| Project/Area Number |
12650204
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Thermal engineering
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| Research Institution | Osaka University |
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Principal Investigator |
MOMOSE Kazunari Graduate School of Engineering Science, Associate Professor, 大学院・基礎工学研究科, 助教授 (00211607)
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| Co-Investigator(Kenkyū-buntansha) |
KIMOTO Hideo Graduate School of Engineering Science, Professor, 大学院・基礎工学研究科, 教授 (70029495)
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| Project Period (FY) |
2000 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2002: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2001: ¥1,200,000 (Direct Cost: ¥1,200,000)
Fiscal Year 2000: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | Convection Heat Transfer / Boundary Condition / Sensitivity Analysis / Optimal Design / Optimal Control / Adjoint Problem / Numerical Analysis |
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| Research Abstract |
With recent progress in computer hardware and numerical simulation techniques, numerical prediction of heat transfer characteristics has been possible when the boundary conditions are specified. However, from the viewpoint of design or control, the prediction of boundary conditions to achieve the desired heat transfer characteristics is more significant than that of heat transfer characteristics under specific boundary conditions. In other words, the problem becomes inverse one.
In this study, we proposed an adjoint approach to inverse analysis of boundary condition effects on convection heat transfer problems. The main features of the present approach can be summarized as follows:
1. For linear forced convection problem, a numerical solution of the adjoint problem directly gives us the optimal thermal boundary conditions both in time and space to control the heat transfer at any given time.
2. For general nonlinear convection problems, we can derive an adjoint system using perturbation principle. The adjoint solution gives us the sensitivity which enables us to optimize both thermal and flow boundary conditions when combined with gradient-based optimization strategies.
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