Development of the MDO Framework for the Helicopter Conceptual Design

6th World Congresses of Structural and Multidisciplinary Optimization

Rio de Janeiro, 30 May - 03 June 2005, Brazil

Development of the MDO Framework for the Helicopter Conceptual Design Sanghun Kim1, Sangook Jun1, Jihoon Jeong1, JeongHwa Kim1, JuHyun Kim1 and Dong-Ho Lee2

(1) School of Mechanical and Aerospace Engineering, College of Engineering, Seoul National University, Seoul 151-744, Republic of

Korea ( pusher2@snu.ac.kr )

(2) School of Mechanical and Aerospace Engineering, College of Engineering, Seoul National University, Seoul 151-744, Republic of

Korea ( donghlee@snu.ac.kr )

1. Abstract

This study presents a conceptual design method for helicopter using Multidisciplinary Design Optimization (MDO). In the conceptual design procedure proposed, we have considered aerodynamic, stability, noise, performance disciplines and developed a multi disciplinary design framework which obtains optimized result from the baseline design. As a preliminary step toward a helicopter conceptual design, we have considered a statistical data of existing helicopter. In the procedure for helicopter conceptual design, we have estimated weight using empirical data and we have used Blade Element Method for aerodynamic analysis. This method is considered to be faster than other accurate method such as Euler equation, and even more it may give more accurate results than momentum theory. Also we have used empirical formulas and statistical data for noise, stability, performance analysis. In the procedure of conceptual design, we use a take-off gross weight and number of blade as an input data and the other variables are calculated in the design tool. We use a range as constraint to obtain endurance time. We have validated the helicopter conceptual design tool using existing helicopter data for example 9,000 lb class (UH-1), 20,000 lb class (UH-60). We have obtained the designed helicopter data is in qualitative agreement with existing helicopter data. From the aforementioned analysis technique, we have constructed the Multidisciplinary Analysis (MDA) as to unite the analysis parts and then we have performed the Multidisciplinary optimization. The optimization tools used in this work are PQRSM, STDQAO which are optimization module in the EMDIOS developed by The Center of Innovation Design Optimization Technology (iDOT). Using this methodology, we have performed the two optimizations of configuration which has maximum endurance time and which has minimum gross weight.

2. Keywords: Helicopter Conceptual Design, MDA, MDO Framework, EMDIOS

3. Introduction

Aircraft design process consists of conceptual design, preliminary design and detail design. As the design is progressed, aircraft’s properties are more detailed. It is in conceptual design that the basic questions of configuration arrangement, size and weight, and performance are answered. Conceptual design is characterized by large number of design alternatives and trade studies, and continuous, evolutionary change to the aircraft concepts under consideration.[1]

Helicopter is a useful vehicle which has vertical take-off and landing ability. Vertical take-off and landing ability is possible because of rotating blades give lift force to helicopter. That is the reason helicopter does not need a runway to take-off landing. And vertical take-off and landing ability give hovering ability to the helicopter. Because of this characteristic, helicopter is widely use in many fields which are SAR (Search And Rescue), transport, suppression of a fire and etc.

But this ability causes very complex mechanism of flight and flow around helicopter. Therefore helicopter analysis and design is very difficult and important. As conventional aircraft design, helicopter design is started from weight assumption and includes many parts of analysis. Helicopter analysis parts include aerodynamics, structural dynamics, propulsion dynamics, control dynamics and so on. And each result from analysis parts is used in the helicopter design as a design parameter. But unlike conventional aircraft analysis, helicopter analysis is more complex because of helicopter rotor blade is rotating. In the rotating blade, tip vortex influence other blade (BVI : blade vortex interaction), fuselage and so on. There are more problems to make helicopter analysis difficult besides aforementioned problem.

Like airplane conceptual design, results from many analysis parts are needed in the helicopter conceptual design. We need to design a helicopter which has best result at each analysis part. But occasionally in real helicopter design process, helicopter configuration which has best result in one analysis part is not best in another analysis part. Or an optimal configuration in a one part, this optimal configuration has a good performance in this part but it has a bad performance in another part. Therefore a design which is considered one analysis part is not an optimal design in whole domain. Because of this reason, we need a multidisciplinary optimization in the helicopter conceptual design

In this research, we have constructed helicopter analysis modules of simple empirical formulas and analysis technique. And this analysis module has been validated existing helicopter data. Using this analysis module, we have constructed MDA then we have performed an optimization. We have used EMDIOS which has been developed as an optimization tool, and we have performed optimization design which has maximum endurance time and minimize take-off gross weight of the helicopter.

