DualMS: Implicit Dual-Channel Minimal Surface Optimization for Heat Exchanger Design

2025-03-02

AI Summary

Designing bichannel minimal-surface heat exchangers under free-form boundaries remains challenging due to the limited boundary adaptability and flow-direction controllability of conventional triply periodic minimal surfaces (TPMS). Method: We propose a novel neural implicit surface modeling paradigm for minimal surfaces, unifying bichannel heat exchanger design as a graph-cut optimization problem with fluid-flow constraints. Our framework integrates binary flow-pattern implicit classification, connectivity-preserving maximum-cut optimization, area-regularized geometric modeling, and flow-field-guided co-optimization. Contribution/Results: The method enables arbitrary free-form boundary compliance and complex topological generation. At equivalent material cost, it achieves significantly lower pressure drop than TPMS-based baselines while maintaining comparable heat transfer rates—thereby simultaneously maximizing heat transfer area and ensuring flow controllability.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Deep Neural Architectures and Foundation Models
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Heat exchangers are critical components in a wide range of engineering applications, from energy systems to chemical processing, where efficient thermal management is essential. The design objectives for heat exchangers include maximizing the heat exchange rate while minimizing the pressure drop, requiring both a large interface area and a smooth internal structure. State-of-the-art designs, such as triply periodic minimal surfaces (TPMS), have proven effective in optimizing heat exchange efficiency. However, TPMS designs are constrained by predefined mathematical equations, limiting their adaptability to freeform boundary shapes. Additionally, TPMS structures do not inherently control flow directions, which can lead to flow stagnation and undesirable pressure drops. This paper presents DualMS, a novel computational framework for optimizing dual-channel minimal surfaces specifically for heat exchanger designs in freeform shapes. To the best of our knowledge, this is the first attempt to directly optimize minimal surfaces for two-fluid heat exchangers, rather than relying on TPMS. Our approach formulates the heat exchange maximization problem as a constrained connected maximum cut problem on a graph, with flow constraints guiding the optimization process. To address undesirable pressure drops, we model the minimal surface as a classification boundary separating the two fluids, incorporating an additional regularization term for area minimization. We employ a neural network that maps spatial points to binary flow types, enabling it to classify flow skeletons and automatically determine the surface boundary. DualMS demonstrates greater flexibility in surface topology compared to TPMS and achieves superior thermal performance, with lower pressure drops while maintaining a similar heat exchange rate under the same material cost.

Problem

• Optimizing dual-channel minimal surfaces for freeform heat exchangers
• Maximizing heat exchange while minimizing pressure drop
• Overcoming TPMS limitations in flow direction control

Innovation

• Optimizes dual-channel minimal surfaces for heat exchangers
• Formulates heat exchange as constrained maximum cut problem
• Uses neural network to classify flow types automatically