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Uplifting the complexity of analysis for probabilistic security of electricity supply assessments using artificial neural networks 利用人工神经网络提高电力供应安全概率评估分析的复杂性
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-02 DOI: 10.1016/j.egyai.2024.100401

The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 min to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time.

能源行业面临着快速的去碳化,决策者需要对电力供应安全进行可靠的评估。为此,需要具有较高时间和技术分辨率的详细模拟模型。面对日益增长的依赖天气的可再生能源发电量,概率模拟模型已得到证实。然而,计算一个情景的巨大计算成本限制了进一步分析的复杂性。代码优化方面的进步以及计算集群的使用仍然导致每个情景的运行时间长达 8 小时。然而,正在进行的研究表明,量身定制的近似值可能是进一步缩短计算时间的关键因素。因此,当前的研究旨在提供一种方法,用于快速预测千差万别的场景。在这项工作中,对人工神经网络(ANN)进行了训练和比较,以逼近概率模拟模型的系统行为。为此,需要以有效的方式从概率模拟中抽取信息。由于在 16 个自变量的整个设计空间中,只有有限的空间是人们感兴趣的,因此开发了一种分类方法。最后,创建回归模型只需要大约 35 分钟,包括设计空间采样、模拟训练数据和训练 ANN。生成的人工智能网络能够预测回归模型有效范围内的所有情况,对独立测试数据(1.051200 个数据点)的判定系数超过 0.9998。它们只需要几毫秒就能预测一个场景,从而能够在短时间内进行深入分析。
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引用次数: 0
Leveraging machine learning to generate a unified and complete building height dataset for Germany 利用机器学习生成统一完整的德国建筑高度数据集
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-31 DOI: 10.1016/j.egyai.2024.100408

Building geometry data is crucial for detailed, spatially-explicit analyses of the building stock in energy systems analysis and beyond. Despite the existence of diverse datasets and methods, a standardized and validated approach for creating a nation-wide unified and complete dataset of German building heights is not yet available. This study develops and validates such a methodology, combining different data sources for building footprints and heights and filling gaps in height data using an XGBoost machine learning algorithm. The XGBoost model achieves a mean absolute error of 1.78 m at the national level and between 1.52 m and 3.47 m at the federal state level. The goal is proving the applicability of the methodology at a large scale and creating a useful dataset. The resulting dataset is thoroughly evaluated on a building-by-building level and spatially resolved statistics on the quality of the dataset are reported. This detailed validation found that the building number and footprint area of German building stock is 90.31 % and 94.84 % correct, respectively, and the building height accuracy is 0.59 m at the national level. However, errors are not homogeneous across Germany and further research is needed into the impact of including additional datasets, especially for regions and building types with lower accuracies. This study proves that the chosen methodology is useful for generating a building height dataset and the workflow, with some modifications for regional data availability, can be transferred to other countries. The generated building dataset for Germany constitutes a valuable data basis for the research community in fields such as energy research, urban planning and building decarbonization policy development.

建筑几何数据对于在能源系统分析及其他方面对建筑群进行详细的空间分析至关重要。尽管存在各种不同的数据集和方法,但目前还没有一种标准化的、经过验证的方法来创建一个全国统一的、完整的德国建筑高度数据集。本研究开发并验证了这种方法,它结合了建筑占地面积和高度的不同数据源,并使用 XGBoost 机器学习算法填补了高度数据的空白。XGBoost 模型在国家一级的平均绝对误差为 1.78 米,在联邦州一级的平均绝对误差为 1.52 米至 3.47 米。目标是证明该方法的大规模适用性,并创建一个有用的数据集。我们对生成的数据集进行了逐栋建筑的全面评估,并报告了数据集质量的空间分辨率统计数据。详细的验证结果表明,德国建筑群的建筑数量和占地面积的正确率分别为 90.31% 和 94.84%,全国范围内的建筑高度精确度为 0.59 米。然而,德国各地的误差并不一致,因此需要进一步研究加入额外数据集的影响,尤其是对精确度较低的地区和建筑类型的影响。这项研究证明,所选择的方法对于生成建筑高度数据集非常有用,而且工作流程在根据地区数据可用性进行一些修改后,也可以推广到其他国家。生成的德国建筑数据集为能源研究、城市规划和建筑脱碳政策制定等领域的研究人员提供了宝贵的数据基础。
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引用次数: 0
A review of control strategies for proton exchange membrane (PEM) fuel cells and water electrolysers: From automation to autonomy 质子交换膜(PEM)燃料电池和水电解槽控制策略综述:从自动化到自主化
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-28 DOI: 10.1016/j.egyai.2024.100406

