IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2026-01-07 DOI: 10.1016/j.energy.2026.139943

Ze Chen , Yandong Gu , Weigang Lu , Mengying Liu

Canned motor pumps are widely used in liquid cooling systems for energy systems, serving as the core component for thermal regulation of various modules. However, particles enter the canned motor gap with coolant. Prolonged operation causes motor wear, jeopardizing equipment longevity and system safety. To address this issue, this study introduces a novel and straightforward L-structure to prevent particle-induced wear in motor gaps. Numerical simulations using the Discrete Phase Model for solid-liquid two-phase flow were validated by experimental results. L-structure geometric parameters were optimized with particle interception and hydraulic performance as objectives. Orthogonal designs and Pearson correlation analysis identified outer diameter and thickness as primary influencing factors. With the optimized L-structure, the particle interception ratio reaches 53.57 %, motor gap erosion amount is reduced by 50.43 %, head coefficient marginally improves by 1.37 %, efficiency slightly increases by 0.89 %, and motor gap leakage loss ratio decreases by 0.1 %. Flow fields reveal significantly reduced particle trajectories and erosion distributions in the motor gap. The L-structure mitigates wear via two mechanisms: inertial-separation and entrainment-mitigation, with inertial-separation being dominant. The inertial-separation mechanism results in a significant reduction in velocity at the L-structure, thereby decreasing particle inertia and facilitating effective particle interception. Meanwhile, the entrainment-mitigation mechanism attenuates leakage by increasing local flow resistance, decreasing particles entering motor gap. Additionally, the L-structure decreases pump entropy generation, marginally improving flow efficiency. This straightforward and cost-effective design enhances the operational performance and service life of canned motor pumps, which is crucial for the reliability and efficiency of energy systems.

屏蔽式电机泵广泛应用于能源系统的液冷系统中，是各模块热调节的核心部件。然而，颗粒进入屏蔽电机间隙与冷却剂。长时间运行导致电机磨损，危及设备寿命和系统安全。为了解决这个问题，本研究引入了一种新颖而直接的l型结构，以防止电机间隙中颗粒引起的磨损。实验结果验证了采用离散相模型对固液两相流动进行数值模拟的正确性。以截留颗粒和水力性能为目标，对l型结构几何参数进行优化。正交设计和Pearson相关分析确定外径和厚度为主要影响因素。优化后的l型结构，颗粒截留率达到53.57%，电机间隙侵蚀量减少了50.43%，水头系数略微提高了1.37%，效率略微提高了0.89%，电机间隙漏损比降低了0.1%。流场显示明显减少粒子轨迹和侵蚀分布在电机间隙。l型结构通过两种机制减轻磨损：惯性分离和夹带减缓，其中惯性分离占主导地位。惯性分离机制导致l结构处的速度显著降低，从而降低了颗粒的惯性，有利于有效的颗粒拦截。同时，夹带-减缓机制通过增加局部流动阻力，减少颗粒进入电机间隙来减弱泄漏。此外，l型结构降低了泵的熵产，略微提高了流动效率。这种简单而经济的设计提高了屏蔽电机泵的运行性能和使用寿命，这对能源系统的可靠性和效率至关重要。

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引用次数: 0

Optimizing blue hydrogen production in Türkiye: Techno-economic and environmental assessment of SMR with carbon capture under evolving carbon policy 优化<s:1> rkiye蓝色氢生产：碳政策下具有碳捕集的SMR的技术经济和环境评价

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2026-01-06 DOI: 10.1016/j.energy.2026.139949