4. Helicopter Conceptual Design (Construction of MDA)

We have constructed helicopter conceptual design module using multiple analysis parts and validate this module using existing helicopter data. Empirical equations which are on the related books [2], [3], [4], [5] and paper [6] are used for this research.

4.1. Mission Profile and Requirements

Aircraft design starts from the analysis of mission profile of the helicopter. It is basic in the aircraft design that we get the information of the maximum take-off gross weight of the helicopter from the analysis of the mission profile. It is same to the helicopter design, in

addition, some mission profile segment of the helicopter is different from mission profile of the airplane. These are hovering, vertical take-off and lading and etc. These mission profile segment is the characteristic of the helicopter differ from the airplane. For example, SAR (Search And Rescue) mission of the helicopter including vertical take-off and lading, and hovering is shown in Figure. 1. This mission profile includes forward flight and forward climbing which are difficult to analysis. Therefore, to analysis these mission segment, helicopter conceptual design module must is included the complicated aerodynamic analysis module. In this study, the goal of the research is to construct the MDO framework of helicopter conceptual design, therefore we choose a simple mission profile which is included hovering shown in Figure. 2.

Figure 1. SAR mission profile

Figure 2. Mission profile for this helicopter conceptual design

Mission requirement is a key of the helicopter conceptual design. In the requirement, we get the information of payload weight and wanted range. This information is the major factor of the fuel weight, empty weight and maximum gross weight which are major factor of the configuration of the helicopter. In this study, we give these information is related with the existing helicopter which has similar size with the helicopter which will have designed.

4.2. Helicopter conceptual design process

From the data of the input file which has a data of mission profile and mission requirement, we have assumed helicopter’s maximum take-off gross weight. And we input wanted payload, wanted gross weight and wanted range to conceptual design module. We calculate main rotor disc area from a statistical equation of disc loading by using helicopter’s maximum gross weight. Because of each helicopter has a different disc loading, we should input a disc loading by input data file. But in this study, we use statistical equation of Ref.4, because equation about the disc loading in this paper is the function of gross weight. According to this equation, we can calculate the main rotor disc area and we can calculate main rotor blade span from main rotor disc area. And helicopter main rotor blade chord is given by the statistical equation of the main rotor solidity. This configuration of the main rotor is given to the aerodynamic analysis module to calculate the aerodynamic performance of the main rotor.

And results from aerodynamic module and main rotor configuration is used in the another module. Each module has statistical equation to calculate the configuration of each part, and we can calculate the performance of each part from the configuration. We calculate helicopter’s empty weight from configuration and performance. From statistical equation, we calculate new take-off gross weight, and we compare take-off gross weight which was estimated and new take-off gross weight. The helicopter conceptual design process is end until take-off weight difference is smaller than 5%.

4.3. Aerodynamic Analysis Module

We have used an analytical method for an aerodynamic analysis module for helicopter conceptual design. An aerodynamic analysis using analytical method is less accurate than Navier-Stokes equation and Euler equation which are high order accurate method and which give particular solution like pressure contour at a particular blade section for us, but in the helicopter aerodynamic analysis, it has a merit of time efficiency than the high order accurate method . In this research, we do not want a particular solution of the helicopter rotor blade but a general performance of the helicopter rotor, the analytical method is efficient.

In the analytical method of helicopter aerodynamic analysis method, there are two types of methodology, Momentum Theory and Blade Element Theory. Both methods are time efficient method and . Momentum theory is obtained by the application of Newtonian mechanics to the overall process. The rotor is conceived as an ‘actuator disc’, across which there is a sudden increase of pressure,

uniformly spread. But the Momentum Theory was not use for this research because of this method does not consider a real helicopter rotor blade configuration. The Momentum Theory does not give us a helicopter configuration, therefore we use the Blade Element Theory. Also we can calculate a drag using the Blade Element Theory although Momentum Theory does not.

