土木工程英文文献及翻译-英语论文

土木工程英文文献及翻译

in Nanjing, China

Zhou Jin, Wu Yezheng *, Yan Gang

Department of Refrigeration and Cryogenic Engineering, School of Energy and Power Engineering, Xi’an Jiao Tong University,

Xi’an , PR China

Received 4 April 2005; accepted 2 October 2005

Available online 1 December 2005

AbstractThe bin method, as one of the well known and simple steady state methods used to predict heating and cooling energy

consumption of buildings, requires reliable and detailed bin data. Since the long term hourly temperature records are not

available in China, there is a lack of bin weather data for study and use. In order to keep the bin method practical in China,

a stochastic model using only the daily maximum and minimum temperatures to generate bin weather data was established

and tested by applying one year of measured hourly ambient temperature data in Nanjing, China. By comparison with the

measured values, the bin weather data generated by the model shows adequate accuracy. This stochastic model can be used

to estimate the bin weather data in areas, especially in China, where the long term hourly temperature records are missing

or not available.

Keywords: Energy analysis; Stochastic method; Bin data; China

1. Introduction

In the sense of minimizing the life cycle cost of a building, energy analysis plays an important role in devel-

oping an optimum and cost e?ective design of a heating or an air conditioning system for a building. Several

models are available for estimating energy use in buildings. These models range from simple steady state mod-

els to comprehensive dynamic simulation procedures.

Today, several computer programs, in which the in?uence of many parameters that are mainly functions

of time are taken into consideration, are available for simulating both buildings and systems and performing

hour by hour energy calculations using hourly weather data. DOE-2, BLAST and TRNSYS are such

* Corresponding author. Tel.: +86 29 8266 8738; fax: +86 29 8266 8725.

E-mail address: yzwu@https://www.360docs.net/doc/d916426760.html, (W. Yezheng).

doi:10.1016/j.enconman.2005.10.006

Nomenclature

Z. Jin et al. / Energy Conversion and Management 47 (2006) 1843–1850

number of days

frequency of normalized hourly ambient temperature

MAPE mean absolute percentage error (%)

number of subintervals into which the interval [0, 1] was equally divided

number of normalized temperatures that fall in subinterval

probability density

hourly ambient temperature (°C)

normalized hourly ambient temperature (dimensionless)

weighting factor

Subscripts

calculated value

measured value

max daily maximum

min daily minimum

programs that have gained widespread acceptance as reliable estimation tools. Unfortunately, along with

the increased sophistication of these models, they have also become very complex and tedious to

use [1].

The steady state methods, which are also called single measure methods, require less data and provide

adequate results for simple systems and applications. These methods are appropriate if the utilization of

the building can be considered constant. Among these methods are the degree day and bin data methods.

The degree-day methods are the best known and the simplest methods among the steady state models.

Traditionally, the degree-day method is based on the assumption that on a long term average, the solar

and internal gains will o?set the heat loss when the mean daily outdoor temperature is 18.3 °C and that

the energy consumption will be proportional to the di?erence between 18.3 °C and the mean daily tempera-

ture. The degree-day method can estimate energy consumption very accurately if the building use and the

e?ciency of the HVAC equipment are su?ciently constant. However, for many applications, at least one

of the above parameters varies with time. For instance, the e?ciency of a heat pump system and HVAC equip-

ment may be a?ected directly or indirectly by outdoor temperature. In such cases, the bin method can yield

good results for the annual energy consumption if di?erent temperature intervals and time periods are

evaluated separately. In the bin method, the energy consumption is calculated for several values of the outdoor

temperature and multiplied by the number of hours in the temperature interval (bin) centered around that

temperature. Bin data is de?ned as the number of hours that the ambient temperature was in each of a set

of equally sized intervals of ambient temperature.

In the United States, the necessary bin weather data are available in the literature [2,3]. Some researchers

[4–8] have developed bin weather data for other regions of the world. However, there is a lack of information

in the ASHRAE handbooks concerning the bin weather data required to perform energy calculations in build-

ings in China. The practice of analysis of weather data for the design of HVAC systems and energy consump-

tion predictions in China is quite new. For a long time, only the daily value of meteorological elements, such as

daily maximum, minimum and average temperature, was recorded and available in most meteorological

observations in China, but what was needed to obtain the bin weather data, such as temperature bin data,

were the long term hourly values of air temperature. The study of bin weather data is very limited in China.

