Open Access
ARTICLE
Faten S. Alamri1, Adil Ali Saleem2, Muhammad I. Khan3, Hafeez Ur Rehman Siddiqui2, Amjad Rehman3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074679
（This article belongs to the Special Issue: Intelligent Monitoring of Rotating Machinery: Diagnostic and Prognostic Paradigms)
Abstract Motor imbalance is a critical failure mode in rotating machinery, potentially causing severe equipment damage if undetected. Traditional vibration-based diagnostic methods rely on direct sensor contact, leading to installation challenges and measurement artifacts that can compromise accuracy. This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar. A dataset of 1802 experimental trials was sourced, covering four imbalance levels (0, 10, 20, 30 g) across varying motor speeds (500–1500 rpm) and load torques (0–3 Nm). Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second… More >
Open Access
ARTICLE
Mohammed Al-Mahbashi1,2,*, Ali Ahmed3, Abdolraheem Khader4,*, Shakeel Ahmad3, Mohamed A. Damos5, Ahmed Abdu6
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075909
（This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
Abstract Reliable detection of traffic signs and lights (TSLs) at long range and under varying illumination is essential for improving the perception and safety of autonomous driving systems (ADS). Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions. To overcome these limitations, this research presents FED-YOLOv10s, an improved and lightweight object detection framework based on You Only look Once v10 (YOLOv10). The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations, an Efficient Multiscale Attention (EMA) mechanism to More >
Open Access
ARTICLE
Van-Viet Nguyen1, Huu-Khanh Nguyen2, Kim-Son Nguyen1, Thi Minh-Hue Luong1, Duc-Quang Vu1, Trung-Nghia Phung3, The-Vinh Nguyen1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075442
Abstract It remains difficult to automate the creation and validation of Unified Modeling Language (UML) diagrams due to unstructured requirements, limited automated pipelines, and the lack of reliable evaluation methods. This study introduces a cohesive architecture that amalgamates requirement development, UML synthesis, and multimodal validation. First, LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements. Then, DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code. Using this dual-LLM pipeline, we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families. Rendering analysis showed that 89.5% of the generated diagrams compile correctly, while… More >
Open Access
ARTICLE
Monalisa Jena1, Noman Khan2,*, Mi Young Lee3,*, Seungmin Rho3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075119
Abstract Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises. When such risks go undetected, consequences can escalate to self-harm, long-term disability, reduced productivity, and significant societal and economic burden. Despite recent advances, detecting risk from online text remains challenging due to heterogeneous language, evolving semantics, and the sequential emergence of new datasets. Effective solutions must encode clinically meaningful cues, reason about causal relations, and adapt to new domains without forgetting prior knowledge. To address these challenges, this paper presents a Continual Neuro-Symbolic Graph… More >
Open Access
ARTICLE
Qi Li, Yifan Wang*, Yuxi Liu, Xingjing She, Yixuan Wu
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074066
（This article belongs to the Special Issue: Incomplete Data Test, Analysis and Fusion Under Complex Environments)
Abstract In data communication, limited communication resources often lead to measurement bias, which adversely affects subsequent system estimation if not effectively handled. This paper proposes a novel bias calibration algorithm under communication constraints to achieve accurate system states of the interested system. An output-based event-triggered scheme is first employed to alleviate transmission burden. Accounting for the limited-communication-induced measurement bias, a novel bias calibration algorithm following the Kalman filtering line is developed to restrain the effect of the measurement bias on system estimation, thereby achieving accurate system state estimates. Subsequently, the Field Programmable Gate Array (FPGA) implementation More >
Open Access
ARTICLE
Abdulaziz Almalaq1, Ambe Harrison2,*, Ibrahim Alsaleh1, Abdullah Alassaf1, Mashari Alangari1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074815
Abstract We present a computer-modeling framework for photovoltaic (PV) source emulation that preserves the exact single-diode physics while enabling iteration-free, real-time evaluation. We derive two closed-form explicit solvers based on the Lambert W function: a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator. Unlike Taylor-linearized explicit models, our proposed formulation retains the exponential nonlinearity of the PV equations. It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding, all while maintaining limited computational costs and a small… More >
Open Access
ARTICLE
Muhammad Farman1,2,3,*, Muhammad Hashir Zubair4, Hua Li4, Kottakkaran Sooppy Nisar5,6, Mohamad Hafez7,8
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074160
（This article belongs to the Special Issue: Innovative Applications of Fractional Modeling and AI for Real-World Problems)
Abstract Lassa Fever (LF) is a viral hemorrhagic illness transmitted via rodents and is endemic in West Africa, causing thousands of deaths annually. This study develops a dynamic model of Lassa virus transmission, capturing the progression of the disease through susceptible, exposed, infected, and recovered populations. The focus is on simulating this model using the fractional Caputo derivative, allowing both qualitative and quantitative analyses of boundedness, positivity, and solution uniqueness. Fixed-point theory and Lipschitz conditions are employed to confirm the existence and uniqueness of solutions, while Lyapunov functions establish the global stability of both disease-free and… More >