Celal Erbay

This study evaluates hydrogen production pathways in Türkiye, focusing on the economic and environmental viability of blue hydrogen produced via steam methane reforming (SMR) with carbon capture and storage (CCS). Using a techno-economic model, levelized cost of hydrogen (LCOH) values are calculated for 2023 and 2035 under carbon tax scenarios ranging from $0 to $100 per ton of CO₂. Results show that although gray hydrogen is the lowest-cost option in 2023 ($1.7/kg), its high emissions make it unsustainable in the long term. By 2035, under a $100 CO₂ tax and under a low domestic natural gas price scenario with a 48 % drop compared to today, SMR with 90 % CCS becomes the most competitive option at $1.31–1.79/kg, achieving significant emission reductions. The study includes sensitivity analyses assessing the influence of CAPEX, OPEX, fuel, and carbon costs on hydrogen pricing. It also compares the environmental performance of gray, blue, and green hydrogen, incorporating CO₂ emissions, water usage, and methane leakage. Findings highlight the importance of high-efficiency CCS technologies and policy tools—particularly carbon pricing—for enabling a cost-effective transition toward low-carbon hydrogen. The results align with Türkiye's Hydrogen Strategy Roadmap and its broader decarbonization and energy security goals. This research offers one of the first region-specific, scenario-based techno-economic assessments of blue hydrogen in Türkiye. It provides actionable insights for policymakers and industry stakeholders aiming to position the country as a competitive player in the global hydrogen economy.

本研究评估了 rkiye的制氢途径，重点研究了通过蒸汽甲烷重整（SMR）和碳捕集与封存（CCS）生产蓝氢的经济和环境可行性。利用技术经济模型，在每吨二氧化碳征收0美元至100美元的碳税情景下，计算了2023年和2035年氢的平准化成本（LCOH）值。结果表明，尽管灰氢在2023年是成本最低的选择（每公斤1.7美元），但其高排放使其长期不可持续。到2035年，在100美元的二氧化碳税和较低的国内天然气价格（与目前相比下降48%）的情况下，拥有90% CCS的SMR成为最具竞争力的选择，价格为1.31-1.79美元/公斤，实现了显著的减排。该研究包括敏感性分析，评估资本支出、运营支出、燃料和碳成本对氢定价的影响。它还比较了灰氢、蓝氢和绿氢的环境性能，包括二氧化碳排放量、用水量和甲烷泄漏。研究结果强调了高效CCS技术和政策工具的重要性，特别是碳定价，以实现向低碳氢的成本效益过渡。结果与 rkiye公司的氢战略路线图及其更广泛的脱碳和能源安全目标一致。这项研究提供了第一个针对 rkiye蓝色氢的特定区域、基于场景的技术经济评估之一。它为政策制定者和行业利益相关者提供了可行的见解，旨在将该国定位为全球氢经济中的竞争对手。

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引用次数: 0

Low-carbon economic dispatch of electrified transportation networks considering grid parity and mixed traffic of human-driven and autonomous vehicles 考虑电网平价和人工驾驶与自动驾驶混合交通的电气化交通网络低碳经济调度

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2026-01-05 DOI: 10.1016/j.energy.2026.139941

Jia Li , Bin Li , Zhitao Liu , Hongye Su

The rapid proliferation of electric vehicles (EVs) has intensified the coupling between transportation networks (TNs) and power distribution networks (PDNs). Simultaneously, the growth of renewable energy sources (RES) and the implementation of grid parity policies, along with the coexistence of autonomous vehicles (AVs) and human-driven vehicles (HVs), present both opportunities and challenges for the realization of low-carbon electrified transportation networks (ETNs). To address these challenges, this paper proposes a low-carbon economic dispatch model for ETNs aimed at minimizing operational costs, reducing wind and solar curtailment, and lowering carbon emissions. The model incorporates the effects of grid parity for RES and centralized AV control, treating EVs (including AVs) as dynamic decarbonization resources. By optimizing the spatiotemporal distribution of charging loads, the model improves RES utilization and reduces carbon emissions within ETNs. Given the high non-convexity of the semi-dynamic traffic assignment under the mixed user equilibrium-system optimal framework, the problem is reformulated as a variational inequality and solved using an efficient fixed-point algorithm. Additionally, a bidirectional independent charging pricing strategy is introduced to optimize the spatiotemporal distribution of EVs, thereby reducing both carbon emissions and economic costs. Then a decentralized iterative algorithm is developed to solve the bi-level ETN model. Numerical results from two test systems demonstrate the effectiveness of the proposed model and strategy, while also highlighting the model’s robustness in handling the uncertainties associated with RES and traffic demand. The impacts of various parameters on ETN operation are analyzed.

