地压矿井311202工作面为现场,开展相关应用㊂4.1㊀工作面概况311202工作面是该矿井12盘区第2个回采工作面,是首个沿空回采工作面,位于12盘区北部,为311201接续工作面,东部以12盘区辅运大巷为界,西部至12盘区西部边界,南部为实体煤,北部为正在回采的311201工作面,保护煤柱宽度6m㊂该工作面采用走向长壁综合机械化一次采全高采煤法,采高5.25m,工作面倾斜长度299m,走向长度3140m,全部垮落法管理顶板,两回采巷道采用液压支架进行超前支护㊂工作面布置如图11所示㊂图11㊀311202工作面布置Fig.11㊀LayoutofNo.311202miningface经鉴定,3-1煤及其顶底板均具有弱冲击倾向性,3-1煤层冲击危险等级为中等冲击危险㊂311202工作面所在地层构造形态总体为一向北西倾斜的单斜构造,倾向300ʎ 320ʎ㊁倾角1ʎ 3ʎ,地层产状沿走向及倾向均有一定变化,沿走向发育有宽缓的波状起伏㊂311202工作面受DF19㊁DF18㊁F22㊁F24断层影响较大,其中DF19断层影响最为显著,该断层走向长度约1200m,落差6.5 10.0m,预计影响311202工作面走向长度560m,对生产过程中的冲击地压灾害影响最大㊂311202工作面主要断层情况见表13,311202工作面煤层顶底板结构特征见表14㊂表13㊀311202工作面断层特征Table13㊀FaultcharacteristicsofNo.311202miningface断层走向/(ʎ)倾向/(ʎ)倾角/(ʎ)性质落差/mDF183124270正断层0 5.0DF192962649正断层6.5 10.0F222851530正断层1.1F243579046正断层0.3表14㊀311202工作面煤层顶底板结构特征Table14㊀StructuralcharacteristicsofcoalseamroofandfloorinNo.311202miningface顶底板岩性厚度/m平均厚度/m基本顶细粒砂岩9.25 19.7015.84直接顶砂质泥岩2.28 12.858.50直接底砂质泥岩4.69 12.997.68基本底细粒砂岩5.21 21.4514.824.2㊀多场耦合冲击危险性动态评价原岩应力场包括重力场和构造应力场,通过地应力测试及三维反演可得到㊂采动应力场通过应力在线监测系统监测得到㊂在311202回风巷生产帮安设应力在线监测系统,距离开切眼60m生产帮侧9212021年第4期煤炭科学技术第49卷安设第1组应力测点,之后每隔40m安设一组,共布置10组,主要监测工作面超前300m范围内回风巷一侧煤体采动应力分布情况;每组垂直于煤壁施工2个ø44mm应力钻孔,孔深分别为11m和16m,钻孔间距1m㊂当测点与工作面距离小于30m时开始回撤,随着工作面回采,测点依次前移,直至回采结束㊂测点布置方案如图12所示㊂收集了311202工作面2019年5月至11月的回风巷采动应力监测数据,并进行了分析和应用㊂图12㊀应力在线监测测点布置Fig.12㊀Layoutofmeasuringpointsforonlinestressmonitoring工作面震动场数据由ARAMISM/E微震监测系统监测得到㊂311202工作面测站布置情况如图13所示㊂井下布置4台微震拾震器(编号S9至S12)和6个移动式监测探头(编号T19至T24),地面布置1台编号为A2矿震测站组成联合监测网,对工作面进行全面监测㊂图13㊀311202回采工作面微震监测系统测站布置Fig.13㊀ArrangementofthestationofmicroseismicmonitoringsysteminNo.311202miningface选取311202工作面回采至距离DF19断层10m时,开始揭露DF19断层时以及揭露DF19断层295m时,3个时间节点311202工作面超前150m范围内的冲击危险性评价情况㊂回采至距离DF19断层10m时,计算原岩应力场冲击危险性指数R,3-1煤层平均采深620m,R1=3;工作面距离断层10m,R2=4;工作面前方无背斜或向斜,R3=1;该区域未发生过冲击地压,R4=1㊂根据式(9)计算得到R=2.3㊂按照式(1)㊁式(2)计算得到γσ=2.3,gσ=3.3㊂311202工作面最大主应力与水平应力比约为1,取a1=b1=c1=0.5,根据式(10)计算得到DRM=4.0㊂同理计算出,揭露断层时DRM=5.0;揭露断层295m时DRM=4.0㊂回采至距离DF19断层10m时,R=2.3;根据式(3)㊁(4)计算得到γθ=3.4,gθ=5.0;工作面最大主应力与水平应力比约为1,取a2=b2=c2=0.5,根据式(11)计算得到DRS=5.4㊂同理计算出,揭露断层时DRS=6.5;揭露断层295m时DRS=4.5㊂回采至距离DF19断层10m时,根据式(1)㊁式(2)计算得到γσ=2.3,gσ=3.3;根据式(3)㊁式(4)计算得到γθ=3.4,gθ=5.0㊂311202工作面最大主应力与水平应力比约为1,取a3=b3=c3=d3=0.5,根据式(12)计算得到DMS=7.0㊂同理计算出,揭露断层时DMS=9.2;揭露断层295m时DMS=6.2㊂根据式(13)计算得到,回采至距离DF19断层10m时D=16.4,具有强冲击危险性;揭露断层时D=20.7,具有强冲击危险性;揭露断层295m时D=14.7,具有中等冲击危险性㊂4.3㊀评价结果验证与对比依据311202工作面回采期间超前工作面300m范围内微震监测数据㊁钻孔应力监测数据平均值验证评价结果㊂在距离DF19断层10m附近,当天微震释放总能量约为19300J,单次最大能量为7000J,微震事件26次;揭露断层时,当天微震释放总能量约为22300J,单次最大能量约为9000J,微震事件17次;揭露断层296m附近,当天微震释放总能量约为7700J,单次最大能量约为6000J,微震事件6次㊂从微震事件能量㊁频次中可以看出冲击危险性降低㊂在距离断层10m附近㊁揭露断层附近以及揭露断层296m附近选取3个煤层钻孔应力测点,3个测点应力监测数据如图14所示㊂工作面推进过程中煤层应力数值增加,强冲击危险区域应力始终高于中等冲击危险区域㊂微震和煤层钻孔应力监测数据验证了冲击危险性动态评价结果的合理性㊂图14㊀煤层钻孔应力监测数据平均值Fig.14㊀Averagevaluesofstressmonitoringdatasincoalseam031。

Predictable and Unpredictable Components of the Su

Predictable and Unpredictable Components of the Su

ADV ANCES IN ATMOSPHERIC SCIENCES,VOL.35,NOVEMBER2018,1372–1380•Original Paper•Predictable and Unpredictable Components of the Summer EastAsia–Pacific Teleconnection PatternXiaozhen LIN1,2,Chaofan LI∗3,Riyu LU1,2,and Adam A.SCAIFE4,51State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing100029,China2University of Chinese Academy of Sciences,Beijing100029,China3Center for Monsoon System Research,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing100029,China4Met Office Hadley Centre,FitzRoy Road,Exeter EX13PB,UK5College of Engineering,Mathematics and Physical Sciences,University of Exeter,Exeter,Devon EX44QF UK(Received17December2017;revised21April2018;accepted06June2018)ABSTRACTThe East Asia–Pacific(EAP)teleconnection pattern is the dominant mode of circulation variability during boreal sum-mer over the western North Pacific and East Asia,extending from the tropics to high latitudes.However,much of this pattern is absent in multi-model ensemble mean forecasts,characterized by very weak circulation anomalies in the mid and high latitudes.This study focuses on the absence of the EAP pattern in the extratropics,using state-of-the-art coupled sea-sonal forecast systems.The results indicate that the extratropical circulation is much less predictable,and lies in the large spread among different ensemble members,implying a large contribution from atmospheric internal variability.However, the tropical–mid-latitude teleconnections are also relatively weaker in models than observations,which also contributes to the failure of prediction of the extratropical circulation.Further results indicate that the extratropical EAP pattern varies closely with the anomalous surface temperatures in eastern Russia,which also show low predictability.This unpredictable circulation–surface temperature connection associated with the EAP pattern can also modulate the East Asian rainband.Key words:EAP pattern,circulation,seasonal forecast,surface temperature,eastern RussiaCitation:Lin,X.Z.,C.F.Li,R.Y.Lu,and A.A.Scaife,2018:Predictable and unpredictable components of the summer East Asia–Pacific teleconnection pattern.Adv.Atmos.Sci.,35(11),1372–1380,https:///10.1007/s00376-018-7305-5.1.IntroductionThe East Asia–Pacific(EAP)teleconnection pattern (Huang and Sun,1992),which is also referred to as the Pacific–Japan pattern(Nitta,1987),dominates the interan-nual variability of summer climate over the western North Pacific and East Asia(WNP-EA).It features anomalous zon-ally elongated centers that appear alternately between the equator and high latitudes in the meridional direction over the WNP-EA(Kosaka and Nakamura,2006;Lu and Lin, 2009).The circulation anomalies associated with the EAP pattern exhibit a meridional wave-like distribution with alter-nate cyclonic and anticyclonic anomalies(e.g.,Kosaka and Nakamura,2006,Fig.4;Lu and Lin,2009,Fig.2).The EAP pattern links closely with variation of the circulation not only over the subtropical WNP,manifesting as a change in the WNP subtropical high(Lu and Dong,2001;Lu,2004), but also over the midlatitude WNP-EA.This teleconnection ∗Corresponding author:Chaofan LIEmail:lichaofan@ pattern modifies water vapor transport and significantly influ-ences summer rainfall over East Asia.Anomalous convection around the Philippine Sea is gen-erally