Engineering Science and Technology-An International Journal-Jestech最新文献_第2页

State-dependent efficiency estimation in electric vehicles using an artificial neural network approach 基于人工神经网络的电动汽车状态相关效率估计

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-02-01 Epub Date: 2026-01-22 DOI: 10.1016/j.jestch.2025.102270

Ahmet Burak Kaydeci , Salih Baris Ozturk

Accurate modeling of powertrain efficiency is essential for optimizing energy management and range prediction in electric vehicles. This is particularly important under varying real-world driving conditions. To address the limitations of fixed efficiency assumptions in conventional models, this study proposes a hybrid approach combining experimental data with physics-based simulation. A feedforward artificial neural network (ANN) is trained to predict powertrain efficiency dynamically using real-world data collected from a prototype electric vehicle. The ANN utilizes four input variables—motor torque, motor speed, battery temperature, and state of charge—selected through a combined physical and experimental data-driven relevance analysis. The trained model is integrated into a longitudinal vehicle simulation framework, enabling dynamic efficiency estimation and energy consumption analysis. The validation was performed by comparing the ANN predictions against a separate set of experimental measurements. Compared to a baseline linear regression model, the ANN demonstrated a 95.2% lower mean squared error (MSE) and 80.4% lower mean absolute error (MAE) during efficiency interpolation, with a coefficient of determination (

R^{2}

) of 0.995. Simulations were conducted on both long-haul and city drive cycles, validating the model’s adaptability in diverse scenarios. These results support its application in predictive energy control, route-specific planning, and on-board performance evaluation.

准确的动力系统效率建模对于优化电动汽车的能量管理和里程预测至关重要。在多变的真实驾驶条件下，这一点尤为重要。为了解决传统模型中固定效率假设的局限性，本研究提出了一种将实验数据与基于物理的模拟相结合的混合方法。通过对前馈人工神经网络（ANN）的训练，利用从原型车收集的真实数据动态预测动力总成效率。人工神经网络利用四个输入变量——电机扭矩、电机速度、电池温度和充电状态——通过物理和实验数据驱动的相关性分析进行选择。将训练后的模型集成到纵向车辆仿真框架中，实现动态效率估计和能耗分析。验证是通过将人工神经网络预测与一组单独的实验测量进行比较来完成的。与基线线性回归模型相比，人工神经网络在效率插值时的均方误差（MSE）降低95.2%，平均绝对误差（MAE）降低80.4%，决定系数（R2）为0.995。在长途和城市驾驶工况下进行了仿真，验证了该模型在不同工况下的适应性。这些结果支持其在预测能量控制、路线特定规划和车载性能评估中的应用。

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引用次数: 0

Front Matter 1 - Full Title Page (regular issues)/Special Issue Title page (special issues) 封面1 -完整的扉页（每期）/特刊扉页（每期）

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-02-01 Epub Date: 2026-02-03 DOI: 10.1016/S2215-0986(26)00024-8

引用次数: 0

A lightweight, GPU-accelerated batch image encryption framework with integrated ECC and multi-attack resilience 一个轻量级的，gpu加速批处理图像加密框架，集成了ECC和多攻击弹性

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-02-01 Epub Date: 2026-01-24 DOI: 10.1016/j.jestch.2026.102287

Shaima Safa Aldin Baha Aldin , Noor Baha Aldin , Mahmut Aykaç

The secure delivery of visual content over noisy or lossy communication networks requires strong cryptographic schemes that combine security with error control and resilience. Despite the security being available for most chaos-based encryption schemes, they are in general sensitive to transmission errors. This paper presents a simple but efficient Graphics Processing Unit (GPU) based image-encryption which combines chaotic encryption and integrated Error Correction Codes (ECC). It consists of a 3D logistic-map for producing different keystreams of rearranged pixels and mixup values using XOR operations. In order to make the cipher more robust to transmission issues, we have integrated a Combined ReedSolomon (RS) and Low-Density ParityCheck (LDPC) ECC layer. All packed in an interactive MATLAB framework for easy test, visualization, and realtime analysis. The experimental results on the USC-SIPI dataset show that the proposed framework has a high entropy of 7.9993, NPCR = 99.63%, and UACI = 33.52%. The systems got a 39 Mbps on a standard GPU with 5 times overall speed compared to the CPU. Thus, this design gives a practical, efficient, and robust approach for secure image communication, as well as a good educational tool for exploring multimedia security concepts.