随着电动汽车的快速发展，交通网络与配电网络之间的耦合日益加剧。同时，可再生能源（RES）的增长和电网平价政策的实施，以及自动驾驶汽车（AVs）和人类驾驶汽车（HVs）的共存，为实现低碳电气化交通网络（etn）提供了机遇和挑战。为了应对这些挑战，本文提出了一个etn的低碳经济调度模型，旨在最大限度地降低运营成本，减少风能和太阳能弃风，降低碳排放。该模型结合了电网平价对可再生能源和自动驾驶汽车集中控制的影响，将电动汽车（包括自动驾驶汽车）视为动态脱碳资源。该模型通过优化充电负荷的时空分布，提高了可再生能源的利用率，降低了etn内的碳排放。针对混合用户均衡-系统最优框架下的半动态流量分配问题的高度非凸性，将该问题重新表述为变分不等式，并采用高效的不动点算法求解。此外，引入双向独立充电定价策略，优化电动汽车的时空分布，从而降低碳排放和经济成本。在此基础上，提出了求解双层ETN模型的分散迭代算法。两个测试系统的数值结果证明了所提出的模型和策略的有效性，同时也突出了模型在处理RES和交通需求相关的不确定性方面的鲁棒性。分析了各参数对ETN运行的影响。

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引用次数: 0

Optimal hydrogen-assisted resilient schedule of networked multi-energy microgrids under flexible responsive load and risk analysis 基于柔性响应负荷和风险分析的网络化多能微电网氢辅助弹性调度优化

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2026-01-05 DOI: 10.1016/j.energy.2026.139938

Tai Li , Xukun Guo

This study proposes a proactive, risk-aware scheduling framework for interconnected multi-energy microgrids (IMMGs) designed to enhance operational resilience and risk management in the face of natural disasters. The model integrates electricity, heating, and hydrogen subsystems into a mixed-integer linear programming formulation and enables energy sharing across microgrids, thereby improving system-wide flexibility. A Bayesian network is employed to model the probabilistic relationships between disaster intensity, pre-disaster preparedness, emergency response, and recovery duration. These dependencies are encoded via conditional probability tables, which facilitates realistic scenario generation and disaster impact modeling. To address broader system uncertainties, the model combines Latin Hypercube Sampling with stochastic programming to capture variability in renewable generation, energy demands, and disaster severity. A conditional value-at-risk (CVaR) framework is used to evaluate and mitigate operational risk under varying degrees of risk aversion. Key findings show that interconnecting IMMGs yields tangible benefits. At moderate and high risk aversion levels, the CVaR cost is reduced by up to 0.28 % compared to individual microgrid operation, reflecting greater resilience during extreme conditions. This reduction is achieved while maintaining operational costs within an economically efficient range, confirming that interconnection supports both cost-effectiveness and risk mitigation. The proposed framework advances the state-of-the-art by integrating disaster-aware uncertainty modeling, transactive energy exchange, and risk-based scheduling into a unified operational strategy. Demonstrated scalability and performance on representative multi-microgrid test systems make it a strong candidate for real-world deployment in resilient, low-carbon energy networks.

本研究提出了一种面向互联多能微电网（imgs）的前瞻性、风险意识调度框架，旨在增强面对自然灾害时的运行弹性和风险管理。该模型将电力、供暖和氢气子系统集成到一个混合整数线性规划公式中，实现了微电网之间的能源共享，从而提高了整个系统的灵活性。采用贝叶斯网络对灾害强度、灾前准备、应急响应和恢复时间之间的概率关系进行建模。这些依赖关系是通过条件概率表进行编码的，这有利于实际场景的生成和灾难影响建模。为了解决更广泛的系统不确定性，该模型将拉丁超立方抽样与随机规划相结合，以捕获可再生能源发电、能源需求和灾害严重程度的可变性。采用条件风险值（CVaR）框架对不同风险规避程度下的操作风险进行评估和降低。主要研究结果表明，相互连接的img产生了切实的效益。在中等和高度的风险规避水平下，与单个微电网运行相比，CVaR成本降低了0.28%，反映了在极端条件下更大的弹性。在实现这一减少的同时，将运营成本保持在经济有效的范围内，确认了互联既支持成本效益，又支持降低风险。提出的框架通过将灾害感知不确定性建模、交互能源交换和基于风险的调度集成到统一的操作策略中，推进了最先进的技术。在具有代表性的多微电网测试系统上展示的可扩展性和性能使其成为现实世界中弹性低碳能源网络部署的有力候选者。