Proton exchange membrane (PEM) based electrochemical systems have the capability to operate in fuel cell (PEMFC) and water electrolyser (PEMWE) modes, enabling efficient hydrogen energy utilisation and green hydrogen production. In addition to the essential cell stacks, the system of PEMFC or PEMWE consists of four sub-systems for managing gas supply, power, thermal, and water, respectively. Due to the system's complexity, even a small fluctuation in a certain sub-system can result in an unexpected response, leading to a reduced performance and stability. To improve the system's robustness and responsiveness, considerable efforts have been dedicated to developing advanced control strategies. This paper comprehensively reviews various control strategies proposed in literature, revealing that traditional control methods are widely employed in PEMFC and PEMWE due to their simplicity, yet they suffer from limitations in accuracy. Conversely, advanced control methods offer high accuracy but are hindered by poor dynamic performance. This paper highlights the recent advancements in control strategies incorporating machine learning algorithms. Additionally, the paper provides a perspective on the future development of control strategies, suggesting that hybrid control methods should be used for future research to leverage the strength of both sides. Notably, it emphasises the role of artificial intelligence (AI) in advancing control strategies, demonstrating its significant potential in facilitating the transition from automation to autonomy.

基于质子交换膜(PEM)的电化学系统可在燃料电池(PEMFC)和水电解槽(PEMWE)模式下运行,从而实现高效氢能利用和绿色制氢。除基本的电池堆外,PEMFC 或 PEMWE 系统还包括四个子系统,分别用于管理气体供应、电力、热力和水。由于系统的复杂性,即使是某个子系统的微小波动也会导致意外反应,从而降低性能和稳定性。为了提高系统的鲁棒性和响应能力,人们致力于开发先进的控制策略。本文全面回顾了文献中提出的各种控制策略,揭示了传统控制方法因其简单性而被广泛应用于 PEMFC 和 PEMWE,但在精度方面存在局限性。相反,先进的控制方法精度高,但动态性能差。本文重点介绍了结合机器学习算法的控制策略的最新进展。此外,本文还对控制策略的未来发展提出了展望,建议在未来的研究中采用混合控制方法,以充分利用双方的优势。值得注意的是,论文强调了人工智能(AI)在推进控制策略方面的作用,展示了人工智能在促进从自动化向自主化过渡方面的巨大潜力。
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引用次数: 0
Engineered wettability-gradient porous structure enabling efficient water manipulation in regenerative fuel cells 可在再生燃料电池中实现高效水处理的工程润湿梯度多孔结构
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-28 DOI: 10.1016/j.egyai.2024.100400

Regenerative fuel cells can operate alternately as an electrolyzer and as a fuel cell, frequently involving water as a reactant or product. Modifying the electrode surface to manipulate water can prevent electrode flooding and enhance the electrode's mass transfer efficiency by facilitating better contact with gaseous reactants. However, conventional electrodes face difficulties in allowing water droplets to penetrate in a single direction leaving electrodes. In this work to address this issue, a wettability gradient electrode is designed and fabricated for efficient water manipulation in regenerative fuel cells. The findings demonstrate that the water removal strategy in the electrolyzer mode yields the highest ammonia yield and Faradaic efficiency of 3.39 × 10-10 mol s-1 cm-2 and 0.49 %, respectively. Furthermore, in the fuel cell mode, the discharging process sustains for approximately 20.5 h, which is six times longer than the conventional strategy. The ability to sustain the discharging process for extended periods is particularly advantageous in regenerative fuel cells, as it enables the cells to operate for longer periods without the need for regeneration.