在嘈杂或有损的通信网络上安全地传输视觉内容需要强大的加密方案，该方案将安全性与错误控制和弹性相结合。尽管大多数基于混沌的加密方案都具有安全性，但它们通常对传输错误很敏感。本文提出了一种简单而高效的基于图形处理器（GPU）的图像加密算法，该算法将混沌加密和集成纠错码（ECC）相结合。它由一个3D逻辑图组成，用于使用异或操作产生不同的重排像素和混合值的键流。为了使密码对传输问题更具鲁棒性，我们集成了一个组合的ReedSolomon （RS）和低密度ParityCheck (LDPC) ECC层。所有包装在一个交互式的MATLAB框架，便于测试，可视化和实时分析。在USC-SIPI数据集上的实验结果表明，该框架具有较高的熵值7.9993,NPCR = 99.63%, UACI = 33.52%。该系统在标准GPU上的速度为39 Mbps，总体速度是CPU的5倍。因此，本设计为安全图像通信提供了一种实用、高效和健壮的方法，也是探索多媒体安全概念的良好教育工具。

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引用次数: 0

Reliability enhancement method for distribution system using a network cooperation recovery optimization technique 基于网络协同恢复优化技术的配电系统可靠性增强方法

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-02-01 Epub Date: 2026-01-21 DOI: 10.1016/j.jestch.2026.102285

Hejun Yang , Yangxu Yue , Jing Ma , Dabo Zhang , Xianjun Qi

The distributed generation (DG) and soft open point (SOP) have been connected to the distribution network, so distribution network fault recovery has changed from the single tie line recovery to collaborated recovery of DG and SOP, resulting in the reliability of distribution network is seriously underestimated under the traditional reliability assessment mode. Therefore, in order to overcome this shortcoming, this paper presents reliability assessment methodology for enhancing reliability of electrical distribution system using a network collaboration recovery technique. The paper employs a highly flexible model to fully exploit the synergistic restoration potential of flexible resources, enabling precise reliability evaluation through the formulation of optimal fault recovery strategies. Firstly, the restoration strategy for SOP and tie line reconfiguration in coordination with DG islanding is proposed in order to consider the mutual influence between SOP and DG in fault recovery and fully explore the collaborative recovery ability of DG and SOP; Secondly, this paper proposes a radial network constraint method that allows island recovery and load shedding operations. The method ensures to obtain the optimal solution for the restoration strategy while constraining the radial operation of the distribution network; Thirdly, in order to improve the computational accuracy of the proposed model, this paper uses the big M method and second-order cone relaxation to transform the model into a mixed-integer second-order cone programming problem and solves the model using a solver; Finally, the effectiveness and superiority of the proposed method is investigated through the case study on IEEE 33 and 54-node distribution systems, and the SAIDI index can be reduced by 5.98% for IEEE 33 system and 3.07% for 54-node system.

分布式发电（DG）和软开路点（SOP）已接入配电网，配电网故障恢复由单一并线恢复向DG和软开路点协同恢复转变，导致传统可靠性评估模式下配电网可靠性严重低估。因此，为了克服这一缺点，本文提出了利用网络协同恢复技术提高配电系统可靠性的可靠性评估方法。本文采用高度灵活的模型，充分挖掘柔性资源的协同恢复潜力，通过制定最优的故障恢复策略，实现精确的可靠性评估。首先，为了考虑SOP与DG在故障恢复中的相互影响，充分挖掘DG与SOP的协同恢复能力，提出了SOP与DG孤岛协调的配线重构恢复策略；其次，提出了一种允许孤岛恢复和减载的径向网络约束方法。该方法在约束配电网径向运行的情况下保证了恢复策略的最优解；第三，为了提高所提模型的计算精度，采用大M法和二阶锥松弛法将模型转化为混合整数二阶锥规划问题，并用求解器对模型进行求解；最后，通过对IEEE 33节点和54节点配电系统的实例分析，验证了所提方法的有效性和优越性，结果表明，IEEE 33节点和54节点配电系统的SAIDI指数分别降低了5.98%和3.07%。

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引用次数: 0

Algorithm-oriented benchmarking of deep learning and hybrid architectures for robust SOC estimation in electric vehicle batteries 面向算法的基于深度学习和混合架构的电动汽车电池稳健SOC评估

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-02-01 Epub Date: 2026-01-23 DOI: 10.1016/j.jestch.2026.102286