Blade Element Theory is basically the application of the standard process of airfoil theory to the rotating blade. Although in reality flexible, the blade is assumed throughout to be rigid, justification for this lying in the fact that at normal rotation speeds the outward centrifugal force is the largest force acting on a blade and in effect is sufficient to hold the blade in rigid form. In vertical flight, including hover, the main complication is the need to integrate the elementary force along the blade span. Offsetting this, useful simplification occurs because the blade incidence and induced flow angles are normally small enough to allow small-angle approximations to be made. [2]

As you can see in the Figure. 3 and Figure. 4, approximate a blade section in the vertical flight, the resultant velocity is 22)()(y V v U C i ?++=

(1)

Figure 3. Top view of the helicopter rotor blade

Figure 4. Blade element

The blade pitch angle, determined by the pilot’s collective control setting is θ. The angle between the flow direction and the plane of the rotation, known as the inflow angle is φ, given by

?????????+=?y v V i C 1tan φ (2) Or for small angles, which we shall assume, y v V i

C ?+=φ (3)

The angle of incidence of the blade section, denoted α, is seen to be

φθα?= (4) The elementary lift and drag forces on the section are

L cdyC U dL 221ρ= (5) D cdyC U dD 22

1ρ= (6)

In the Eq.(5) and Eq.(6), ρ is an air density, c is the blade chord, dy is the elementary span, and , is the aerodynamic coefficient of the airfoil.

L C D C Resolving these normal and parallel to the disc plane gives an element of thrust and torque shown as Eq.(7), Eq.(8)

(7) φφsin cos dD dL dT ?=

(8)

y dD dL dQ )cos sin (φφ+=And power required is shown as Eq.(9)

Q P ?= (9) In the Blade Element Theory, we can calculate the lift force and drag force from the airfoil aerodynamic data, and using lift and drag force we can calculate thrust, torque and power. For the rotor blade, we have used NACA0012 airfoil aerodynamic coefficient in this analysis and we have used rectangular planform.

4.4. Noise Module

The Helicopter get a lift force by the rotating blade, therefore there are many source of noise in the helicopter. The helicopter is the quietest VTOL aircraft, but its noise level can still be high enough to compromise its utility unless specific attention is given to designing for low noise. As the restrictions on aircraft noise increase, the rotor noise becomes an increasingly important factor in helicopter design.

There are many types of noise of helicopter. First, helicopter rotor noise tends to be concentrated at harmonics of the blade passage frequency, because of the periodic nature of the rotor. Rotor vortex noise is a high frequency sound produced by random fluctuations of the forces on the blade. The principal source of vortex noise appears to be the lift fluctuations resulting from operation of the blade in the turbulent rotor wake; the random loads induced by the tip vortices in the wake are especially important. Rotor rotational noise is a periodic sound pressure disturbance and rotor blade slap is an impulsive type of sound pressure disturbance occurring at the blade passage frequency.

There are many source of noise in the helicopter, the noise analysis is complex and difficult. In this analysis, we have used empirical equation for the noise analysis. [7]

4.4. Stability Module

As with an airplane, both static stability and dynamic stability contribute to the flying qualities of a helicopter. Static stability refers to the initial tendency of the aircraft to return to its trimmed condition following a displacement. Dynamic stability considers the subsequent motion in time, which may consist of a dead-beat return, an oscillatory return, a no-change motion, an oscillatory divergence or a non-return divergence; the first two signifying positive stability, the third neutral stability and the last two negative stability. A statically unstable also dynamically unstable but a statically stable motion may be either stable or unstable dynamically. The subject of stability and control in totality is a formidable one. The part played by the rotor is highly complicated, because strictly each blade possesses its own degrees of freedom and makes an individual contribution to any disturbed motion. However, analysis can almost always be made satisfactorily by considering the behavior of the rotor as a whole. [8]

In this study, we have performed simple stability analysis. We consider tail rotor as anti-torque device and controller, according to this concept, we have calculated the tail rotor configuration by the statistical equation and we have calculated an aerodynamic forces by Blade Element Theory. And we have obtained horizontal and vertical stabilizer from statistical equation.

4.5. Propulsion Module

All forms of aircraft propulsion develop thrust by pushing air backward, in a helicopter develop thrust by pushing air horizontal. In the helicopter conceptual design, engine sizing is the major factor of performance, for example climb rate, range and endurance. Helicopter has turbine engine as its propulsion device. For this study, we have colleted turbine engine data which is used in a helicopter from engine maker. [9] From this data, we have got the range of SFC (Specific Fuel Consumption) and engine weight in terms of designed power.