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引用次数: 0

The resilience of energy trade networks in countries along “The Belt and Road” and the risk resistance capacity of China “一带一路”沿线国家能源贸易网络弹性与中国抗风险能力

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2026-01-05 DOI: 10.1016/j.energy.2025.139856

Kai Wu, Yingying Qiu, Jing Deng, Zimo Feng

In the context of the Belt and Road trade cooperation and energy transition, analyzing the trade network characteristics and evolutionary trends of traditional and renewable energy in the countries along the route aids in optimizing and transforming their energy structures. Furthermore, assessing the resilience of different energy trade networks and the risk resistance of key countries is crucial for maintaining energy security. This paper constructs trade networks for crude oil, natural gas, and solar photovoltaic energy, comparing their characteristics and evolution. It also evaluates the resilience of these energy networks and China’s risk resistance through simulated attacks. The main conclusions are as follows:First, the indicators of the solar photovoltaic network show a significant gap compared to crude oil and natural gas, with high density, efficiency, strong clustering, and short paths. Second, countries with extensive traditional energy trading, such as Qatar and Kazakhstan, lack significant node importance, whereas Singapore has emerged as the solar photovoltaic network’s central hub due to its strategic location. Third, the resilience of three energy trade networks exhibits an upward trend, with the solar photovoltaic network demonstrating greater resilience. Ultimately, China plays a significant bridging role and has the highest resistance to risks in the natural gas network. These findings highlight the policy need to prioritize renewable cooperation, strengthen hub connectivity, and foster resilient photovoltaic investment.

在“一带一路”贸易合作和能源转型背景下，分析沿线国家传统能源和可再生能源贸易网络特征及其演化趋势，有助于沿线国家能源结构的优化和转型。此外，评估不同能源贸易网络的弹性和主要国家的风险抵抗能力对于维护能源安全至关重要。本文构建了原油、天然气和太阳能光伏的贸易网络，比较了它们的特点和演变。它还通过模拟攻击评估了这些能源网络的弹性和中国的风险抵抗能力。主要结论如下：第一，太阳能光伏网络各项指标与原油、天然气相比存在明显差距，具有密度大、效率高、集聚性强、路径短等特点；其次，拥有广泛传统能源贸易的国家，如卡塔尔和哈萨克斯坦，缺乏显著的节点重要性，而新加坡由于其战略位置而成为太阳能光伏网络的中心枢纽。三种能源交易网络弹性均呈上升趋势，其中太阳能光伏网弹性更强。最终，中国在天然气网络中发挥着重要的桥梁作用，具有最高的抗风险能力。这些发现强调了政策需要优先考虑可再生能源合作，加强枢纽连通性，促进有弹性的光伏投资。

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引用次数: 0

Performance enhancement of thermoelectric modules through the introduction of macro-porous thermoelectric leg 通过引入大孔热电腿提高热电模块的性能

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2026-01-05 DOI: 10.1016/j.energy.2026.139942

Boyang Liang , Xiangning Meng , Zhuang Miao , Xi Li

Thermoelectric modules (TEMs) can directly convert heat energy into electrical energy through thermoelectric conversion, but their conversion efficiency remains relatively low. The dense arrangement of thermoelectric legs (TELs) restricts airflow and causes heat concentration, which degrades performance and shortens service life. Introducing pore structures in TELs has been proposed as an effective approach to alleviate heat concentration and improve thermal uniformity. However, their impact on entire TEMs has generally been overlooked. This study employs numerical simulations to investigate the effects of pore structures on TELs and entire TEMs. The results show that circular pores of medium size provide the most balanced thermomechanical performance. They reduce the maximum thermal stress and deformation by 8.34 % and 5.98 %, respectively, while improving the uniformity of current distribution. The analysis of entire TEMs shows that the number of macro-porous thermoelectric legs (MPTELs) significantly affects internal convection and power generation performance. An optimized configuration enhances heat transfer and alleviates heat concentration. As a result, the maximum output power and conversion efficiency reach 6.31 W and 5.19 %, respectively. These results demonstrate that optimizing pore structures can simultaneously enhance thermal stability and power generation performance of TEMs. This provides important insights for the design of high-performance thermoelectric systems.

