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

This Special Issue aims to bring together cutting-edge research addressing emerging trends, challenges, and innovations in thermal sciences and energy-related applications. The issue provides a dedicated platform for researchers, scientists, and engineers to disseminate original findings and exchange insights that advance the understanding of heat transfer mechanisms, thermal systems design, and sustainable energy technologies. The scope of this Special Issue spans a broad range of theoretical, computational, and experimental investigations that contribute to the development and optimization of modern thermal and energy systems. Contributions showcasing novel materials, advanced computational models, data-driven techniques, nanofluid applications, and innovative thermal management solutions are particularly encouraged. Topics of Interest (but not limited to):

Thermal systems and advanced heat transfer processes

Renewable energy–based heat transfer units

Heat transfer in bioengineering and biomedical applications

Reactive flows involving heat and mass transfer

Multiphase, particulate, and complex fluid flows

Heat transfer and transport phenomena in porous media

Thermal enhanced oil recovery and petroleum reservoir modeling

Desalination technologies and thermal water treatment processes

Heat transfer in turbulent, transitional, and mixed convection flows

Thermal control and management in manufacturing and industrial systems

Thermophysical characterization and transport properties of nanofluids

Stability, rheology, and preparation techniques of mono- and hybrid nanofluids

Solar collectors, photovoltaic-thermal (PVT) systems, and solar-assisted thermal devices

Applications of mono- and hybrid nanofluids in sustainable and advanced energy systems

Data-driven modeling, AI/ML-based prediction, and optimization of thermal and nanofluid performance

Multi-objective optimization, metaheuristic algorithms, and intelligent design frameworks

This Special Issue invites high-quality manuscripts, including original research articles, comprehensive reviews, and case studies that address both foundational concepts and emerging applications in the domain of thermal sciences and sustainable energy technologies. We welcome contributions that offer new insights, propose innovative methodologies, or demonstrate impactful engineering applications.