Only a few attempts [9,10] in which bin weather data for several cities was given have been found in China.

Obviously, this cannot meet the need for actual use and research. So, there is an urgent need for developing bin

weather data in China. The objective of this paper, therefore, is to study the hourly measured air temperature

distribution and then to establish a model to generate bin weather data for the long term daily temperature

data.2. Data used

Z. Jin et al. / Energy Conversion and Management 47 (2006) 1843–1850

In this paper, to study the hourly ambient temperature variation and to establish and evaluate the model, a

one year long hourly ambient temperature record for Nanjing in 2002 was used in the study. These data are

taken from the Climatological Center of Lukou Airport in Nanjing, which is located in the southeast of China

(latitude 32.0°N, longitude 118.8°E, altitude 9 m).

In addition, in order to create the bin weather data for Nanjing, typical weather year data was needed.

Based on the long term meteorological data from 1961 to 1989 obtained from the China Meteorological

Administration, the typical weather year data for most cities in China has been studied in our former research

[11] by means of the TMY (Typical Meteorological Year) method. The typical weather year for Nanjing is

shown in Table 1. As only daily values of the meteorological elements were recorded and available in China,

the data contained in the typical weather year data was also only daily values. In this study, the daily maxi-

mum and minimum ambient temperature in the typical weather year data for Nanjing was used.

3. Stochastic model to generate bin data

Traditionally, the generation of bin weather data needs long term hourly ambient temperature records.

However, in the generation, the time information, namely the exact time that such a temperature occurred

in a day, was omitted, and only the numerical value of the temperature was used. So, the value of each hourly

ambient temperature can be treated as the independent random variable, and its distribution within the daily

temperature range can be analyzed by means of probability theory.

3.1. Probability distribution of normalized hourly ambient temperature

Since the daily maximum and minimum temperatures and temperature range varied day by day, the con-

cept of normalized hourly ambient temperature should be introduced to transform the hourly temperatures in

each day into a uniform scale. The new variable, normalized hourly ambient temperature is de?ned by

^ ?ttmintmaxtmin

where ^ may be termed the normalized hourly ambient temperature, tmaxand tminare the daily maximum and

minimum temperatures, respectively, t is the hourly ambient temperature. Obviously, the normalized hourly ambient temperature ^ is a random variable that lies in the interval [0, 1].

To analyze its distribution, the interval [0, 1] can be divided equally into several subintervals, and by means of

the histogram method [12]i

in each subinterval can be calculated by1137

土木工程英文文献及翻译

Based on the one year long hourly ambient temperature data in Nanjing, China, the probability density pi

was calculated for the whole day and the 08:00–20:00 period, where the interval [0, 1] was equally divided into

50 subintervals, namely n equals 50. The results are shown in Fig. 1.

According to the discrete probability density data in Fig. 1, the probability density function of ^ can be

obtained by a ?tting method. In this study, the quadratic polynomials

were used to ?t the probability density data, where a, b and c are coe?cients. According to the property of the

probability density function, the following equation should be satis?ed

As shown in Fig. 1, the probability density curve obtained according to the probability density data points

is also shown. The probability density functions that are ?tted are described by

p ? 2:7893^23:1228^ t 1:6316 for the whole day period

p ? 2:2173^20:1827^ t 0:3522 for the 08 : 00–20 : 00 period

3.2. The generation of hourly ambient temperature

As stated in the beginning of this paper, the objective of this study is to generate the hourly ambient tem-

perature needed for bin weather data generation in the case that only the daily maximum and minimum tem-

peratures are known. To do this, we can use the obtained probability density function to generate the

normalized hourly ambient temperature and then transform it to hourly temperature. This belongs to the

problem of how to simulate a random variable with a prescribed probability density function and can be done

on a computer by the method described in the literature [13]. For a given probability density function f e^T, if

its distribution function F e^T can be obtained and if u is a random variable with uniform distribution on [0, 1],

then

F, we need only set

As stated above, the probability density function of the normalized ambient temperature was ?tted using a

one year long hourly temperature data. Based on the probability density function obtained, the random nor-

malized hourly temperature can be generated. When the daily maximum and minimum temperature are

known, the normalized hourly temperature can be transformed to an actual temperature by the following

equation

When the hourly temperature for a particular period of the day has been generated using the above method,

the bin data can also be obtained. Because the normalized temperature generated using the model in this study

is a random variable, the bin data obtained from each generation shows some

di?erence, but it has much sim-

ilarity. To obtain a stable result of bin data, the generation of the bin data can be performed enough times,

and the bin data can be obtained by averaging the result of each generation. In this paper, 50 generations were

averaged to generate the bin weather data.