recognized as one of the wave sources for the EAP pat-tern,which propagates northward in the lower troposphere (Kawamura et al.,1996;Lu,2001;Kosaka and Nakamura, 2006).Nevertheless,the wave activity excited by the anoma-lous convection around the Philippine Sea appears mainly in the low-latitude regions to the south of35◦N(Kosaka and Nakamura,2006).In view of the remarkable circulation anomalies over midlatitude regions of the WNP-EA associ-ated with the EAP teleconnection pattern,it suggests that the underlying physical mechanisms may be related to Rossby wave propagation into the midlatitude regions(e.g.,Scaife et al.,2017),but the mechanisms behind the EAP pattern are not fully understood.As for the prediction of the EAP teleconnection pattern, forecast models generally capture the component associated with the tropical air–sea interactions(Kosaka et al.,2012, 2013;Li et al.,2012,2014a).These good forecasts man-ifest mainly over the subtropical WNP as variation of the©Institute of Atmospheric Physics/Chinese Academy of Sciences,and Science Press and Springer-Verlag GmbH Germany,part of Springer Nature2018NOVEMBER2018LIN ET AL.1373WNP subtropical high(Wang et al.,2009;Li et al.,2012). In the lower troposphere,high prediction skill shown by cur-rent coupled forecast systems is found over the WNP south of Japan for the zonal wind(Li et al.,2012).However,the pre-diction skill decreases rapidly northward to the midlatitude regions,particularly north of35◦N.In particular,the lower-tropospheric circulation related to the WNP subtropical high shows significant correlation with an anomalous cyclone or anticyclone over the midlatitude regions in observations,but no notable anomalies in the ensemble mean model output, as illustrated in Li et al.(2012).This implies either that this high-latitude component is simply unpredictable or that cur-rent coupled models may not capture the observed midlati-tude components of the EAP pattern.It further suggests that different(unpredictable)mechanisms are responsible for the midlatitude circulation associated with the EAP pattern,in addition to the tropical forcing.It is crucial to gain a better understanding of the underlying mechanism for the variation of the midlatitude circulation associated with the EAP tele-connection pattern,since this midlatitude circulation signifi-cantly affects the summer climate over East Asia.The summer of1998is special for several aspects.First, the EAP teleconnection pattern is clear in this summer(Fig. 1a).Second,associated with this EAP pattern,there is a strong anticyclonic anomaly over the WNP,which is typi-cal for the El Ni˜n o decaying summer and leads tofloods in East Asia(e.g.,Wang et al.,2000;Xie et al.,2016;Li et al., 2017).Finally,and most importantly,associated with strong tropical signals,the climate anomalies of this summer,at least in the tropics and subtropics,show high prediction skill(Li et al.,2012;MacLachlan et al.,2015).Therefore,this summer provides a good opportunity for us to investigate tropical–extratropical interaction.For this purpose,in this study we analyze the outputs of state-of-the-art coupled seasonal fore-cast systems,and compare the model results with observa-tions.The organization of this paper is as follows:Section2 introduces the data used.Section3analyzes the prediction of the EAP teleconnection pattern in1998and the related responses of summer prediction for surface temperature and precipitation.Section4provides a summary and discussion.2.Observed datasets and retrospective fore-castsWe use the monthly mean National Centers for Envi-ronmental Prediction–National Centers for Atmospheric Re-search reanalysis data(Kalnay et al.,1996)from1979to 2015in summer(June–July–August).We also use precipi-tation data from the Global Precipitation Climatology Project dataset(Adler et al.,2003),from1979to2015.Here,we only focus on the interannual variation and exclude the decadal or long-term component by removing a nine-year running aver-age.Two sets of retrospective forecast(hindcast)data are examined in this study.Thefirst is from the Ensembles-Based Predictions of Climate Change and their Impacts(EN-SEMBLES)seasonal forecast project(Van Der Linden and Mitchell,2009),which was an EU-funded integrated predic-tion project based onfive coupled atmosphere–ocean–land global models.It comprises hindcasts for the46-year period of1960–2005.For each year,seasonal forecasts were initial-ized on1May and run for seven months with nine members for each model.Therefore,there are45members for each year.We also use output from the Met Office Global Sea-sonal forecast system5(GloSea5)(MacLachlan et al.,2015). Hindcasts from GloSea5increase the ensemble size in our study;plus,GloSea5exhibits good prediction skill for East Asian precipitation and the WNP subtropical high(MacLach-lan et al.,2015;Li et al.,2016).The model used in this forecast system is the Hadley Center Global Environmental Model version3,with a horizontal resolution of0.83◦×0.56◦for the atmosphere and0.25◦for the ocean and sea-ice model. The retrospective forecasts in GloSea5were performed for each summer from1992to2011,with24members each year.The two hindcasts show similarity in the simulation of the EAP teleconnection pattern.The temporal correlation coeffi-cient for the EAP index[defined by Huang(2004)]between the ENSEMBLES(GloSea5)system and observations during 1960–2005(1992–2011)is0.57(0.50).Therefore,we com-bine all the ensemble members for1998in these two forecast systems together to investigate the predictability,with suffi-cient ensemble members(69)and using the overlapping hind-cast period(1992–2005)as climatology,and the anomalies are calculated by removing the climatology of the ensemble mean.3.Results3.1.Prediction of the EAP teleconnection pattern in1998Figure1shows the850-hPa horizontal wind anomalies in observations and model predictions in1998.In observations, the wind anomalies over the WNP-EA present a clear merid-ional teleconnection pattern with three centers along East Asia(Fig.1a).There are two anomalous anticyclones over the Philippine Sea and Northeast Asia,and one anomalous cyclone over East