Osman Demirci , Sezai Taskin

Accurate state-of-charge (SOC) estimation is a key requirement for the safe and efficient management of lithium-ion batteries in electric vehicles, especially under varying thermal and dynamic operating conditions. This study presents a comprehensive, algorithm-oriented assessment of several deep learning and hybrid SOC estimation architectures—including feedforward neural networks (FNN), gated recurrent networks (GRU), long short-term memory networks (LSTM), temporal convolutional networks (TCN), and their hybrid combinations—using a multi-temperature dataset collected at 10 °C, 25 °C, and 40 °C under diverse dynamic load profiles and standardized drive cycles such as UDDS, HWFET, US06, and LA92. All architectures were trained and evaluated under a unified preprocessing and training configuration to ensure methodological consistency and a fair basis for comparison.

The evaluation highlights how different recurrent, convolutional, and hybrid architectures respond to thermal variations and dynamic load transitions, revealing model-specific strengths and limitations under realistic operating conditions. Among the evaluated models, the hybrid FNN + GRU architecture demonstrated the most reliable overall performance, achieving an RMSE of 1.11 % and reducing peak estimation errors to 3.6 % under nominal temperature conditions. SOC-zone analysis further showed characteristic error amplification at low and high SOC levels, emphasizing the importance of architectures capable of capturing nonlinear boundary dynamics. Computational benchmarking indicated that hybrid structures—particularly FNN + GRU—also provide an advantageous balance between estimation accuracy and inference speed, supporting their suitability for embedded Battery Management Systems (BMSs) with real-time constraints.

Overall, this study contributes a unified evaluation framework that simultaneously addresses thermal robustness, dynamic load variability, SOC-dependent behavior, and computational efficiency, offering practical guidance for selecting reliable and deployable SOC estimation models for next-generation electric vehicle BMSs.

准确的荷电状态（SOC）估算是电动汽车锂离子电池安全高效管理的关键要求，特别是在不同的热动态运行条件下。本研究对几种深度学习和混合SOC估计架构进行了全面的、面向算法的评估，包括前馈神经网络（FNN）、门控循环网络（GRU）、长短期记忆网络（LSTM）、时间卷积网络（TCN）及其混合组合，使用在10°C、25°C和40°C下收集的多温度数据集，在不同的动态负载配置和标准化驱动循环（如UDDS、HWFET、US06和LA92）下进行。所有架构都在统一的预处理和训练配置下进行训练和评估，以确保方法的一致性和公平的比较基础。评估强调了不同的循环、卷积和混合架构如何响应热变化和动态负载转换，揭示了模型在实际操作条件下的特定优势和局限性。在评估的模型中，混合FNN + GRU架构表现出最可靠的整体性能，在标称温度条件下实现了1.11%的RMSE，并将峰值估计误差降低到3.6%。SOC-zone分析进一步显示了低SOC和高SOC水平下的特征误差放大，强调了能够捕获非线性边界动力学的架构的重要性。计算基准测试表明，混合结构-特别是FNN + gru -还在估计精度和推理速度之间提供了有利的平衡，支持它们适用于具有实时约束的嵌入式电池管理系统（bms）。总体而言，该研究提供了一个统一的评估框架，同时解决了热鲁棒性、动态负载可变性、SOC依赖行为和计算效率问题，为下一代电动汽车bms选择可靠和可部署的SOC评估模型提供了实用指导。

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引用次数: 0

Integrated quantum-classical hybrid architectures for robust lung lesion segmentation in volumetric CT video data samples 基于集成量子经典混合架构的体积CT视频数据样本鲁棒肺病变分割

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 DOI: 10.1016/j.jestch.2025.102272

Sai Babu Veesam , Lalitha Kumari Pappala , Aravapalli Rama Satish , Sravan Kumar Chirumamilla , Vunnava Dinesh Babu , Shonak Bansal , Krishna Prakash , Mohamad A. Alawad , Mohammad Tariqul Islam

