Advanced Simulation Research Articles

Highly insulated hydrogen powered vacuum gasochromic smart windows for energy-efficient buildings

Switchable active hydrogen powered gasochromic smart windows present an appealing option for energy-efficient building integration, offering two controllable transparencies between ON and OFF states compared to traditional windows. Implementing such glazing in cold-climate buildings requires minimizing the total heat transfer across the glazing, achievable through vacuum-insulated glazing resulting in the retrofitting to obtain energy efficient building. This study investigates the potential of vacuum gasochromic (VGC) windows to enhance the energy efficiency of buildings in diverse climatic conditions, focusing on London, UK, and New Delhi, India. Comparing traditional single-glazing and double-glazing windows with vacuum-gasochromic windows in terms of thermal performance, energy demand, and dynamic control over solar heat gain and visible light transmission are evaluated using advanced simulation techniques with SketchUp, OPTICS6, WINDOW 7.8 and EnergyPlus 9.6. Results reveal that vacuum gasochromic windows exhibit superior thermal insulation with significantly lower U-values of 1.559W/m2K, reducing heat transfer and energy demand for heating and cooling. Vacuum gasochromic opaque states demonstrate exceptional performance in minimizing energy transfer, making them the most energy-efficient option, especially in warm climates like New Delhi with 9544.4 kWh. Conversely, vacuum gasochromic transparent state shows promise in reducing heating demand in London's cold climate by 9.43 %, highlighting their adaptability. Moreover, vacuum gasochromic windows allow dynamic control over solar heat gain and visible light transmission, enabling occupants to adjust transparency based on weather conditions and preferences.

Unlocking the potential of mixed-valence silver oxide for electrochemical valorization of 5-hydroxymethylfurfural into valuable products

The burgeoning interest in the electrocatalytic conversion of biomass-derived compounds, exemplified by 5-hydroxymethylfurfural (HMF), into economically valuable products underscores the significance of such studies. Within this context, the electrocatalytic oxidation of HMF into 2,5-diformylfuran (DFF) using the mixed-valence silver (I, III) oxide (AgO) as the catalyst is presented for the first time. A thorough investigation has been carried out to explore the complex factors influencing the electrochemical transformation of the HMF to DFF, involving applied potentials, reactant concentrations, and the significant implications of mass transfer phenomena. Under optimized conditions, DFF, one of the highest value-added intermediates, can be produced with selectivity as high as 54%. Additionally, a yield of 10.8 μmol cm−2 h−1 was obtained under mild basic condition. Another pivotal aspect of this work involves meticulously examining the reaction process, bolstered by a comprehensive analytical approach that integrates high-performance liquid chromatography (HPLC), and operando Raman spectroscopy. The amalgamation of operando Raman spectroscopy with advanced simulation techniques represents a novel endeavor, offering a groundbreaking pathway to unravel the complexities inherent in these compounds and contribute substantially to the understanding of HMF oxidation and its intermediates. By looking closely at the catalyst surface during the reaction, a valuable insight into the steps involved was developed, helping in proposing an in-depth reaction pathway.

Monte Carlo Workflow Unification for Nuclear Reactor Analysis with Metamodel-Driven Modeling

Monte Carlo (MC) transport codes are a cornerstone of nuclear reactor analysis frameworks, providing reference solutions and multigroup cross sections, and even as core components in multiphysics couplings. These applications can be seen in toolkits from the Nuclear Energy Advanced Modeling and Simulation program and the U.S. Nuclear Regulatory Commission’s BlueCRAB (Comprehensive Reactor Analysis Bundle). Contrary to their ubiquitousness in reactor physics modeling and simulation, popular MC codes, such as MCNP, Serpent, and KENO, are still reliant on antiquated textual input formats. These input languages use a plethora of keywords with terse syntax to specify all facets of a model, including its geometry, materials, physics settings, and tally options. This poses a steep learning curve and a poor user experience. Being tied to unique text-based input formats also significantly complicates programmatic input generation or modification that may be desired and/or required within a multiphysics framework. This work demonstrates how the development of programmatic interfaces for MC codes can support model unification and translation activities. Building on the Python application program interface (API) development of MCNPy, similar capabilities are being implemented for Serpent that are able to support model translation. In future work, the Serpent API capabilities will be made more robust and the work will be further expanded to include translations with KENO.

