发表机构
NED University of Engineering and Technology（国立工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对炎热干燥气候下低收入农村住房墙体材料热排序问题，提出两阶段计算框架，先利用有限差分法求解热方程并采样，再用物理信息神经算子学习，实现高精度排序，还得出相关指标，支持循证材料选择。
AI 中文摘要
确定能使热量穿透墙体最小化的经济高效本土建筑材料，对炎热干燥气候下低收入农村住房的室内热舒适度至关重要。本文提出了一个两阶段计算框架，用于对五种低成本本土墙体材料进行热排序。首先，经验证的Crank-Nicolson有限差分法求解一维瞬态热方程，通过拉丁超立方采样在九维参数空间生成1500个周期日解。其次，以傅里叶神经算子为骨干的物理信息神经算子学习参数到解的算子，同时保证数据保真度和偏微分方程一致性。训练后的物理信息神经算子在峰值内表面温度上达到了5.14e-4的相对L2场误差和0.201K的平均绝对误差，精确保持了有限差分法的材料排序。周期性日公式还得出了ISO 13786时间滞后和衰减因子。在名义炎热干燥夏季条件下，粘土秸秆土坯在广泛可用材料中实现了最佳性价比指数。气候扫描揭示了一个 regime 边界：在低于环境室外条件下，排序反转到传导性烧制粘土砖，划定了排热和散热 regime。该框架支持热干地区洪水后重建的循证材料选择。
英文摘要
Identifying cost-effective indigenous building materials that minimise heat penetration through walls is critical for indoor thermal comfort in low-income rural housing in hot-dry climates, where summer temperatures routinely exceed 45 C. We present a two-stage computational framework for thermal ranking of five low-cost indigenous wall materials: mud brick, clay-straw adobe, lime-stabilised bamboo panel, fired clay brick, and lime-mud composite. First, a validated Crank-Nicolson finite difference method (FDM) solves the one-dimensional transient heat equation with Robin boundary conditions under diurnal solar and outdoor air-temperature forcing, generating 1500 periodic-day solutions across a nine-dimensional parameter space by Latin Hypercube sampling. Second, a Physics-Informed Neural Operator (PINO) with a Fourier Neural Operator (FNO) backbone learns the parameter-to-solution operator mu -> T(x,t), enforcing both data fidelity and PDE consistency. The trained PINO attains a relative L2 field error of 5.14e-4 and a 0.201 K mean absolute error on the peak inner surface temperature, preserving the FDM material ranking exactly; PINO trained on 150 FDM samples matches a data-only FNO trained on twice as many, so the physics loss is most valuable when data are scarce. The periodic-day formulation also yields the ISO 13786 time lag and decrement factor, reproduced to within 0.99 h and 0.010. At nominal hot-dry summer conditions, clay-straw adobe achieves the best cost-performance index among widely available materials. A climate sweep, confirmed by FDM spot checks, reveals a regime boundary: under sub-ambient outdoor conditions the ranking inverts to conductive fired clay brick, delineating heat-exclusion and heat-rejection regimes. The framework supports evidence-based material selection for post-flood reconstruction in hot-dry regions.
Comments41 pages, 13 figures. Code and data: https://doi.org/10.5281/zenodo.21311299