热电模块（TEMs）可以通过热电转换直接将热能转化为电能，但其转换效率相对较低。热电腿（tel）的密集排列限制了气流，导致热量集中，从而降低了性能并缩短了使用寿命。在电晶体中引入孔隙结构是缓解热集中和改善热均匀性的有效途径。然而，它们对整个tem的影响通常被忽视了。本文采用数值模拟的方法研究了孔隙结构对电晶体和整个电晶体的影响。结果表明，中等尺寸的圆形孔隙提供了最平衡的热力学性能。使最大热应力和最大变形分别降低8.34%和5.98%，同时提高了电流分布的均匀性。整个tem的分析表明，大孔热电腿的数量对内部对流和发电性能有显著影响。优化的配置增强了传热，减轻了热量集中。最大输出功率和转换效率分别达到6.31 W和5.19%。这些结果表明，优化孔隙结构可以同时提高tem的热稳定性和发电性能。这为高性能热电系统的设计提供了重要的见解。

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引用次数: 0

Source domain selection with early-cycle features for transfer learning-based prediction of lithium-ion battery degradation trajectories 基于迁移学习的锂离子电池退化轨迹预测的早期周期特征源域选择

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2026-01-04 DOI: 10.1016/j.energy.2026.139928

Kai Huang , XinYu Zhang , Yongfang Guo , MengShi Li

Predicting the remaining useful life (RUL) of batteries is critical for ensuring equipment safety and optimizing maintenance strategies. However, due to the complicated nonlinear degradation of batteries, accurate prediction based on early-cycle data remains a significant challenge. Thus, a transfer learning prediction framework based on source domain selection using early-cycle degradation features is proposed. First, features reflecting the internal aging state are extracted from charging voltage-capacity data in early cycles. Then, a hybrid distance-dynamic time warping (HD-DTW) source domain battery selection method is developed to select similar batteries based on the features and capacity data. Finally, based on the selected source domain batteries and the early-cycle data of the target battery, an elastic net model is established to generate the degradation trajectory for the target battery. The performance of the proposed framework is validated based on open-source datasets. The results show that it can accurately predict RUL in the early stage, achieving an average absolute percentage error of 7.23 % and a root mean square error of 81.52 cycles.

预测电池的剩余使用寿命（RUL）对于确保设备安全和优化维护策略至关重要。然而，由于电池复杂的非线性退化，基于早期循环数据的准确预测仍然是一个重大挑战。在此基础上，提出了一种基于源域选择的迁移学习预测框架。首先，从前期充电电压-容量数据中提取反映内部老化状态的特征；然后，提出了一种混合距离-动态时间规整（HD-DTW）源域电池选择方法，根据特征和容量数据选择相似的电池。最后，基于选定的源域电池和目标电池的早期循环数据，建立弹性网络模型，生成目标电池的退化轨迹。基于开源数据集验证了该框架的性能。结果表明，该方法能较准确地在早期预测RUL，平均绝对百分比误差为7.23%，均方根误差为81.52个周期。

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引用次数: 0

Thermal energy storage and leakage prevention of phase change materials via one-step impregnation and in-situ polymerization process in hardwood 硬木一步浸渍原位聚合相变材料的储热防漏

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2026-01-04 DOI: 10.1016/j.energy.2025.139874

Jakub Grzybek , Gabriel Zsembinszki , Emiliano Borri , Alina Meindl , Zuzana Paschová , Alexander Petutschnigg , Luisa F. Cabeza , Thomas Schnabel

Wood is a versatile material widely used in building construction, but its low thermal mass limits its ability to regulate indoor temperatures and mitigate thermal load peaks. Phase change materials are effective at storing thermal energy, but when impregnated into wood, they leak out, compromising performance and restricting their use in buildings.