In this module, required power of main rotor and tail rotor from aerodynamic module and stability module has been used. Also statistical equation of power in terms of gross weight is used for estimation of helicopter’s engine sizing. From this propulsion module, we have got the data of SFC and weight of helicopter’s turbine engine. Turbine engine’s SFC varies from 0.4 to 0.6 lb/hr/lb, shown as Figure 5.

Figure 5. SFC vs. Power

4.6. Performance Module

The assessment of helicopter performance, like that of airplane, is at bottom a matter of comparing the power required with that available, in order to determine whether a particular flight task is feasible. The number of different performance calculations that can be made for a particular aircraft is of course unlimited, but aircraft specification sets the scene in allowing meaningful limits to be prescribed.

Before performance analysis can be done, it is necessary to collect the individual items of information that are required: the performance of the individual rotors, the installed engine performance, the power losses in transmission and accessories, the vertical drag in hover, the tail rotor-fin interference, and the parasite drag in forward flight. These information effect a performance of the whole helicopter. Representative performance of a helicopter is vertical climb rate, forward flight speed, range, endurance time and etc. Equation for the range of the helicopter is shown Eq. (10).

. (10)

.)(....W dG Range Specific Range off

take W G landing W G ∫=Where,

hr lb hr Km flow Fuel speed

Ground Range Specific //,= (11)

And G.W is a gross weight. Equation for the range of the helicopter is shown Eq. (12).

. (12) .)(....W dG Endurance Specific Endurance off

take W G landing W G ∫=

Where,

hr

lb flow Fuel Endurance Specific /1,1= (13)

In this study, we have calculated an endurance time of the designed helicopter using empirical equation. We will add range and vertical climb rate calculation model to the performance analysis module in the future.

4.7. Weight Module

A key process in the conceptual design process is the weight analysis. This analysis bases estimates both on the empirical equation and future engineering trends. This analysis include equations for each aircraft component that have been derived form weight data on previous helicopter subjected to a mathematical process known as multiple linear regression. This determines sensitivity with respect to very parameter that logically affects the weight of the component. In this study, we use weight estimation equations of Ref. 3. H-1, therefore e use data of UH-1 as baseline data for conceptual design. And result which has been obtained is shown as Table 1.

Table 1. UH-1 vs. Designed case

Configurati Designed e

4.8. Validation

We have constructed helicopter conceptual design module and have validated this module with an existing helicopter data. First we gave 9,000lb class of helicopter input data to conceptual design module. Representative of 9,000lb class helicopter is U w on UH-1 Case

Chord (m) 0.59 0.60

Rotor Nu de mber of Bla 2 2

1.9

Chord(m) 0 0.31 .29Rotor Number of blade 2 2

Power plant Engine Power (hp)783 x 2 745 x 2

Range (km) 500 500

Performance 3 h n 2

Endurance r 18 mi hr 50 min Empty (lb) 5,210 5,633.2

P ayload (lb)2,677 2,975.4

Fuel (lb) 1,573 1,296.6

Weight Take-off gross (lb)9,500 9,905.2

As you can see in the Table 1, the designed case is similar to UH-1. This helicopter conceptual design module has a tendency of over-estimate in rotor configuration. And in the power plant point of view, this conceptual design module has a tendency of under-estimate. These tendencies come from an effect of empirical and statistical equation. And in the result of endurance time, designed case is smaller than baseline. It comes from that the fuel weight calculation using statistical equation is smaller than the fuel e we use data of UH-60 as baseline data for the conceptual design. And result which has been obtained is shown as Table 2. .

Table 2. UH-60 vs. Designed case

Configurati U Designed weight of the UH-1.

Second, we gave 20,000lb class of helicopter input data to conceptual design module. Representative of 20,000lb class helicopter is UH-60, therefor on H-60Case

Radius (m) 8.18 9.41

Chord (m) 0.53 0.55

Main Rotor 4 4

Radius(m) 1.675 1.58

Chord(m) 0 0.21 .23Tail Rotor 4 4

Power plant Engine Power (hp)1,8 2 1,00 x 770 x 2

Range (km) 600 600

Performance 2 Endurance h 18 min 3 h

Empty (lb) 10,625 11,066

P ayload (lb)8,000 5,909

Fuel (lb) 1,575 3,048

Weight Take-off gross (lb)20,200 20,023

As you can see in the Table 2, the designed case is similar to UH-60. But in the endurance time point of view, designed case has greater than UH-60 and this result caused by the fuel weight difference. In this study, we have used statistical equation which is obtained by data from large number of helicopter and each helicopter has a different mission profile. Because of this reason, the design result of this study is not identical with an existing helicopter. But the configuration of the designed helicopter and gross weight is same as the , we have validated that this helicopter design module which are constructed in this study is by the data of the xisting helicopter.