Segmentation of lung lesions in volumetric CT data is crucial for the clinical aspects of diagnosis, therapy planning, and monitoring disease progression. Currently, deep learning applications are unable to model spatiotemporal coherency alongside anatomical consistency and uncertainty-aware refinement across sequential slices. In this study, we propose a hybrid quantum–classical framework that would accommodate multiple innovative modules. The architecture features a Quantum Latent Entanglement Consistency validator to establish spatiotemporal coherence across slices by maximizing von Neumann entropy. A Quantum-Classical Interventional Gradient Alignment ensures the harmony of gradients between classical CNN encoders and quantum discriminators. Further, the Temporal Quantum Attention for Boundary Stabilization captures the temporal context in the boundary refinement using controlled quantum gates. Alongside these, a Quantum-Enhanced Structural Similarity Feedback mechanism is proposed that exploits anatomical priors for retrofitting spatial lesion structures, as well as a Hybrid Quantum Adversarial Ensemble Validation, which provides confidence-aware validity through disagreement modeling. Collection and experimental evaluations over LIDC IDRI, NSCLC-Radiomics, and MosMedData datasets depict that the entirety of the systems significantly increases the Dice Similarity Coefficient by 5–7%, holds Hausdorff Distance lower at 10–12%, narrows down the over-segmentation errors by 8–10%, while reducing overall false positives near lung boundaries by 15% or even less. This represents a significant advancement toward fusing quantum learning with clinical-grade imaging pipelines, demonstrating clear improvements in segmentation stability, precision, and trustworthiness in real-world settings.

体积CT数据中肺病变的分割对于临床诊断、治疗计划和监测疾病进展至关重要。目前，深度学习应用程序无法模拟时空一致性以及跨序列切片的解剖一致性和不确定性感知细化。在这项研究中，我们提出了一个混合量子-经典框架，将容纳多个创新模块。该架构具有量子潜在纠缠一致性验证器，通过最大化冯·诺伊曼熵来建立跨片的时空相干性。量子-经典干涉梯度对准保证了经典CNN编码器和量子鉴别器之间梯度的和谐。此外，用于边界稳定的时间量子注意在使用受控量子门的边界细化中捕获时间上下文。除此之外，还提出了一种量子增强结构相似性反馈机制，该机制利用解剖先验来改造空间病变结构，以及一种混合量子对抗集成验证，该验证通过分歧建模提供信心感知有效性。对LIDC IDRI、NSCLC-Radiomics和MosMedData数据集的收集和实验评估表明，整个系统显着将Dice Similarity Coefficient提高了5-7%，将Hausdorff Distance降低了10-12%，将过度分割错误降低了8-10%，同时将肺边界附近的总体假阳性降低了15%甚至更少。这代表了将量子学习与临床级成像管道融合的重大进步，在现实世界的分割稳定性、精度和可信度方面有了明显的提高。

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引用次数: 0

Secure and imperceptible medical image watermarking via multiscale QR embedding and attention-based optimization 基于多尺度QR嵌入和注意力优化的安全且不易察觉的医学图像水印

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 Epub Date: 2025-12-12 DOI: 10.1016/j.jestch.2025.102250

Ahmed Said Beggari , Ali Wali , Amine Khaldi , Med Redouane Kafi , Aditya Kumar Sahu

The exponential growth of telemedicine and digital health platforms has introduced serious challenges in maintaining the confidentiality, authenticity, and diagnostic integrity of medical images transmitted over insecure networks. This study specifically addresses these challenges by developing a blind and imperceptible watermarking architecture that ensures both data privacy and image reliability. The proposed method integrates four complementary techniques—Non-Subsampled Shearlet Transform (NSST) for multiscale feature extraction, QR decomposition for numerically stable embedding, Particle Swarm Optimization (PSO) for adaptive block selection, and Grad-CAM attention maps for perceptual guidance. Together, these components solve three long-standing issues in medical image protection: (1) preserving diagnostic quality while embedding sensitive data, (2) achieving robustness against signal and geometric distortions without reference to the original image, and (3) reducing computational complexity for real-time telemedicine integration. The watermark encodes both compressed patient metadata and biometric images using BCH error correction and XOR encryption. Experiments on colorized CT and X-ray datasets show high imperceptibility (PSNR = 45.21 dB, SSIM = 0.9864), strong robustness (NCC ≥ 0.897), and fast runtime (≈ 2 s per image), confirming the method’s suitability for secure and practical clinical deployment.