蓄热式燃料电池可交替作为电解器和燃料电池运行,通常以水作为反应物或产物。对电极表面进行改造以处理水,可以防止电极浸水,并通过促进电极与气态反应物更好地接触来提高传质效率。然而,传统电极很难让水滴从单一方向渗透离开电极。为解决这一问题,本研究设计并制造了一种润湿性梯度电极,用于再生燃料电池中的高效水处理。研究结果表明,在电解槽模式下,水去除策略可产生最高的氨产量和法拉第效率,分别为 3.39 × 10-10 mol s-1 cm-2 和 0.49 %。此外,在燃料电池模式下,放电过程可持续约 20.5 小时,是传统策略的六倍。延长放电过程的持续时间对于再生燃料电池尤为有利,因为它能使电池运行更长时间而无需再生。
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引用次数: 0
Performance decay prediction model of proton exchange membrane fuel cell based on particle swarm optimization and gate recurrent unit 基于粒子群优化和栅极递归单元的质子交换膜燃料电池性能衰减预测模型
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-26 DOI: 10.1016/j.egyai.2024.100399

The durability of proton exchange membrane fuel cells (PEMFC) is an important issue that restricts their large-scale application. To improve their reliability during use, this paper proposes a short-term performance degradation prediction model using particle swarm optimization (PSO) to optimize the gate recurrent unit (GRU). After training using only the data from the first 300 h, good prediction accuracy can be achieved. Compared with the traditional GRU algorithm, the proposed prediction method reduces the root mean square error (RMSE) and mean absolute error (MAE) of the prediction results by 44.8 % and 35.1 %, respectively. It avoids the problem of low accuracy in predicting performance during the temporary recovery phase in traditional GRU models, which is of great significance for the health management of PEMFC.

质子交换膜燃料电池(PEMFC)的耐用性是限制其大规模应用的一个重要问题。为了提高质子交换膜燃料电池在使用过程中的可靠性,本文提出了一种短期性能退化预测模型,利用粒子群优化(PSO)来优化栅极递归单元(GRU)。仅使用前 300 小时的数据进行训练后,就能获得良好的预测精度。与传统的 GRU 算法相比,所提出的预测方法将预测结果的均方根误差(RMSE)和平均绝对误差(MAE)分别降低了 44.8% 和 35.1%。它避免了传统 GRU 模型在临时恢复阶段性能预测准确度低的问题,对 PEMFC 的健康管理具有重要意义。
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引用次数: 0
Deep learning-based prediction of 3-dimensional silver contact shapes enabling improved quality control in solar cell metallization 基于深度学习的三维银触点形状预测有助于改进太阳能电池金属化的质量控制
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-25 DOI: 10.1016/j.egyai.2024.100404

The industrial metallization of Si solar cells predominantly relies on screen printing, with silver as the preferred electrode material. However, the design of commercial screens often leads to suboptimal silver usage and increased electrical resistance due to print-related inhomogeneities like mesh marks, constrictions and spreading. Real-time monitoring of quality parameters during production has thus become increasingly critical. Current inline optical quality control systems usually only include 2D visualizations of the printed layout, which limits their effectiveness in quality control. Options that allow 3D measurements are usually slow, expensive, and therefore not worth considering in most cases. This research focuses on the development of a model that can estimate the three-dimensional shape of printed contact fingers from a single 2D image without the need of additional hardware using deep learning. Furthermore, a workflow for the generation of training data, which involves the creation of image pairs from a 2D microscope and a 3D confocal laser scanning microscope (CLSM) to accurately represent solar cell fingers, is presented. After model training, the predicted height maps are compared with the ground truth height maps, and the robustness of the model with respect to a paste variation and screen parameter variation is examined. The results confirm the feasibility and reliability of deep learning-based 3D shape estimation, extending its applicability to new, previously unseen data from screen-printed contact fingers. With a structural similarity index (SSIM) score of 0.76, a strong correlation between the estimated and ground truth height maps is established. In summary, our deep learning-based approach for height map estimation offers an effective and reliable solution for fast inline detection and analysis of the cross-sectional area of the printed contact fingers.