Asia.The multi-model ensemble(MME) mean result,by contrast,shows horizontal wind anomalies in the midlatitude regions that are small compared with the anti-cyclonic anomalies over the subtropical WNP(Fig.1b).The MME mean result only predicts the anticyclonic anomalies over the subtropical WNP.The extratropical part of the EAP teleconnection pattern is not well predicted in the ensemble mean,despite the strong tropical forcing in the year of1998. There are two possible causes of this difference:the extra-tropical EAP nodes could simply be unpredictable,or there could be model errors preventing its simulation in response to tropical forcing.To assess this further we examine the model integration members to determine if they can simulate the ex-tratropical part of the EAP pattern via internal unpredictable variability in the model.1374PREDICTION OF SUMMER EAST ASIA-PACIFIC PATTERN VOLUME35 Fig.1.850-hPa horizontal wind anomalies in the(a)observation and(b)MME mean posite850-hPa horizontal wind anomalies for(c)negative integration members and(d)positive integration membersin1998(units:m s−1).The“A”and“C”represent anticyclonic and cyclonic circulation anomalies,respec-tively.The green box indicates the domain of the midlatitude component of the EAP teleconnection pattern(40◦–50◦N,90◦–150◦E).According to the distinct difference of850-hPa horizon-tal wind anomalies between observations and the MME mean prediction,the zonal wind anomalies over(40◦–50◦N,110◦–150◦E)is defined as the midlatitude zonal wind index.To avoid the influence of the anticyclonic anomalies over the subtropical WNP,only the north part of cyclonic anomalies is adopted to define the index,for further investigation of the teleconnection between midlatitude zonal wind and the tropical part of the EAP pattern.A positive(negative)zonal wind index represents westerly(easterly)wind anomalies in the midlatitudes.In total,11/69(14/69)integration mem-bers have zonal wind indexes that are smaller(larger)than a standard deviation of−0.8(0.8).Figures1c and d show the composite spatial distribution of850-hPa horizontal wind anomalies for these negative and positive integration mem-bers.The wind anomalies for negative integration members (Fig.1c)show a tripolar pattern of anomalous centers,re-sembling well the observed wind anomalies(Fig.1a).It indi-cates that the negative index integration members can capture the EAP pattern over the WNP-EA.In contrast,the anticy-clonic anomaly over the subtropical WNP in positive integra-tion members(Fig.1d)extends northward to the midlatitudes,associated with a cyclonic anomalous center to the north of 50◦N,which is opposite to that in negative index integrations and observations in the midlatitude regions.The opposite anomalous circulation patterns between negative and positive integration members therefore lead to the weak circulation anomalies in midlatitude regions for MME mean prediction (Fig.1b),suggesting that large spread exists among model in-tegration members and that the extratropical part of the EAP pattern is reproduced but may not be predictable.A scatterplot of zonal wind indexes from the69integra-tion members in1998is shown in Fig.2(y-axis).It can be seen that zonal wind indexes are quite dispersed,with about half of the indexes being negative and the other half posi-tive.The zonal wind index in the MME mean prediction is small(0.003m s−1),while the zonal wind index in observa-tions is−1.40m s−1.The spread of the ensemble members does include the observed value.In comparison,the subtrop-ical component of the EAP pattern in1998,represented by the WNP monsoon index(WNPMI)following Wang and Fan(1999)[the850-hPa zonal wind anomalies between (5◦–15◦N,100◦–130◦E)and(20◦–30◦N,110◦–140◦E)],is well predicted by the ensemble members,as shown in Fig.2NOVEMBER 2018LIN ET AL.1375Fig.2.Scatterplot for the anomalies of the midlatitude zonal wind index (y -axis,as shown by the green box in Fig.1)and WNPMI (x -axis)from 69integrations in 1998.Black and grey dots represent the indexes in the MME mean and observations,respectively.Units:m s −1.(x -axis).Almost all members are negative,indicating that the models are able to simulate and predict the anomalous anti-cyclonic circulation over the subtropical WNP in 1998.The WNPMI is negative both in the observations and MME mean prediction,and the intensity of circulation anomalies in the MME mean prediction (−5.10m s −1)is close to that in ob-servations (−5.87m s −1).This is in accordance with the high prediction skill of the anticyclonic circulation anomaly in the subtropical WNP (Kosaka et al.,2012;Li et al.,2012),which to a large extent contributes to the prediction skill of the EAP index shown previously.However,the correlation coe fficient of these ensemble members between the zonal wind index and WNPMI is quite low (0.12),implying a largely indepen-dent variation of these two components of the EAP pattern in model predictions in 1998,regardless of strong tropical forc-ing.Further evidence of a lack of predictability in the mid-latitude component of the EAP pattern can be found from hindcast years besides 1998.The correlation coe fficient be-tween the predicted and observed zonal wind indices is only 0.21(0.23)for all hindcast years of ENSEMBLES (GloSea5).Similarly,the interannual variance of the ensemble mean pre-diction in ENSEMBLES (GloSea5)is 0.01(0.03)m 2s −2,which is much lower than that in observations (0.56m 2s −2for 1960–2005and 0.43m 2s −2for 1992–2011),while in all ensemble members,concatenated after subtracting the clima-tology of individual models from 1992to 2005,it is 0.42m 2s −2,which is similar to that in observation.These results confirm that little prediction skill exists in the midlatitude circulations of the EAP pattern,even though the pattern is realistically simulated by the model.However,there is also some evidence for model error in the teleconnection betweenthe midlatitude zonal wind and the WNP subtropical high:the correlation coe fficient between the zonal wind index and the WNPMI in all ensemble members from all hindcast years (1992to 2005)is 0.07,which is lower than that in observa-tions (0.52for 1979–2015),but still exceeds the 95%confi-dence level according to the Student’s t -test because of the large sample size.This implies that while the models can reproduce an EAP pattern through internal variability,they are not able to reproduce well the tropical–mid-latitude tele-connection,and an improvement in prediction skill may be expected if a better teleconnection to the variation in mid-latitude circulation associated with the EAP pattern can be reproduced.Similar situations exist in the five individual models of ENSEMBLES,and all models show significant inter-member variability (uncertainty of the prediction)for the midaltitude circulation related to the variations in the WNP subtropical high (Li et al.,2012,Fig.11).In addition,none of these models shows good prediction skill of the zonal wind index;correlations range between −0.11and 0.21.Di fferences in model performance might lie in the di fferent parameteriza-tions or residual internal variability among di fferent models and are not discussed further in this study.3.2.Response of surface temperature in eastern Russia Consistent with the atmospheric circulation,the surface temperature anomalies also show a meridional wave-like pat-tern with negative anomalies along the mei-yu rainband and positive anomalies over eastern Russia and the subtropical WNP (Fig.3a).The positive surface temperature anomalies averaged over the land area of eastern Russia (50◦–70◦N,120◦–160◦E)reach 1.16◦C.For the MME prediction,the anomalies are quite weak and even have the opposite sign in the midlatitude regions.The averaged temperature anomaly in eastern Russia is only −0.33◦C,suggesting a poor predic-tion of the observed temperature variation.Furthermore,the surface temperature anomalies over the midlatitude WNP-EA also demonstrate large contrast among di fferent groups of ensemble members,as shown in Fig. 3.For negative index cases (Fig.3c),the surface temperature anomalies show a relatively similar meridional wave-like pat-tern to observations (Fig 3a).In contrast,for positive integra-tion members (Fig.3d),the surface temperature anomalies to the north of 30◦N are opposite to those for negative in-tegration members and observations,with negative anoma-lies in eastern Russia and positive anomalies along 40◦N of the WNP-EA.The surface temperature anomalies around the Philippine Sea are positive for both integration groups,cor-responding to a good capability of models in predicting trop-ical temperatures.In general,the integration members that reproduce easterly (westerly)anomalies over the midlatitude regions,tend to predict the EAP teleconnection pattern well (badly),and predict positive (negative)surface temperature anomalies in eastern Russia and negative (positive)anoma-lies along the mei-yu rge spread in surface tem-perature among the integrations is found over the midlatitude regions.1376PREDICTION OF SUMMER EAST ASIA-PACIFIC PATTERN VOLUME35 Fig.3.As Fig.1but for surface temperature anomalies(units:◦C).The green box indicates the domain ofeastern Russia(50◦–70◦N,120◦–160◦E).An intimate relationship between the midlatitude circula-tion and surface temperature is further revealed via a scatter-plot of surface temperature in eastern Russia and zonal windindex among all model integrations(Fig.4a).Here,the sur-face temperature in eastern Russia is defined by the tempera-ture anomalies averaged over the land area in the region(50◦–70◦N,120◦–160◦E).This temperature index and the zonalwind index exhibit a negative relationship,with a correla-tion coefficient of−0.54,which exceeds the99%confidencelevel according to the Student’s t-test.A positive(negative)surface temperature anomaly in eastern Russia correspondsto an easterly(westerly)anomaly in the midlatitude WNP.Furthermore,the850-hPa wind anomalies regressed onto thetemperature index(Fig.4b)mainly appear over the midlati-tude regions north of30◦N,with an anomalous anticyclone ineastern Russia and a relatively weaker cyclonic anomaly overthe WNP-EA.The wind anomalies in the tropics are weak,suggesting that the surface temperatures in eastern Russia areroughly independent of the tropical anomalies in the modelintegrations.The relationship between the midlatitude zonal windanomalies and surface temperature anomalies in eastern Rus-sia not only exists among models integrations,but also inobservations,as shown in Fig.5.The correlation coefficientbetween the temperature index and zonal wind index for ob-servations during1979–2015(Fig.5a)is−0.60,also exceed-ing the99%confidence level according to the Student’s t-test.This is close to that in the model integrations,suggesting a re-alistic relationship in the model.Moreover,the850-hPa windanomalies related to the surface temperature in eastern Rus-sia(Fig.5b)resemble well those for models integrations inthe extratropical regions(Fig.4b).In the tropical/subtropicalWNP,on the other hand,there is an anticyclonic anomaly inobservations,which is absent in model integrations.This dif-ference between the observations and integrations is consis-tent with the idea that the extratropical part of the EAP is rel-atively independent of the tropical part in model predictionsat least.Given the close relationship between surface temper-ature in eastern Russia and midlatitude circulation,the limita-tion of prediction for the extratropical component of the EAPpotentially leads to the poor prediction of surface temperaturein eastern Russia,and the surface temperature in this regionin turn may enhance the spread of midlatitude zonal windsthrough modulating the meridional gradient of temperatures.3.3.Influence on the prediction of East Asian summerrainfallInterannual variation of East Asian summer rainfall issignificantly affected by the circulation anomalies associatedwith the EAP teleconnection pattern,particularly the subtrop-ical components(Lu and Dong,2001;Zhou and Yu,2005;Yang et al.,2010;Kosaka et al.,2012;Li et al.,2012).ThisNOVEMBER 2018LIN ET AL.1377Fig.4.