Open Access
ARTICLE
Huda Talib Najm1,2, Ahmed Sabah Al-Araji3, Nur Syazreen Ahmad1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.071837
Abstract Mobile service robots (MSRs) in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions, including model uncertainties and external disturbances. This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller (NFIDC) with a Feedback Radial Basis Function Neural Network (FRBFNN). The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1. The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.… More >
Open Access
ARTICLE
Burhan Baraklı1,*, Can Yüzkollar2, Tuğrul Taşçı3, ˙Ibrahim Yıldırım2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074292
（This article belongs to the Special Issue: Advanced Image Segmentation and Object Detection: Innovations, Challenges, and Applications)
Abstract Salient object detection (SOD) models struggle to simultaneously preserve global structure, maintain sharp object boundaries, and sustain computational efficiency in complex scenes. In this study, we propose SPSALNet, a task-driven two-stage (macro–micro) architecture that restructures the SOD process around superpixel representations. In the proposed approach, a “split-and-enhance” principle, introduced to our knowledge for the first time in the SOD literature, hierarchically classifies superpixels and then applies targeted refinement only to ambiguous or error-prone regions. At the macro stage, the image is partitioned into content-adaptive superpixel regions, and each superpixel is represented by a high-dimensional region-level… More >
Open Access
REVIEW
Wenfeng Zheng1, Guangyu Xu2, Siyu Lu3, Junmin Lyu4, Feng Bao5,*, Lirong Yin6,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075741
（This article belongs to the Special Issue: The Collection of the Latest Reviews on Advances and Challenges in AI)
Abstract Graph Neural Networks (GNNs), as a deep learning framework specifically designed for graph-structured data, have achieved deep representation learning of graph data through message passing mechanisms and have become a core technology in the field of graph analysis. However, current reviews on GNN models are mainly focused on smaller domains, and there is a lack of systematic reviews on the classification and applications of GNN models. This review systematically synthesizes the three canonical branches of GNN, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sampling Aggregation Network (GraphSAGE), and analyzes their integration pathways More >
Open Access
ARTICLE
Menwa Alshammeri1,2,*, Noshina Tariq3, N. Z. Jhanji4,5, Mamoona Humayun6, Muhammad Attique Khan7
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074627
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Artificial Intelligence (AI) is changing healthcare by helping with diagnosis. However, for doctors to trust AI tools, they need to be both accurate and easy to understand. In this study, we created a new machine learning system for the early detection of Autism Spectrum Disorder (ASD) in children. Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning. For this, we combined several different models, including Random Forest, XGBoost, and Neural Networks, into a single, more powerful framework. We used two different types More >
Open Access
ARTICLE
Junmin Lyu1, Guangyu Xu2, Feng Bao3,*, Yu Zhou4, Yuxin Liu5, Siyu Lu5,*, Wenfeng Zheng5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075239
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract Graph neural networks (GNN) have shown strong performance in node classification tasks, yet most existing models rely on uniform or shared weight aggregation, lacking flexibility in modeling the varying strength of relationships among nodes. This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node. Unlike traditional methods, the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance. The model operates in the spatial domain, utilizing adjacency list structures for efficient… More >
Open Access
REVIEW
Moein Tosan1,*, Vahid Nourani2,3, Ozgur Kisi4,5,6, Yongqiang Zhang7, Sameh A. Kantoush8, Mekonnen Gebremichael9, Ruhollah Taghizadeh-Mehrjardi10, Jinhui Jeanne Huang11
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074768
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract The integration of machine learning (ML) into geohazard assessment has successfully instigated a paradigm shift, leading to the production of models that possess a level of predictive accuracy previously considered unattainable. However, the black-box nature of these systems presents a significant barrier, hindering their operational adoption, regulatory approval, and full scientific validation. This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence (XAI) as applied to geohazard science (GeoXAI), a domain that aims to resolve the long-standing trade-off between model performance and interpretability. A rigorous synthesis of 87 foundational… More >
Open Access
REVIEW
Jihoon Moon*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.071052
（This article belongs to the Special Issue: Deep Learning for Energy Systems)
Abstract Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data, a challenge that becomes especially pronounced when commissioning new facilities where operational records are scarce. This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such “cold-start” forecasting problems. It primarily covers three interrelated domains—solar photovoltaic (PV), wind power, and electrical load forecasting—where data scarcity and operational variability are most critical, while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective. To this end, we examined… More >
Open Access
ARTICLE
Guebin Choi*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073985