硅太阳能电池的工业金属化主要依靠丝网印刷,银是首选的电极材料。然而,商业丝网的设计往往会导致银的使用量达不到最佳水平,并且由于印刷相关的不均匀性(如网痕、收缩和扩张)而导致电阻增加。因此,在生产过程中对质量参数进行实时监控变得越来越重要。目前的在线光学质量控制系统通常只能实现印刷布局的二维可视化,这限制了其质量控制的有效性。可进行 3D 测量的方案通常速度慢、成本高,因此在大多数情况下不值得考虑。本研究的重点是开发一种模型,利用深度学习技术,无需额外硬件,即可从单张二维图像中估算出印刷接触手指的三维形状。此外,还介绍了生成训练数据的工作流程,其中包括从二维显微镜和三维共焦激光扫描显微镜(CLSM)创建图像对,以准确表示太阳能电池指。模型训练完成后,将预测的高度图与地面实况高度图进行比较,并检验了模型对浆料变化和屏幕参数变化的稳健性。结果证实了基于深度学习的三维形状估计的可行性和可靠性,并将其适用性扩展到了来自丝网印刷接触手指的以前未见过的新数据。结构相似性指数(SSIM)得分为 0.76,在估计高度图和地面实况高度图之间建立了很强的相关性。总之,我们基于深度学习的高度图估算方法为快速联机检测和分析印刷接触手指的横截面积提供了有效而可靠的解决方案。
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引用次数: 0
Deep learning from three-dimensional Lithium-ion battery multiphysics model Part II: Convolutional neural network and long short-term memory integration 三维锂离子电池多物理场模型的深度学习 第二部分:卷积神经网络与长短期记忆整合
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-18 DOI: 10.1016/j.egyai.2024.100398

Lithium-ion (Li-ion) batteries have emerged as a cornerstone of electric vehicles (EVs), enabling the road transportation towards net zero. The success of electric vehicles largely hinges on the battery performance and safety. It is challenging to test and predict battery performance and safety issues by conventional methods, which are usually time-consuming and expensive, involving significant human and measurement errors. To enable the quick estimation of battery performance and safety, we developed three data-driven machine learning (ML) models, namely a convolutional neural network (CNN), a long short-term memory (LSTM), and a CNN-LSTM to predict battery discharge curves and local maximum temperature (hot spot) under various operating conditions. The developed ML models mitigated data scarcity by employing a three-dimensional multi-physics Li-ion battery model to generate enormous and diverse high-quality data. It was found the CNN-LSTM model outperforms the others and achieved high accuracy of 98.68% to learn discharge curves and battery maximum temperature, owing to the integration of spatial and sequential feature extraction. The battery safety can be improved by comparing the predicted maximum battery temperature against safe temperature threshold. The proposed data development and data-driven ML models are of great potential to provide digital tools for engineering high-performance and safe EVs.

锂离子(Li-ion)电池已成为电动汽车(EV)的基石,使道路交通实现零排放。电动汽车的成功在很大程度上取决于电池的性能和安全性。采用传统方法测试和预测电池性能和安全问题具有挑战性,因为传统方法通常耗时长、成本高,而且存在很大的人为误差和测量误差。为了快速评估电池性能和安全性,我们开发了三种数据驱动的机器学习(ML)模型,即卷积神经网络(CNN)、长短期记忆(LSTM)和 CNN-LSTM,用于预测各种工作条件下的电池放电曲线和局部最高温度(热点)。所开发的 ML 模型通过采用三维多物理场锂离子电池模型来生成大量多样的高质量数据,从而缓解了数据稀缺的问题。研究发现,CNN-LSTM 模型在学习放电曲线和电池最高温度方面优于其他模型,其准确率高达 98.68%,这得益于空间和序列特征提取的整合。通过比较预测的电池最高温度与安全温度阈值,可以提高电池的安全性。所提出的数据开发和数据驱动的 ML 模型具有巨大潜力,可为高性能和安全电动汽车的工程设计提供数字化工具。
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引用次数: 0
Optimizing hybrid electric vehicle coupling organic Rankine cycle energy management strategy via deep reinforcement learning 通过深度强化学习优化混合动力电动汽车耦合有机朗肯循环能源管理策略
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-14 DOI: 10.1016/j.egyai.2024.100392

