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data-driven framework with theoretical modelling to evaluate fuel savings through air lubrication system

OCEAN ENGINEERING 2025年 316卷

作者： Kim, Daehyuk Rhee, Shin Hyung Seoul Natl Univ Res Inst Marine Syst Engn Seoul South Korea Seoul Natl Univ Dept Naval Architecture & Ocean Engn Seoul South Korea

This study introduces a data-driven and theoretical framework to evaluate the Air Lubrication System (ALS) in maritime operations, analyzing its impact on fuel consumption under varied conditions. The operational data from three 325k series ships is used to examine ALS performance over operational scenarios, including different drafts and speeds. The Partial Cavity Drag Reduction (PCDR) model consistently outperformed the Air Layer Drag Reduction (ALDR) model, providing an average fuel consumption savings of 10-15%. Notably, ALS demonstrated enhanced effectiveness at operational speeds above the critical threshold speed of about 9 knots, defined as the minimum speed at which ALS begins to yield net positive energy savings. Additionally, increased draft conditions significantly influenced ALS efficiency;higher drafts increased the compressor power required, reducing the net savings, with net power savings of up to about 4000 kW (approximately 25% reduction in power consumption). This comprehensive evaluation highlights the pivotal role of operational speed and draft in optimizing ALS performance. The integration of data-driven analytics with theoretical insights significantly strengthens the evaluation process, offering a powerful approach that substantiates the ALS's potential to reduce fuel consumption and meet stringent environmental standards, thus providing crucial operational insights for maritime operators.

关键词： Air lubrication system (ALS) data-driven framework Theoretical modelling Fuel consumption Operational data Energy saving technology

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Unraveling the Complexity of Divalent Hydride Electrolytes in Solid-State Batteries via a data-driven framework with Large Language Model

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ANGEWANDTE CHEMIE-INTERNATIONAL EDITION 2025年 e202506573页

作者： Wang, Qian Yang, Fangling Wang, Yuhang Zhang, Di Sato, Ryuhei Zhang, Linda Cheng, Eric Jianfeng Yan, Yigang Chen, Yungui Kisu, Kazuaki Orimo, Shin-ichi Li, Hao Tohoku Univ Adv Inst Mat Res WPI AIMR Sendai 9808577 Japan Sichuan Univ Inst New Energy & Low Carbon Technol Chengdu 610207 Peoples R China Univ Tokyo Dept Mat Engn 7-3-1 Hongo Bunkyo Ku Tokyo 1138656 Japan Minist Educ Engn Res Ctr Alternat Energy Mat & Devices Nanjing Peoples R China Shibaura Inst Technol Coll Engn Tokyo 1358548 Japan Tohoku Univ Inst Mat Res IMR Sendai 9808577 Japan

Solid-state electrolytes (SSEs) are essential for next-generation energy storage technologies. However, the exploration of divalent hydrides is hindered by complex ionic migration mechanisms and reliance on "trial-and-error" methodologies. Conventional approaches, which focus on individual materials and predefined pathways, remain inefficient. Herein, we present a data-driven artificial intelligence framework that integrates a comprehensive SSE database with large language models and ab initio metadynamics (MetaD) simulations to accelerate the discovery of hydride SSEs. Our study reveals that hydrides incorporating neutral molecules have great potential, with MetaD revealing novel "two-step" ion migration mechanisms. Predictive models developed using both experimental and computational data accurately forecast ionic migration activation energies for various types of hydride SSEs. In particular, some SSEs with carbon-containing neutral molecules exhibit notably low activation energy, with barriers as low as 0.62 eV. This framework enables the rapid identification of optimized SSE candidates and establishes a transformative tool for advancing sustainable energy storage technologies.

关键词： AI for materials Big data analytics data-driven framework Hydride materials Solid-state electrolyte

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A generalized incremental harmonic balance method by combining a data-driven framework for initial value selection of strongly nonlinear dynamic systems

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INTERNATIONAL JOURNAL OF NON-LINEAR MECHANICS 2025年 169卷

作者： Li, Y. L. Huang, J. L. Zhu, W. D. Sun Yat Sen Univ Dept Appl Mech & Engn Guangzhou 510275 Peoples R China Univ Maryland Baltimore Cty Dept Mech Engn 1000 Hilltop Circle Baltimore MD 21250 USA