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
Abstract This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series. Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks (STGNNs). However, such definitions are prone to generating spurious correlations due to the dominance of periodic structures. To address this limitation, we adopt the Elastic-Band Transform (EBT) to decompose solar radiation into periodic and amplitude-modulated components, which are then modeled independently with separate graph neural networks. The periodic component, characterized by strong More >
Open Access
ARTICLE
Kang-Jia Wang1, Hongzhi Zhang2, Runsheng Lin3,*, Jiabin Li4, Xiao-Yong Wang1,5,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074787
（This article belongs to the Special Issue: Advanced Modeling and Simulation for Sustainable Construction Materials and Structures)
Abstract Conventional low-carbon concrete design approaches have often overlooked carbonation durability and the progressive loss of cover caused by surface scaling, both of which can increase the long-term risk of reinforcement corrosion. To address these limitations, this study proposes an improved design framework for low-carbon slag concrete that simultaneously incorporates carbonation durability and cover scaling effects into the mix proportioning process. Based on experimental data, a linear predictive model was developed to estimate the 28-day compressive strength of slag concrete, achieving a correlation coefficient of R = 0.87711 and a root mean square error (RMSE) of… More >
Open Access
ARTICLE
Zia Ur Rahman1, Nigar Ali1,2, Dragan Pamucar3, Imtiaz Ahmad1,2, Haci Mehmet Baskonus2,*, Naseer Ul Haq1, Zeeshan Ali4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.070268
（This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
Abstract Hepatitis B Virus (HBV) infection and heavy alcohol consumption are the two primary pathogenic causes of liver cirrhosis. In this paper, we proposed a deterministic mathematical model and a logistic equation to investigate the dynamics of liver cirrhosis progression as well as to explain the implications of variations in alcohol consumption on chronic hepatitis B patients, respectively. The intricate interactions between liver cirrhosis, recovery, and treatment dynamics are captured by the model. This study aims to show that alcohol consumption by Hepatitis B-infected individuals accelerates liver cirrhosis progression while treatment of acutely infected individuals reduces… More >
Open Access
ARTICLE
Farhad Soleimanian Gharehchopogh*, Keyvan Fattahi Rishakan
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073808
Abstract Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences. Metaheuristic algorithms, in particular, have proven highly effective in complex optimization scenarios characterized by high dimensionality and intricate variable relationships. The Mountain Gazelle Optimizer (MGO) is notably effective but struggles to balance local search refinement and global space exploration, often leading to premature convergence and entrapment in local optima. This paper presents the Improved MGO (IMGO), which integrates three synergistic enhancements: dynamic chaos mapping using piecewise chaotic sequences to boost exploration diversity; Opposition-Based Learning (OBL) with adaptive, diversity-driven activation to speed up… More >
Open Access
ARTICLE
Shengkang Zhang1, Yong Jin2,*, Soon Poh Yap1,*, Haoyun Fan1, Shiyuan Li3, Ahmed El-Shafie4, Zainah Ibrahim1, Amr El-Dieb5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075351
Abstract Concrete-filled steel tubes (CFST) are widely utilized in civil engineering due to their superior load-bearing capacity, ductility, and seismic resistance. However, existing design codes, such as AISC and Eurocode 4, tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core. To address this limitation, this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer (PKO), a nature-inspired algorithm, to enhance the accuracy of shear strength prediction for CFST columns. Additionally, quantile regression is employed to construct prediction intervals for… More >
Open Access
ARTICLE
Ehsan Arshid*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074898
Abstract In this work, a computational modelling and analysis framework is developed to investigate the thermal buckling behavior of doubly-curved composite shells reinforced with graphene-origami (G-Ori) auxetic metamaterials. A semi-analytical formulation based on the First-Order Shear Deformation Theory (FSDT) and the principle of virtual displacements is established, and closed-form solutions are derived via Navier’s method for simply supported boundary conditions. The G-Ori metamaterial reinforcements are treated as programmable constructs whose effective thermo-mechanical properties are obtained via micromechanical homogenization and incorporated into the shell model. A comprehensive parametric study examines the influence of folding geometry, dispersion arrangement, More >
Open Access
ARTICLE
Xianrui Lyu, Xiaodan Ren*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074388
（This article belongs to the Special Issue: AI-Enhanced Computational Methods in Engineering and Physical Science)
Abstract Inverse design of advanced materials represents a pivotal challenge in materials science. Leveraging the latent space of Variational Autoencoders (VAEs) for material optimization has emerged as a significant advancement in the field of material inverse design. However, VAEs are inherently prone to generating blurred images, posing challenges for precise inverse design and microstructure manufacturing. While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent, it simultaneously imposes a substantial burden on target optimization due to an excessively high search space. To address these limitations, this study adopts a Variational… More >
Open Access
ARTICLE
