Results in Engineering最新文献_第8页

Assessing the environmental impacts of passive and active fire protection strategies 评估被动和主动防火策略对环境的影响

IF 7.9 Q1 ENGINEERING, MULTIDISCIPLINARY

Results in Engineering

Pub Date : 2025-12-01 DOI: 10.1016/j.rineng.2025.108530

Mansoure Dormohamadi , Kai Kanafani , Harpa Birgisdottir , Anders Dragsted , Endrit Hoxha

Buildings significantly contribute to climate change, prompting growing interest in timber construction for its lower environmental footprint and carbon storage potential. However, biogenic materials introduce notable fire safety challenges. This study presents the Fire Safety Life Cycle Assessment (FS-LCA) framework to quantify environmental impacts of passive and active fire protection strategies. Building products are functionally decomposed and classified according to whether fire safety is their primary or secondary function.

The FS-LCA framework is applied to three case studies: a concrete building, a non-sprinklered timber building, and a sprinklered timber building. Across all, fire-regulated elements, encompassing passive fire protection materials, account for approximately 50 % of total embodied impacts across most indicators, largely from the production stage. Active fire protection systems contribute to both production and replacement stages, the latter due to periodic renewals. Fire-regulated burdens are mainly driven by structural support and thermal comfort functions. In the concrete building, primary fire safety is negligible. In the non-sprinklered timber building, primary fire safety comes solely from fire-specific materials, with maximum shares in ADPf (3.95 %) and GWP (3.3 %). In the sprinklered timber building, primary fire safety combines PFP and AFP, with a GWP share of 3.22 %. Secondary fire functions account for over 90 % of FR-related impacts in all cases.

These findings demonstrate that the environmental burden of fire protection is shaped by both material selection and the adopted fire safety strategy. Moreover, over-attributing multifunctional materials’ impacts to fire safety risks overestimation, highlighting the need for refined, function-based FS-LCA methods.

建筑对气候变化的影响很大，这促使人们对木结构建筑越来越感兴趣，因为它具有更低的环境足迹和碳储存潜力。然而，生物材料带来了显著的消防安全挑战。本研究提出了消防安全生命周期评估（FS-LCA）框架来量化被动和主动消防策略的环境影响。根据消防安全是主要功能还是次要功能，对建筑产品进行功能分解和分类。FS-LCA框架应用于三个案例研究：混凝土建筑、非洒水木结构建筑和洒水木结构建筑。总体而言，包括被动式防火材料在内的火灾调节要素在大多数指标中约占总具体影响的50%，主要来自生产阶段。主动消防系统有助于生产和更换阶段，后者由于定期更新。火灾调节负荷主要由结构支撑和热舒适功能驱动。在混凝土建筑中，初级消防安全可以忽略不计。在非洒水木结构建筑中，主要的防火安全性仅来自于防火专用材料，其中ADPf（3.95%）和GWP（3.3%）的比例最大。在喷淋木结构建筑中，初级消防安全组合了PFP和AFP， GWP份额为3.22%。在所有案例中，次生火灾功能占森林灾害相关影响的90%以上。这些研究结果表明，消防的环境负担是由材料选择和采用的消防安全策略共同决定的。此外，将多功能材料的影响过度归因于对消防安全风险的高估，突出了对改进的、基于功能的FS-LCA方法的需求。

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引用次数: 0

Identifying frequency-domain operating deflection shapes and internal damage in structures using geometric vision method 利用几何视觉方法识别结构的频域工作挠度形状和内部损伤

IF 7.9 Q1 ENGINEERING, MULTIDISCIPLINARY

Results in Engineering

Pub Date : 2025-11-30 DOI: 10.1016/j.rineng.2025.108474

Haoran Zhang , Jiaren Zuo , Zhangyu Yao , Deyang Zhang , Lei Lu , Wei Pan , Zhilong Su