5ed Multidisciplinary optimization. In this analysis, we have performed two cases of optimization, 9,500 lb class and 20,000 lb class. existing helicopter, we have validated this MDA module.

Through this process e

. MDO Framework

We have constructed the Multidisciplinary Analysis (MDA) as to unite the analysis parts in the previous part. And we have perform

5.1. Selection of Design Variables

The design space handled in this study consists of parameters related to the whole helicopter, therefore there are many design variables. But we want to observe the performance of the helicopter and weight of the helicopter which are related to the main rotor, SFC of engine, wanted range and weight. Therefore we use these parameters as design variable, and these variables are summarized in Table 3, nd Table 4.

Table 3.n variable (Ca 0 lb class)

a Range of desig se 1 : 9,50AoA(°)

0 5 10 Take-off weight 8,550 9,500 10,450

gross (lb)

SFC (lb/hr/lb)0.4 0.5 0.6

450 500 550

Table 4. variable (Cas 0 lb class)

Design Variable Lower Bound Baseline Upper Bound

Range of design e 2 : 20,00AoA(°)

0 5 10 Take-off weight 18,180 20,200 22,200

gross (lb)

SFC (lb/hr/lb)0.4 0.5 0.6

540 600 660

Wanted range is determined 500 km and 600 km, because of SAR helicopter using in Korea National Emergency Management Agency has this amount of range. Each design parameter upper and lower bound varies 10% from a baseline parameter, but AoA and SFC does not. AoA varies from 0° to 10°, it is because of helicopter main rotor blade pitch angle varies within this region. And SFC is analyzed in e 4.5

e have performed four cases o

f optimization. The multidisciplinary optimizations which are we have erformed are defined as:

ase 1-1: Maximize Endurance Time in 9,500 lb class helicopter th

5.2Objective Function and Constraints

Optimization design is performed at each design point by considering performance in each module. In the helicopter design, best helicopter design is maximum endurance hour while the less gross weight or minimum gross weight while the greater payload weight and endurance time. In this study, we have performed multidisciplinary optimization using Sequential Quadratic Programming (SQP) which is an optimization module of the EMDIOS developed by The Center of Innovation Design Optimization Technology (iDOT). Using this methodology w p C

Fuel Weight < 1,231.7 lb (decrease 5% than baseline)

ase 1-2: Minimize Gross Weight in 9,500 lb class helicopter

Objective: Maximize Endurance Time

Constraints: Total gross weight < 9,905 lb

C

3.214 hr (increase 10% than baseline)

Range = 500 km

ase 2-1: Maximize Endurance Time in 20,200 lb class helicopter

Objective: Minimize Gross Weight

Constraints: Payload weight > 2,980 lb

Endurance time > C

Fuel Weight < 2895.67 lb (decrease 5% than baseline)

ase 2-2: Minimize Gross Weight in 20,200 lb class helicopter

Objective: Maximize Endurance Time

Constraints: Total gross weight < 20,023 lb

C

3.256 hr (increase 10% than baseline)

Range = 600 km

ultidisciplinary optimization results are shown in the Table 5,6,7,8.

Objective: Minimize Gross Weight

Constraints: Payload weight > 5,908.9 lb

Endurance time >

5.3. Optimization Results

M

Table 5. Case 1-1 Re Baseline Optim sult sult ization Re Objective

Endurance Time (hr) 2.678 3.411 AoA (°) 5 5 SFC (lb/hr/lb) 0.5 0.4 9,905.2 9,297.7

5,633.2 5,310.6

Fuel Weight (lb) 1,296.5 1,231.7

Payload (lb) 2,975.5 2,755.4

Design Variables Range (km) 500 513

tor of increase in endurance time is decrease of the SFC and gross weight. Optimized result is improved than the baseline in general.