A multi-objective method for large multi-lane roundabout design through microscopic traffic simulation and SSAM analysis

Urban traffic congestion poses a significant challenge, impacting both economic efficiency and environmental sustainability. Roundabouts, recognized for their ability to improve traffic flow and reduce collisions, have become a focal point in traffic management strategies in urban areas. Different types and sizes of single and multi-lane roundabouts can be used, including conventional and innovative roundabouts with small, medium or large size diameters. Many optimization models are available for designing conventional roundabouts but not large roundabouts. To partially cover this gap, the present paper introduces a novel approach integrating simulation-based analysis with a goal programming method to optimize large roundabout design. By leveraging advanced traffic simulation tools like AIMSUN Next and Surrogate Safety Assessment Models (SSAMs), this methodology evaluates multiple design scenarios to enhance traffic flow, safety, and environmental performance. A case study of a complex large roundabout in the Italian greenest and sustainable city demonstrates the efficacy of this methodological approach. The findings provide valuable insights for traffic and highway engineers, emphasizing a data-driven, holistic approach to sustainable traffic management and roundabout optimization.

Light-Induced Ring-to-Chain Transformations of Elemental Sulfur: Nonadiabatic Dynamics Simulations.

The emergence of high-sulfur content polymeric materials and their diverse applications underscore the need for a comprehensive understanding of the ring-to-chain transformation of elemental sulfur. In this study, we delve into the ultrafast transformation of the elemental sulfur S8 ring upon photoexcitation employing advanced nonadiabatic dynamics simulations. Our findings reveal that the bond breaking of the S8 ring occurs within tens of femtoseconds. At the time of bond breaking, most molecules are in the lowest singlet excited state S1. S1 survives for 40-450 fs before relaxing to the quasi-degenerate manifolds formed by the T1 and S0 states of the S8 chain. This suggests that upon photoexcitation the polymerization of the S8 chains might proceed before the chains relax to their lowest energy states. The derived temporal resolution provides a detailed perspective on the dynamics of S8 rings upon photoexcitation, shedding light on the intricate processes involved in its excited-state transformations.

Assessing The Thermal Damage Induced By Radiofrequency Ablation for Localized Liver Cancer

Abstract Clinical trials are already established for high-temperature treatment of localized cancer, i.e., rise of tissue temperature to more than 550 C as an effective non-invasive method for the treatment of localized cancer. However, as the computational techniques and capacity have enhanced considerably personalized treatment planning has become a manured tool. In the present investigation, a novel treatment planning framework is being proposed for radiofrequency ablation of cancer tissue based on the tomographic image-based actual model. In patient-specific modelling, different thermal parameters like temperature history during ablation, and thermal damage profile have been virtually determined based on Penne's bioheat transfer model with appropriate boundary conditions. This advancement promises to significantly enhance the capabilities of healthcare practitioners in tailoring personalized treatment strategies for their clinical cases. By leveraging simulation outcomes, clinicians can precisely determine the most effective parameters, such as ablation power and frequency. Unfortunately, the current landscape in India presents a scarcity of specialized medical experts in the field of ablation oncology. Moreover, those who practice in this niche often rely on empirical charts rather than data-driven approaches, highlighting a critical need for increased expertise and the integration of advanced simulation technology to optimize cancer tissue ablation procedures. Last but not least optimizing different operating parameters of RF in this patient-centric approach for accurate treatment has also been discussed.

Characterization of an Advanced Blast Simulator for Investigation of Large Scale Blast Traumatic Brain Injury Studies.

Blast traumatic brain injury (bTBI) is a prominent military health concern. The pervasiveness and long-term impacts of this injury highlight the need for investigation of the physiological outcomes of bTBI. Preclinical models allow for the evaluation of behavioral and neuropathological sequelae associated with bTBI. Studies have implemented rodent models to investigate bTBI due to the relative small size and low cost; however, a large animal model with similar neuroanatomical structure to humans is essential for clinical translation. Small blast simulators are used to induce bTBI in rodents, but a large animal model demands a larger device. This study describes a large advanced blast simulator (ABS4) that is a gas-detonation-driven system consisting of 5 sections totaling 40ft in length with a cross-section of 4 × 4 ft at the test section. It is highly suitable for large animals and human surrogate investigations. This work characterized the ABS4 in preparation of large-scale bTBI testing. An array of tests were conducted with target overpressures in the test section ranging from 10 to 50 psi, and the pressure-time profiles clearly illustrate the essential characteristics of a free-field blast wave, specifically a sharp peak pressure and a defined negative phase. Multiple blast tests conducted at the same target pressure produced very similar pressure profiles, exhibiting the reproducibility of the ABS4 system. With its extensive range of pressures and substantial size, the ABS4 will permit military-relevant translational blast testing.