Operating deflection shape (ODS) analysis characterizes structural vibration in the frequency domain without requiring prior knowledge of excitation, offering an automatic means to assess dynamic behavior and detect damage in service. Building on this, we present a geometric vision–based sensing method for ODS identification under a depth-parameterization framework, enabling direct recovery of full-field vibration responses from spatiotemporal image motion captured by a stereo imaging system. To ensure accurate ODS reconstruction, a random sample consensus–based calibration algorithm is developed to rectify stereo geometry and establish a depth-parameterized projective system. Within this framework, we formulate a linear model that maps measured image motion of object points to their ODS responses in the frequency domain. In addition, a wavelet-based algorithm is proposed to extract damage parameters from defective structures using the reconstructed ODS fields. Experimental validations confirm that the proposed vision-based sensing method and damage identification approach are both effective and robust, providing a promising pathway for structural vibration monitoring and health assessment in complex operational environments.

工作挠度形状（ODS）分析在频域上表征结构振动，而不需要事先了解激励，为评估动态行为和检测使用中的损伤提供了一种自动手段。在此基础上，我们提出了一种在深度参数化框架下的基于几何视觉的ODS识别方法，该方法可以从立体成像系统捕获的时空图像运动中直接恢复全场振动响应。为了保证ODS重建的准确性，提出了一种基于随机样本共识的校正算法，对立体几何进行校正，并建立了深度参数化投影系统。在这个框架内，我们制定了一个线性模型，将测量到的物体点的图像运动映射到它们在频域的ODS响应。此外，提出了一种基于小波的算法，利用重建的ODS场从缺陷结构中提取损伤参数。实验验证了所提出的基于视觉的感知方法和损伤识别方法的有效性和鲁棒性，为复杂作战环境下的结构振动监测和健康评估提供了一条有前景的途径。

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引用次数: 0

Predicting particle-induced permeability impairment during water injection using ensemble machine learning models 利用集成机器学习模型预测注水过程中颗粒引起的渗透率损害

IF 7.9 Q1 ENGINEERING, MULTIDISCIPLINARY

Results in Engineering

Pub Date : 2025-11-30 DOI: 10.1016/j.rineng.2025.108516

Armin Mansoori , Rafat Parsaei , Azim Kalantariasl , Hossein Parsaei

Water injection is a widely applied secondary recovery method, yet the presence of solid particles in injected water often causes pore throat plugging and deposition, leading to permeability impairment near the wellbore. Accurate prediction of this impairment is critical for optimizing injection strategies and reservoir management. In this study, machine learning was employed to predict permeability reduction using six input parameters: pore volume injection (PVI), initial permeability, porosity, particle size, particle concentration, and injection rate. A dataset of 1003 samples was compiled and analyzed using five supervised learning algorithms, Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), and Categorical Boosting (CatBoost), optimized through Optuna and evaluated via 10-fold cross-validation. All ensemble models achieved strong performance, with correlation coefficients (R²) above 0.95 and mean squared error (MSE) ranging below 0.039 across models and datasets. LightGBM provided the most accurate predictions (R² = 0.981, MSE = 0.018), closely followed by XGBoost and Gradient Boosting, while residual and learning curve analyses confirmed strong generalization and the absence of overfitting. External validation using two independent experimental datasets further demonstrated model robustness with MSE < 0.039, where partial pore reopening introduced additional complexity. SHAP-based feature importance highlighted PVI and particle concentration as the dominant predictors, followed by injection rate and initial permeability, with particle size and porosity contributing secondarily. Overall, the study demonstrates that boosting algorithms, particularly LightGBM and XGBoost, provide a fast, cost-effective, and physically consistent approach for predicting permeability impairment, with strong potential for integration into reservoir management and digital twin frameworks.