(a)Scatterplot for the anomalies of temperature index (y -axis)and zonal wind index (x -axis)from 69integrations.The temperature index is defined by surface temperature anomalies averaged over the land area in the region (50◦–70◦N,120◦–160◦E),as shown by the green box in Fig.3.The value in the top-right corner of the diagram is the correlation coe fficient be-tween them.(b)850-hPa horizontal wind anomalies regressed onto the temperature index.Shading indicates regions exceed-ing the 95%significance level.Units:m s −1.section further explores the contribution from midlatitude cir-culation anomalies described in the preceding section to the East Asian summer rainfall.Figure 6shows the distribution of precipitation anomalies in 1998.Corresponding to the atmospheric circulation (Fig.1),the precipitation also demonstrates a meridional wave-like pattern in observations (Fig.6a),with positive anomalies along the East Asian mei-yu rainband and negative anoma-lies around the South China Sea,Philippine Sea and south of eastern Russia.The negative anomalies around the South China Sea and Philippine Sea result in an intensified WNP subtropical high (Fig.1a),which would further transfer more water vapor to East Asia and induce more rainfall along the East Asian mei-yu rainband.In addition,because of exces-sive water vapor transported by the midlatitude easterly wind (Fig.1a),more rainfall also appears around Northeast China,which resulted in serious flooding in the SonghuajiangandFig.5.As in Fig.4but for the observations from 1979to 2015.Nenjiang basin in that year (Li et al.,2014b).As the models generally predict the WNP subtropical high (Figs.1and 2),they predict well the associated rainfall in the subtropical WNP-EA regions,for both the MME mean and the ensemble members (Figs.6b–d).However,the MME mean result produces quite weak precipitation anomalies in the midlatitude WNP-EA,including the negative anomalies around the south of eastern Russia and positive anomalies around Northeast China (Fig.6b).Furthermore,although the models generally reproduce the positive rainfall anomalies along the East Asian mei-yu rainband,the large rainfall over the upper reaches of the Yangtze River basin,Korean Penin-sula and the Sea of Japan are also quite weak.The inability of the MME predictions in predicting the above precipitation anomalies connects closely to the lack of skill for the extra-tropical part of the EAP (Figs.6c and d).The precipitation anomalies in the negative integrations resemble well those in observations,especially in the midlatitude regions.The positive rainfall anomalies around the upper reaches of the Yangtze River basin,Korean Peninsula and the Sea of Japan are also successfully predicted in these integrations (Fig.6c).In contrast,in the positive integrations with westerly anoma-lies in the midlatitude regions,these positive precipitation anomalies together with the midlatitude anomalies do not ap-1378PREDICTION OF SUMMER EAST ASIA-PACIFIC PATTERN VOLUME35 Fig.6.As in Fig.3but for the precipitation anomalies(units:mm d−1).pear(Fig.6d),but show an opposite response to the observa-tions and the negative integrations.The difference between different categories of integra-tions demonstrate the impact from the midlatitude circulation (Fig.7).Corresponding to an easterly(westerly)anomaly, less(more)rainfall appears around the south of eastern Rus-sia and more(less)rainfall appears along the East Asian mei-yu rainband.These intimate relationships are detected not just in model integrations from1998,but also in all ensem-ble members concatenated after subtracting the climatology of individual models from all overlapping hindcast years and observations for all years from1979to2015.While the pre-cipitation anomalies in1998are largely modulated by the WNP subtropical high with strong tropical forcing,these sig-nificant relationships further suggest that larger differences in precipitation among integrations could still be anticipated along the East Asian mei-yu rainband,in other years with weak tropical forcing.In summary,the lack of skill in pre-dicting the midlatitude components of the EAP teleconnec-tion pattern suggests a considerable limitation in the seasonal prediction of East Asian summer rainfall,albeit with some potential improvement if modeled tropical teleconnections could be improved.4.ConclusionThis study focuses on the variation of midlatitude circu-lation associated with the EAP teleconnection pattern,based on seasonal forecasts from state-of-the-art coupled forecast systems.In association with strong tropical forcing,the EAP teleconnection pattern in1998,which is typically organized over the WNP-EA in observations,is not well predicted and instead shows quite weak anomalies in the extratropics.The predictions among different model integrations are further in-vestigated to reveal the decoupled tropical–extratropical pre-dictable patterns in models.A close relationship is detected between the variation of surface temperatures in eastern Russia and midlatitude circu-lation associated with the EAP pattern,not just among differ-ent model integrations,but also among all years in observa-tions.The integrations that predict positive(negative)surface temperature anomalies in eastern Russia tend to reproduce easterly(westerly)anomalies over the midlatitude regions. Similarly,anomalous easterly(westerly)winds tend to ap-pear over the midlatitude WNP/EA in the summer when the surface temperature is warm(cold)in eastern Russia.This coupled relationship increases the uncertainty and difficulty of the prediction of the extratropical component of the EAP pattern and the surface temperature in eastern Russia.Despite this,the midlatitude circulation anomalies asso-ciated with the EAP pattern do significantly modulate East Asian summer rainfall.The integrations predicted with east-erly(westerly)anomalies in the midlatitude WNP-EA tend to predict more(less)rainfall along the East Asian mei-yu rainband.As a result,the lack of skill for northern parts of the EAP suggest an important limit to seasonal prediction of。

基于多目标优化NSGA2改进算法的结构动力学模型确认

基于多目标优化NSGA2改进算法的结构动力学模型确认

