Regression Analysis of Triply Periodic Minimal Surface (TPMS) Models to Achieve Multi-Objective Optimization
Source abstract
Abstract
Freshwater scarcity demands innovative solutions, and Atmospheric Water Generation (AWG) technology offers a promising approach. This study applies a data-driven optimization methodology to enhance AWG efficiency by improving condensation surface design using Triply Periodic Minimal Surface (TPMS) structures. Five TPMS types (Gyroid, Diamond, Lidinoid, SplitP, and Schwartz) were evaluated using thermal simulations in nTop, and a regression-based predictive model was developed to assess the impact of lattice thickness and cell size on surface area to volume ratio (SA/VS) and temperature difference (ΔT). A multi-objective optimization approach, incorporating sensitivity-weighted desirability analysis, identified optimal design parameters under varying performance priorities. Results show Schwartz exhibits the highest tunability, with both factors significantly influencing its performance, while other TPMS types are primarily governed by lattice thickness. By integrating regression modeling, multi-objective optimization, and sensitivity analysis, this study provides a systematic framework for AWG surface design, offering scalable insights for thermal management and moisture collection efficiency in sustainable water harvesting applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Material Properties and Applications · Advanced Theoretical and Applied Studies in Material Sciences and Geometry · Microplastics and Plastic Pollution
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Materials Science, Engineering, and Commercialization, Texas State University, 601 University Dr, San Marcos, TX 78666, USA
Funding
US Department of Agriculture (USDA)
References (50)
Show source-provided references
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