Trucks consume a lot of energy. Hybrid technology maintains a long range while realizing energy savings. Hybrid is therefore an effective energy-saving technology for trucks. Recovery of engine waste heat through the organic Rankine cycle further enhances engine efficiency and provides effective thermal management. However, the powertrain greatly increases the complexity of energy management system. In order to design an energy management system with high efficiency and robustness, this study proposes a deep reinforcement learning embedded rule-based energy management system. This method optimises the key parameters of the rule-based energy management system by inserting deep reinforcement learning into it. Therefore, this scheme combines the good optimization effect of deep reinforcement learning and the excellent robustness of rule. In order to verify the feasibility of this scheme, this study builds the system dynamic model and carries out a simulation study. Subsequently, a hybrid powertrain semi physical experimental bench was constructed and a rapid control prototype experimental study was carried out. The simulation results show that the deep reinforcement learning embedded rule-based energy management system can reduce the energy consumption by 4.31 % compared with the rule-based energy management system under the C-WTVC driving cycle. In addition, energy saving and safe operation can also be achieved under other unfamiliar untrained driving cycles. The rapid control prototype experimental study shows that the deep reinforcement learning embedded rule-based energy management system has good agreement in experiment and simulation, which demonstrates the potential for real vehicle engineering applications and promotes the engineering application of deep reinforcement learning.

卡车消耗大量能源。混合动力技术在实现节能的同时,还能保持较长的续航里程。因此,混合动力技术是一种有效的卡车节能技术。通过有机朗肯循环回收发动机废热可进一步提高发动机效率,并提供有效的热管理。然而,动力系统大大增加了能源管理系统的复杂性。为了设计出高效、稳健的能源管理系统,本研究提出了一种基于深度强化学习嵌入式规则的能源管理系统。该方法通过将深度强化学习植入基于规则的能源管理系统,优化了该系统的关键参数。因此,该方案结合了深度强化学习的良好优化效果和规则的卓越鲁棒性。为了验证该方案的可行性,本研究建立了系统动态模型并进行了仿真研究。随后,构建了混合动力系统半实物实验台,并进行了快速控制原型实验研究。仿真结果表明,与基于规则的能源管理系统相比,基于深度强化学习的嵌入式规则能源管理系统在 C-WTVC 驾驶循环下可降低能耗 4.31%。此外,在其他不熟悉的非训练驾驶循环下也能实现节能和安全运行。快速控制原型实验研究表明,深度强化学习嵌入式基于规则的能量管理系统在实验和仿真中具有良好的一致性,展示了其在实际车辆工程应用中的潜力,促进了深度强化学习的工程应用。
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引用次数: 0
A machine learning driven 3D+1D model for efficient characterization of proton exchange membrane fuel cells 用于质子交换膜燃料电池高效表征的机器学习驱动 3D+1D 模型
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-14 DOI: 10.1016/j.egyai.2024.100397

The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the catalyst layers and membrane. However, existing studies often simplify the 1D domain to a linearized 0D lumped model, potentially resulting in significant errors at high loads. In this study, we present a computationally efficient neural network driven 3D+1D model for proton exchange membrane fuel cells. The 3D sub-model captures transport in the gas channels and gas diffusion layers and is coupled with a 1D electrochemical sub-model for microporous layers, membrane, and catalyst layers. To reduce computational intensity of the full 1D description, a neural network surrogates the 1D electrochemical sub-model for coupling with the 3D domain. Trained by model-generated large synthetic datasets, the neural network achieves root mean square errors of less than 0.2%. The model is validated against experimental results under various relative humidities. It is then employed to investigate the nonlinear distribution of internal states under different operating conditions. With the neural network operating at 0.5% of the computing cost of the 1D sub-model, the hybrid model preserves a detailed and nonlinear representation of the internal fuel cell states while maintaining computational costs comparable to conventional 3D+0D models. The presented hybrid data-driven and physical modeling framework offers high accuracy and computing speed across a broad spectrum of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry.