注水是一种应用广泛的二次采油方法，但注入水中存在的固体颗粒往往会造成孔喉堵塞和沉积，导致井筒附近的渗透率下降。准确预测这种损害对于优化注入策略和油藏管理至关重要。在这项研究中，使用机器学习来预测渗透率降低，使用六个输入参数：孔隙体积注入（PVI）、初始渗透率、孔隙度、粒径、颗粒浓度和注入速率。使用随机森林（RF）、梯度增强（GB）、极限梯度增强（XGBoost）、光梯度增强（LightGBM）和分类增强（CatBoost）五种监督学习算法编译和分析了1003个样本的数据集，并通过Optuna进行了优化，并通过10倍交叉验证进行了评估。所有集成模型均取得了较好的性能，模型和数据集的相关系数（R2）均在0.95以上，均方误差（MSE）均在0.039以下。LightGBM提供了最准确的预测（R²= 0.981,MSE = 0.018），紧随其后的是XGBoost和Gradient Boosting，而残差和学习曲线分析证实了强泛化和不存在过拟合。使用两个独立实验数据集的外部验证进一步证明了模型的稳健性，MSE < 0.039，其中部分孔隙重新开放引入了额外的复杂性。基于shap的特征强调PVI和颗粒浓度是主要预测因子，其次是注入速率和初始渗透率，其次是颗粒尺寸和孔隙度。总的来说，研究表明，增强算法，特别是LightGBM和XGBoost，为预测渗透率损害提供了一种快速、经济、物理一致的方法，具有整合到油藏管理和数字孪生框架中的强大潜力。

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引用次数: 0

Technical and organizational barriers and solution strategies for the implementation of BIM: An empirical study with a focus on the lighting sector BIM实施的技术和组织障碍及解决方案策略：以照明行业为重点的实证研究

IF 7.9 Q1 ENGINEERING, MULTIDISCIPLINARY

Results in Engineering

Pub Date : 2025-11-30 DOI: 10.1016/j.rineng.2025.108520

Sascha Hammes , Johannes Strohm , David Geisler-Moroder , Johannes Weninger , Josef Miller , Martin Hauer , Philipp Zech , Mathias Wambsganß , Rainer Pfluger

Building Information Modelling (BIM) offers considerable potential for the construction industry, particularly in terms of transparency, increased efficiency and integrated data management throughout the entire life cycle of buildings. However, technical and organizational barriers exist, which limit the widespread adoption of BIM. This study therefore employed two large-scale, online surveys to analyze the technical and organizational aspects of BIM implementation in lighting design. The results show that, in addition to technical barriers, successful BIM implementation hinges on knowledge, acceptance, and organizational learning ability. Training, internal processes and a supportive corporate culture are crucial for the effective use of new workflows. Fragmented planning processes and asynchronous participation reinforce technical limitations. To fully realize the potential of BIM, an integrated approach combining technological innovations with organizational learning and collaborative structures is essential. Key solutions include modular, interoperable BIM systems, open interfaces, and standardized data structures.

建筑信息模型（BIM）为建筑行业提供了巨大的潜力，特别是在透明度、提高效率和在建筑整个生命周期内集成数据管理方面。然而，存在技术和组织障碍，这限制了BIM的广泛采用。因此，本研究采用了两个大规模的在线调查来分析BIM在照明设计中实施的技术和组织方面。结果表明，除了技术障碍外，BIM的成功实施还取决于知识、接受度和组织学习能力。培训、内部流程和支持性的企业文化对于有效使用新的工作流程至关重要。分散的规划过程和异步参与强化了技术限制。为了充分发挥BIM的潜力，将技术创新与组织学习和协作结构相结合的综合方法是必不可少的。关键解决方案包括模块化、可互操作的BIM系统、开放接口和标准化数据结构。

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引用次数: 0

Exploring the feasibility of using recycled fines from diverse sources as partial cement substitutes in high-performance cementitious materials 探索在高性能胶凝材料中使用不同来源的再生细粉作为部分水泥替代品的可行性

IF 7.9 Q1 ENGINEERING, MULTIDISCIPLINARY

Results in Engineering

Pub Date : 2025-11-30 DOI: 10.1016/j.rineng.2025.108506

Oussama Douidi , Ahmed Tafraoui , Pedro Serna , Abdelkadir Makani , Liberato Ferrara