The incremental harmonic balance (IHB) method is a semi-analytical and semi-numerical method that consists of the harmonic balance process and the Newton-Raphson iteration process, which can accurately obtain solutions of strongly nonlinear dynamic systems, and has been successfully applied to many practical problems. However, it is difficult to choose proper initial values for the Newton-Raphson iteration process, especially for strongly nonlinear dynamic systems, which can cause divergence of the IHB method. A novel generalized IHB method by combining a data-driven framework to obtain feasible initial values for strongly nonlinear dynamic systems is proposed in this work. In the proposed data-driven framework, an artificial neural network (ANN) is trained to learn the mapping between small system parameters of a weakly nonlinear dynamic system and corresponding solutions of the IHB method. The amount of data required for training is determined by the number of system parameters, which is usually only a few hundreds to a few thousands, making the training process very expeditious. Once the trained ANN receives other system parameters of a nonlinear dynamic system, it can immediately output a set of feasible initial values for the IHB method, which can make the IHB method quickly converge. Results from extensive testing indicate that the mapping learned by the ANN effective not only for weakly nonlinear dynamic systems that are characterized by smaller system parameters, but also for strongly nonlinear dynamic systems inmost cases that involve larger system parameters. During the testing for strongly nonlinear dynamic systems, successful convergences generate new training data. These data can be leveraged to further fine-tune the ANN to yield an improved ANN, which allows continuous refinement of the ANN and enhances prediction of initial values for strongly nonlinear dynamic systems. With the introduction of this simple yet highly efficient data-driven initial valu

关键词： Strongly nonlinear dynamic system Incremental harmonic balance method Initial values data-driven framework Artificial neural network

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data-driven framework for predicting ground temperature during ground freezing of a silty deposit

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GEOMECHANICS AND ENGINEERING 2021年第3期26卷 235-251页

作者： Pham, Khanh Park, Sangyeong Choi, Hangseok Won, Jongmuk Int Univ Dept Civil Engn Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Korea Univ Sch Civil Environm & Architectural Engn Seoul 02841 South Korea Univ Ulsan Dept Civil & Environm Engn Ulsan 44610 South Korea

Predicting the frozen zone near the freezing pipe in artificial ground freezing (AGF) is critical in estimating the efficiency of the AGF technique. However, the complexity and uncertainty of many factors affecting the ground temperature cause difficulty in developing a reliable physical model for predicting the ground temperature. This study proposed a data-driven framework to accurately predict the ground temperature during the operation of AGF. Random forest (RF) and extreme gradient boosting (XGB) techniques were employed to develop the prediction model using the dataset of a field experiment in the silty deposit. The developed ensemble models showed relatively good performance (R-2 > 0.96), yet the XGB model showed higher accuracy than the RF model. In addition, the evaluated mutual information and importance score revealed that the environmental attributes (ambient temperature, surface temperature, humidity, and wind speed) can be critical in predicting ground temperature during the AFG operation. The prediction models presented in this study can be utilized in evaluating freezing efficiency at the range of geotechnical and environmental attributes.

关键词： artificial ground freezing data-driven framework extreme gradient boosting mutual information random forest

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data-driven framework for the prediction of cutting force in turningInspec keywordsOther keywords

IET COLLABORATIVE INTELLIGENT MANUFACTURING

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IET COLLABORATIVE INTELLIGENT MANUFACTURING 2020年第2期2卷 87-95页

作者： Chatterjee, Kaustabh Zhang, Jian Dixit, Uday Shanker Indian Inst Technol Guwahati Dept Mech Engn Gauhati 781039 Assam India Shantou Univ Shantou Inst Light Ind Equipment Res Shantou 515063 Peoples R China

Cutting force is one of the most important parameters for assessing power consumption and tool wear. This work attempts to utilise the concept of data analytics for collecting and building a data warehouse in a cloud-based platform called Central database Repository (CDR), which can share the information about the machining forces in turning operations directly to the process planner or shop floor operator. The estimation of cutting force is accomplished based on the input of process parameters. CDR uses multiple-linear regression on the data collected and stored in the data bank. Uncertainties in machining operations are taken care of in a separate cloud-based platform known as mini-repository. This study estimates the interval of cutting force with a certain level of confidence and a new concept of dynamic reliability is attached to it. The information from the CDR can be accessed from anywhere across the globe. There is a provision to update the database based on the feedback and filter out unnecessary data by evaluating Cook's distance. The proposed framework is explained through illustrative case studies.