远程医疗和数字卫生平台的指数级增长，在维护通过不安全网络传输的医学图像的保密性、真实性和诊断完整性方面带来了严峻挑战。本研究通过开发一种盲的和难以察觉的水印架构来解决这些挑战，该架构可以确保数据隐私和图像可靠性。该方法集成了四种互补技术：用于多尺度特征提取的非下采样Shearlet变换（NSST）、用于数值稳定嵌入的QR分解、用于自适应块选择的粒子群优化（PSO）和用于感知引导的grado - cam注意图。这些组件共同解决了医学图像保护中三个长期存在的问题：(1)在嵌入敏感数据的同时保持诊断质量；(2)在不参考原始图像的情况下实现对信号和几何畸变的鲁棒性；(3)降低实时远程医疗集成的计算复杂度。水印使用BCH纠错和异或加密对压缩的患者元数据和生物特征图像进行编码。在彩色CT和x射线数据集上的实验表明，该方法具有较高的不可见性（PSNR = 45.21 dB, SSIM = 0.9864）、较强的鲁棒性（NCC≥0.897）和较快的运行时间（每张图像≈2 s），适合安全实用的临床部署。

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引用次数: 0

Leveraging uncertainty for transmission period control in IoT applications 利用物联网应用中传输周期控制的不确定性

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 Epub Date: 2025-12-12 DOI: 10.1016/j.jestch.2025.102248

Gyeong Ho Lee , Sungpil Woo , Jaeseob Han

In numerous Internet of Things (IoT) applications, vast amounts of data are continuously collected and transmitted by IoT sensors to enable precise, real-time environmental monitoring. However, frequent data transmission significantly increases energy consumption, posing a critical challenge for IoT systems. To address this issue, we propose the uncertainty-based optimal transmission period control (U-OTPC) model, which adaptively adjusts the transmission period of each IoT sensor to minimize energy consumption while preserving data accuracy. The optimal transmission period is derived by formulating a min–max optimization problem that balances energy consumption and data quality, where data quality is quantified using a combination of reconstruction error and uncertainty. To estimate predictive uncertainty, we leverage the Monte Carlo (MC) dropout technique, a factor often overlooked in transmission period control models. To efficiently solve this problem, the U-OTPC integrates theoretical propositions with a

k

-ary search algorithm. The effectiveness of the proposed model is rigorously validated through extensive evaluations on three distinct open datasets collected from real-time monitoring. Experimental results show that the U-OTPC model surpasses other benchmarks in the period control score (PCS) metric, effectively balancing data collection accuracy and energy consumption.

在众多物联网（IoT）应用中，物联网传感器不断收集和传输大量数据，以实现精确、实时的环境监测。然而，频繁的数据传输大大增加了能源消耗，对物联网系统构成了严峻的挑战。为了解决这个问题，我们提出了基于不确定性的最优传输周期控制（U-OTPC）模型，该模型自适应调整每个物联网传感器的传输周期，以最大限度地减少能耗，同时保持数据准确性。通过制定平衡能耗和数据质量的最小-最大优化问题推导出最优传输周期，其中数据质量使用重构误差和不确定性的组合来量化。为了估计预测不确定性，我们利用蒙特卡罗（MC） dropout技术，这是一个在传输周期控制模型中经常被忽视的因素。为了有效地解决这一问题，U-OTPC将理论命题与k-ary搜索算法相结合。通过对从实时监测中收集的三个不同的开放数据集进行广泛评估，严格验证了所提出模型的有效性。实验结果表明，U-OTPC模型在周期控制分数（PCS）指标上优于其他基准，有效地平衡了数据采集精度和能耗。

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引用次数: 0

An innovative stochastic fuzzy-optimized dispatch strategy for multi-energy parks with uncertain opportunity constraints 具有不确定机会约束的多能园区随机模糊优化调度策略

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 Epub Date: 2025-12-17 DOI: 10.1016/j.jestch.2025.102260

Liu Jiang , Jia Cui , Xingyang Xu , Jiajia Zhang , Tianhe Fu , Yuanzhong Li , Ximing Zhang

Amid the wave of green and low-carbon energy transition, unprecedented acceleration is urgently required for global renewable energy deployment. However, complex interdependencies are created by the interplay between the random nature of renewable power generation and diversified energy demands, and the scheduling robustness of Regional Integrated Energy Systems (RIES) is undermined by these interdependencies. A Regional Integrated Energy System solution based on a fuzzy adaptive scheduling approach is proposed in this paper. Energy flexibility is maximized through the implementation of multi-domain collaborative optimization to dynamically balance supply–demand uncertainties. Firstly, a fuzzy probabilistic constraint programming approach is proposed, in which wind power, photovoltaic power generation, and load are treated as fuzzy variables, and a credibility measure is introduced to mitigate decision ambiguity. Secondly, novel fuzzy membership functions are designed to comprehensively characterize the uncertainty in renewable energy generation and electricity consumption. Thirdly, robust flexibility coordination for bidirectional source-load matching is achieved through a fuzzy adaptive mechanism, with combined membership functions enhancing optimization reliability. Finally, fuzzy constraints are converted into deterministic equations via an exact equivalence class solver, and the confidence level of the time step is optimized by an improved particle swarm optimization (IPSO) algorithm—characterized by a linear decreasing inertia weight based on the arctangent function. Research findings indicate that dispatch costs are significantly increased by the uncertainty in power supply loads (costs under multi-source uncertainty scenarios are 51.2% higher than those under deterministic scenarios), while a confidence level of 0.7 is critical for balancing system reliability and economic efficiency.