Advancing sustainable construction requires the development of durable materials while extending the life of end-of-life resources traditionally considered as waste. Construction and demolition waste represents a rapidly growing stream requiring effective recycling and valorisation strategies. Current directives promote the use of secondary raw materials to reduce environmental impacts and conserve natural resources. This study aims to reduce the environmental footprint of cementitious materials by repurposing fine fractions derived from diverse waste streams, including construction and demolition waste, recycled concrete, brick debris, and glass, as partial cement replacements in high-performance cement-based composites. A multi-scale experimental approach was adopted, starting with mortar mixtures to evaluate fresh properties, mechanical performance, and environmental behaviour, and subsequently extended to ultra-high-performance concrete formulations. Optimized ultra-high-performance concrete formulations achieved reduced cement content while maintaining superior performance and minimizing environmental impact. Results demonstrate that recycled fines can shift from disposal burdens to valuable resources, supporting circular-economy principles and enabling next-generation sustainable cementitious composites with enhanced technical performance and reduced environmental footprint.

推进可持续建筑需要开发耐用材料，同时延长传统上被视为废物的报废资源的寿命。建筑和拆除废物是一种快速增长的废物流，需要有效的回收和增值战略。目前的指令促进使用二次原料，以减少对环境的影响和保护自然资源。本研究旨在通过重新利用来自不同废物流的细组分，包括建筑和拆除废物、再生混凝土、砖屑和玻璃，作为高性能水泥基复合材料的部分水泥替代品，减少胶凝材料的环境足迹。采用了多尺度实验方法，从砂浆混合物开始评估新鲜性能，机械性能和环境行为，随后扩展到超高性能混凝土配方。优化的高性能混凝土配方在保持优异性能的同时减少了水泥含量，并最大限度地减少了对环境的影响。研究结果表明，回收细粉可以从处理负担转变为宝贵的资源，支持循环经济原则，并使下一代可持续胶凝复合材料具有更高的技术性能和更少的环境足迹。

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引用次数: 0

Influences of Zn flow rate and temperature on the defect state-related photoelectronic properties of MOCVD-grown ZnGa2O4 锌流量和温度对mocvd生长ZnGa2O4缺陷态相关光电性能的影响

IF 7.9 Q1 ENGINEERING, MULTIDISCIPLINARY

Results in Engineering

Pub Date : 2025-11-30 DOI: 10.1016/j.rineng.2025.108511

Jiamin Liu , Zhe Chuan Feng , Yao Liu , Manika Tun Nafisa , Qingxuan Li , Chuanwei Zhang , Ray-Hua Horng , Dong-Sing Wuu , Jeffrey Yiin , Lingyu Wan , Hao-Hsiung Lin , Benjamin Klein , Ian T. Ferguson , Shiyuan Liu

Spinel-structural ZnGa₂O₄ has attracted considerable attention for novel high-power device applications due to its broad transparent range, wide bandgap of ∼4.80 eV, and high breakdown voltage characteristics. Herein, we focus on the evolution of the optical, electronic, and structural properties of ZnGa₂O₄ films under different Zn flow rates and temperature loading, as determined by spectroscopic ellipsometry, optical transmittance, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), photoluminescence (PL), and first-principles calculations. Remarkably, non-classical temperature-dependent evolution of the crystal-related band gap E_g and defect-related Urbach tail energy E_U has been observed in an ultra-wide temperature range of 10–873 K using the two optical characterization techniques combined with a temperature-loading module. Specifically, the temperature dependency of E_U only partially conforms to the Bose-Einstein distribution, due to the effects of oxygen-based defect emission. The results from XRD-based structural observation, PL-based sub-bandgap emission, and XPS-based atomic-ratio scanning also collectively demonstrate a strong correlation between E_U and oxygen-based defect emission. The valence-band maxima and valence-band offset, as determined by ultraviolet photoelectron spectroscopy, further support the elucidation of the impacts of the different-level oxygen-based defect states caused by the Zn flow rate on the band structure of ZnGa₂O₄. These systematic consequences related to the defect stage are thus promising as a beneficial reference in the continual developments of ZnGa₂O₄-based optoelectronic, spintronic, electronic, sensing, optical, and related applications.