This study introduces a novel one-step impregnation process combined with in-situ polymerization using furfuryl alcohol and a capric-stearic acid phase change material mixture to create a sustainable material for thermal energy storage. Various formulations were tested on European beech (Fagus sylvatica L.) to evaluate effectiveness of the approach.

The results confirm that this method successfully prevents phase change material leakage. Moreover, differential scanning calorimetry and nuclear magnetic resonance verified that phase change materials retain their thermal energy storage functionality, with no chemical cross-linking between the phase change materials and furfuryl alcohol. The treated wood showed up to 185 % higher thermal energy storage capacity, enhanced dimensional stability (anti-swelling efficiency up to 87 %), and 28 % higher compressive strength than untreated wood. It is a step towards sustainable, multifunctional, leakage-free, enhanced mechanical properties, improved dimensional stability wood for thermal energy storage for building applications, with potential for further optimisation and characterisation.

木材是一种广泛用于建筑施工的多功能材料，但其低热质量限制了其调节室内温度和减轻热负荷峰值的能力。相变材料在储存热能方面是有效的，但是当渗透到木材中时，它们会泄漏，从而影响性能并限制其在建筑物中的使用。本研究介绍了一种新的一步浸渍工艺，并结合原位聚合，利用糠醇和卡普-硬脂酸相变材料混合物制备了一种可持续的储能材料。在欧洲山毛榉（Fagus sylvatica L.）上试验了各种配方，以评价该方法的有效性。结果表明，该方法成功地防止了相变材料的泄漏。此外，差示扫描量热法和核磁共振验证了相变材料保留了其储热功能，相变材料与糠醇之间没有化学交联。与未经处理的木材相比，经过处理的木材的储热能力提高了185%，尺寸稳定性增强（抗膨胀效率高达87%），抗压强度提高了28%。这是朝着可持续、多功能、无泄漏、增强机械性能、改善尺寸稳定性的一步，用于建筑应用的热能储存，具有进一步优化和表征的潜力。

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引用次数: 0

AI-supported decision framework for sustainable reconstruction: Case study on TOKİ housing after the 2023 Kahramanmaraş earthquake 人工智能支持的可持续重建决策框架：2023年kahramanmaraki地震后TOKİ住房案例研究

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2026-01-03 DOI: 10.1016/j.energy.2025.139891

Gürkan Kavuran , Gonca Özer Yaman , Bahar Başarır , Ebru Doğan , Beyzanur İnce , Gökçe Dağteke

This study presents a hybrid analytical and machine learning-based framework to evaluate and classify the electricity performance of standardized TOKİ housing units planned for reconstruction in the aftermath of the February 6, 2023, Kahramanmaraş earthquakes. While a standardized building model was analyzed using dynamic energy simulation (DesignBuilder) for 11 affected provinces, machine learning techniques were integrated to enhance the interpretability and decision support capabilities of the output. According to local climate data and building specifications, annual electricity consumption was simulated, and units were classified into ‘low’ or ‘high’ consumption categories using thresholds defined by Türkiye's Energy Market Regulatory Authority (EPDK). To improve classification reliability and computational efficiency, a wrapper-based feature selection approach was employed. The Whale Optimization Algorithm (WOA), guided by K-Nearest Neighbors (KNN) fitness evaluation, was used to identify a subset of the most relevant features, and a Support Vector Machine (SVM) was trained on this reduced feature set. The WOA-KNN-SVM model outperformed the baseline SVM classifier across all performance metrics, achieving 98.2 % classification accuracy, with notable improvements in sensitivity, specificity, and Matthews Correlation Coefficient. The results demonstrate that this integrated methodology can effectively support climate-sensitive and energy-efficient design decisions for mass housing in disaster-prone regions. By providing a replicable and scalable decision-support tool aligned with real-world tariff structures, the proposed approach contributes a novel perspective to post-disaster sustainable reconstruction planning.