Table 6. Case 1-2 Re As shown in the Table 5, endurance time increase 27%, gross weight decrease 6.2%, fuel weight decrease 5%, range increase 2.6%, and payload decrease 7.4%. Significant fac sult

AoA (°) 5 5 SFC (lb/hr/lb) 0.5 0.4 E mpty Weight (lb)5,633.2 5,640.6 Fuel Weight (lb) 1,296.5 1,298.5

2,975.5 2,980.0

3.344 2.678 Design Variables Range (km) 500 500

y weight calculation odule which has an over-estimated tendency. But generally, optimization result is improved than the baseline.

Table 7. Case 2-1 Re Baseline Optim sult As shown in the Table 6, weight is almost same, but range increase 2.6%, and payload decrease 7.4%. In this case, range is fixed as 500km. The slight increase of gross weight is affected by the constraint of increase in endurance time and empt m sult

ization Re Objective Endurance Time (hr) 2.96 3.518

AoA (°) 5 5 SFC (lb/hr/lb) 0.5 0.4 20,023.0 20,015.1 11,066.0 11,059.6

Fuel Weight (lb) 3,048.0 2,897.6

Payload (lb) 5,909.0 6,057.9

Design Variables Range (km) 600 554.6

Endurance time increases 19%, payload weight increases 2%. Significant factor of increase in endurance time is decrease of the SFC as the result of case 1-1. Fuel weight has larger than upper bound of constraint, it is because that the violation in the optimization program allowed 0.001.

Table 8. Case 2-2 Re Baseline Optim esult is sult

ization R Objective Gross 20,023.0 19,640.3 Weight (lb) AoA (°) 5 5 0.5 0.4 E mpty Weight (lb)11,066.0 10,904.5 Fuel Weight (lb) 3,048.0 2,826.8

Payload (lb) 5,909.0 5,909.0

Endurance Time (hr) 3.517 2.96 Design Variables Range (km) 600 600

As shown in the Table 8, gross weight decrease 2%, and endurance time increase 19%, and fuel weight decrease 7%. And in this case, nge is fixed as 600km. In the endurance time and fuel weight point of view, optimization result is improved than the baseline. ra

6. Conclusions

In the present study, we have constructed the MDA for the helicopter conceptual design, and performed the MDO for the helicopter onceptual design. By the results, some conclusions are drawn as follows;

2. and stability and noise and performance analysis

3. dule with data of existing 9,500 lb and 20,200 lb class helicopter and designed result is satisfied with

0 lb class helicopter is performed.

5. There is the optimum results are satisfied with the constraints for each four cases.

a 21 Project in 2005 and Center of Innovative Design Optimization Technology (ERC of orea Science and Engineering Foundation)

craft Design: A Conceptual Approach 3rd ed. Virginia: AIAA – American Institute of Aeronautics and mpany, 1995

. Jane’s All The World’s Aircraft, Eighty-ninth year of issue 1998~99. Coulsdon, Surrey: Jane’s Information Group J. Contributions to the dynamic stability of rotary wing aircraft with articulated blades. Air Material Command Trans. 9. Turbine engine data from website. Http://www.aric.or.kr/info/spec/list.asp?la=2&mi=0c 1. Develop MDO framework using MDA which is composed four disciplines.

Aerodynamic analysis discipline is using Blade Element Theory,disciplines are composed a statistical equation or empirical equation

Validate the MDA mo a certain error range

4. Using the MDA module, optimization of two cases for the each 9,500 lb and 20,20

8. Acknowledgement

This research was supported by the Brain Kore K

9. References

1. Daniel P. Raymer. Air Astronautics, 1999

2. J. Seddon. Basic Helicopter aerodynamics. Oxford: BSP Professional Books, 1990

3. Raymond W. Prouty. Helicopter Performance, Stability, and Control. Malabar, Florida: Krieger Publishing Co

4. J. Gordon Leishman, Principles of Helicopter Aerodynamics. Cambridge: Cambridge University Press, 2002

5. Paul Jackson Limited, 1998

6. O. Rand and V. Khromov. Helicopter sizing by statistics. American Helicopter Society 58th Annual Forum, 2002, Montreal

7. Korean Science Foundation. Report of the Preliminary Study for an Advanced Helicopter Development. KOSEF 87-0212-05, 1990

8. Sissingh, G.F-TS-690-RE