尖晶石结构的ZnGa2O4由于其宽透明范围、约4.80 eV的宽带隙和高击穿电压特性，在新型大功率器件应用中引起了相当大的关注。本文通过椭偏光谱、透射率、x射线衍射（XRD）、x射线光电子能谱（XPS）、光致发光（PL）和第一性原理计算，研究了不同Zn流速和温度负载下ZnGa2O4薄膜的光学、电子和结构性质的演变。值得注意的是，利用两种光学表征技术结合温度加载模块，在10-873 K的超宽温度范围内观察到晶体相关带隙Eg和缺陷相关乌尔巴赫尾能EU的非经典温度依赖演化。具体来说，由于氧基缺陷发射的影响，EU的温度依赖关系仅部分符合玻色-爱因斯坦分布。基于xrd的结构观测、基于pl的亚带隙发射和基于xps的原子比扫描结果也共同证明了EU和氧基缺陷发射之间存在很强的相关性。紫外光电子能谱测定的价带最大值和价带偏移量进一步支持了Zn流速引起的不同氧基缺陷态对ZnGa2O4能带结构的影响。这些与缺陷阶段相关的系统结果有望为基于znga2o4的光电、自旋电子、电子、传感、光学和相关应用的持续发展提供有益的参考。

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引用次数: 0

Modeling of recycled coarse aggregates concrete characteristics via hybrid improved Grey Wolf Optimizer deep neural network and multi-objective Grey Wolf Optimization 基于改进灰狼优化器、深度神经网络和多目标灰狼优化的再生粗骨料混凝土特性建模

IF 7.9 Q1 ENGINEERING, MULTIDISCIPLINARY

Results in Engineering

Pub Date : 2025-11-30 DOI: 10.1016/j.rineng.2025.108512

Cherif Belebchouche , Abdelkader Hammoudi , Karim Moussaceb , Aissa Laouissi , Mostefa Hani , Mohamed Sahraoui , Ahmed Belaadi , Yazid Chetbani , Ibrahim M.H. Alshaikh , Djamel Ghernaout , Yacine Karmi

This study presents a thorough methodology for forecasting and enhancing the essential performance characteristics of concrete with Recycled Coarse Aggregates (RCA), including water content (Wt), density, and Ultrasonic Pulse Velocity (UPV). The attributes are represented as functions of cement content (300–400 kg/m³), RCA percentage (0–100 %), and slump (5–12 ± 1 cm). An experimental database was created using a three-factor, three-level design, followed by exploratory analysis using correlation matrices and three-dimensional response surface plots to identify significant input-output associations. Various modeling techniques, such as Response Surface Methodology (RSM), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and a Deep Neural Network (DNN) enhanced by the Improved Grey Wolf Optimizer (IGWO), were assessed. The accuracy of the model was meticulously evaluated by 4-fold cross-validation, Taylor diagrams, and radar plots, demonstrating that the DNN-IGWO hybrid surpassed all other models, achieving the minimal prediction errors and the maximal correlation coefficient (R²) values for all target responses. The validated models were later employed as surrogates inside a multi-objective optimization framework employing the Multi-Objective Grey Wolf Optimizer (MOGWO). The optimization produced well-distributed Pareto fronts, emphasizing the intrinsic trade-off between density and UPV. The resultant "knee" solutions identify specific concrete compositions that optimize UPV (3.6–3.7) while ensuring a high density (≥2400 kg/m³) and moderate water content. This study provides a scientifically robust, data-driven paradigm for the sustainable design of concrete mixtures, facilitating the appropriate incorporation of recycled aggregates while enhancing material qualities.

本研究提出了一种全面的方法来预测和提高再生粗骨料（RCA）混凝土的基本性能特征，包括含水量（Wt）、密度和超声波脉冲速度（UPV）。这些属性表示为水泥含量（300-400 kg/m³）、RCA百分比（0 - 100%）和坍落度（5-12±1 cm）的函数。采用三因素、三水平设计建立实验数据库，然后利用相关矩阵和三维响应面图进行探索性分析，以确定显著的投入-产出关联。评估了各种建模技术，如响应面方法（RSM）、决策树（DT）、k近邻（KNN）、支持向量机（SVM）和由改进的灰狼优化器（IGWO）增强的深度神经网络（DNN）。通过4倍交叉验证、泰勒图和雷达图对模型的准确性进行了仔细评估，结果表明DNN-IGWO混合模型优于所有其他模型，实现了最小的预测误差和所有目标响应的最大相关系数（R2）值。验证后的模型随后在多目标灰狼优化器（MOGWO）的多目标优化框架中作为替代。优化产生了分布良好的帕累托前沿，强调了密度和UPV之间的内在权衡。由此产生的“膝盖”解决方案确定了特定的混凝土成分，以优化UPV(3.6-3.7)，同时确保高密度（≥2400 kg/m³）和适度的含水量。这项研究为混凝土混合物的可持续设计提供了一个科学可靠的、数据驱动的范例，促进了回收骨料的适当结合，同时提高了材料质量。