Integrating machine learning and parametric design for energy-efficient building cladding systems in arid climates: Sport hall in Kerman

Climate change, driven by fossil fuel dependence, presents a significant challenge for the construction industry, particularly in energy-intensive regions like arid climates. This research investigates the potential of integrating machine learning and parametric optimization to enhance the energy efficiency of spatial structure domes in such environments. Focusing on a sports pavilion in Kerman, Iran, the study examines the crucial role of cladding systems in building energy performance. Employing a rigorous four-phase methodology, the research optimizes dome cladding materials for hot, dry climates using a dual objective function: energy cost and material cost. The process involves a comprehensive literature review, data-driven material selection, advanced energy simulations, and optimization analysis. Parametric modeling tools (Rhino, Grasshopper, Honeybee) facilitate the comparative analysis of various cladding systems. Multivariate Polynomial Regression (MPR) enables predictive modeling of energy consumption and material costs, streamlining the design process for architects. The optimized solution is a hybrid cladding model composed of 10 % polycarbonate and 90 % aluminum. Analysis reveals that the hybrid system offers superior energy optimization compared to pure aluminum (4.58 %) and polycarbonate (5.70 %). While polycarbonate has a lower initial material cost, the hybrid system achieves a balance between material expenditure and long-term energy efficiency. This highlights the importance of considering life-cycle costs when evaluating building envelope materials. This research advances a framework that leverages machine learning and parametric design for building envelope optimization. This framework empowers architects and engineers to create energy-efficient structures within arid environments, promoting a more sustainable built environment.

Seismic performance assessment of pile supported wharves with seismic isolation system considering pile-soil interaction

Pile-Supported Wharves (PSW) are critical for maritime operations but are highly vulnerable to seismic events, which can disrupt port activities. Previous seismic events have highlighted that short free length piles in wharf structures are particularly prone to earthquake damage. This paper aims to mitigate damage between the wharf deck and piles connections by adopting the seismic isolation systems. Conventional Wharf (CW) and Isolated Wharf (IW) structures were comprehensively assessed, focusing on Pile-Soil Interaction (PSI), using the finite element software OpenSees for advanced numerical simulations. Non-Linear Time History Analysis (NLTHA) of CW and IW has been conducted to verify the design under Contingency Level Earthquake (CLE) and Maximum Considered Earthquake (MCE) scenarios. The analysis aims to enhance the performance of short free length piles within the IW structure. Comparative analysis of fragility curves between CW and IW structures shows that isolation systems significantly reduce seismic fragility by 49%, 60%, and 67% at the MCE level for minimal damage, control & repairable damage and life safety protection across three performance levels, respectively. These findings indicate that the implementation of isolation measures has significantly enhanced the seismic performance and safety of PSW structures.

Recurrent neural network-aided processing of incomplete free induction decays in 1H-MRS of the brain

In the case of limited sampling windows or truncation of free induction decays (FIDs) for artifact removal in proton magnetic resonance spectroscopy (1H‐MRS) and spectroscopic imaging (1H‐MRSI), metabolite quantification needs to be performed on incomplete FIDs. Given that FIDs are naturally time-domain sequential data, we investigated the potential of recurrent neural network (RNN)-types of neural networks (NNs) in the processing of incomplete human brain FIDs with or without FID restoration prior to quantitative analysis at 3.0T.First, we employed an RNN encoder-decoder and developed it to restore incomplete FIDs (rRNN) with different amounts of sampled data. The quantification of metabolites from the rRNN-restored FIDs was achieved by using LCModel. Second, we modified the RNN encoder-decoder and developed it to convert incomplete brain FIDs into incomplete metabolite-only FIDs without restoration, followed by linear regression using a metabolite basis set for quantitative analysis (cRNN). In consideration of the practical benefit of the FID restoration with respect to pure zero-filling, development and analysis of the NNs were focused particularly on the incomplete FIDs with only the first 64 data points retained. All NNs were trained on simulated data and tested mainly on in vivo data acquired from healthy volunteers (n = 27).Strong correlations were obtained between the NN-derived and ground truth metabolite content (LCModel-derived content on fully sampled FIDs) for myo‐inositol, total choline, and total creatine (normalized to total N-acetylaspartate) on the in vivo data using both rRNN (R = 0.83–0.94; p ≤ 0.05) and cRNN (R = 0.86–0.91; p ≤ 0.05).RNN-types of NNs have potential in the quantification of the major brain metabolites from the FIDs with substantially reduced sampled data points. For the metabolites with low to medium SNR, the performance of the NNs needs to be further improved, for which development of more elaborate and advanced simulation techniques would be of help, but remains challenging.