关键词： cloud computing data analysis power consumption data warehouses production engineering computing turning (machining) regression analysis cutting data-driven framework cutting force data analytics data warehouse dynamic reliability mini-repository cloud-based platform machining operations data bank process parameters shop floor operator turning operations machining forces CDR Central database Repository

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data-driven framework to improve collaborative human-robot flexible manufacturing applications 47

Data-driven framework to improve collaborative human-robot f...

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47th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Sibona, Fiorella Indri, Marina Politecn Torino Dipartimento Elettron & Telecomunicaz Corso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (纸本)9781665435543

The manufacturing assembly lines of the future are foreseen to dismiss fully unmanned systems in favour of anthropocentric solutions. However, bringing in the human complexity leads to modeling and control questions that only data can answer. Moreover, many human-robot collaborative applications in flexible manufacturing involve manipulator cobots, whereas little attention is given to the role of mobile robots. This work outlines a data-driven framework, which is the core of a brand new project to be fully developed in the very next future, to let human-robot collaborative processes overcome the barriers to successful interaction, leveraging mobile and fixed-base robots.

关键词： Flexible manufacturing Human-robot collaboration data-driven framework Mobile robotics

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Evaluating Water Withdrawals for Regional Water Management Under a data-driven framework

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Chinese Geographical Science 2022年第3期32卷 521-536页

作者： LU Yan WANG Jinxin LIU Jianzhong QIN Fen WANG Jiayao School of Geoscience and Technology Zhengzhou UniversityZhengzhou 450001China Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Ministry of EducationHenan UniversityZhengzhou 475000China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan UniversityZhengzhou 475000China College of Geography and Environmental Science Henan UniversityKaifeng 475004China

With an increase in population and economic development,water withdrawals are close to or even exceed the amount of water available in many regions of the *** water withdrawals could help water planners improve the efficiency of water use,water resources allocation,and management in order to alleviate water ***,minimal information has been obtained on how water withdrawals have changed over space and time,especially on a regional or local *** research proposes a data-driven framework to help estimate county-level distribution of water *** this framework,spatial statistical methods are used to estimate water withdrawals for agricultural,industrial,and domestic purposes in the Huaihe River watershed in China for the period 1978–*** water withdrawals were found to have more than doubled,from 292.55×10^(8)m^(3) in 1978 to 642.93×10^(8)m^(3) in 2009,and decreased to 602.63×10^(8)m^(3) in *** water increased from 208.17×10^(8)m^(3) in 1978 to 435.80×10^(8)m^(3) in 2009 and decreased to 360.84×10^(8)m^(3) in *** and domestic water usage constantly increased throughout the 1978–2018 *** 1978,industrial and domestic demands were 20.35×10^(8)m^(3) and 60.04×10^(8)m^(3),respectively,and up until 2018,the figures were 105.58×10^(8)m^(3) and 136.20×10^(8)m^(3).From a spatial distribution perspective,Moran’s I statistical results show that the total water withdrawal has significant spatial autocorrelation during 1978–*** overall trend was a gradual increase in 1978–2010 with withdrawal beginning to decline in 2010–*** results of Getis-Ord G_(i)^(*)statistical calculations showed spatially contiguous clusters of total water withdrawal in the Huaihe River watershed during1978–2010,and the spatial agglomeration weakened from 2010 to *** study provides a data-driven framework for assessing water withdrawals to enable a deeper understanding of competing water use among economic sectors a

关键词： water withdrawal data-driven framework spatial data analysis water coefficient Huaihe River watershed,China

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Secure data Transmission and Trustworthiness Judgement Approaches Against Cyber-Physical Attacks in an Integrated data-driven framework

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第12期52卷 7799-7809页

作者： Jiang, Yuchen Wu, Shimeng Yang, Hongyan Luo, Hao Chen, Zhiwen Yin, Shen Kaynak, Okyay Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China Ind Intelligence Academician Studio Peng Cheng Lab Shenzhen 518066 Peoples R China Beijing Univ Technol Dept Control Sci & Engn Beijing 100124 Peoples R China Cent South Univ Dept Automat Changsha 410083 Peoples R China Norwegian Univ Sci & Technol Fac Engn Dept Mech & Ind Engn N-7491 Trondheim Norway Bogazici Univ Dept Elect & Elect Engn TR-34342 Istanbul Turkey Turkish Acad Sci TR-06670 Ankara Turkey

