Combustor Design Optimization Using Co-Kriging of Steady and Unsteady Turbulent Combustion

2011 ◽  
Vol 133 (12) ◽  
Author(s):  
Moresh J. Wankhede ◽  
Neil W. Bressloff ◽  
Andy J. Keane
Keyword(s):  
Design Optimization ◽  
Optimization Process ◽  
Design Stage ◽  
Reacting Flow ◽  
High Fidelity ◽  
Optimization Strategy ◽  
Function Calls ◽  
Unsteady Rans ◽  
Computational Budget ◽  
High Level

In the gas turbine industry, computational fluid dynamics (CFD) simulations are often used to predict and visualize the complex reacting flow dynamics, combustion environment and emissions performance of a combustor at the design stage. Given the complexity involved in obtaining accurate flow predictions and due to the expensive nature of simulations, conventional techniques for CFD based combustor design optimization are often ruled out, primarily due to the limits on available computing resources and time. The design optimization process normally requires a large number of analyses of the objective and constraint functions which necessitates a careful selection of fast, reliable and efficient computational methods for the CFD analysis and the optimization process. In this study, given a fixed computational budget, an assessment of a co-Kriging based optimization strategy against a standard Kriging based optimization strategy is presented for the design of a 2D combustor using steady and unsteady Reynolds-averaged Navier Stokes (RANS) formulation. Within the fixed computational budget, using a steady RANS formulation, the Kriging strategy successfully captures the underlying response; however with unsteady RANS the Kriging strategy fails to capture the underlying response due to the existence of a high level of noise. The co-Kriging strategy is then applied to two design problems, one using two levels of grid resolutions in a steady RANS formulation and the other using steady and unsteady RANS formulations on the same grid resolution. With the co-Kriging strategy, the multifidelity analysis is expected to find an optimum design in comparatively less time than that required using the high-fidelity model alone since less high-fidelity function calls should be required. However, using the applied computational setup for co-Kriging, the Kriging strategy beats the co-Kriging strategy under the steady RANS formulation whereas under the unsteady RANS formulation, the high level of noise stalls the co-Kriging optimization process.

Combustor Design Optimisation Using Co-Kriging of Steady and Unsteady Turbulent Combustion

2011 ◽  
Author(s):  
Moresh J. Wankhede ◽  
Neil W. Bressloff ◽  
Andy J. Keane
Keyword(s):  
Navier Stokes ◽  
Design Stage ◽  
Reacting Flow ◽  
High Fidelity ◽  
Design Optimisation ◽  
Design Problems ◽  
Function Calls ◽  
Unsteady Rans ◽  
Computational Budget ◽  
High Level

In the gas turbine industry, computational fluid dynamics (CFD) simulations are often used to predict and visualize the complex reacting flow dynamics, combustion environment and emissions performance of a combustor at the design stage. Given the complexity involved in obtaining accurate flow predictions and due to the expensive nature of simulations, conventional techniques for CFD based combustor design optimisation are often ruled out, primarily due to the limits on available computing resources and time. The design optimisation process normally requires a large number of analyses of the objective and constraint functions which necessitates a careful selection of fast, reliable and efficient computational methods for the CFD analysis and the optimization process. In this study, given a fixed computational budget, an assessment of a co-Kriging based optimisation strategy against a standard Kriging based optimisation strategy is presented for the design of a 2D combustor using steady and unsteady Reynolds-averaged Navier Stokes (RANS) formulation. Within the fixed computational budget, using a steady RANS formulation, the Kriging strategy successfully captures the underlying response, however with unsteady RANS the Kriging strategy fails to capture the underlying response due to the existence of a high level of noise. The co-Kriging strategy is then applied to two design problems, one using two levels of grid resolutions in a steady RANS formulation and the other using steady and unsteady RANS formulations on the same grid resolution. With the co-Kriging strategy, the multi-fidelity analysis is expected to find an optimum design in comparatively less time than that required using the high-fidelity model alone since less high-fidelity function calls should be required. However, using the applied computational setup for co-Kriging, the Kriging strategy beats the co-Kriging strategy under the steady RANS formulation whereas under the unsteady RANS formulation, the high level of noise stalls the co-Kriging optimisation process.

A Virtual Engineering Tool for Product Design Using High Fidelity CFD Analysis Models

2005 ◽  
Author(s):  
Gengxun Huang ◽  
Angran Xiao ◽  
Kenneth M. Bryden
Keyword(s):  
Design Optimization ◽  
Product Design ◽  
Engineering Design ◽  
Direct Interaction ◽  
Design Tool ◽  
Optimization Process ◽  
Initial Population ◽  
High Fidelity ◽  
Design And Optimization ◽  
Virtual Engineering

