The study titled "Uncertainty Modeling of Thermal Fields in Underground Cable Networks Using Fuzzy Inference Systems" presents a novel approach to analyzing and predicting the thermal behavior of underground power cables in non-homogeneous soils. By employing fuzzy inference systems, this work captures the inherent variability in soil thermal properties, cable loading conditions, and environmental influences. The proposed model integrates expert knowledge and linguistic variables to offer a more flexible and adaptive method for estimating thermal fields around buried cables. This approach enhances the accuracy of temperature predictions and improves the decision-making process for optimizing cable layout and material selection. The fuzzy system accommodates imprecise data without relying solely on deterministic values, enabling more resilient thermal management. Simulation results demonstrate the system’s capability to deliver robust temperature forecasts, aiding in the prevention of overheating and prolonging the operational life of underground electrical infrastructure. This framework is a valuable contribution to smart power grid design and thermal optimization.

The implementation of fuzzy inference systems (FIS) for uncertainty modeling in underground cable networks presents several challenges. One of the primary issues lies in accurately defining membership functions for soil properties, cable loading, and environmental conditions, which are often site-specific and influenced by seasonal variability. Another challenge is the integration of expert knowledge into the fuzzy rule base, which can be subjective and may introduce inconsistencies if not carefully validated. Additionally, the lack of high-resolution field data can limit the calibration and validation of the FIS model, affecting prediction accuracy. Computational complexity also arises when dealing with large-scale cable networks and multiple interacting variables. Moreover, translating the fuzzy model outputs into actionable insights for real-time thermal management can be difficult, especially in systems where quick responses are critical. Finally, aligning fuzzy-based models with conventional engineering design standards and regulatory frameworks remains a key hurdle for widespread adoption in practical underground cable system planning. This theme encompasses advanced fuzzy inference techniques, soft computing approaches, and intelligent systems for addressing uncertainties in the thermal analysis and optimization of underground cable networks. It invites contributions that explore modeling, simulation, predictive diagnostics, and decision support systems under variable soil, environmental, and load conditions. Potential topics included, but not limited to:

1. Fuzzy logic-based thermal simulation for underground power cables in heterogeneous soils

2. Hybrid fuzzy-neural models for predicting cable temperature under load variability

3. Application of type-2 fuzzy systems in soil thermal property estimation

4. Uncertainty quantification in cable ampacity calculations using soft computing

5. Fuzzy rule-based systems for dynamic thermal rating of underground cables

6. Integration of fuzzy inference with finite element models for subsurface heat transfer

7. Modeling the impact of soil moisture uncertainty on cable thermal behavior

8. Fuzzy logic controllers for real-time thermal monitoring of underground cables

9. Multi-criteria decision analysis using fuzzy sets for thermal backfill material selection

10. Risk assessment of underground cable overheating using fuzzy probability models

The global transition toward low-carbon energy systems has intensified the demand for precise thermodynamic and kinetic insights into the life cycle of energy—from generation and storage to conversion and utilization. As energy architectures grow in complexity, understanding heat flow, mass variation, and phase transitions is no longer just a laboratory requirement; it is a prerequisite for operational safety and system efficiency.

Thermal analysis and calorimetry serve as the fundamental pillars for this understanding. By providing a granular view of reaction pathways and thermal stability under realistic operating conditions, these methodologies allow for the rigorous characterization of energy release and absorption. Such data is critical not only for fundamental science but for the scaling, hazard analysis, and regulatory compliance of large-scale industrial applications.

However, the modern energy landscape generates high-volume, multi-dimensional thermal datasets that challenge traditional analytical boundaries. This Special Issue explores the integration of classical thermal methods with Artificial Intelligence (AI) and Machine Learning (ML). By leveraging data-driven modelling, researchers can now extract hidden patterns from non-isothermal experiments, automate kinetic parameter estimation, and enable predictive simulations of system behaviour under variable thermal stresses.

Contributions may include (but are not limited to):

• Thermal behaviour of hydrogen systems
• Battery thermal performance and safety analysis
• Fuel cell performance evaluation
• Bioenergy conversion processes
• Combustion and alternative fuel evaluation
• Thermal management in renewable energy systems
• Kinetic modelling of combustion and decomposition processes
• Thermodynamic evaluation of energy storage systems
• Catalytic reaction thermal analysis
• Pyrolysis and gasification studies
• Phase transitions in thermal energy storage systems
• ML-based prediction of thermal stability and reaction behaviour
• AI-assisted kinetic parameter estimation
• Data-driven modelling of calorimetric curves
• Neural networks for thermophysical property prediction
• AI for anomaly detection in thermal measurements
• Digital twins for thermal performance assessment
• Energy efficiency assessment via calorimetric methods
• Waste-to-energy thermal evaluation
• Thermal degradation in recycling and circular energy systems
• Optimization of energy processes through thermal monitoring

By bridging the gap between rigorous experimental calorimetry and intelligent data analysis, this Special Issue seeks to define a forward-looking direction for thermal engineering—one that ensures sustainable energy systems are as reliable and safe as they are efficient.

Guest Editor Details:

Edwin Geo Varuvel

Professor

Department of Mechanical Engineering,

Faculty of Engineering and Natural Sciences,

Istinye University,

Istanbul, Turkiye

Mob: 00-90-5013482199

Email: vedwingeo@gmail.com, edwin.varuvel@istinye.edu.tr

Ankit Sonthalia

Assistant Professor

Department of Automobile Engineering, Faculty of Engineering and Technology,

SRM Institute of Science and Technology, Delhi – NCR Campus, Modinagar, India

Mob: 00-91-9962466825

Email: ankit.sont@gmail.com, ankitsog@srmist.edu.in