质子交换膜燃料电池三维连续模型的计算要求仍然很高。一种流行的方法是将 2D/3D 流场与催化剂层和膜的 1D 子模型相结合的分层模型。然而,现有的研究通常将一维领域简化为线性化的零维块状模型,这可能会导致在高负载时出现重大误差。在本研究中,我们提出了一种计算高效的神经网络驱动质子交换膜燃料电池 3D+1D 模型。三维子模型捕捉气体通道和气体扩散层中的传输,并与微孔层、膜和催化剂层的一维电化学子模型相结合。为了降低完整一维描述的计算强度,神经网络替代了一维电化学子模型,以便与三维领域进行耦合。通过模型生成的大型合成数据集进行训练,神经网络的均方根误差小于 0.2%。该模型根据各种相对湿度下的实验结果进行了验证。然后,它被用来研究不同工作条件下内部状态的非线性分布。神经网络的计算成本仅为一维子模型的 0.5%,混合模型保留了燃料电池内部状态的详细非线性表示,同时计算成本与传统的三维+零维模型相当。所提出的数据驱动和物理建模混合框架可在各种操作条件下提供高精度和计算速度,从而有助于快速优化膜电极组件和气体通道几何形状。
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引用次数: 0
Gas exchange optimization in aircraft engines using sustainable aviation fuel: A design of experiment and genetic algorithm approach 使用可持续航空燃料的飞机发动机气体交换优化:实验设计和遗传算法方法
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-14 DOI: 10.1016/j.egyai.2024.100396

The poppet valves two-stroke (PV2S) aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio, uniform torque output, and flexible valve timings. However, its high-altitude gas exchange performance remains unexplored, presenting new opportunities for optimization through artificial intelligence (AI) technology. This study uses validated 1D + 3D models to evaluate the high-altitude gas exchange performance of PV2S aircraft engines. The valve timings of the PV2S engine exhibit considerable flexibility, thus the Latin hypercube design of experiments (DoE) methodology is employed to fit a response surface model. A genetic algorithm (GA) is applied to iteratively optimize valve timings for varying altitudes. The optimization process reveals that increasing the intake duration while decreasing the exhaust duration and valve overlap angles can significantly enhance high-altitude gas exchange performance. The optimal valve overlap angle emerged as 93 °CA at sea level and 82 °CA at 4000 m altitude. The effects of operating parameters, including engine speed, load, and exhaust back pressure, on the gas exchange process at varying altitudes are further investigated. The higher engine speed increases trapping efficiency but decreases the delivery ratio and charging efficiency at various altitudes. This effect is especially pronounced at elevated altitudes. The increase in exhaust back pressure will significantly reduce the delivery ratio and increase the trapping efficiency. This study demonstrates that integrating DoE with AI algorithms can enhance the high-altitude performance of aircraft engines, serving as a valuable reference for further optimization efforts.

以可持续航空燃料为燃料的动阀二冲程(PV2S)航空发动机具有功率重量比高、扭矩输出均匀、气门正时灵活等优点,是通用航空和无人机推进的理想选择。然而,其高空气体交换性能仍有待探索,这为通过人工智能(AI)技术进行优化提供了新的机遇。本研究使用经过验证的一维+三维模型来评估 PV2S 飞机发动机的高空气体交换性能。PV2S 发动机的气门定时具有相当大的灵活性,因此采用了拉丁超立方实验设计(DoE)方法来拟合响应面模型。应用遗传算法(GA)对不同高度的气门正时进行迭代优化。优化过程表明,增加进气持续时间,同时减少排气持续时间和气门重叠角,可以显著提高高海拔地区的气体交换性能。最佳气门重叠角在海平面为 93 °CA,海拔 4000 米为 82 °CA。进一步研究了发动机转速、负荷和排气背压等运行参数对不同海拔高度气体交换过程的影响。在不同海拔高度,发动机转速越高,捕集效率越高,但输送比和充气效率却越低。这种影响在高海拔地区尤为明显。排气背压的增加会显著降低输送比,提高捕集效率。这项研究表明,将 DoE 与人工智能算法相结合可以提高飞机发动机的高空性能,为进一步的优化工作提供有价值的参考。
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