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data-driven models for Yacht Hull Resistance Optimization: Exploring Geometric Parameters Beyond the Boundaries of the Delft Systematic Yacht Hull Series

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IEEE ACCESS 2024年 12卷 76102-76120页

作者： Walker, Jake M. Coraddu, Andrea Oneto, Luca Delft Univ Technol Fac Mech Engn NL-2628 CD Delft Netherlands Univ Genoa Dept Informat Bioengn Robot & Syst Engn I-16126 Genoa Italy

Optimizing vessel hull resistance is pivotal for enhancing maritime performance and minimizing environmental impacts. Traditional methods combine expert intuition with data-driven models (DDMs), relying on parametrization to predict and optimize hull geometries using Experimental Fluid Dynamics (EFD) or Computational Fluid Dynamics (CFD) data. However, these conventional approaches are hampered by several limitations: they require significant human input, are computationally intensive and costly, and lack flexibility in adapting to new families of geometries or parameters beyond predefined ranges. Addressing these challenges, our research introduces a novel method that significantly reduces the need for human intervention, computational resources, and costs, while also improving the model's adaptability. By proposing a new a parametrization technique that accurately encompasses the Delft Systematic Yacht Hull Series (DSYHS), we demonstrate that DDMs can be effectively trained directly on EFD datasets. This eliminates the dependency on extensive CFD simulations or the generation of new EFD data tailored to a specific investigation. Our approach matches the performance of leading-edge CFD models, even in extrapolating conditions, with physical plausibility and minimal human oversight. The validation of our method under various and increasingly complex extrapolating scenarios, employing statistical analyses on the DSYHS EFD dataset and comparisons with state-of-the-art CFD models, underscores the effectiveness of our proposal. Furthermore, we demonstrated that our model can successfully optimize hull resistance when navigating geometric parameters outside the confines of the DSYHS validating our results through leading-edge CFD simulations. This work addresses the limitations of existing methodologies by offering a novel approach more accurate, efficient, cost-effective, flexible, automated, and robust to extrapolation for hull resistance optimization.

关键词： Computational fluid dynamics data-driven models DelftBlue Delft Systematic Yacht Hull Series extrapolation hull parametrization hull resistance optimization sailing yachts

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data-driven models of Monotone Systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第8期69卷 5294-5309页

作者： Makdesi, Anas Girard, Antoine Fribourg, Laurent Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst F-91190 Gif sur Yvette France Univ Paris Saclay CNRS Lab Methodes Formelles ENS Paris Saclay F-91190 Gif sur Yvette France

In this article, we consider the problem of computing from data guaranteed set-valued over-approximations of unknown monotone functions with additive disturbances. We provide a characterization of a simulating map that provably contains all monotone functions that are consistent with the data. This map is also minimal in the sense that any set-valued map containing all consistent monotone functions would also include the map we are proposing. We show that this minimal simulating map is interval-valued and admits a simple construction on a finite partition induced by the data. As the complexity of the partition increases with the amount of data, we also consider the problem of computing minimal interval-valued simulating maps defined on partitions that are fixed a priori. We present an efficient algorithm for their computation. We then use those data-driven over-approximations to build models for partially unknown systems where the unknown part is monotone. The resulting models are used to construct finite-state symbolic abstractions, paving the way for discrete controller synthesis methods to be applied. We extend our approach to handle systems with bounded derivatives and introduce an algorithm to calculate the bounds on those derivatives and on the disturbances from the data. We present several numerical experiments to test the performance of the introduced method and show that the data-driven abstractions are suitable for controller synthesis purposes.

关键词： Computational modeling data models Nonlinear systems Dynamical systems Probabilistic logic Predictive models Picture archiving and communication systems data-driven abstraction data-driven models monotone maps monotone systems symbolic control

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State of health estimation of lithium-ion batteries based on feature optimization and data-driven models

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ENERGY 2025年 316卷

作者： Mu, Guixiang Wei, Qingguo Xu, Yonghong Li, Jian Zhang, Hongguang Yang, Fubin Zhang, Jian Li, Qi North Univ China Sch Energy & Power Engn Taiyuan 030051 Peoples R China Beijing Informat Sci & Technol Univ Mech Elect Engn Sch Beijing 100192 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Beijing Univ Technol Key Lab Enhanced Heat Transfer & Energy Conservat Beijing Key Lab Heat Transfer & Energy Convers MOEFac Environm & Life Beijing 100124 Peoples R China Univ Wisconsin Green Bay Richard J Resch Sch Engn Mech Engn Green Bay WI 54311 USA