Product design optimization is a complex decision-making process requiring intensive interactions between designers and the designed product. However, most current optimization tools do not support this type of direct interaction. Typically, resolving a converged result with an optimization tool takes a long solution time and high computing cost. However, designers are not involved in the optimization process and cannot control the quality of the so-called optimal result. In this paper, we introduce a virtual engineering design tool that expands the application scope of virtual reality from visualization to interaction and decision support. This design tool allows designers to easily experiment with different product designs using high fidelity CFD solver and observe the effects in an almost real-time manner. This can help designers understand the nature of the product and make superior decisions. Most importantly, the design tool enables designers to control the optimization computing process by selecting superior starting points or changing an obviously unpromising search direction. Hence, by adding human creativity and experience into the optimization process, designers can resolve the design optimization problem more efficiently. A coal pipe design and optimization scenario is presented to demonstrate the efficacy of this virtual engineering design tool. The goal of this tool is to enable a designer to modify the size and shape of a coal pipe to obtain evenly distributed coal at the outlet. In this tool after the initial population was chosen, a standard evolutionary algorithm was used to find the most superior pipe design within a much shorter time.

scholarly journals Pipe route optimization to avoid undesired vibration by using JuliaFEM

2019 ◽  
Vol 52 (3) ◽  
pp. 177-191 ◽  
Author(s):  
Marja Rapo ◽  
Joona Vaara ◽  
Teemu Kuivaniemi ◽  
Niclas Liljenfeldt ◽  
Antti Vuohijoki ◽  
...  
Keyword(s):  
Finite Element Method ◽  
High Pressure ◽  
Route Optimization ◽  
Optimization Process ◽  
Quick Response ◽  
Optimization Strategy ◽  
Development Environment ◽  
Optimization Routine ◽  
High Level ◽  
Embedded Implementation

An optimization routine was applied to high pressure fuel pipes to avoid resonance in a heavily vibrating environment. The optimization process and also the natural frequency calculations in every iteration were completely performed with the high-level programming language Julia; the optimization process was performed with the JuMP optimization environment, and the frequencies where calculated with JuliaFEM finite element method solver platform. The benefit of this kind of embedded implementation is a quick response which yields a pleasant development environment to focus on the essential—the choice of the optimization strategy.

Metamodel Based Design Automation: Applied on Multidisciplinary Design Optimization of Industrial Robots

2012 ◽  
Author(s):  
Mehdi Tarkian ◽  
Bhanoday Vemula ◽  
Xiaolong Feng ◽  
Johan Ölvander
Keyword(s):  
Design Optimization ◽  
Design Automation ◽  
Multidisciplinary Design Optimization ◽  
Industrial Robots ◽  
Multidisciplinary Design ◽  
Proof Of Concept ◽  
Optimization Strategy ◽  
Concept Generation ◽  
Multi Level ◽  
High Level

Intricate and complex dependencies between multiple disciplines require iterative intensive optimization processes. To this end, multidisciplinary design optimization (MDO) has been established as a convincing concurrent technique to manage inherited complexities. This paper presents a high level CAD and CAE design automation methodology which enables fast, efficient concept generation for MDO. To increase the evaluation speed, global metamodels are introduced to replace computationally expensive CAD and CAE models. In addition, various techniques are applied to drastically decrease the number of samplings required to create the metamodels. In the final part of the paper, a multi-level optimization strategy is proposed to find the optimal concept. As proof of concept, a real world design problem, from ABB industrial robotics, is presented.

scholarly journals An efficient low-speed airfoil design optimization process using multi-fidelity analysis for UAV flying wing

2016 ◽  
Vol 19 (3) ◽  
pp. 43-52
Author(s):  
Bao Anh Dinh ◽  
Hieu Khanh Ngo ◽  
Van Nhu Nguyen
Keyword(s):  
Design Optimization ◽  
Wind Tunnel Test ◽  
Aircraft Design ◽  
Aerodynamic Characteristics ◽  
Optimization Process ◽  
Design Stage ◽  
Low Speed ◽  
Design Requirements ◽  
Long Endurance ◽  
The Given

This paper proposes an efficient low-speed airfoil selection and design optimization process using multi-fidelity analysis for a long endurance Unmanned Aerial Vehicle (UAV) flying wing. The developed process includes the low speed airfoil database construction, airfoil selection and design optimization steps based on the given design requirements. The multi-fidelity analysis solvers including the panel method and computational fluid dynamics (CFD) are presented to analyze the low speed airfoil aerodynamic characteristics accurately and perform inverse airfoil design optimization effectively without any noticeable turnaround time in the early aircraft design stage. The unconventional flying wing UAV design shows poor reaction in longitudinal stability. However, It has low parasite drag, long endurance, and better performance. The multi-fidelity analysis solvers are validated for the E387 and CAL2463m airfoil compared to the wind tunnel test data. Then, 29 low speed airfoils for flying wing UAV are constructed by using the multi-fidelity solvers. The weighting score method is used to select the appropriate airfoil for the given design requirements. The selected airfoil is used as a baseline for the inverse airfoil design optimization step to refine and obtain the optimal airfoil configuration. The implementation of proposed method is applied for the real flying-wing UAV airfoil design case to demonstrate the effectiveness and feasibility of the proposed method.