Z. Jin et al. / Energy Conversion and Management 47 (2006) 1843–1850

3.4. Methods of model evaluation

The performance of the model was evaluated in terms of the following statistical error test:

土木工程英文文献及翻译

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图表和图片图表和图片的题目格式与正文相同，位于图表和图片的下部。题目前加Figure 后跟数字，表示此文的第几个图表。图表及题目都居中。只允许使用黑白图片和表格。举例： Figure 1. The Trend of Economic Development 注：Figure与Table都不要缩写。引用格式与参考文献 1. 在论文中的引用采取插入作者、年份和页数方式，如"Doe (2001, p.10) reported that …" or "This在论文中的引用采取作者和年份插入方式，如"Doe (2001, p.10) reported that …" or "This problem has been studied previously (Smith, 1958, pp.20-25)。文中插入的引用应该与文末参考文献相对应。举例：Frankly speaking, it is just a simulating one made by the government, or a fake competition, directly speaking. (Gao, 2003, p.220). 2. 在文末参考文献中，姓前名后，姓与名之间以逗号分隔；如有两个作者，以and连接；如有三个或三个以上作者，前面的作者以逗号分隔，最后一个作者以and连接。 3. 参考文献中各项目以“点”分隔，最后以“点”结束。 4. 文末参考文献请按照以下格式：

土木工程外文文献翻译

专业资料学院：专业：土木工程姓名：学号：外文出处：Structural Systems to resist (用外文写) Lateral loads 附件：1.外文资料翻译译文；2.外文原文。

附件1：外文资料翻译译文抗侧向荷载的结构体系常用的结构体系若已测出荷载量达数千万磅重，那么在高层建筑设计中就没有多少可以进行极其复杂的构思余地了。确实，较好的高层建筑普遍具有构思简单、表现明晰的特点。这并不是说没有进行宏观构思的余地。实际上，正是因为有了这种宏观的构思，新奇的高层建筑体系才得以发展，可能更重要的是：几年以前才出现的一些新概念在今天的技术中已经变得平常了。如果忽略一些与建筑材料密切相关的概念不谈，高层建筑里最为常用的结构体系便可分为如下几类： 1.抗弯矩框架。 2.支撑框架，包括偏心支撑框架。 3.剪力墙，包括钢板剪力墙。 4.筒中框架。 5.筒中筒结构。 6.核心交互结构。 7. 框格体系或束筒体系。特别是由于最近趋向于更复杂的建筑形式，同时也需要增加刚度以抵抗几力和地震力，大多数高层建筑都具有由框架、支撑构架、剪力墙和相关体系相结合而构成的体系。而且，就较高的建筑物而言，大多数都是由交互式构件组成三维陈列。将这些构件结合起来的方法正是高层建筑设计方法的本质。其结合方式需要在考虑环境、功能和费用后再发展，以便提供促使建筑发展达到新高度的有效结构。这并

不是说富于想象力的结构设计就能够创造出伟大建筑。正相反，有许多例优美的建筑仅得到结构工程师适当的支持就被创造出来了，然而，如果没有天赋甚厚的建筑师的创造力的指导，那么，得以发展的就只能是好的结构，并非是伟大的建筑。无论如何，要想创造出高层建筑真正非凡的设计，两者都需要最好的。虽然在文献中通常可以见到有关这七种体系的全面性讨论，但是在这里还值得进一步讨论。设计方法的本质贯穿于整个讨论。设计方法的本质贯穿于整个讨论中。抗弯矩框架抗弯矩框架也许是低，中高度的建筑中常用的体系，它具有线性水平构件和垂直构件在接头处基本刚接之特点。这种框架用作独立的体系，或者和其他体系结合起来使用，以便提供所需要水平荷载抵抗力。对于较高的高层建筑，可能会发现该本系不宜作为独立体系，这是因为在侧向力的作用下难以调动足够的刚度。我们可以利用STRESS，STRUDL 或者其他大量合适的计算机程序进行结构分析。所谓的门架法分析或悬臂法分析在当今的技术中无一席之地，由于柱梁节点固有柔性，并且由于初步设计应该力求突出体系的弱点，所以在初析中使用框架的中心距尺寸设计是司空惯的。当然，在设计的后期阶段，实际地评价结点的变形很有必要。支撑框架支撑框架实际上刚度比抗弯矩框架强，在高层建筑中也得到更广泛的应用。这种体系以其结点处铰接或则接的线性水平构件、垂直构件和斜撑构件而具特色，它通常与其他体系共同用于较高的建筑，并且作为一种独立的体系用在低、中高度的建筑中。