With the widespread application of lithium-ion batteries in electric vehicles, accurately estimating their state of health (SOH) has become a key focus of research. This paper explores various feature optimization methods and data-driven models with different structures, and constructs various SOH estimation models suitable for lithiumion batteries. Based on battery testing data, multiple features are extracted from voltage and temperature to characterize the battery aging process. To reduce information redundancy among features, filtering methods, Principal Component Analysis (PCA), and Multi-dimensional Scaling (MDS) are applied for optimization, aiming to maximize feature information utilization. This paper compares four common and structurally different datadriven models: linear regression (LR), Gaussian process regression (GPR), support vector regression (SVR), and long short-term memory (LSTM) networks. The effectiveness of each model is validated using multi-feature inputs, and a multi-dimensional assessment of feature selection and data-driven model performance in SOH estimation is conducted, the average absolute error of all models under 60 % training set conditions is 0.8 %. The average absolute error of estimating the four batteries using the fused PCA features as input and the GPR model is less than 1.2 %. At the same time, using the optimized features as input reduces the average training time by 46.63 % compared to using multiple features as input. In summary, the combination of PCA features and GPR models has good performance in both estimation accuracy and computational efficiency for different batteries.

关键词： Lithium-ion battery State of health estimation Feature optimization data-driven models Principal component analysis Gaussian process regression

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Research on estimation of permeability coefficients in microbial geotechnical soils based on data-driven models

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2025年第1期25卷 22-31页

作者： Cheng, Mayao Chen, Linsheng Foshan Univ Coll Transportat & Civil Engn & Architecture Foshan 528225 Peoples R China

Microbial geotechnical soil permeability coefficient estimation prediction is extremely valuable for the development of soil engineering. The study proposes an integrated data-driven model combining three base learners, RVM, ANFIS and iTLBO-ELM, assigning corresponding weights to each base learner through the PLS integrated model combination model, and applying the model to the prediction of microbial geotechnical soil permeability coefficient estimation. The MAE of the proposed integrated model has lower values compared to the single model, but the two metrics MAPE and RMSE are not the lowest;however, the integrated model outperforms both iTLBO-ELM and RVM in terms of MAPE, and the estimated predictions of permeability coefficients for November data are better than those for May. For iTLBO-ELM and RVM, the MAPE of PLS decreased by 5.51% and 1.56% respectively in May and 3.46% and 1.24% respectively in October. The integrated data-driven model proposed in the study can effectively achieve the estimated prediction of microbial geotechnical soil permeability coefficients and facilitate the intelligent acquisition of engineering permeability coefficients.

关键词： data-driven models microorganisms geotechnical land permeability coefficients

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Guidance on the construction and selection of relatively simple to complex data-driven models for multi-task streamflow forecasting

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2024年第9期38卷 3657-3675页

作者： Tran, Trung Duc Kim, Jongho Univ Ulsan Sch Civil & Environm Engn Ulsan South Korea

With the goal of forecasting streamflow time series with sufficient lead time, we evaluate the efficiency and accuracy of data-based models ranging from relatively simple to complex. Based on this, we systematically explain the model construction and selection process according to lead time, type and amount of data, and optimization method. This analysis involved optimizing the inputs and hyperparameters of four unique data-driven models: Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Transformer (TRANS), which were applied to the Soyang watershed, South Korea. The type and amount of model inputs are determined through a fine-tuning process that samples based on a correlation threshold, correlation to predictand, and autocorrelation to historical data and evaluates the simulated objective function. Hyperparameters are simultaneously optimized using three conventional optimization methods: Bayesian optimization (BO), particle swarm optimization (PSO), and gray wolf optimization (GWO). The experimental results provide insight into the role of input predictors, data preparations (e.g., wavelet transform), hyperparameter optimization, and model structures. From this, we can provide guidelines for model selection. Relatively simple models can be used when the dataset is small or there are few input variables, when only the near future is predicted, or when the selection of optimization methods is limited. However, a more complex model should be selected if the type and amount of data are sufficient, various optimization methods can be applied, or it is necessary to secure more lead time. More parameters, more complex model structures, and more training materials make this possible.

