In this paper, we propose a sensitivity-free and multi-objective structural design methodology called
data-driven topology design
. It is schemed to obtain high-performance material distributions ...from initially given material distributions in a given design domain. Its basic idea is to iterate the following processes: (i) selecting material distributions from a dataset of material distributions according to eliteness, (ii) generating new material distributions using a deep generative model trained with the selected elite material distributions, and (iii) merging the generated material distributions with the dataset. Because of the nature of a deep generative model, the generated material distributions are diverse and inherit features of the training data, that is, the elite material distributions. Therefore, it is expected that some of the generated material distributions are superior to the current elite material distributions, and by merging the generated material distributions with the dataset, the performances of the newly selected elite material distributions are improved. The performances are further improved by iterating the above processes. The usefulness of data-driven topology design is demonstrated through numerical examples.
Heat exchangers are devices that typically transfer heat between two fluids. The performance of a heat exchanger such as heat transfer rate and pressure loss strongly depends on the flow regime in ...the heat transfer system. In this paper, we present a density-based topology optimization method for a two-fluid heat exchange system, which achieves a maximum heat transfer rate under fixed pressure loss. We propose a representation model accounting for three states, i.e., two fluids and a solid wall between the two fluids, by using a single design variable field. The key aspect of the proposed model is that mixing of the two fluids can be essentially prevented. This is because the solid constantly exists between the two fluids due to the use of the single design variable field. We demonstrate the effectiveness of the proposed method through three-dimensional numerical examples in which an optimized design is compared with a simple reference design, and the effects of design conditions (i.e., Reynolds number, Prandtl number, design domain size, and flow arrangements) are investigated.
This paper presents a topology optimization method for a coupled thermal–fluid problem based on the two- and three-dimensional steady-state Navier–Stokes and energy equations. In this research, the ...optimization problem is formulated as a heat exchange maximization problem to obtain structures that function as high-performance cooling devices. Such devices, for example, liquid-cooled heat sinks, have recently attracted considerable attention as an engineering application for thermal cooling devices. The proposed optimization method employs level set boundary expressions and a Tikhonov-based regularization scheme enables qualitative control of the geometric complexity of the optimal configurations. Using the developed methodology, we provide two- and three-dimensional numerical examples that confirm the applicability, from an engineering standpoint, of the optimization method for the design of cooling devices.
•The novel designs of micromixer were obtained based on topology optimization method.•The mixing quality maximization problem of topology optimization was formulated.•The mixing mechanism for ...non-Newtonian fluid at low Reynolds number was investigated.
Improving the mixing performance is a crucial and widely concerned topic in microfluidic devices for low Reynolds numbers (Re). In this paper, the mixing unit as the design domain in a typical T-shape micromixer considering non-Newtonian fluid effects is investigated using topology optimization. The objective of topology optimization is to maximize the mixing quality under the prescribed pressure drop of the micromixer. Based on the optimized structures obtained by topology optimization, high-fidelity calculations are performed to eliminate the influence of the greyscale problem. For the Newtonian fluid, the optimized structure has better mixing performance than the reference structure with the same pressure drop. For the non-Newtonian fluid, cross-comparison is conducted to obtain the optimized structure. The results show that the mixing qualities of the 25-period mixing unit are 93.90% (Newtonian fluid, Re = 8) and 93.84% (non-Newtonian fluid, Re = 0.24), respectively.
•Novel winglet is designed considering its shape, arrangement, and number.•Promising features to enhance heat transfer is obtained from topology optimization.•Curved winglets along the flow can ...enhance heat transfer at moderate pressure loss.•The proposed fin demonstrates higher quality factor than reference fins.
This paper presents a novel winglet design of a fin-and-tube heat exchanger, and its performance regarding heat transfer enhancement is numerically investigated. In this study, the fin pattern is designed considering the shape, arrangement, and number of the winglets, because a higher degree of freedom for the structural design results in further improvement of the performance. To obtain promising features for the winglet design, topology optimization is applied in a simplified model that incorporates key factors of the physical phenomena. The optimization problem is formulated as a maximization problem of the heat extraction by the winglets in a two-dimensional simplified model under lowered Reynolds number flows. The optimized configurations are evaluated via three-dimensional high-fidelity analyses to obtain a promising design candidate. A manufacturable fin pattern is then designed from the promising candidate. The results indicate that the gradually curved winglets along the flow can enhance the heat transfer at a moderate pressure loss. The proposed fin demonstrates up to 16.0% improvement in the quality factor j/f compared with a fin with rectangular winglet pairs.
