Adjoint-Based Multi-Point and Multi-Objective Optimization of a Turbocharger Radial Turbine

Mueller, Lasse; Verstraete, Tom

doi:10.3390/ijtpp4020010

Cited by 17 publications

(11 citation statements)

References 36 publications

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“…When considering gradient-based optimisation, the adjoint method provides an effective way to calculate the gradients of an objective function with respect to design variables and alleviates the computational workload greatly (Jameson 1988; Giles & Pierce 2000; Economon, Palacios & Alonso 2013; Kline, Economon & Alonso 2016; Zhou et al. 2016), but the number of required adjoint computational fluid dynamics simulations is typically still prohibitively expensive when multiple optimisation objectives are considered (Mueller & Verstraete 2019). In gradient-free methods (e.g.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates

Chen

Cakal

et al. 2021

J. Fluid Mech.

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Section: Introductionmentioning

confidence: 99%

Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates

Chen

Cakal

et al. 2021

J. Fluid Mech.

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“…The gradient-based optimization method is a search optimization algorithm, which takes the gradient of the objective function with respect to the design variables (DVs) as the optimization direction. The global optimization method and the gradient-based method were applied to turbine design, and shown that the gradient-based method calculated the Pareto front at a remarkably lower computational cost [19]. Unfortunately, the gradient-based optimization method is effective for the single objective function, but has limitations for obtaining the global optimal solution of multiple optimization objectives [20].…”

Section: Reference Design Variables Optimization Objectives Methods Optimization Algorithmmentioning

confidence: 99%

Many-Objective Hybrid Optimization Method for Impeller Profile Design of Low Specific Speed Centrifugal Pump in District Energy Systems

2021

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Low specific speed centrifugal pumps (LSSCP) are widely utilized in district energy systems to promote the integration of renewable energy. However, the performance of LSSCP becomes inefficient due to harsh operating conditions resulting in substantial increase in energy consumption. Many-objective optimization is significant in improving the performance of LSSCP and promoting the sustainability of district energy systems. Among the existing optimization methods, global optimization methods are limited by high computational cost when solving many-objective optimization problems, and gradient-based optimization methods face difficulties in locating the global optimum. In the present study, a hybrid optimization method was developed for solving many-objective optimization problems of LSSCP. The LSSCP optimization result of the hybrid algorithm was compared with that of the non-dominated sorting genetic algorithm (NSGA), so as to demonstrate the capacity of the proposed method. In the designed flow condition without cavitation, the hydraulic efficiency obtained by the hybrid optimization algorithm was found to be 9.5%, 5.4%, and 4.7% higher than those of the original, NSGA-II, and NSGA-III optimized results, respectively. The shaft power was 10.3%, 8.7% and 5.1% less than said three optimized results. The maximum turbulent kinetic energy in the flow passage obtained from the hybrid optimization was only 2.2 J/kg, which was 67% and 46% less than that of the NSGA-II and NSGA-III optimized results, respectively. In the designed flow condition with cavitation, the net positive suction head critical optimized by the hybrid model was 0.857 m, which was substantially reduced compared with the original and NSGA- II optimized results.

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“…As the importance of including the structural integrity within the design and optimisation process became more apparent, work on turbomachinery adjoint structural optimisation to reduce the maximum stress has recently emerged, with a focus on radial turbines [23,24]. This was followed by a multidisciplinary approach involving aerodynamics and structural analysis and using decoupled adjoint optimisation for improving the efficiency of radial turbines either at design conditions [25] or at two operating points [26,27] while constraining the maximum von Mises stress [25,26] or its moment of inertia [27].…”

Section: Adjoint Optimisation Of Turbomachinerymentioning

confidence: 99%

Structurally Constrained Aerodynamic Adjoint Optimisation of Highly Loaded Compressor Blades

Cuciumita

John

Qin

et al. 2021

Volume 2D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomach

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Adjoint aerodynamic optimisation has recently gained increased popularity for turbomachinery applications due to the large number of parameters that can be used without incurring additional major computational costs. This work presents an adjoint based aero-structural optimisation method having efficiency as the objective function and maximum von Mises stress set as a constraint. The full optimisation loop was set up with free-form deformation for geometry parametrisation. A response surface was created beforehand for computing the maximum von Mises stress using a meshless method. A discrete adjoint approach was used to obtain the gradients of the objective function with respect to each design parameter, while the constraint gradients were computed using finite differences. A sequential least squares programming algorithm was used as the optimizer. Tests carried out on a highly loaded compressor blade showed that the method successfully increases the efficiency by more than 3% while maintaining the maximum stress under the imposed value. The results also showed that the constrained optimisation loses about 1% in potential efficiency gain compared to the same optimisation process without stress constraint. Overall, the work provides a methodology for conducting structurally constrained adjoint aerodynamic optimisation that can be applied for large number of design parameters while maintaining low computational costs. It also provides reference for constructing and selecting a response surface to be used in the optimisation process.

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Adjoint-Based Multi-Point and Multi-Objective Optimization of a Turbocharger Radial Turbine

Cited by 17 publications

References 36 publications

Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates

Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates

Many-Objective Hybrid Optimization Method for Impeller Profile Design of Low Specific Speed Centrifugal Pump in District Energy Systems

Structurally Constrained Aerodynamic Adjoint Optimisation of Highly Loaded Compressor Blades

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