arXiv subjects

Hiroki Kawabe

Publications and source records attributed to Hiroki Kawabe.

6 recordsLinked to original sources

arXiv2026

Topology optimization of two-fluid turbulent heat exchangers: A Darcy flow-based multifidelity approach

This paper presents a topology optimization method for designing two-fluid heat exchangers under turbulent conditions using a Darcy flow-based low-fidelity (LF) model. The LF model is calibrated against a high-fidelity (HF) model based on the Reynolds-averaged Navier-Stokes (RANS) equations to increase the accuracy of predictions for fluid flow and heat transfer characteristics. Since the discrepancies between the LF and HF models can be significant, particularly for pressure drops, a multifidelity topology optimization framework is adopted to leverage the strengths of both models. Using the calibrated LF model, we perform topology optimization for various inlet velocities in the boundary conditions and trade-off parameters in the objective function to obtain diverse optimized designs. The optimized designs are then evaluated using the HF model to assess their performance with higher accuracy. The results demonstrate that the optimized designs significantly improve overall heat transfer coefficients while maintaining manageable pressure drops, achieving up to a 22% higher performance evaluation criterion (PEC) compared to a reference design enhanced by conventional twisted tape insertion. The improvements are attributed to the optimized configurations that promote enhanced fluid mixing and increased surface area for heat exchange, yet maintain streamlined flow paths to minimize pressure losses. Overall, the proposed topology optimization method using the Darcy flow-based LF model proves effective in designing high-performance double pipe heat exchangers, showcasing the potential of the multifidelity approach in overcoming the challenges of optimizing heat exchangers under turbulent flow conditions.

physics.flu-dyn↗

arXiv2026

Distortion-minimized de-homogenization for optimization of cell-size distribution in TPMS structures

This paper presents a homogenized topology optimization (TO) method for spatially optimizing cell-size distribution of triply-periodic minimal surface (TPMS) structures, with high accuracy in the optimized structural response after de-homogenization. To achieve this, we introduce a novel de-homogenization technique that directly minimizes the difference between the wavenumbers obtained from the target and actual size distributions. This minimization problem is efficiently solved as a typical Poisson's equation utilizing the discrete cosine transform. We first verify the proposed de-homogenization method through numerical examples, showcasing its capability in significantly reducing the known distortion of the de-homogenized TPMS structures from the conventional periodic modulation (PM) method. Then, we apply the proposed method to a stiffness maximization problem, to demonstrate its effectiveness in improving the structural response compared to the PM method. The proposed method successfully reduced the distortion of the de-homogenized structures compared to the PM method, leading to 0.8% difference in the strain energy compared to the homogenized model, as opposed to 63.6% difference in the PM method. The optimized structure from the proposed method shows a significant improvement in the strain energy by 50.1% compared to the uniform case in the FE analysis on the de-homogenized models, while the PM method results in a significant decrease of 45.8%. The experimental validation shows that the effective stiffness of the optimized structure from the proposed method is 54.2% higher than that of the uniform case, while the PM method results in a significant decrease by 77.3%. These results exhibit the proposed method effectively increases the accuracy of the de-homogenization, thereby maximizing the potential of the homogenized TO for the spatial cell-size optimization of TPMS structures.

math.OC↗

arXiv2025

Homogenization-based optimization of wall thickness distribution for TPMS two-fluid heat exchangers

Triply Periodic Minimal Surface (TPMS) structures are attracting growing attention as promising geometries for next-generation high-performance heat exchangers (HXs), due to their continuous flow paths and high surface-area-to-volume ratio that enhance heat transfer performance. Among these, graded TPMS structures with spatially varying thickness have emerged as a potential means to further improve performance. This study proposes an optimization method of wall thickness distribution for TPMS HXs based on an effective porous media model, which allows accurate performance prediction while significantly reducing computational cost. The proposed method is applied to a gyroid two-fluid HX aiming to improve the thermal-hydraulic performance. Furthermore, full-scale numerical simulations of the optimized-thickness design show a 12.2% improvement in the performance evaluation criterion (PEC) compared to the uniform-thickness design. The improvement is primarily attributed to the optimized non-uniform wall thickness, which directs more flow toward the core ends and enhances velocity uniformity. As a result, heat transfer is enhanced at the core ends, leading to more effective use of the entire HX core and improved overall thermal performance.