在绿色低碳能源转型的浪潮中，全球可再生能源的部署迫切需要前所未有的加速。然而，可再生能源发电的随机性与能源需求的多样性之间的相互作用产生了复杂的相互依赖关系，这些相互依赖关系破坏了区域综合能源系统（RIES）的调度鲁棒性。提出了一种基于模糊自适应调度方法的区域综合能源系统解决方案。通过多域协同优化实现能源灵活性最大化，动态平衡供需不确定性。首先，提出了一种模糊概率约束规划方法，将风电、光伏发电和负荷作为模糊变量，引入可信度度量来减轻决策的模糊性；其次，设计了新的模糊隶属函数，对可再生能源发电和电力消耗的不确定性进行综合表征；第三，通过模糊自适应机制实现双向源负荷匹配的鲁棒柔性协调，结合隶属函数增强优化可靠性。最后，通过精确等价类求解器将模糊约束转化为确定性方程，并采用基于arctan函数的惯性权值线性递减的改进粒子群优化算法（IPSO）优化时间步长的置信水平。研究结果表明，电力负荷的不确定性显著增加了调度成本（多源不确定性情景下的调度成本比确定性情景下的调度成本高51.2%），而要平衡系统可靠性和经济效率，置信水平必须达到0.7。

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引用次数: 0

Comparison of pareto fronts for pump impeller design using sobol sequence sampling with water and methanol 用sobol顺序水和甲醇取样的泵叶轮设计的帕累托面比较

IF 5.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Engineering Science and Technology-An International Journal-Jestech

Pub Date : 2026-01-01 Epub Date: 2025-12-20 DOI: 10.1016/j.jestch.2025.102265

Muhammed Donmez , Onur Yemenici

This study focuses on the optimization and performance evaluation of pump impellers for water and methanol using Sobol sequence sampling, Artificial Neural Network (ANN)-based metamodeling, and Multi-Objective Genetic Algorithm (MOGA) optimization. Initially, 40 design points generated via Sobol sequences facilitate the exploration of a multidimensional design space, enabling the design of impellers with varied geometrical parameters. The resulting head and efficiency values are used to train an ANN model, achieving high accuracy, with overall R-values above 0.99 for both fluids. Optimized impellers for water and methanol show improved flow uniformity and energy efficiency, as evidenced by smoother velocity distributions. For water, the optimized impeller achieved a head of 10.01 m and an efficiency of 72.41 %, while for methanol, it reached a head of 10.01 m and an efficiency of 73.62 %, as obtained by CFD. Pareto analysis reveals that water designs are constrained around a 10 m head, whereas methanol allows flexibility, achieving optimal efficiency across a 10–15 m head range. These findings confirm the efficacy of the optimization framework, offering an adaptable approach for enhancing pump impeller performance across different fluid applications.

采用Sobol序列采样、基于人工神经网络（ANN）的元建模和多目标遗传算法（MOGA）优化对水和甲醇泵叶轮进行优化和性能评价。最初，通过Sobol序列生成的40个设计点促进了对多维设计空间的探索，使设计具有不同几何参数的叶轮成为可能。所得的水头和效率值用于训练人工神经网络模型，获得了很高的精度，两种流体的总体r值都在0.99以上。优化后的水和甲醇叶轮表现出更好的流动均匀性和能量效率，速度分布更平滑。对于水，优化后的叶轮扬程为10.01 m，效率为72.41%；对于甲醇，优化后的叶轮扬程为10.01 m，效率为73.62%。帕累托分析显示，水的设计受到10米水头的限制，而甲醇则具有灵活性，可以在10 - 15米水头范围内实现最佳效率。这些发现证实了优化框架的有效性，为提高泵叶轮在不同流体应用中的性能提供了一种适应性方法。

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引用次数: 0