Proposal of Educational Models and a Simulation Program for Learning Piezosurgery in Stomatology, Implantology and Maxillofacial Surgery

Background and Objective: The acquisition of technical skills, using piezosurgery devices, would be facilitated by simulation on educational models. This study aims to disseminate a simulation program and propose educational models of animal origin enabling progressive achievement of psychomotor skills in learning piezosurgical procedures. Material: As part of the national continuing education program in stomatology, implantology, and maxillofacial surgery that we founded and have been leading since 2015, seven procedural simulation sessions for piezosurgery training were realized. The simulation program included two steps: first, specific tasks carried out on a sheep's head to learn psychomotor skills using piezoelectric device. Then, similar tasks were reproduced on a hen egg to reach a higher level of mastery and proficiency. The program and simulation models were evaluated according to a questionnaire by 200 practitioners at the end of each session. Results: Almost all participants reported that working on a sheep head authentically reproduced the tasks, that were completed after at least two attempts, with a feeling of projection into the clinical environment. All participants considered that undermining the membrane from the eggshell reproduced the same gesture on the Schneiderian membrane. However, only 30% of practitioners achieved this task on the hen egg requiring more than five attempts. Conclusion: The simulation program using the educational models that we have proposed allows us to carry out in the first stage of the simulation the basic learning of technical gestures in a high-fidelity environment and the second stage considered as an advanced simulation, to acquire a higher level of competence and technical capacity. Our simulation method is simple, inexpensive, and reliable to become standardized and followed on a larger scale in learning piezosurgical skills.

An integral approach to plasma-wall interaction modelling for EU-DEMO

Abstract The integral approach to plasma-wall interaction (PWI) modelling for DEMO is presented, which is a part of EUROfusion Theory and Advanced Simulation Coordination (E-TASC) activities that were established to advance the understanding and predictive capabilities for modelling of existing and future fusion devices using a modern advanced computing approach. In view of DEMO design, the aim of PWI modelling activities is to assess safety-relevant information regarding erosion of plasma-facing components (PFC), including its impact on plasma contamination, dust production, fuel inventory, and material response to transient events. This is achieved using a set of powerful and validated computer codes that deal with particular PWI aspects and interact with each other by means of relevant data exchange. Steady state erosion of tungsten PFC and subsequent transport and re-deposition of eroded material are simulated with the ERO2.0 code using a DEMO plasma background produced by dedicated SOLPS-ITER simulations. Dust transport simulations in steady state plasma also rely on the respective SOLPS-ITER solution and are performed with the MIGRAINe code. On the way to improved simulations of tungsten erosion in the divertor of DEMO, relevant high density sheath models are being developed based on Particle-in-Cell (PIC) simulations with the state-of-the-art BIT code family. PIC codes of the SPICE code family, in turn, provide relevant information on multi-emissive sheath physics, such as semi-empirical scaling laws for field-assisted thermionic emission. These scalings laws are essential for simulations of material melting under transient heat loads that are performed with the recently developed MEMENTO code, the successor of MEMOS-U. Fuel retention simulations assess tritium retention in tungsten and structural materials and permeation to coolant accounting for neutron damage, in particular for divertor monoblocks of different sizes, using the FESTIM code, and for the first wall, in particular in view of tritium self-sufficiency, using the TESSIM code. Respective code-code dependencies and interactions, as well as modelling results achieved to date are discussed in the contribution.