math.OC↗

arXiv2025

Data-driven multifidelity and multiscale topology optimization based on phasor-based evolutionary de-homogenization

Multiscale topology optimization is crucial for designing porous infill structures with high stiffness-to-weight ratios and excellent energy absorption. Although gradient-based methods provide a rigorous framework, they are computationally expensive and struggle to capture cross-scale sensitivities in nonlinear settings. Moreover, the resulting hierarchical geometries are often overly complex and lack macroscopically meaningful features. To overcome these issues, we propose an evolutionary de-homogenization framework that couples MultiFidelity Topology Design (MFTD) with a phasor-based de-homogenization technique. The framework translates low-dimensional geometric descriptors into manufacturable high-resolution structures through a hybrid evolutionary algorithm integrating NSGA-II selection, VAE-enabled latent space crossover, and a novel image deformation-based mutation operator. This gradient-free approach achieves efficient optimization while ensuring geometric continuity. Numerical results confirm that the method effectively balances efficiency and design flexibility, offering a scalable pathway for fabrication-aware multiscale structural optimization.

math.OC↗

arXiv2024

Evolutionary de-homogenization using a generative model for optimizing solid-porous infill structures considering the stress concentration issue

The design of porous infill structures presents significant challenges due to their complex geometric configurations, such as the accurate representation of geometric boundaries and the control of localized maximum stress. In current mainstream design methods, such as topology optimization, the analysis is often performed using pixel or voxel-based element approximations. These approximations, constrained by the optimization framework, result in substantial geometric discrepancies between the analysis model and the final physical model. Such discrepancies can severely impact structural performance, particularly for localized properties like stress response, where accurate geometry is critical to mitigating stress concentration. To address these challenges, we propose evolutionary de-homogenization, which is a design framework based on the integration of de-homogenization and data-driven multifidelity optimization. This framework facilitates the hybrid solid-porous infill design by bridging the gap between low-fidelity analysis and high-fidelity physical realizations, ensuring both geometric accuracy and enhanced structural performance. The low-fidelity level utilizes commonly used density control variables, while the high-fidelity level involves stress analysis based on structures with precise geometric representations. By employing a de-homogenization-based mapping method, a side-by-side correspondence between low-fidelity and high-fidelity results is established. The low-fidelity control variables are iteratively adjusted to optimize the high-fidelity results by integrating deep generative model with multi-objective evolutionary algorithm. Finally, numerical experiments demonstrate the effectiveness of the proposed method.

math.OC↗

arXiv2024

Data-driven multifidelity topology design with multi-channel variational auto-encoder for concurrent optimization of multiple design variable fields

The objective of this study is to establish a gradient-free topology optimization framework that facilitates more global solution searches to avoid entrapping in undesirable local optima, especially in problems with strong non-linearity. The framework utilizes a data-driven multifidelity topology design, where solution candidates resulting from low-fidelity optimization problems are iteratively updated by a variational auto-encoder (VAE) and high-fidelity (HF) evaluation. A key step in the solution update involves constructing HF models by extruding VAE-generated material distributions to a constant thickness (the HF modeling parameter) across all candidates, which limits exploration of the parameter space and requires extensive parametric studies outside the optimization loop. To achieve comprehensive optimization in a single run, we propose a multi-channel image data architecture that stores material distributions and HF modeling parameters in separate channels, allowing simultaneous optimization of the HF parameter space. We demonstrated the efficacy of the proposed framework by solving a maximum stress minimization problem, characterized by strong non-linearity due to its minimax formulation.

math.OC↗