基于多目标优化NSGA2改进算法的结构动力学模型确认赖文星;邓忠民;张鑫杰【摘要】传统结构动力学模型确认方法通常采用单目标优化,存在精度不足和稳定性差等缺点,难以满足实际工程需求.基于此,提出一种采用神经网络作为代理模型,建立以马氏距离和鲁棒性为不确定性量化指标的多目标优化模型,并将NSGA2多目标进化算法用于求解.针对NSGA2存在无法有效识别伪非支配解、计算效率低和解集质量较差等设计缺陷,提出一种基于支配强度的NSGA2改进算法INSGA2-DS.INSGA2-DS将支配强度引入非支配排序,采用新型拥挤距离公式和自适应精英保留策略,以提高收敛效率和解集质量.GARTEUR飞机算例的仿真结果表明,INSGA2-DS求解复杂工程问题时具有更好的收敛性和分布性,而考虑鲁棒性的结构动力学模型确认方法可以获得同时满足多种目标要求的Parcto解集,提高了模型确认的精度和稳定性.【期刊名称】《计算力学学报》【年(卷),期】2018(035)006【总页数】6页(P669-674)【关键词】NSGA2;模型确认;结构动力学;鲁棒性;多目标优化【作者】赖文星;邓忠民;张鑫杰【作者单位】北京航空航天大学宇航学院,北京100191;北京航空航天大学宇航学院,北京100191;北京航空航天大学宇航学院,北京100191【正文语种】中文【中图分类】TH212;O3131 引言多目标进化算法从20世纪90年代开始迅速发展,Deb等[1]提出第二代带精英保留策略的快速非支配排序算法NSGA2。

NSGA2采用快速非支配排序方法,基于拥挤距离的分布性方法和精英保留策略,凭借简单及高效等优点,广泛应用于科学计算和工程设计等领域。

Kollat等[2]将Epsilon支配概念引入 NSGA2,提出Epsilon-NSGA2算法;Zhang等[3]提出了基于分解的多目标进化算法MOEA/D,MOEA/D将多个目标分为若干组,再并行优化求解;Elhossini等[4]提出粒子群算法和进化算法的混合算法;Deb等[5]提出一种基于参考点的NSGA2算法,以提高高维优化能力;Shim等[6]将非支配排序与目标分解结合,以提高算法优化性能;Qiu等[7]提出用于多目标优化的自适应交叉差分演化算子。

三维多物质弹塑性流体动力学euler方法的并行算法研究及程序测试

三维多物质弹塑性流体动力学euler方法的并行算法研究及程序测试

第25卷第6期高压物理学报V o l.25,N o.6 2011年12月C H I N E S EJ O U R N A L O F H I G H P R E S S U R E P H Y S I C S D e c.,2011文章编号:1000-5773(2011)06-0508-06三维多物质弹塑性流体动力学E u l e r方法的并行算法研究及程序测试*马天宝1,费广磊1,张文耀2(1.北京理工大学爆炸科学与技术国家重点实验室,北京100081;2.北京理工大学计算机学院智能信息技术北京市重点实验室,北京100081)摘要:并行计算是解决爆炸与冲击问题大规模数值模拟最有效的手段之一㊂针对E u l e r方法并行程序设计的复杂性,阐述了三维多物质弹塑性流体动力学程序MM I C-3D并行设计的总体策略,基于消息传递接口(M P I)设计出相应的P MM I C-3D并行程序,并提出了一套实用的程序测试方案㊂结合聚能射流形成过程的数值模拟算例,在八节点的集群上测试了加速比㊁并行效率及可扩放性,分析了影响并行性能的因素㊂关键词:爆炸与冲击;E u l e r方法;并行计算;消息传递接口;程序测试中图分类号:O347;O358文献标识码:A1引言近年来,随着大规模科学与工程计算的需求,许多计算问题已经超出单机所能承受的能力范围,并行计算对于大规模科学与工程计算越来越重要[1]㊂目前,并行计算机的基本存储方式主要有共享存储与分布式存储两种㊂M P I(M e s s a g eP a s s i n g I n t e r f a c e) 消息传递接口是消息传递函数库的标准规范,是目前广泛使用的并行编程工具[2],M P I基于分布式存储,但同样适应于共享存储,具有移植性好㊁功能强大㊁效率高等多种优点㊂MM I C-3D(M u l t i-M a t e r i a l i nC e l l f o r3D)[3]是基于E u l e r型有限差分方法的用于爆炸与冲击问题仿真计算的三维多物质弹塑性流体动力学程序,能处理3种及3种以上物质混合格的界面计算问题,解决了三维E u l e r方法中多物质计算的难题,实现了对空中爆炸㊁密实介质中爆炸及聚能射流形成等典型的爆炸与冲击问题的数值模拟㊂在单机32位操作系统下,受限于计算机内存和计算速度,原有MM I C-3D 串行程序最多只能计算200万网格的问题,远远达不到工程和科研的需求,迫切需要将原有的串行程序改造为并行程序,以扩大计算规模,加快求解速度㊂并行程序设计不但包含了串行程序设计,而且还包含了更多富有挑战性的问题[4],由于并行程序需要通信㊁同步等操作,使得并行程序设计远比串行程序设计复杂得多㊂L a g r a n g e方法,无论采用显式或隐式差分格式,最后都会归结为代数或矩阵运算,对于这类运算都有现成的并行算法可用㊂采用E u l e r 方法,由于需要处理物质在网格间的输运问题,隐含在输运算法中的数据相关性及子区域间的关联性不易发现㊂本文针对E u l e r方法并行设计的复杂性,阐述了处理E u l e r输运算法中处理数据相关性的一种方法,以及挖掘子区域间关联性的过程㊂程序测试是程序开发的一个重要环节,考虑到并行程序的测试复杂性,将测试分为两个阶段,提出了一些实用的测试策略,缩短了整个程序的开发周期,并在八节点集群上测试了程序的并行性能㊂*收稿日期:2010-08-20;修回日期:2010-11-26基金项目:国家重点基础研究发展计划(2010C B832706);爆炸科学与技术国家重点实验室自主课题(Z D K T10-03b);国家自然科学基金(10972041)作者简介:马天宝(1981 ),男,博士,副教授,主要从事计算爆炸力学研究.E-m a i l:m a d a b a l@b i t.e d u.c n2 MM I C -3D 数学模型及数值方法介绍MM I C -3D 串行程序采用不考虑外力㊁外源和热传导,非守恒形式的E u l e r 流体弹塑性动力学偏微分方程组[5]㊂数值计算采用算子分裂格式,将上述非守恒形式的方程组分为L a g r a n g e 步和E u l e r 步进行计算,并在计算中按空间x ㊁y ㊁z 3个方向进行分裂㊂MM I C -3D 采用模糊界面方法,该方法是指在一个混合网格中,不区分物质界面;根据模糊方法计算体积比,把体积比作为模糊权重系数;对网格进行分类,不同类网格之间视为物质界面;对介质进行模糊排序,决定输运优先权和模糊输运表;根据模糊权重计算输运量;按模糊输运表进行输运;在建模和计算中应用模糊方法,故称为 模糊界面方法[6]㊂3 P MM I C -3D 并行算法实现的关键性问题P MM I C -3D (P a r a l l e l i z a t i o n f o rM u l t i -M a t e r i a l i nC e l l -3D )采用域分解并行策略,域分解是指把计算域分成若干子区域,一个处理器处理一个或多个子区域㊂采用域分解并行策略,P MM I C -3D 的并行算法设计主要应考虑如下问题:(1)如果原有串行算法有数据相关性,如何解除相关性;(2)采用计算域分区的并行方式,各子区域间关联性如何,即需要多少额外的网格储存临近子区域的信息㊂3.1 数据相关性分析MM I C -3D 程序采用欧拉型有限差分算法,分L a g r a n ge 步和E u l e r 输运步分别进行计算,使得当前计算网格最多受到周围26个网格的影响,这是能采用计算域分区方式对MM I C -3D 并行的有利条件,同时必须分析计算程序所有语句间的依赖关系,称之为相关分析(D e p e n d e n c y A n a l y s i s )㊂对于采用域分解策略的三维多物质流体弹塑性并行程序来说,在1个时间步内,在同1个空间三重循环下若满足:当前网格物理量的更新依赖于周围网格相关变量更新后的值,则该算法会存在数据相关性㊂用表达式表示为a ᶄi ,j ,k =f (a i ,j ,k ,a ᶄi ʃ1,jʃ1,k ʃ1)(1)其中a 的更新直接或间接依赖于周边网格a 更新后的值㊂采用E u l e r 方法的数值模拟,数据相关性一般出现在E u l e r 输运步上㊂模糊界面方法采用方向分裂 输运,3个方向的输运在1个空间三重循环下进行,当前网格的物理量的更新依赖于周边相关物理量更新后的值,更新后网格物理量影响周围网格相关量的更新,因此存在数据依赖性,且这种依赖关系影响到整个计算域㊂如何解除数据相关性,且保持原有的计算精度,要与具体算法结合起来㊂为消除P MM I C -3D 中E u l e r 输运步的数据相关性,当前网格输运采用相邻网格更新前的值,同时为了消除因此而带来过量输运的问题,将原有在1个空间三重循环下完成的3个方向的输运改为在3个空间三重循环下完成,即每个空间三重循环只进行1个方向的输运,3个方向的输运顺序随时间步交替变换㊂为考核因解除数据相关性而带来的精度影响,采用H a r i v e 和F l e t c h e [7-8]设计的测试精度的方法㊂该方法定义了L 1误差,其公式如下E =ði ,j ,k A (i ,j ,k )ΩF (i ,j ,k )t =T-F (i ,j ,k)e (2)式中:A(i ,j ,k )Ω为网格的体积,F (i ,j ,k )t =T为数值计算的网格介质体积分数,F (i ,j ,k )e为准确的网格介质体积分数㊂数值算例计算参数见表1,其结果为计算到400步的结果㊂由表1可以看出,并行算法的精度和原有串行算法的精度基本一致,说明所采用的解除数据相关性的策略是可行的㊂表1 计算参数和结果T a b l e 1 C a l c u l a t i o n p a r a m e t e r s a n d r e s u l t sC e l l n u m b e r S p a t i a l s t e pT i m e s t e p F l u i dv e l o c i t yEP a r a l l e l a l go r i t h m S e r i a l a l go r i t h m 50ˑ50ˑ500.10.1u x =0.5;u y =0.50.1640.162905 第6期 马天宝等:三维多物质弹塑性流体动力学E u l e r 方法的并行算法研究及程序测试3.2 子区域边界网格(层)数量的确定 子区域边界网格是用来储存临近子区域相关变量的额外网格㊂采用E u l e r 数值方法,子区域间的关联性,即子区域边界网格的数量取决于两个因素:(1)因算法本身所固有的因素,当前网格物理量依赖于周边网格物理量的更新;(2)当前网格物理量的更新需要周边网格的物理量㊂考虑到通信所占用的开销,子区域边界网格(层)的数量应该考虑以上两个因素所增加的网格(层)数的最小值㊂以一维E u l e r 输运步为例,说明子区域边界网格数量的确定过程㊂如图1(a )所示,其中u 为当前k 网格的速度值㊂k 网格的更新(输运)影响k +1网格的物理量,也就是说k +1网格的物理量依赖于k 网格和k +1网格物理量的更新(输运),即k 网格为k +1网格的依赖网格,k +1网格为k 网格的影响网格㊂因此若想保证图1(a )左端1网格更新正确,需要在计算域左侧增加1层网格,并参与计算更新(输运),如图1(a )的灰色虚网格㊂由于在实际的更新运算中,往往需要周边网格物理量的信息,因此考虑如下的差分格式u n +1k =(1-2r )u n k +r (u n k +1+u n k -1)(3)则k 网格更新需要k ㊁k +1㊁k -1网格的物理量㊂综合以上两种因素,对于一维E u l e r 输运步的运算来说,需要在计算域增加3层子区域边界网格,左边增加2层,右边增加1层,如图1(b)所示,其中左边灰色虚网格参与运算,两端的虚网格不参与运算更新㊂(a )E x t r a c e l l a d d e d c o n s i d e r i n g t h e f i r s t f a c t o r (b )E x t r a c e l l s a d d e d c o n s i d e r i n g tw o f a c t o r s 图1 一维E u l e r 输运步子区域边界虚网格确定过程F i g.1 T h e p r o c e s s o f e x t r a c e l l s a d d e d i nE u l e rm o d e l 4 P MM I C -3D 并行程序测试G.J .M ye r s 在他的名著‘软件的测试技巧“一书中给出测试的定义: 程序测试是为了发现错误而执行程序的过程 [9]㊂基于M P I 并行程序的测试与调试的主要困难在于:除了串行程序的所有问题之外,并行程序还会有一些其它的问题存在㊂具体体现在:(1)并行算法设计的复杂性,对于一个串行程序很容易实现的问题,并行实现起来可能就困难重重,潜在的算法逻辑错误不易发现;(2)多进程之间协同作业(任务的映射与分发),导致并行程序比串行程序难于驾驭;(3)基于M P I 的并行程序增加了通信㊁进程同步等操作,增加了程序潜在的风险,一些不当的操作可能会导致程序异常中断或死锁㊂考虑到基于M P I 并行程序的测试与调试的困难,把对P MM I C -3D 并行程序的测试分为2个阶段:阶段1,不依赖于实际的物理模型㊁测试分区对计算结果的影响;阶段2,设计合理的物理模型,测试计算结果是否符合物理规律㊂阶段1是阶段2的基础,只有在阶段1测试正确的基础上才能进行阶段2的测试㊂(a )2D g e o m e t r i cm o d e l (b )3D m o d e l 图2 聚能装药二维几何模型及三维模型图F i g .2 2D g e o m e t r i cm o d e l a n d 3D m o d e l o f s h a p e d c h a r ge 聚能射流算例的计算几何模型如图2所示,其中图2(a )为二维尺寸结构图,图2(b )为三维模型图㊂药柱直径为60mm ,高为90mm ㊂依据网格步长设计了4个算例,见表2㊂聚能射流的数值模拟涉及到的材料包括炸药㊁金属药型罩及空气3种介质㊂炸药采用B 炸药,金属罩采用45钢㊂爆轰产物采用J W L 状态方程;空气采用理想气体状态方程;对于金属材料,考虑其在高温㊁高压㊁高应变率下表现的动态行为,采用M i e -G r ün e i s e n 