关键词： data-driven models Multi-task forecasting Input predictor optimization Hyperparameter optimization Streamflow forecasting

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Prediction-Based Power Consumption Monitoring of Industrial Equipment Using Interpretable data-driven models

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第2期21卷 1312-1322页

作者： Xiao, Hui Hu, Wenshan Zhou, Hong Liu, Guo-Ping Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China

Efficient energy optimization and scheduling in industrial factories depend on accurate, reasonable, and real-time monitoring of equipment power consumption. However, the power prediction of industrial equipment requires a large number of process data, which could be inevitably contaminated by some imperfect data, due to the harsh environments. Monitoring or predicting equipment power consumption is usually not feasible using data-driven black-box models with imperfect data. This paper proposes a prediction-based approach for power consumption monitoring using an interpretable data-driven model. First, a data preprocessing method is used to remove outliers and fill in missing values. Then, a Volterra polynomial basis function (VPBF) model is built to predict equipment power consumption. This model decomposes power values into a series of basis functions consisting of input parameters. Moreover, to compensate for data dropouts during the power consumption monitoring process, a networked predictive monitoring system is also proposed. Finally, this paper presents two case studies based on actual production equipment in an industrial manufacturing factory. The results demonstrate that the proposed approach can achieve satisfactory monitoring accuracy and adaptation ability. Note to Practitioners-This paper is motivated by the problem of power consumption monitoring of industrial equipment in harsh production environments. A conventional solution is to predict the power consumption using data-driven black-box models, with limited feasibility and interpretability. This paper proposes a new power consumption monitoring approach, utilizing an interpretable data-driven model and a networked predictive method. This approach accurately reveals the transparent relationships between the output and input parameters. The power prediction result is consequently interpretable and more reasonable. Meanwhile, this approach actively compensates for data dropouts in the network, which c

关键词： Power demand Predictive models Monitoring data models Real-time systems Neural networks Manufacturing Power consumption production equipment imperfect data data-driven models networked prediction

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Forecasting operation of a chiller plant facility using data-driven models

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INTERNATIONAL JOURNAL OF REFRIGERATION 2024年 167卷 70-89页

作者： Rizi, Behzad Salimian Faramarzi, Afshin Pertzborn, Amanda Heidarinejad, Mohammad IIT Dept Civil Architectural & Environm Engn 3201 S Dearborn St Chicago IL 60616 USA NIST Mech Syst & Controls Grp Engn Lab Gaithersburg MD USA

In recent years, data-driven models have enabled accurate prediction of chiller power consumption and chiller coefficient of performance (COP). This study evaluates the usage of time series Extreme Gradient Boosting (XGBoost) models to predict chiller power consumption and chiller COP of a water-cooled chiller plant. The 10second measured data used in this study are from the Intelligent Building Agents Laboratory (IBAL), which includes two water-cooled chillers. Preprocessing, data selection, noise analysis, and data smoothing methods influence the accuracy of these data-driven predictions. The data intervals were changed to 30 s, 60 s, and 180 s using down-sampling and averaging strategies to investigate the impact of data preprocessing methods and data resolutions on the accuracy of chiller COP and power consumption models. To overcome the effect of noise on the accuracy of the models of chiller power consumption and COP, two data smoothing methods, the moving average window strategy and the Savitzky-Golay (SG) filter, are applied. The results show that both methods improve the predictions compared to the baseline, with the SG filter slightly outperforming the moving average. Particularly, the mean absolute percentage error of the chiller COP and power consumption models improved from 4.8 to 4.9 for the baseline to 1.9 and 2.3 with the SG filter, respectively. Overall, this study provides a practical guide to developing XGBoost data-driven chiller power consumption and COP prediction models.