本研究提出了一个基于混合分析和机器学习的框架，用于评估和分类2023年2月6日kahramanmaraku地震后计划重建的标准化TOKİ住房单元的电力性能。在使用动态能源模拟（DesignBuilder）对11个受影响省份的标准化建筑模型进行分析的同时，还集成了机器学习技术，以增强输出的可解释性和决策支持能力。根据当地的气候数据和建筑规范，模拟了年用电量，并根据 rkiye能源市场监管局（EPDK）定义的阈值将单元划分为“低”或“高”消耗类别。为了提高分类可靠性和计算效率，采用了一种基于包装器的特征选择方法。在k -近邻（KNN）适应度评估的指导下，使用鲸鱼优化算法（WOA）来识别最相关的特征子集，并在此简化的特征集上训练支持向量机（SVM）。WOA-KNN-SVM模型在所有性能指标上都优于基线SVM分类器，分类准确率达到98.2%，灵敏度、特异性和马修斯相关系数都有显著提高。结果表明，这种综合方法可以有效地支持灾害多发地区大规模住房的气候敏感和节能设计决策。通过提供与现实世界电价结构相一致的可复制和可扩展的决策支持工具，该方法为灾后可持续重建规划提供了新的视角。

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引用次数: 0

A two-stage deep learning method for predicting turbine vane temperature fields under active cooling air flow modulation 主动冷却气流调节下涡轮叶片温度场预测的两阶段深度学习方法

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2026-01-03 DOI: 10.1016/j.energy.2026.139911

Zhenhua Long , Zeqiu Liu , Yiran Shao , Bo Jiang , Minghao Ren , Yang Bai , Fuxiang Dong , Jinfu Liu , Daren Yu

Active modulation of turbine cooling air flow is a pivotal technology for enhancing the partial load operational efficiency of gas turbines. However, its practical implementation is impeded by the absence of real-time, precise vane temperature feedback. To address this challenge, this study introduces a novel two-stage deep learning model designed to achieve fast, high-fidelity prediction of turbine vane temperature fields under varying operating conditions and cooling air flow modulation states. The method integrates a gas turbine thermodynamic model and a turbine vane numerical model to generate a comprehensive dataset. The proposed two-stage model first employs a novel upsampling network, termed n-Net, to map low-dimensional inputs representing the gas turbine operating conditions and coolant modulation coefficients to high-resolution temperature fields. Subsequently, an Attention Pix2PixHD network, enhanced with a spatial attention mechanism, performs end-to-end super-resolution to refine the initial predictions and correct local errors. The results demonstrate that the two-stage model achieves excellent predictive performance on the test set, with a maximum relative error of 3.5408 % and a structural similarity index of 0.8460. Crucially, compared to conventional CFD simulations, the model reduces the prediction time by a factor of approximately 10⁵, decreasing the time required per case to around 0.1358 s. The proposed method enables near-real-time, high-fidelity temperature field prediction for applications in active cooling air flow modulation and design optimization.

涡轮冷却气流的主动调节是提高燃气轮机部分负荷运行效率的关键技术。然而，由于缺乏实时、精确的叶片温度反馈，其实际实施受到阻碍。为了应对这一挑战，本研究引入了一种新的两阶段深度学习模型，旨在实现不同操作条件和冷却气流调制状态下涡轮叶片温度场的快速、高保真预测。该方法将燃气轮机热力学模型和涡轮叶片数值模型相结合，生成一个综合的数据集。提出的两阶段模型首先采用一种称为n-Net的新型上采样网络，将代表燃气轮机运行条件和冷却剂调制系数的低维输入映射到高分辨率温度场。随后，利用空间注意机制增强的注意力Pix2PixHD网络执行端到端超分辨率，以改进初始预测并纠正局部错误。结果表明，两阶段模型在测试集上取得了良好的预测性能，最大相对误差为3.5408%，结构相似度指数为0.8460。关键是，与传统的CFD模拟相比，该模型将预测时间缩短了约105倍，将每个案例所需的时间减少到0.1358秒左右。提出的方法可以实现近实时、高保真的温度场预测，用于主动冷却气流调制和设计优化。

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