Threats of cyberattacks have penetrated from disclosing critical user information to destroying/manipulating industrial control systems. Study on data security during network transmission has raised increasing attention in the systems and control community, which is found very necessary and timely in the context of Industry 4.0. In most existing approaches, the protection of the transmitted data from eavesdropping attacks and the detection of malicious integrity attacks are usually carried out separately. In this study, an integrated data-driven framework applicable at the control level is proposed to deal with secure transmission and attack detection simultaneously. In the framework, a secure correlation-based encryption/decryption approach and a trustworthiness judgement approach are proposed. Comprehensive discussions are made regarding the analysis of the sensitivity to attacks, the introduced time delay, and the design degree-of-free. Executable algorithms are presented, corresponding to which hardware is modularized and can work standalone independent from the configuration of the monitoring and control systems or any third-party authentication agencies. Evaluation results on a simulated two-area frequency-load control power grid system are provided to show the effectiveness and performance of the proposed approaches.

关键词： Sensors Security Monitoring Control systems Eavesdropping data communication Safety Attack detection cyber-physical system (CPS) data-driven framework industrial security

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A data-driven framework to predict ignition delays of straight-chain alkanes

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COMBUSTION THEORY AND MODELLING 2022年第5期26卷 943-967页

作者： Rana, Pragneshkumar Rajubhai Narayanaswamy, Krithika Ambikasaran, Sivaram Indian Inst Technol Madras Dept Mech Engn Chennai Tamil Nadu India Indian Inst Technol Madras Dept Math Chennai Tamil Nadu India

Ignition delay time (IDT) is an important global combustion property that affects the thermal efficiency of the engine and emissions (particularly NOX). IDT is generally measured by performing experiments using Shock-tube and Rapid Compression Machine (RCM). The numerical calculation of IDT is a computationally expensive and time-consuming process. Arrhenius type empirical correlations offer an inexpensive alternative to obtain IDT. However, such correlations have limitations as these typically involve ad-hoc parameters and are valid only for a specific fuel and particular range of temperature/pressure conditions. This study aims to formulate a data-driven scientific way to obtain IDT for new fuels without performing detailed numerical calculations or relying on ad-hoc empirical correlations. We propose a rigorous, well-validated data-driven study to obtain IDT for new fuels using a regression-based clustering algorithm. In our model, we bring in the fuel structure through the overall activation energy (Ea) by expressing it in terms of the different bonds present in the molecule. Gaussian Mixture Model (GMM) is used for the formation of clusters, and regression is applied over each cluster to generate models. The proposed algorithm is used on the ignition delay dataset of straight-chain alkanes (C-n H2n+2 for n = 4 to 16). The high level of accuracy achieved demonstrates the applicability of the proposed algorithm over IDT data. The algorithm and framework discussed in this article are implemented in python and made available at https://***/10.5281/zenodo.5774617.

关键词： Ignition delay machine learning data-driven framework fuel prediction clustering

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Monitoring exhaust air temperature and detecting faults during milk spray drying using data-driven framework

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DRYING TECHNOLOGY 2023年第12期41卷 1991-2011页

作者： Wagh, Nivedita Agashe, S. D. Savitribai Phule Pune Univ Coll Engn Pune Dept Instrumentat & Control Pune India Coll Engn Pune Dept Instrumentat & Control Wellesley Rd Pune 411005 India

The advanced data analytics platform bridges the gap between industrial automation technology and new cloud-based technology. The information on the implementation of a data analytics platform to convert huge data into valuable information and use it to serve the scheduled maintenance of the components involved in the food processing industry is rarely reported in the literature. This work reports a data-driven framework for prediction and fault detection in key performance parameters for a milk spray drying process plant. The framework consists of different data analysis methods and it helps to take decisions about the selection of key performance parameters involved in improving the spray drying thermal efficiency. The neural network-based NARX model demonstrates a better performance than the linear models in the prediction of cyclone exit air temperature which is the key performance parameter in spray drying as it governs thermal efficiency. The performance of the predictive model is validated using RMSE. The ML-based classification methods are also used in the present work to classify the different faults and the decisions regarding the maintenance of the components responsible for the faults. The performance of these models was verified using a confusion matrix. It is proposed that the decision tree classifier and random forest classifier are best suitable for fault finding as their accuracy is highest at 99.83%.

关键词： Spray dryer data-driven framework key parameter prediction fault diagnosis

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