Limit states of thin-walled composite structures with closed sections under axial compression

The subjects of the study were thin-walled composite columns with closed cross sections manufactured using the autoclave technique. The composite profiles were characterized by the fact that they had a constant height and arrangement of laminate layers, however, varied cross-sectional shapes. The study was conducted using several interdisciplinary experimental research methods and advanced numerical simulations. In the course of the research, both forms of structural stability loss were registered, and damage to composite structures was assessed. In the course of the research, the influence of the shape of the cross-section on the stability and load-carrying capacity of the structure was evaluated. A measurable effect of the conducted research was the determination of the structure's post-buckling equilibrium paths, which made it possible to determine the structure's behavior in the full range of loading. In addition, the author's numerical models developed enabled validation of parallel experimental studies. The developed numerical models were based on a failure criterion known as progressive failure analysis - which allowed a thorough assessment of the failure mechanism of the composite material.

Efficacy of modified anterior maxillary segmental distraction osteogenesis based on 3D visualisation for the treatment of maxillary hypoplasia among adolescents with cleft lip and palate

BackgroundThis study evaluates a three-dimensional (3D) visualisation design combined with customized surgical guides to assist anterior maxillary segmental distraction osteogenesis (AMSDO) in correcting maxillary hypoplasia in adolescents with cleft lip and palate (CLP), focusing on treatment outcomes, satisfaction and the validity of 3D planning.MethodsThis retrospective cohort study was conducted at a single hospital in China. Between January 2020 and December 2023, 12 adolescents with CLP with maxillary hypoplasia were included. An advanced 3D simulation was used to convey the treatment strategy to the patients and their families. A customized surgical guide and distraction osteogenesis device were designed. Cephalometric analysis evaluated AMSDO changes and long-term stability. Patient satisfaction was assessed. The Chinese version of the Child Oral Health Impact Profile was used to evaluate the children’s oral health-related quality of life before and after treatment. The postoperative outcomes were compared with the planned outcomes by superimposing the actual postoperative data onto the simulated soft tissue models and calculating the linear and angular differences between them.ResultsOne patient experienced postoperative gingivitis, yielding an 8.33% complication rate. Most patients (83.33%) were highly satisfied with the target position, with the rest content. Cephalometric analysis showed significant improvements in various indices post-traction. Quality-of-life scores significantly improved post-treatment. The discrepancies in facial soft tissue between the simulated and actual results were within clinically satisfactory ranges.ConclusionsDigitally designed surgical guides effectively treat maxillary hypoplasia in adolescents with CLP, ensuring stability, reducing complications, reducing dependency on operator experience, and enhancing satisfaction and health outcomes. Although the simulated results were clinically acceptable, it is important to inform patients of potential variations in the predicted soft tissue.

Application of Industry 4.0 and digital twin in the field of smart healthcare

There is an increasing need for preventive health care as well as precise diagnosis and tailored treatment of different diseases in recent years. Providing customized treatment for each patient and maximizing accuracy and efficiency are main goals of a good healthcare system. This thesis explores the integration of digital twin technology with Industry 4.0 in healthcare. Digital twins create virtual representations of physical systems, enabling real-time monitoring and tailored treatments. Key elements include the living human body, IoT, digital twin, cloud computing, and simulation. The architecture comprises data acquisition, data munging, data storage, data simulation with analytics, and user access layers. Creating a digital twin involves precise 3D modeling, representing the entire body or specific organs. A case study demonstrates real-time monitoring using wearable sensors for blood pressure, blood sugar, and heart rate. Data is transmitted, integrated with the digital twin, and accessible via a website with alarms for abnormal readings. While Industry 4.0 and digital twin adoption in healthcare is evolving, the architecture serves as a reference. It offers real-time data for diagnosis and treatment, with potential for advanced simulations. Challenges include automated data systems and privacy concerns. Despite limitations, this integration holds promise for precision medicine and personalized healthcare.

Implementation of matrix compression in the coupling of JOREK to realistic 3D conducting wall structures

Abstract JOREK is an advanced non-linear simulation code for studying MHD instabilities in magnetically confined fusion plasmas and their control and/or mitigation. A free-boundary and resistive wall extension was introduced via coupling to the STARWALL and CARIDDI codes, both able to provide dense response matrices describing the electromagnetic interactions between plasma and conducting structures. For detailed CAD representations of the conducting structures and high resolutions for the plasma region, memory and computing time limitations restrict the possibility of simulating the ITER tokamak. In the present work, the Singular Value Decomposition provided by routines from the ScaLAPACK library has been successfully applied to compress some of the dense response matrices and thus optimize memory usage. This is demonstrated for simulations of Tearing Mode and Vertical Displacement Event instabilities. An outlook to future applications on large production cases and further extensions of the method are discussed.

