Development of Simplified Analysis Process for Multidisciplinary Design Optimization (MDO) of Exhaust Manifold During Concept Stage

2021 ◽  
Author(s):  
Nilesh Ghodke ◽  
Prashant Pimpalkar ◽  
Bhaskarjyoti Saikia ◽  
Joshua Miller ◽  
Girish Kulkarni ◽  
...  

Abstract The exhaust manifold is one of the key components of an engine exhaust system. Exhaust manifold simulations are time-consuming as they require modeling of complex thermal loading and multiple non-linearities like friction and plasticity. This proves to be a big constraint for using Multidisciplinary Design Optimization (MDO) for exhaust manifolds as it involves running a large number of models specified by a Design of Experiments (DOE). Also, during the initial phase of design development, it seems reasonable to compromise the accuracy of simulations at the cost of speed for getting correct feedback on design direction. Hence, the main objective of the current work was to a develop simplified analysis process for Thermomechanical Fatigue (TMF) and modal analysis of exhaust manifold. At the concept stage, due to the lack of availability of accurate thermal Boundary Conditions (BCs) and the goal to simplify modeling, thermal BCs are assumed with the help of thermal data history instead of accurate thermal BCs from test cells. Similarly, other aspects such as ‘level of component assembly required’, ‘mechanical loading’, and ‘outputs to be monitored for making design decisions were also investigated to come up with a simplified approach. The proposed approach was quick compared to the conventional one. This approach was implemented on a few heavy-duty and mid-range engine programs to check repeatability. It was observed that the proposed analysis approach provides correct design direction with a significantly reduced computational time of up to 80%. Incorporating the simplified approach for the MDO process has made it more practical and feasible for implementation during the concept design cycle in the early stage of an engine development program.

2010 ◽  
Vol 139-141 ◽  
pp. 1396-1399
Author(s):  
Lei Li ◽  
Zhen Zhou Lv ◽  
Liang Bo Ao ◽  
Ming Yu ◽  
Zhu Feng Yue

In this paper, the multidisciplinary design optimization based on Approximation Model for supercharge turbo is studied. Temperature and pressure loads are transferred to the solid model by distance-weighted function, and structure deformation is transferred to aerodynamic model by mesh regenerated method in order to avoid mesh aberration. The Multidisciplinary analysis (MDA) model of supercharge turbo considering aerodynamic, heat transfer, strength and vibration is obtained on the basis of information transferring, which is solved by iterated three times. The Kriging Approximation Model which fits the sample space accurately is employed in the MDO process to reduce computational cost. Results show that performance of supercharge turbo is improvement on the MDO system based on Approximation Model, meanwhile the computational time of the optimization system is saved. Also, this method is suitable for other Multidisciplinary Design Optimization problems.


2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.


Author(s):  
Dongqin Li ◽  
Yifeng Guan ◽  
Qingfeng Wang ◽  
Zhitong Chen

The design of ship is related to several disciplines such as hydrostatic, resistance, propulsion and economic. The traditional design process of ship only involves independent design optimization within each discipline. With such an approach, there is no guarantee to achieve the optimum design. And at the same time improving the efficiency of ship optimization is also crucial for modem ship design. In this paper, an introduction of both the traditional ship design process and the fundamentals of Multidisciplinary Design Optimization (MDO) theory are presented and a comparison between the two methods is carried out. As one of the most frequently applied MDO methods, Collaborative Optimization (CO) promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, Design Of Experiment (DOE) and a new support vector regression algorithm are applied to CO to construct statistical approximation model in this paper. The support vector regression algorithm approximates the optimization model and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method. Then this new Collaborative Optimization (CO) method using approximate technology is discussed in detail and applied in ship design which considers hydrostatic, propulsion, weight and volume, performance and cost. It indicates that CO method combined with approximate technology can effectively solve complex engineering design optimization problem. Finally, some suggestions on the future improvements are proposed.


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