Weiqi Mao1,2,3, Enbo Yu1,*, Guoji Xu3, Xiaozhen Li3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.069733
（This article belongs to the Special Issue: Deep Learning for Energy Systems)
Abstract Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration. This study presents a novel machine learning model that integrates clustering, deep learning, and transfer learning to mitigate accuracy degradation in 24-h forecasting. Initially, an optimized DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm clusters wind fields based on wind direction, probability density, and spectral features, enhancing physical interpretability and reducing training complexity. Subsequently, a ResNet (Residual Network) extracts multi-scale patterns from decomposed wind signals, while transfer learning adapts the backbone network across clusters, cutting training time by over… More >
Open Access
ARTICLE
Sanjog Chhetri Sapkota1,2, Liborio Cavaleri3, Ajaya Khatri4, Siddhi Pandey5, Satish Paudel6, Panagiotis G. Asteris7,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.069691
（This article belongs to the Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
Abstract Optimization is the key to obtaining efficient utilization of resources in structural design. Due to the complex nature of truss systems, this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints. Two new algorithms, the Red Kite Optimization Algorithm (ROA) and Secretary Bird Optimization Algorithm (SBOA), are utilized on five benchmark trusses with 10, 18, 37, 72, and 200-bar trusses. Both algorithms are evaluated against benchmarks in the literature. The results indicate that SBOA always reaches a lighter optimal. Designs with reducing structural weight ranging from 0.02%… More >
Open Access
ARTICLE
Sana Shahab1, Vladimir Simic2,*, Ashit Kumar Dutta3,4, Mohd Anjum5,*, Dragan Pamucar6,7,8
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073945
Abstract Environmental problems are intensifying due to the rapid growth of the population, industry, and urban infrastructure. This expansion has resulted in increased air and water pollution, intensified urban heat island effects, and greater runoff from parks and other green spaces. Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies. This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization (AAROM-TN), enhanced by a dual weighting strategy. The weighting approach integrates the Criteria Importance Through Intercriteria Correlation… More >
Open Access
ARTICLE
Junmin Lyu1, Feng Bao2,*, Guangyu Xu3, Siyu Lu4,*, Bo Yang5, Yuxin Liu5, Wenfeng Zheng5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075178
Abstract Robust teleoperation in image-guided interventions faces critical challenges from latency, deformation, and the quasi-periodic nature of physiological motion. This paper presents a fully integrated, latency-aware visual servoing system leveraging stereo vision, hand–eye calibration, and learning-based prediction for motion-compensated teleoperation. The system combines a calibrated binocular camera setup, dual robotic arms, and a predictive control loop incorporating Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) models. Through experiments using both in vivo and phantom datasets, we quantitatively assess the prediction accuracy and motion-compensation performance of both models. Results show that TCNs deliver more stable and precise More >
Open Access
ARTICLE
Amel Ksibi*, Hatoon Albadah, Ghadah Aldehim, Manel Ayadi
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073533
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
Abstract Sustainable energy systems will entail a change in the carbon intensity projections, which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions. The present article outlines the TransCarbonNet, a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory (Bi-LSTM) network to forecast the carbon intensity of the grid several days. The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data; hence, it is able to give… More >
Open Access
ARTICLE
Guangyu Xu1,2, Yuxin Liu1, Bo Yang1, Siyu Lu3,*, Chao Liu4, Junmin Lyu5, Wenfeng Zheng1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074938
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)
Abstract Soft-tissue motion introduces significant challenges in robotic teleoperation, especially in medical scenarios where precise target tracking is critical. Latency across sensing, computation, and actuation chains leads to degraded tracking performance, particularly around high-acceleration segments and trajectory inflection points. This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking. Three models—autoregressive (AR), long short-term memory (LSTM), and temporal convolutional network (TCN)—were implemented and evaluated on both synthetic and real datasets. By aligning the prediction horizon with the end-to-end system delay, we demonstrate that prediction-based compensation significantly reduces tracking errors. Among the models, TCN More >
Open Access
ARTICLE
Maddina Dinesh Kumar1,#, S. U. Mamatha2, Khalid Masood3, Nehad Ali Shah4,#, Se-Jin Yook1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074680
（This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-III)
Abstract Fluid dynamic research on rectangular and trapezoidal fins is aimed at increasing heat transfer by means of large surfaces. The trapezoidal cavity form is compared with its thermal and flow performance, and it is revealed that trapezoidal fins tend to be more efficient, particularly when material optimization is critical. Motivated by the increasing need for sustainable energy management, this work analyses the thermal performance of inclined trapezoidal and rectangular porous fins utilising a unique hybrid nanofluid. The effectiveness of nanoparticles in a working fluid is primarily determined by their thermophysical properties; hence, optimising these properties… More >
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