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E-raamat: Inverse Design Methods for the Built Environment

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, , (Purdue University, USA),
  • Formaat: 264 pages
  • Ilmumisaeg: 14-Jul-2017
  • Kirjastus: Routledge
  • Keel: eng
  • ISBN-13: 9781315467993
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Inverse Design Methods for the Built EnvironmentSuurem pilt
  • Formaat: 264 pages
  • Ilmumisaeg: 14-Jul-2017
  • Kirjastus: Routledge
  • Keel: eng
  • ISBN-13: 9781315467993
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The inverse design approach is new to the built environment research and design community, though it has been used in other industries including automobile and airplane design. This book, from some of the pioneers of inverse design applications in the built environment, introduces the basic principles of inverse design and the specific techniques that can be applied to built environment systems. The authors' inverse design concept uses the desired enclosed environment as the design objective and inversely determines the systems required to achieve the objective.

The book discusses a number of backward and forward methods for inverse design. Backward methods, such as the quasi-reversibility method, the pseudo-reversibility method, and the regularized inverse matrix method, can be used to identify contaminant sources in an enclosed environment. However, these methods cannot be used to inversely design a desired indoor environment. Forward methods, such as the computational-fluid-dynamics (CFD)-based genetic algorithm (GA) method, the CFD-based adjoint method, the CFD-based artificial neural network (ANN) method, and the CFD-based proper orthogonal decomposition (POD) method, show the promise in the inverse design of airflow and heat transfer in an enclosed environment.

The book describes the fundamentals of the methods for beginners, provides exciting design examples for the reader to duplicate, discusses the pros and cons of each design method and points out the knowledge gaps for further development.
Preface viii
Nomenclature x
List of abbreviations
xiv
1 Methods for inverse/optimal design of an enclosed environment
1(23)
1.1 Introduction
1(1)
1.2 Inverse design for an enclosed environment
2(13)
1.2.1 Backward methods
3(4)
1.2.2 Forward methods
7(8)
1.3 Discussion
15(2)
1.4 Conclusions
17(7)
2 CFD-based genetic algorithm method
24(33)
2.1 Introduction
24(2)
2.2 Principles of the CFD-based genetic algorithm
26(5)
2.2.1 General principles of a genetic algorithm
26(2)
2.2.2 Integration of a genetic algorithm with CFD
28(1)
2.2.3 Improvements to the integrated GA and CFD method
29(2)
2.3 Applications of the CFD-based genetic algorithm
31(20)
2.3.1 Inverse identification of ventilation parameters in a built environment
31(2)
2.3.2 Inverse design of an enclosed environment
33(18)
2.4 Discussion
51(2)
2.5 Summary
53(4)
3 CFD-based adjoint method
57(51)
3.1 Introduction
57(1)
3.2 CFD-based adjoint method
58(12)
3.2.1 Design objective vs. objective function
58(1)
3.2.2 Principle of the CFD-based adjoint method
59(11)
3.2.3 Numerical method
70(1)
3.3 Application of the CFD-based adjoint method
70(34)
3.3.1 Inverse identification problems in built environments
70(20)
3.3.2 Optimal design of the built environment
90(14)
3.4 Conclusions
104(4)
4 CFD-based artificial neural network method
108(45)
4.1 Introduction
108(1)
4.2 Principles of the CFD-based ANN method
109(21)
4.2.1 Structure of the ANN
109(2)
4.2.2 Sampling method for the ANN
111(3)
4.2.3 Normalization methods for the ANN
114(1)
4.2.4 Training algorithms for the ANN
115(8)
4.2.5 Integration of the ANN with CFD
123(7)
4.3 Application of the CFD-based ANN method
130(19)
4.3.1 Inverse identification of a built environment
130(10)
4.3.2 Optimal design of a built environment
140(9)
4.4 Discussion
149(1)
4.4.1 Design accuracy
149(1)
4.4.2 Design efficiency
149(1)
4.5 Conclusions
150(3)
5 CFD-based proper orthogonal decomposition method
153(28)
5.1 Introduction
153(1)
5.2 Principles of inverse design with the CFD-based POD method
154(8)
5.2.1 Fundamentals of POD
155(2)
5.2.2 Interpolation of coefficients of POD modes
157(3)
5.2.3 Original CFD data preparation and criteria for data saturation
160(1)
5.2.4 POD-based design procedure
161(1)
5.3 Application of the CFD-based POD method
162(17)
5.3.1 Inverse solution of air-supply parameters in a two-dimensional air cavity
163(6)
5.3.2 Inverse design of air-supply parameters in an office
169(4)
5.3.3 Inverse design of air-supply parameters in an aircraft cabin
173(6)
5.4 Conclusions
179(2)
6 Enhancement of the inverse design methods
181(59)
6.1 Introduction
181(1)
6.2 Integration of genetic algorithm and adjoint method
182(6)
6.2.1 Comparison of genetic algorithm and adjoint method
182(2)
6.2.2 External integration of genetic algorithm and adjoint method
184(2)
6.2.3 Internal integration of genetic algorithm and adjoint method
186(2)
6.3 Integration of genetic algorithm method and artificial neural network
188(11)
6.3.1 Classification of combination modes
189(2)
6.3.2 Multi-objective problems
191(2)
6.3.3 Factors affecting convergence and computational cost
193(1)
6.3.4 Methods for improving GA and ANN performance
194(1)
6.3.5 Application examples
194(5)
6.4 Enhancement of computing speed by fast fluid dynamics
199(23)
6.4.1 Fast fluid dynamics
200(5)
6.4.2 Validation of fast fluid dynamics
205(17)
6.5 Enhancement of computing speed by semi-Lagrangian PISO algorithm
222(12)
6.5.1 Principle of semi-Lagrangian scheme in combination with PISO algorithm
223(2)
6.5.2 Accuracy analysis
225(3)
6.5.3 Stability analysis
228(1)
6.5.4 Validation of the SLPISO algorithm
229(5)
6.6 Discussion
234(2)
6.7 Conclusions
236(4)
Index 240
Qingyan Chen is the Vincent P. Reilly Professor of Mechanical Engineering at Purdue University, USA. He also serves as the Editor-in-Chief of the international journal Building and Environment.



Zhiqiang Zhai is a Professor of Architectural Engineering at the University of Colorado at Boulder, USA.



Xueyi You is a Professor of Environmental Engineering at Tianjin University, China.



Tengfei Zhang is a Professor of HVAC at Dalian University of Technology, China.
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