土木工程专业外文文献及翻译

（二〇一二年六月外文文献及翻译题目： About Buiding on the Structure Design 学生姓名：学院：土木工程学院系别：建筑工程系专业：土木工程(建筑工程方向) 班级：土木08-4班指导教师：

英文原文： Building construction concrete crack of prevention and processing Abstract The crack problem of concrete is a widespread existence but again difficult in solve of engineering actual problem, this text carried on a study analysis to a little bit familiar crack problem in the concrete engineering, and aim at concrete the circumstance put forward some prevention, processing measure. Keyword:Concrete crack prevention processing Foreword Concrete's ising 1 kind is anticipate by the freestone bone, cement, water and other mixture but formation of the in addition material of quality brittleness not and all material.Because the concrete construction transform with oneself, control etc. a series problem, harden model of in the concrete existence numerous tiny hole, spirit cave and tiny crack, is exactly because these beginning start blemish of existence just make the concrete present one some not and all the characteristic of quality.The tiny crack is a kind of harmless crack and accept concrete heavy, defend Shen and a little bit other use function not a creation to endanger.But after the concrete be subjected to lotus carry, difference in temperature etc. function, tiny crack would continuously of expand with connect, end formation we can see without the

商业建筑外文文献翻译)

Commercial Buildings Abstract: A guide and general reference on electrical design for commercial buildings is provided. It covers load characteristics; voltage considerations; power sources and distribution apparatus; controllers; services, vaults, and electrical equipment rooms; wiring systems; systems protection and coordination; lighting; electric space conditioning; transportation; communication systems planning; facility automation; expansion, modernization, and rehabilitation; special requirements by occupancy; and electrical energy management. Although directed to the power oriented engineer with limited commercial building experience, it can be an aid to all engineers responsible for the electrical design of commercial buildings. This recommended practice is not intended to be a complete handbook; however, it can direct the engineer to texts, periodicals, and references for commercial buildings and act as a guide through the myriad of codes, standards, and practices published by the IEEE, other professional associations, and governmental bodies. Keywords: Commercial buildings, electric power systems, load characteristics 1. Introduction 1.1 Scope This recommended practice will probably be of greatest value to the power oriented engineer with limited commercial building experience. It can also be an aid to all engineers responsible for the electrical design of commercial buildings. However, it is not intended as a replacement for the many excellent engineering texts and handbooks commonly in use, nor is it detailed enough to be a design manual. It should be considered a guide and general reference on electrical design for commercial buildings. 1.2 Commercial Buildings The term “commercial, residential, and institutional buildings”as used in this chapter, encompasses all buildings other than industrial buildings and private dwellings. It includes office and apartment buildings, hotels, schools, and churches, marine, air, railway, and bus terminals, department stores, retail shops, governmental buildings, hospitals, nursing homes, mental and correctional institutions, theaters, sports arenas, and other buildings serving the public directly. Buildings, or parts of buildings, within industrial complexes, which are used as offices or medical facilities or for similar nonindustrial purposes, fall within the scope of this recommended practice. Today’s commercial buildings, because of their increasing size and complexity, have become more and more dependent upon adequate and reliable electric systems. One can better understand the complex nature of modern commercial buildings by examining the systems, equipment, and facilities listed in 1.2.1. 1.2.2 Electrical Design Elements In spite of the wide variety of commercial, residential, and institutional buildings, some electrical design elements are common to all. These elements, listed below, will be discussed generally in this section and in detail in the remaining sections of this recommended practice. The principal design elements considered in the design of the power, lighting, and auxiliary systems include: 1) Magnitudes, quality, characteristics, demand, and coincidence or diversity of loads and load factors 2) Service, distribution, and utilization voltages and voltage regulation 3) Flexibility and provisions for expansion