Thermo, Volume 5, Issue 3 (September 2025) – 3 articles

Modern energy storage systems for both power and transportation are highly related to lithium-ion batteries (LIBs). However, their safety depends on a potentially hazardous failure mode known as thermal runaway (TR). Predicting and classifying TR causes can widely enhance the safety of power and transportation systems. This paper presents an advanced machine learning method for forecasting and classifying the causes of TR. A generative model for synthetic data generation was used to handle class imbalance in the dataset. Hyperparameter optimization was conducted using Optuna for four classifiers: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), tabular network (TabNet), and Extreme Gradient Boosting (XGBoost). A three-fold cross-validation approach was used to guarantee a robust evaluation. An open-source database of LIB failure events is used for model training and testing. The XGBoost model outperforms the other models across all TR categories by achieving 100% accuracy and a high recall (1.00). Model results were interpreted using SHapley Additive exPlanations analysis to investigate the most significant factors in TR predictors. The findings show that important TR indicators include energy adjusted for heat and weight loss, heater power, average cell temperature upon activation, and heater duration. These findings guide the design of safer battery systems and preventive monitoring systems for real applications. They can help experts develop more efficient battery management systems, thereby improving the performance and longevity of battery-operated devices. By enhancing the predictive knowledge of temperature-driven failure mechanisms in LIBs, the study directly advances thermal analysis and energy storage safety domains. Full article

Cryosurgery employs extremely low temperatures to destroy abnormal or cancerous tissue. Conventional systems use cryogenic fluids like liquid nitrogen or argon, which pose challenges in handling, cost, and precise temperature control. This study explores thermoelectric (TE) cooling using the Peltier effect as an efficient alternative. A numerical optimization of multi-stage TE coolers using bismuth telluride (Bi₂Te₃) is performed through finite element analysis in COMSOL Multiphysics. Results show that the optimized multi-stage TE system achieves a minimum temperature of −70 °C, a 90 K temperature difference, and 4.0 W cooling power—outperforming single-stage (SS) systems with a maximum ΔT of 73.27 K. The study also investigates the effects of material properties, current density, and geometry on performance. An optimized multi-stage (MS) configuration improves cooling efficiency by 22.8%, demonstrating the potential of TE devices as compact, energy-efficient, and precise solutions for cryosurgical applications. Future work will explore advanced nanomaterials and hybrid systems to further improve performance in biomedical cooling. Full article

Cast iron is a longtime reliable material for the production of heat-treated stressed castings, i.e., those that are long, are cyclically heated, and heat-stressed. The durability of thermally stressed castings used in practice is dependent on the choice of the optimum chemical composition, metallurgy of production, macro- and microstructures, construction, and the way of exploitation. Today, the successful solution of this problem is dominated by simulation programs. The comprehensive analysis of heat stress is very important, i.e., the impacts of various physical quantities on its rise, progress, and size. This paper provides a comprehensive analysis of thermal stress mechanisms in gray iron castings, with a particular emphasis on the relationships between the material properties, microstructural characteristics, and component performance under thermal loading conditions. The theoretical foundations are complemented by experimental data, establishing practical guidelines for optimizing cast iron compositions and processing parameters for thermal applications. Full article