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引用次数: 0

Fine-grained segmentation of high-resolution crack images based on transformer and rendering techniques 基于变换和渲染技术的高分辨率裂纹图像的细粒度分割

IF 7.9 Q1 ENGINEERING, MULTIDISCIPLINARY

Results in Engineering

Pub Date : 2025-11-29 DOI: 10.1016/j.rineng.2025.108492

Honghu Chu , Weiwei Chen , Lu Deng

High-resolution (HR) imaging is crucial for structural defect detection, yet conventional deep learning models struggle to balance edge segmentation precision with computational efficiency in HR crack image analysis. To address these challenges, a HR Crack Image Rendering Segmentation Network (HRCRSN) is proposed. Three customized improvements were made, enabling the HRCRSN to exploit the advantages of edge-aware rendering technique from the field of computer graphics in the precise segmentation of HR crack images. First, a Transformer-based crack localization module with adaptive multi-scale feature fusion (MSFAWFS) enhances pixel-level guidance while preserving micro-crack details. Second, dynamic point sampling prioritizes ambiguous boundaries and sub-millimeter defects via asymmetric density allocation during training/inference. Third, a synthetic augmentation framework recombines crack objects to address data scarcity. Experiments on UAV-acquired datasets achieve state-of-the-art performance (IoU: 85.36 %, mBA: 92.07 %, DICE: 91.78 %). This approach improves inspection efficiency and establishes a new framework for UAV-based civil infrastructure monitoring.

高分辨率（HR）成像对于结构缺陷检测至关重要，然而传统的深度学习模型在高分辨率裂纹图像分析中难以平衡边缘分割精度和计算效率。为了解决这些问题，提出了一种HR裂缝图像绘制分割网络（HRCRSN）。对HRCRSN进行了三次定制化改进，使HRCRSN能够利用计算机图形学领域的边缘感知渲染技术在HR裂纹图像的精确分割方面的优势。首先，基于transformer的自适应多尺度特征融合（MSFAWFS）的裂纹定位模块在保留微裂纹细节的同时增强了像素级制导；其次，在训练/推理过程中，动态点采样通过不对称密度分配优先处理模糊边界和亚毫米缺陷。第三，合成增强框架重组裂缝对象，解决数据稀缺性问题。在无人机采集数据集上的实验达到了最先进的性能（IoU: 85.36%, mBA: 92.07%, DICE: 91.78%）。该方法提高了检测效率，为基于无人机的民用基础设施监测建立了新的框架。

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引用次数: 0

Energy conversion characteristics of gas-liquid two-phase flow inside a drainage self-priming pump under different rotational speeds during self-priming 不同转速下排水自吸泵内气液两相流自吸过程的能量转换特性