状态方程描述㊂起爆方式采用点起爆,并采用简单的燃烧模型模拟爆轰波在炸药中的传播过程㊂015 高 压 物 理 学 报 第25卷表2 射流算例模型T a b l e 2 M o d e l o f s h a p e d c h a r g e je t M o d e lC h a r g em a s s /(g )S p a t i a l s t e p/(m mˑm mˑm m )C e l ln u m b e r M o d e lC h a r g em a s s /(g )S p a t i a l s t e p/(m mˑm mˑm m )C e l ln u m b e rE x a m p l e 13401.00ˑ1.00ˑ1.0080ˑ80ˑ180E x a m p l e 33420.04ˑ0.04ˑ0.04227ˑ227ˑ480E x a m pl e 23440.06ˑ0.06ˑ0.06151ˑ151ˑ300E x a m pl e 43420.03ˑ0.03ˑ0.03301ˑ301ˑ600对于射流算例,阶段1的测试采用射流头部速度作为监测变量,监测精度为10-11m /s,算例1的分区方式为:3ˑ1ˑ1,1ˑ3ˑ1,1ˑ1ˑ3;算例2的分区方式为:1ˑ4ˑ1,1ˑ1ˑ4;算例3的分区方式为:2ˑ2ˑ2,1ˑ2ˑ4;算例4的分区方式为:2ˑ2ˑ8,2ˑ2ˑ6㊂测试表明,不同分区方式下的计算结果保持一致,表明P MM I C -3D 并行程序在网格数较多的情况下,分区方式不影响计算结果(算例4网格数为5436万)㊂图3展示了4种算例的三维射流图,从图3中可以看出,算例2㊁算例3及算例4的射流形状明显优于算例1,算例3和算例4射流形状差别不大㊂图4为4种算例的射流头部速度随时间的变化曲线,从图4中可以看出,随着网格数的增多,射流最大头部速度略有增加,算例2㊁算例3及算例4比较接近,三者最大头部速度分别为4.4㊁4.5和4.7k m /s ,比实验结果略小,而算例1最大头部速度仅为4.0k m /s㊂图3 32.51μs 时4种算例的三维数值模拟图F i g .3 T h r e e -d i m e n s i o n a l s i m u l a t i o n g r a p h s a t 32.51μs o f f o u r e x a m pl e s 图4 4种算例的射流头部速度随时间变化曲线F i g .4 T h e j e t t i p v e l o c i t y c h a n ge sw i t h t i m e 5 P MM I C -3D 并行性能测试并行程序除了满足分区方式不影响计算结果的最基本要求外,加速比㊁并行效率及可扩放性也是衡量并行程序质量的主要性能指标㊂P MM I C -3D 程序并行性能测试在自主定制的八节点集群上进行,每个节点包含两颗I n t e l 四核E 5620C P U ,主节点24G 内存,其余节点12G 内存㊂5.1 加速比及并行效率测试并行系统的加速比(S p e e d u p)是指对于一个给定的应用,并行程序的执行速度相对于串行程序的执行速度加快了多少倍,也称为 绝对加速 (A b s o l u t eS p e e d u p );对于给定问题,同一程序在单C P U 的运行时间除以在多个C P U 运行的时间,称为 相对加速 (R e l a t i v eS p e e d u p)㊂加速比除以处理机个数,称为并行效率㊂加速比及并行效率测试采用第4节中的射流测试算例中的算例2㊁算例3作为测试算例,二者的网格数分别为684万和2473万㊂射流测试中的算例2㊁算例3的加速比及并行效率如图5所示㊂从图5中可以看出:加速比随着进程数的增加而增加,用64进程时算例2和算例3的加速比可达到16倍和20倍;并行效率随着进程数的增多而降低,用64进程时算例2和算例3的并行效率只有25%和30%,这是由于进程数增多,通信开销所占的比重也会增加;一样的进程数下,加速比及并行效率随着网格数的增多而增大㊂115 第6期 马天宝等:三维多物质弹塑性流体动力学E u l e r 方法的并行算法研究及程序测试图5 加速比及并行效率F i g .5 T h e s p e e d u p a n de f f i c i e n c y ve r s u s t h en u m b e r of p r o c e s s o r s 5.2可扩放性测试图6 可扩放性折线F i g .6 S c a l a b i l i t y可扩放性是指在确定的应用背景下,计算机系统(或算法或编程等)的性能随处理器的增加而按比例提高的能力㊂可扩放性是和并行算法以及并行计算机体系结构放在一起讨论的,某个算法在某个机器上的可扩放性反映了该算法是否能有效利用不断增加的C P U 的能力㊂采用射流算例作为测试算例,其结果如图6所示,计算域为10c mˑ10c mˑ20c m ㊂对于不同的进程数,每个进程分配的网格数固定为100万㊂测试的进程数如图6横轴所示,纵轴为计算1000个时间步的总时间㊂理想的可扩放性折线应该是一条平行于横轴的直线㊂由图6可知:随着进程数的增多,平均每个进程处理固定网格数的时间逐渐增加,64进程下处理时间是单进程下处理时间的2.8倍㊂5.3 影响并行性能的因素图7 通信时间在总时间的百分比F i g .7 T h e p e r c e n t a ge of c o mm u n i c a t i o n t i m e t o t h e t o t a l t i m e影响并行性能的因素主要包括通信所占用的开销以及负载不均衡所造成的同步等待时间等㊂图7为射流测试算例3在不同进程数下通信时间占总时间的百分比曲线㊂从图7中可以看出,通信时间在计算总时间的百分比随着进程数的增多而快速增大,64进程时达到48%㊂这是由于:(1)P MM I C -3D 中每一个子模块中都有诸如密度㊁质量㊁速度㊁能量及动量等变量更新,而这些变量的变化会影响下一个子模块的计算,因此需要数据通信;(2)每个节点采用千兆网络及千兆交换机连接,实测数据通信速度为35~45M ,网络通信延迟及带宽也影响通信㊂通信开销比重过大是影响并行性能的最重要因素㊂6 结 论(1)E u l e r 数值方法由于需要处理物质在网格间的输运问题,隐含在输运算法中的数据相关性及子区域间的关联性不易发现,因此并行算法设计中潜在的逻辑性错误不易发觉㊂215 高 压 物 理 学 报 第25卷(2)由于P MM I C -3D 中通信过多及自主定制的集群通信延迟及带宽的限制,通信开销比重过大,影响了P MM I C -3D 的并行性能㊂优化程序结构㊁减少通信量是改善P MM I C -3D 并行性能的重要手段㊂(3)从射流算例的模拟结果看,随着网格数的增多计算精度越来越高,数值模拟三维图片的分辨率越来越高,表明所采用的并行算法是合理的;P MM I C -3D 并行程序增大了计算规模,加快了计算速度,达到了并行程序设计的目的㊂R e f e r e n c e s:[1] D o n g a r r a J ,F o s t e r I ,F o xG ,e t a l .S o u r c e b o o k o f P a r a l l e l C o m p u t i n g [M ].T r a n s l a t e d b y MoZY ,C h e n J ,C a oXL .B e i j i n g :P u b l i s h i n g H o u s e o fE l e c t r o n i c s I n d u s t r y ,2005.(i nC h i n e s e )D o n ga r r a J ,F o s t e r I ,F o xG ,等.并行计算综论[M ].莫则尧,陈 军,曹小林,译.北京:电子工业出版社,2005.[2] M a l a r d J .M P I :A M e s s a g e -P a s s i n g I n t e r f a c eS t a n d a r d [R ].U K :T h eU n i v e r s i t y o fE d i nb u r g h ,1994.[3] X u eM Y ,N i n g JG.R e s e a rc ho n 3DE u l e r i a nN u m e r i c a l S i m u l a t i o n f o rE x p l o s i o n [J ].A c t aA r m a m e n t a r i i ,2006,27(6A ):129-132.(i nC h i n e s e)薛妙轶,宁建国.三维爆炸问题的E u l e r 数值方法研究[J ].兵工学报,2006,27(6A ):129-132.[4] C h e nGL ,A nH ,C h e nL .T h eA r c h i t e c t u r e o f P a r a l l e l C o m p u t e [M ].B e i j i n g :H i gh e rE d u c a t i o nP r e s s ,2004.(i nC h i n e s e )陈国良,安 虹,陈 崚,等.并行算法实践[M ].北京:高等教育出版社,2004.[5] M aTB ,W a n g C ,N i n g JG.M u l t i -M a t e r i a l E u l e r i a nF o r m u l a t i o n s a n dH y d r o c o d e f o r t h e S i m u l a t i o n o f E x p l o s i o n s [J ].C M E S :C o m p u t e rM o d e l i n g i nE n g i n e e r i n g &Sc i e n c e s ,2008,33(2):155-178.[6] N i n g JG ,C h e nL W.F u z z y I n t e r f a c eT r e a t m e n t i nE u l e r i a n M e t h od [J ].S c iC h i n aSe rE-E n g M a t e rS c i ,2004,47(5):550-568.[7] H a r v i eDJE ,F l e c t c h e rD F .A N e w V o l u m eo fF l u i dA d v e c t i o nA l g o r i t h m :T h eS t r e a m S c h e m e [J ].JC o m p u t P h y s ,2000,162:1-32.[8] H a r v i eDJE ,F l e c t c h e rDF .A N e w V o l u m e o fF l u i dA d v e c t i o nA l g o r i t h m :T h eD if i n e dD o n a t i ng R e g i o nS ch e m e [J ].I n t JN u m e rM e t hF l ,2001,35:151-172.[9] S hi JM.S o f t w a r eE n g i n e e r i n g -P r i n c i p l e ,M e t h o d a n dA p p l y [M ].B e ij i n g :H i gh e rE d u c a t i o nP r e s s ,2002.(i nC h i n e s e )史济民.软件工程 原理㊁方法与应用[M ].北京:高等教育出版社,2002.S t u d y o nP a r a l l e lA l g o r i t h mo fE u l e r i a n M e t h o d f o rT h r e e -D i m e n s i o n a l M u l t i -M a t e r i a l P l a s t i c -E l a s t i cH yd r o k i ne t i c s MA T i a n -B a o 1,F E IG u a n g -L e i 1,Z H A N G W e n -Y a o 2(1.S t a t eK e y L a b o r a t o r y o f E x p l o s i o nS c i e n c e a n dT e c h n o l o g y ,B e i j i n g I n s t i t u t e o f T e c h n o l o g y ,B e i j i n g 100081,C h i n a ;2.B e i j i n g L a b o r a t o r y o f I n t e l l i g e n t I n f o r m a t i o nT e c h n o l o g y ,S c h o o l o f Co m p u t e rS c i e n c e a n dT e c h n o l o g y ,B e i j i n g I n s t i t u t e o f T e c h n o l o g y ,B e i j i n g 100081,C h i n a )A b s t r a c t :P a r a l l e l c o m p u t i n g o f 3De x p l o s i o na n d s h o c k p r o c e s s e s o n t h e p a r a l l e l c o m pu t e r i s e f f e c t i v e m e a n s f o r t h e l a r g e -s c a l en u m e r i c a l s t u d y o f e x p l o s i o na n d s h o c k p r o c e s s .C o n s i d e r i n g t h e c o m p l e x i t y o f t h e p a r a l l e l p r o g r a mm i n g ,t h e o v e r a l l s t r a t e