Design of High Performing Duct-Burners for Combined-Cycle Plants Using Progressive Optimization

2002 ◽  
Author(s):  
L. A. Catalano ◽  
A. Dadone ◽  
D. Manodoro
Keyword(s):  
Design Optimization ◽  
Flow Analysis ◽  
Combined Cycle ◽  
Inverse Design ◽  
Optimization Process ◽  
Computational Time ◽  
Optimization Strategy ◽  
High Performing ◽  
Commercial Code ◽  
Progressive Optimization

A general efficient strategy for the design optimization of duct-burners for combined-cycle plants is presented. This methodology combines a widely employed commercial code, used for the flow analysis, with a progressive optimization strategy, whose efficiency relies on the simultaneous convergence of both the flow solution and the optimization process, as well as on the use of progressively finer grid levels. The proposed strategy has been initially tested versus two inverse design examples with known solutions; then, it has been employed to flatten the outlet thermal profile of a new enhanced-mixing after-burner. The presented results show that the overall optimization process requires a computational time compared to that required by 5 ÷ 14 flow analyses.

scholarly journals Developing a BIM-Based MUVR Treadmill System for Architectural Design Review and Collaboration

2021 ◽  
Vol 11 (15) ◽  
pp. 6881
Author(s):  
Calvin Chung Wai Keung ◽  
Jung In Kim ◽  
Qiao Min Ong
Keyword(s):  
Architectural Design ◽  
Human Locomotion ◽  
Virtual Space ◽  
Design Stage ◽  
Design Review ◽  
Team Members ◽  
Physical Constraints ◽  
Simulated Environment ◽  
Set Up ◽  
High Level

Virtual reality (VR) is quickly becoming the medium of choice for various architecture, engineering, and construction applications, such as design visualization, construction planning, and safety training. In particular, this technology offers an immersive experience to enhance the way architects review their design with team members. Traditionally, VR has used a desktop PC or workstation setup inside a room, yielding the risk of two users bump into each other while using multiuser VR (MUVR) applications. MUVR offers shared experiences that disrupt the conventional single-user VR setup, where multiple users can communicate and interact in the same virtual space, providing more realistic scenarios for architects in the design stage. However, this shared virtual environment introduces challenges regarding limited human locomotion and interactions, due to physical constraints of normal room spaces. This study thus presented a system framework that integrates MUVR applications into omnidirectional treadmills. The treadmills allow users an immersive walking experience in the simulated environment, without space constraints or hurt potentialities. A prototype was set up and tested in several scenarios by practitioners and students. The validated MUVR treadmill system aims to promote high-level immersion in architectural design review and collaboration.

Transfer Reinforcement Learning for Autonomous Driving

2021 ◽  
Vol 31 (3) ◽  
pp. 1-26
Author(s):  
Aravind Balakrishnan ◽  
Jaeyoung Lee ◽  
Ashish Gaurav ◽  
Krzysztof Czarnecki ◽  
Sean Sedwards
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Transfer Problem ◽  
Autonomous Driving ◽  
High Fidelity ◽  
Rule Based ◽  
High Level ◽  
Real Vehicle

Reinforcement learning (RL) is an attractive way to implement high-level decision-making policies for autonomous driving, but learning directly from a real vehicle or a high-fidelity simulator is variously infeasible. We therefore consider the problem of transfer reinforcement learning and study how a policy learned in a simple environment using WiseMove can be transferred to our high-fidelity simulator, W ise M ove . WiseMove is a framework to study safety and other aspects of RL for autonomous driving. W ise M ove accurately reproduces the dynamics and software stack of our real vehicle. We find that the accurately modelled perception errors in W ise M ove contribute the most to the transfer problem. These errors, when even naively modelled in WiseMove , provide an RL policy that performs better in W ise M ove than a hand-crafted rule-based policy. Applying domain randomization to the environment in WiseMove yields an even better policy. The final RL policy reduces the failures due to perception errors from 10% to 2.75%. We also observe that the RL policy has significantly less reliance on velocity compared to the rule-based policy, having learned that its measurement is unreliable.

Shape-design optimization of hull structures considering thermal deformation

2013 ◽  
Vol 228 (13) ◽  
pp. 2266-2277
Author(s):  
Myung-Jin Choi ◽  
Min-Geun Kim ◽  
Seonho Cho
Keyword(s):  
Optimal Design ◽  
Design Optimization ◽  
Thermal Deformation ◽  
Parametric Design ◽  
Optimization Method ◽  
Optimization Process ◽  
Design Parameters ◽  
Shape Design ◽  
Shape Design Optimization ◽  
Modified Method

We developed a shape-design optimization method for the thermo-elastoplasticity problems that are applicable to the welding or thermal deformation of hull structures. The point is to determine the shape-design parameters such that the deformed shape after welding fits very well to a desired design. The geometric parameters of curved surfaces are selected as the design parameters. The shell finite elements, forward finite difference sensitivity, modified method of feasible direction algorithm and a programming language ANSYS Parametric Design Language in the established code ANSYS are employed in the shape optimization. The objective function is the weighted summation of differences between the deformed and the target geometries. The proposed method is effective even though new design variables are added to the design space during the optimization process since the multiple steps of design optimization are used during the whole optimization process. To obtain the better optimal design, the weights are determined for the next design optimization, based on the previous optimal results. Numerical examples demonstrate that the localized severe deviations from the target design are effectively prevented in the optimal design.

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