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Iffa, Emishaw [ORNL]

Publications and source records attributed to Iffa, Emishaw [ORNL].

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2025

Next generation retrofit wall panels with integrated vacuum insulation panels

Approximately two-thirds of residential buildings in the United States were constructed before the Department of Energy established energy conservation measures. These buildings present major opportunities for improving energy efficiency, though retrofitting them remains technically and economically challenging. This study presents the development and the durability evaluation of an innovative retrofit panel system that integrates vacuum insulation panels (VIP) with a nail-base panel (called a retrofit insulated panel) to enhance thermal performance with minimal disruption to occupants and without altering standard nail-based panel installation practices. Hygrothermal simulations were conducted to assess the moisture behavior of wall assemblies before and after retrofit installation under varying water vapor control strategies and climate conditions. Results indicate that, with appropriate moisture control strategy, the retrofit system effectively prevents moisture accumulation, keeping mold index values and relative humidity levels below critical thresholds. Additionally, Guarded Hot Box testing was performed to compute the effective R-value of the panel under different coverage areas that demonstrates its effectiveness in enhancing both thermal and moisture performance in existing residential buildings.

Iffa, Emishaw [ORNL]↗