Mohamed S. El-Genk^a,b,c,∗, Amir F. Ali^a,c

Frontiers in Heat and Mass Transfer, Vol.3, No.4, pp. 1-14, 2012, DOI:10.5098/hmt.v3.4.3001

Abstract Advanced spreaders for cooling a 10 x 10 mm underlying computer chip with a central hot spot (CHS) could remove > 85 W of dissipated thermal power at junctions’ temperature < 100o C. The spreaders comprise a 1.6 - 3.2 mm thick Cu substrate and an 80-μm thick micro-porous copper (MPC) surface cooled by saturation nucleate boiling of PF-5060 dielectric liquid. Investigated are the effects of varying the heat flux at the chip’s 1 and 4 mm² CHS and the impedance of thermal interface material (TIM) between the Cu substrate and underlying chip. Results confirmed the effectiveness of the MPC… More >

Rohollah Fallah Madvari¹, Hamideh Bidel², Ahmad Mehri³, Fatema Babaee⁴, Fereydoon Laal^5,*

Sound & Vibration, Vol.58, pp. 119-131, 2024, DOI:10.32604/sv.2024.048861

Abstract Noise is one of the environmental factors with mental and physical effects. The workload is also the multiple mental and physical demands of the task. Therefore, his study investigated the relationship between noise exposure and mood states at different levels of workload. The study recruited 50 workers from the manufacturing sector (blue-collar workers) as the exposed group and 50 workers from the office sector (white-collar workers) as the control group. Their occupational noise exposure was measured by dosimetry. The Stress-Arousal Checklist (SACL) and the NASA Task Load Index (NASA-TLX) were used to measure mood and workload, respectively. The equivalent noise… More >

Vasishta Bhargava Nukala^*, Chinmaya Prasad Padhy

Sound & Vibration, Vol.58, pp. 133-150, 2024, DOI:10.32604/sv.2024.047762

Abstract A significant aerodynamic noise from wind turbines arises when the rotating blades interact with turbulent flows. Though the trailing edge of the blade is an important source of noise at high frequencies, the present work deals with the influence of turbulence distortion on leading edge noise from wind turbine blades which becomes significant in low-frequency regions. Four quasi-empirical methods are studied to verify the accuracy of turbulent inflow noise predicted at low frequencies for a 2 MW horizontal axis wind turbine. Results have shown that all methods exhibited a downward linear trend in noise spectra for a given mean wind… More >

Carlo Cattani¹, Haci Mehmet Baskonus^2,*, Armando Ciancio³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2261-2264, 2024, DOI:10.32604/cmes.2024.050209

Abstract This article has no abstract. More >

Mohammad Mehedi Hassan^1,*, Salman A. AlQahtani², Mabrook S. AlRakhami¹, Ahmed Zohier Elhendi³

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3101-3123, 2024, DOI:10.32604/cmes.2024.047940

Abstract In the current landscape of the COVID-19 pandemic, the utilization of deep learning in medical imaging, especially in chest computed tomography (CT) scan analysis for virus detection, has become increasingly significant. Despite its potential, deep learning’s “black box” nature has been a major impediment to its broader acceptance in clinical environments, where transparency in decision-making is imperative. To bridge this gap, our research integrates Explainable AI (XAI) techniques, specifically the Local Interpretable Model-Agnostic Explanations (LIME) method, with advanced deep learning models. This integration forms a sophisticated and transparent framework for COVID-19 identification, enhancing the capability of standard Convolutional Neural Network… More >

Debiao Meng^1,2,*, Yipeng Guo^1,2, Yihe Xu³, Shiyuan Yang^1,2,*, Yongqiang Guo⁴, Lidong Pan⁴, Xinkai Guo²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2329-2359, 2024, DOI:10.32604/cmes.2024.047507

Abstract The escalating need for reliability analysis (RA) and reliability-based design optimization (RBDO) within engineering challenges has prompted the advancement of saddlepoint approximation methods (SAM) tailored for such problems. This article offers a detailed overview of the general SAM and summarizes the method characteristics first. Subsequently, recent enhancements in the SAM theoretical framework are assessed. Notably, the mean value first-order saddlepoint approximation (MVFOSA) bears resemblance to the conceptual framework of the mean value second-order saddlepoint approximation (MVSOSA); the latter serves as an auxiliary approach to the former. Their distinction is rooted in the varying expansion orders of the performance function as… More >

Di Peng^1,2, Guoqing Chen¹, Jiale Yan^1,*, Shiping Wang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2947-2964, 2024, DOI:10.32604/cmes.2024.047265

Abstract Cavitation is a prevalent phenomenon within the domain of ship and ocean engineering, predominantly occurring in the tail flow fields of high-speed rotating propellers and on the surfaces of high-speed underwater vehicles. The re-entrant jet and compression wave resulting from the collapse of cavity vapour are pivotal factors contributing to cavity instability. Concurrently, these phenomena significantly modulate the evolution of cavitation flow. In this paper, numerical investigations into cloud cavitation over a Clark-Y hydrofoil were conducted, utilizing the Large Eddy Simulation (LES) turbulence model and the Volume of Fluid (VOF) method within the OpenFOAM framework. Comparative analysis of results obtained… More > Graphic Abstract

Haitao Liu^1,2,*, Jiaming Wang¹, Xiuliang Zhang¹, Yanji Jiang², Qian Xiao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2747-2772, 2024, DOI:10.32604/cmes.2024.047129

Abstract The expansion chamber serves as the primary silencing structure within the exhaust pipeline. However, it can also act as a sound-emitting structure when subjected to airflow. This article presents a hybrid method for numerically simulating and analyzing the unsteady flow and aerodynamic noise in an expansion chamber under the influence of airflow. A fluid simulation model is established, utilizing the Large Eddy Simulation (LES) method to calculate the unsteady flow within the expansion chamber. The simulation results effectively capture the development and changes of the unsteady flow and vorticity inside the cavity, exhibiting a high level of consistency with experimental… More > Graphic Abstract

Rui Wang¹, Yao Zhou^3,*, Guangchun Luo¹, Peng Chen², Dezhong Peng^3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3011-3027, 2024, DOI:10.32604/cmes.2023.047065

Abstract Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data. Due to the challenges associated with annotating anomaly events, time series reconstruction has become a prevalent approach for unsupervised anomaly detection. However, effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series. In this paper, we propose a cross-dimension attentive feature fusion network for time series anomaly detection, referred to as CAFFN. Specifically, a series and feature mixing block is introduced to learn representations in 1D space. Additionally, a… More >

Xingli Wu^{1,{{sup}}#{{/sup}}}, Huchang Liao^{1,{{sup}}#{{/sup}}}, Shuxian Sun¹, Zhengjun Wan^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3125-3146, 2024, DOI:10.32604/cmes.2023.047031

Abstract Agricultural investment project selection is a complex multi-criteria decision-making problem, as agricultural projects are easily influenced by various risk factors, and the evaluation information provided by decision-makers usually involves uncertainty and inconsistency. Existing literature primarily employed direct preference elicitation methods to address such issues, necessitating a great cognitive effort on the part of decision-makers during evaluation, specifically, determining the weights of criteria. In this study, we propose an indirect preference elicitation method, known as a preference disaggregation method, to learn decision-maker preference models from decision examples. To enhance evaluation ease, decision-makers merely need to compare pairs of alternatives with which… More >