g y f o r p a r a l l e l p r o g r a mm i n g o f 3D m u t i l -m a t e r i a l h y d r o -e l a s t o p l a s t i ch y d r o c o d eMM I C -3Dw a s d i s c u s s e d ,a n d t h e P MM I C -3D p a r a l l e l h y d r o c o d ew a s d e s i gn e d b a s e do nM P I (M e s s a g e P a s s i n g I n t e r f a c e ).I n a d d i t i o n ,a p r a c t i c a l p l a n o f p r o g r a mt e s t i n g w a s p r e s e n -t e d .T h e s p e e d u p ,e f f i c i e n c y a n ds c a l a b i l i t y o f t h eP MM I C -3D p a r a l l e lh yd r o c o d ewe r e t e s t e do nt h e c l u s t e r c o n s i s t i n g of 8n o d e s b a s e d o n t h e n u m e r i c a l e x a m p l e o f s h a p e d c h a rg e j e t ,a n d th e e f f e c t o f t h e b o t t l e n e c k s o f P MM I C -3D p a r a l l e l h yd r o c o d ew a s d i s c u s se d .K e y wo r d s :e x p l o s i o na n d s h o c k ;p a r a l l e l c o m p u t i n g ;M e s s a g eP a s s i n g I n t e r f a c e (M P I );p r o g r a mt e s t 315 第6期 马天宝等:三维多物质弹塑性流体动力学E u l e r 方法的并行算法研究及程序测试。

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Received December 20, 2013; revised March 20, 2013; accepted March 27, 2013
Abstract: Laboratory experiments were conducted for falling U-chain, but explicit analytic form of the general equations of motion was not presented. Several modeling methods were developed for fish robots. However, they just focused on the whole fish’s locomotion which does little favor to understand the detailed swimming behavior of fish. Udwadia-Kalaba theory is used to model these two multi-body systems and obtain explicit analytic equations of motion. For falling U-chain, the mass matrix is non-singular. Second-order constraints are used to get the constraint force and equations of motion and the numerical simulation is conducted. Simulation results show that the chain tip falls faster than the freely falling body. For fish robot, two-joint Carangiform fish robot is focused on. Quasi-steady wing theory is used to approximately calculate fluid lift force acting on the caudal fin. Based on the obtained explicit analytic equations of motion (the mass matrix is singular), propulsive characteristics of each part of the fish robot are obtained. Through these two cases of U chain and fish robot, how to use Udwadia-Kalaba equation to obtain the dynamical model is shown in detail and the modeling methodology for multi-body systems is presented. It is also shown that Udwadia-Kalaba theory is applicable to systems whether or not their mass matrices are singular. In the whole process of applying Udwadia-Kalaba equation, Lagrangian multipliers and quasi-coordinates are not used. Udwadia-Kalaba theory is creatively applied to dynamical modeling of falling U-chain and fish robot problems and explicit analytic equations of motion are obtained. Key words: Udwadia-Kalaba equation, multi-body systems, falling U-chain, fish robot
1
Introduction

The general problem of obtaining equations of motion for constrained discrete mechanical systems has been an area of considerable interest among scientists and engineers. It is also one of the central issues in multi-body dynamics. The problem has been aggressively and continuously pursued by many scientists, engineers and mathematicians since constrained motion was initially described by LAGRANGE[1]. He invented the special Lagrange multiplier method to deal with constrained motion. However, the Lagrange multiplier method relies on problem-specific approaches to determine the multipliers; it is often very difficult to find the multipliers to obtain the explicit equations of motion for systems with large numbers of degrees of freedom and a mass of non-integrable constraints. GAUSS[2] introduced a new general principle of mechanics for handling constrained motion. Gauss's Principle gives a clear description of the general nature of constrained motion through minimization of a function of the accelerations of the particles of a system. GIBBS[3] and APPELL[4] have independently
·839·
Dynamic Modeling and Simulation of Multi-body Systems Using the Udwadia-Kalaba Theory
ZHAO Han1, ZHEN Shengchao1, 2, *, and CHEN Ye-Hwa2
1 Mechanical Engineering, Hefei University of Technology, Hefei 230009, China 2 Mechanical Engineering, Georgia Institute of Technology, Atlanta 30332, USA
CHINESE JOURNAL OF MECHANICAL ENGINEERING
Vol. 26, No. 5, 2013
DOI: 10.3901/CJME.2013.05.839, available online at ; ;
பைடு நூலகம்
·840·
YZHAO Han, et al: Dynamic Modeling and Simulation of Multi-body Systems Using the Udwadia-Kalaba TheoryY
* Corresponding author. E-mail: zhenshengchao@
© Chinese Mechanical Engineering Society and Springer-Verlag Berlin Heidelberg 2013
developed equations of constrained motion when the constraints satisfy D’ Alember’ s principle. In the treatise on the analytical dynamics, PARS[5] refers to the Gibbs-Appell equations as “probably the most comprehensive equations of motion so far discovered ”. But the Gibbs-Appell equations require a “lucky” choice of problem-specific quasi-coordinates and they suffer from similar problems when dealing with systems with a large number of degrees of freedom and many non- integrable constraints. DIRAC[6] used Poisson brackets, a recursive scheme for determining the Lagrange multipliers, for singular Hamiltonian systems where the constraints do not exactly depend on time. On the other hand, UDWADIA, et al[7–8], obtained a concise, explicit set of equations of motion for constrained discrete dynamic systems which lead to a simple and new fundamental view of Lagrangian mechanics. They derived the fundamental equation of motion that describes the dynamics of constrained systems from Gauss’ s principle which seems somewhat less popular than the principles of Lagrange, Hamilton, Gibbs and Appell. The equations can deal with holonomic and also non-holonomic constraints. UDWADIA, et al[9–10], observed that all the research above has used D’ Alember’ s principle as their starting point. D’ Alember’ s principle assumes that the forces of constraints are considered to be ideal and the total work done by the
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