This paper proposes a density-based topology optimization method for natural convection problems using the lattice Boltzmann method (LBM). As the LBM can be developed as a completely explicit scheme, ...its attractive features over the traditional ones, such as the finite element method, are (1) suitability for solving unsteady flow problems and (2) scalability for large-scale parallel computing. We develop an LBM code for solving unsteady natural convection problems and provide its sensitivity analysis based on the so-called adjoint lattice Boltzmann method. Notably, the adjoint equation is derived from the discrete particle velocity Boltzmann equation and can be solved similarly to the original LBM concerning unsteady natural convection problems. We first show that the proposed method can produce similar results to the previous work in a steady-state natural convection problem. We then demonstrate the efficacy of the proposed method through 2D numerical examples concerning unsteady natural convection. As a large-scale problem, we tackle a 3D unsteady natural convection problem on a parallel supercomputer. All the developed codes written in C++ are available at
https://github.com/PANFACTORY/PANSLBM2.git
.
This paper presents topology optimization for the design of flow fields in vanadium redox flow batteries (VRFBs), which are large-scale storage systems for renewable energy resources such as solar ...and wind power. It is widely known that, in recent VRFB systems, one of the key factors in boosting charging or discharging efficiency is the design of the flow field around carbon fiber electrodes and in flow channels. In this study, topology optimization is applied in order to achieve optimized flow field designs. The optimization problem is formulated as a maximization problem for the generation rate of the vanadium species governed by a simplified electrochemical reaction model. A typical porous model is incorporated into the optimization problem for expressing the carbon fiber electrode; furthermore, a mass transfer coefficient that depends on local velocity is introduced. We investigate the dependencies of the optimized configuration with respect to the porosity of the porous electrode and the pressure loss. Results indicate that patterns of interdigitated flow fields are valid designs for VRFBs.
This note briefly reports on the applicability of the local-in-time (LT) adjoint-based method to a large-scale topology optimization problem with unsteady thermal-fluid. The basic idea of the LT ...method is to divide a time-dependent optimization problem into reasonable subproblems to reduce memory cost. We demonstrate that the proposed method solves the large-scale topology optimization problem by incorporating the LT method, the lattice Boltzmann method, and parallel computing.
This paper proposes a topology optimization method for thermal-fluid flow problems using the lattice Boltzmann method (LBM). The design sensitivities are derived based on the adjoint lattice ...Boltzmann method (ALBM), whose basic idea is that the adjoint problem is first formulated using a continuous adjoint approach, and the adjoint problem is then solved using the LBM. In this paper, the discrete velocity Boltzmann equation, in which only the particle velocities are discretized, is introduced to the ALBM to deal with the various boundary conditions in the LBM. The novel sensitivity analysis is applied in two flow channel topology optimization problems: 1) a pressure drop minimization problem, and 2) a heat exchange maximization problem. Several numerical examples are provided to confirm the utility of the proposed method.
In this study, we present a framework based on the concept of multifidelity design optimization with the purpose of indirectly solving complex—computationally heavy and/or unstable—topology ...optimization problems. Our primary idea is to divide an original topology optimization problem into two types of subproblems for low-fidelity optimization and high-fidelity evaluation. To realize this idea, artificial design parameters, which we refer to as seeding parameters, are incorporated into the low-fidelity optimization problem for generating various patterns of topology-optimized candidates. The low-fidelity optimization problem is deliberately formulated as an easily solvable one by decreasing the nonlinearity of the original physical phenomena. Notably, selecting valid seeding parameters in the low-fidelity optimization problem is essential for employing the proposed framework. The aim of high-fidelity evaluation is to obtain a satisfactory solution using a high-fidelity analysis model, which considers the nonlinearity of the original physical phenomena, from the data set of the topology-optimized candidates, via the design of experiments. We apply the proposed framework to two case studies of isothermal and thermal turbulent flow problems, and discuss its efficacy as an alternative strategy for solving complex topology optimization problems.
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