Switching Response in Organic Electrochemical Transistors by Ionic Diffusion and Electronic Transport.

The switching response in organic electrochemical transistors (OECT) is a basic effect in which a transient current occurs in response to a voltage perturbation. This phenomenon has an important impact on different aspects of the application of OECT, such as the equilibration times, the hysteresis dependence on scan rates, and the synaptic properties for neuromorphic applications. Here we establish a model that unites vertical ion diffusion and horizontal electronic transport for the analysis of the time-dependent current response of OECTs. We use a combination of tools consisting of a physical analytical model; advanced 2D drift-diffusion simulation; and the experimental measurement of a poly(3-hexylthiophene) (P3HT) OECT. We show the reduction of the general model to simple time-dependent equations for the average ionic/hole concentration inside the organic film, which produces a Bernards-Malliaras conservation equation coupled with a diffusion equation. We provide a basic classification of the transient response to a voltage pulse, and the correspondent hysteresis effects of the transfer curves. The shape of transients is basically related to the main control phenomenon, either the vertical diffusion of ions during doping and dedoping, or the equilibration of electronic current along the channel length.

Denoising graph neural network based on zero-shot learning for Gibbs phenomenon in high-order DG applications

With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.

CT imaging‐enhanced numerical simulation of microscopic structure and resilient safety of asphalt concrete

AbstractAsphalt concrete is a foundational material in water conservancy projects, serving a critical function in the construction of impermeable structures such as dams. The seismic response characteristics and resilient safety of concrete dams are heavily influenced by the arrangement and evolution of the microscopic structure of the dam material. In this study, a high‐precision computed tomography (CT) scanning technique, in conjunction with advanced numerical simulations, was employed to analyze the internal damage and crack extension mechanism of asphalt concrete. Microstructural images of the asphalt concrete specimen were accurately captured by CT scanning, followed by the construction of corresponding numerical models. Presented simulation results show that the displacement deformation of asphalt concrete reaches its maximum value in the top region of the model and subsequently decreases with depth. Material damage was first observed at the interface between aggregate and asphalt matrix, where microcracks emerge and extend to the entire asphalt matrix, resulting in a gradual deterioration of the model performance. The simulation results indicate that the overall strength of asphalt concrete is primarily influenced by the strength characteristics of its aggregates. The stress–strain curves obtained from the numerical simulations exhibit a hyperbolic relationship, which is in high agreement with the physical test results. This study not only enhances our comprehension of the mechanical behavior of concrete but also contributes to the analysis of seismic response and risk assessment in dam engineering through dynamic experimental testing and numerical simulation.

A study of XLPE insulation failure in power cables under electromagnetic stress

Underground cables with cross-linked polyethylene (XLPE) insulation are integral to medium voltage (MV) power transmission systems, ensuring continuous electricity supply amidst operational challenges and environmental conditions. However, the reliability of these cables can be compromised over time due to aging and installation-related factors, particularly at joints and terminations. This study offers a comprehensive analysis of how various defects, including spherical air voids, pinholes, and irregularities in semiconducting layers, affect electric field and potential distributions within cable end terminations using COMSOL Multiphysics software. Through detailed simulations, the study identifies significant variations in electric field strength caused by these defects, highlighting critical stress concentration areas. In this study, it assumed that the cable and termination have the same cross-section. By analyzing these simulations, the study provides insights into optimizing cable design and installation practices to enhance the reliability and lifespan of underground power transmission systems. This study introduces a novel approach by combining advanced COMSOL Multiphysics simulations with a detailed analysis of defect impacts on electric field distributions, offering new insights into stress concentrations and degradation at cable terminations. Simulation outcomes reveal significant variations in electric field strengths due to air voids and pinholes in cables and terminations: for 2 mm voids, up to 2.45×106 V mm−1 near the conductor and 56.5 V mm−1 at termination. These findings enhance the understanding of XLPE-insulated cable behavior under electromagnetic stress, providing a basis for mitigating failures and improving overall system performance.