关键词： Chiller plant data-driven models XGBoost Energy efficiency Coefficient of performance

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Quantifying Streamflow Prediction Uncertainty Through Process-Aware data-driven models

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HYDROLOGICAL PROCESSES 2024年第11期38卷

作者： Roy, Abhinanda Kasiviswanathan, K. S. Indian Inst Technol Roorkee Dept Water Resources Dev & Management Roorkee India Indian Inst Technol Roorkee Mehta Family Sch Data Sci & Artificial Intelligenc Roorkee India

The hydrological model simulation accompanied with uncertainty quantification helps enhance their overall reliability. Since uncertainty quantification including all the sources (input, model structure and parameter) is challenging, it is often limited to only addressing model parametric uncertainty, neglecting other uncertainty sources. This paper focuses on exploiting the potential of state-of-the-art data-driven models (or DDMs) in quantifying the prediction uncertainty of process-based hydrological models. This is achieved by integrating the robust predictive ability of the DDMs with the process understanding ability of the hydrological models. The Bayesian-based data assimilation (DA) technique is used to quantify uncertainty in process-based hydrological models. This is accomplished by choosing two DDMs, random forest algorithm (RF) and support vector machine (SVM), which are distinctly integrated with two process-based hydrological models: HBV and HyMOD. Particle filter algorithm (PF) is chosen for uncertainty quantification. All these combinations led to four different process-aware DDMs: HBV-PF-RF, HBV-PF-SVM, HyMOD-PF-RF and HyMOD-PF-SVM. The assessment of these models on the Baitarani, Beas and Sunkoshi river basins exemplified an improvement in the accuracy of the daily streamflow simulations with a reduction in the prediction uncertainty across all the models for all the basins. For example, the nash-sutcliffe efficiency improved by 54.69% and 10.61% in calibration and validation of the Baitarani river basin, respectively. Equivalently, average bandwidth improved by 79.37% and 71.59%, respectively. This signified the (a) potential of the DDMs in quantifying and reducing the prediction uncertainty of the hydrological model simulations, (b) transferability of the model with an appreciable performance irrespective of the choice of basins having varying topography and climatology and (c) ability to perform significantly irrespective of different process-bas

关键词： data-driven models hydrological models particle filter algorithm predictive uncertainty streamflow simulation

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A review of the state of the art in solar photovoltaic output power forecasting using data-driven models

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ELECTRICAL ENGINEERING 2025年第4期107卷 4727-4770页

作者： Gupta, Ankur Kumar Singh, Rishi Kumar Maulana Azad Natl Inst Technol Dept Elect Engn Bhopal Madhya Pradesh India

The integration of Photovoltaic (PV) systems into grid has a detrimental effect on grid stability, dependability, reliability, efficiency, economy, planning and scheduling. Thus, a reliable PV output prediction is necessary for grid stability. This paper presents a detailed review on PV power forecasting technique. A detailed evaluation of forecasting techniques reveals that solar irradiance is highly correlated with PV output which makes it mandatory to classify the weather as well as analyze the cloudy motion. The researchers in literature dominantly preferred very short term and short-term horizon for PV forecasting. Different PV forecasting models categorized into physical, persistence, statistical, machine learning and hybrid model used in literature are studied and evaluated using various performance metrics such as root mean square error and mean absolute error for accuracy. A thorough comparison of various forecasting techniques suggests that deep neural networks with ensemble technique or hybrid techniques supersedes the traditional approaches of PV power forecasting in terms of efficiency and accuracy.

关键词： Forecast models Solar power Forecast horizon data-driven models Machine learning Solar radiation forecasting PV performance models

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Shale gas production evaluation framework based on data-driven models

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Petroleum Science 2023年第3期20卷 1659-1675页

作者： You-Wei He Zhi-Yue He Yong Tang Ying-Jie Xu Ji-Chang Long Kamy Sepehrnoori State Key Laboratoryofiland Gas Rseroir Geoogy and Exloitation Southwest Petroleum UniverstyChengdu00SichuanChina Department of Petroleum and Geosystems Engineering The University of Texas at AustinAustinTX78712UA

Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy *** decline of gas production has been widely observed in shale gas *** to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and *** work develops an integrated framework for evaluating shale gas well production based on data-driven ***,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production *** processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production ***,the random forest algorithm is used to forecast shale gas *** prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in ***-one wells are randomly selected to predict cumulative gas production using the optimal regression *** proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.

关键词： Shale gas Production evaluation Production prediction data-driven models Carbon neutrality

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