IF 7.9 Q1 ENGINEERING, MULTIDISCIPLINARY

Results in Engineering

Pub Date : 2025-11-29 DOI: 10.1016/j.rineng.2025.108503

Yu-Liang Zhang , Ze-Zhou Yang , Shao-Han Zheng , Yan-Juan Zhao

A self-priming pump is a type of centrifugal pump that can automatically draw and transport water without the need to fill the pump and suction pipeline with water prior to startup. As a result, it is widely used in applications such as agricultural irrigation and municipal flood drainage. The self-priming performance is one of the core indicators for evaluating the quality of a self-priming pump. In addition to the internal geometric structure of the pump, external operating conditions, such as variations in rotational speed, also significantly influence the self-priming performance. Therefore, understanding the specific impact of rotational speed changes on self-priming performance is essential. In this study, using computational fluid dynamics (CFD) methods, three constant rotational speed schemes were designed to numerically simulate the self-priming process in a recirculating pipeline system that includes a self-priming pump. The influence of constant rotational speed on the self-priming performance was thoroughly investigated, highlighting the hydraulic performance, self-priming performance, and internal flow characteristics under different rotational speeds. The results show that the self-priming time is significantly affected by rotational speed and is inversely proportional to it, with a maximum reduction of up to 36.69 %. At rated and high rotational speeds, the speed mainly affects the duration of the accelerated exhaust stage, which can be shortened by up to 37.5 %. At low rotational speeds, the duration of the oscillatory exhaust stage is significantly extended, approximately twice as long as that at medium and high rotational speeds.

自吸泵是一种可以自动吸水和输送水的离心泵，无需在启动前将泵和吸入管道注满水。因此，它被广泛应用于农业灌溉和城市防洪等应用。自吸性能是评价自吸泵质量的核心指标之一。除了泵的内部几何结构，外部运行条件，如转速的变化，也显著影响自吸性能。因此，了解转速变化对自吸性能的具体影响至关重要。本研究采用计算流体力学（CFD）方法，设计了三种恒定转速方案，对含自吸泵的循环管道系统的自吸过程进行了数值模拟。深入研究了恒转速对自吸性能的影响，重点研究了不同转速下的水力性能、自吸性能和内部流动特性。结果表明：自吸时间受转速影响显著，且与转速成反比，最大可降低36.69%；在额定转速和高转速下，转速主要影响加速排气阶段的持续时间，最多可缩短37.5%。在低转速下，振荡排气阶段的持续时间明显延长，大约是中高转速下的两倍。

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引用次数: 0

An Open-Source graphical tool for class E PA design exploration and optimization 一个开源图形工具，用于类epa设计的探索和优化

IF 7.9 Q1 ENGINEERING, MULTIDISCIPLINARY

Results in Engineering

Pub Date : 2025-11-29 DOI: 10.1016/j.rineng.2025.108472

Pallab Kr Gogoi , Ştefan Ştefănescu , Jurgen Vanhamel , Jérôme Loicq

This paper presents a novel platform for the efficient analysis, design, and optimization of ideal single-ended Class-E power amplifiers (PAs). It employs a comprehensive time-domain analytical model, which extends the conventional design space by incorporating variable duty cycles, variable voltage switching (VVS), and variable derivative voltage switching (VDS), enabling precise evaluation of key performance parameters such as harmonic efficiency, maximum output power capability, maximum operating frequency, and device stress. To facilitate practical design verification, an open-source, GUI-based CAD tool has been developed, providing researchers with an accessible and interactive environment for analysis and validation. In addition, a Python-based global optimization algorithm is integrated into the framework to automate component selection and enhance design robustness, particularly in scenarios involving finite DC-feed inductance. The accuracy and applicability of the proposed methodology are validated through nonlinear harmonic balance (HB) simulations. The results confirm the model’s ability to predict system behavior with high fidelity, making it a valuable resource for both academic and industrial design applications.

本文为理想的单端e类功率放大器（pa）的高效分析、设计和优化提供了一个新颖的平台。它采用了一个全面的时域分析模型，通过结合可变占空比、可变电压开关（VVS）和可变导数电压开关（VDS）扩展了传统的设计空间，能够精确评估关键性能参数，如谐波效率、最大输出功率能力、最大工作频率和器件应力。为了便于实际的设计验证，开发了一个基于gui的开源CAD工具，为研究人员提供了一个可访问的交互式分析和验证环境。此外，基于python的全局优化算法集成到框架中，以自动选择组件并增强设计鲁棒性，特别是在涉及有限直流馈电电感的情况下。通过非线性谐波平衡（HB）仿真验证了该方法的准确性和适用性。结果证实了该模型具有高保真度预测系统行为的能力，使其成为学术和工业设计应用的宝贵资源。

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引用次数: 0