As the low-carbon economy continues to advance, New Energy Vehicles (NEVs) have risen to prominence in the automotive industry. The design and utilization of lithium-ion batteries (LIBs), which are core component of NEVs, are directly related to the...
A graph is a relational data structure suitable for representing non-Euclidean structured data. In recent years, graph neural networks (GNN) and their subsequent variants, which utilize deep neural networks to complete graph analysis and representati...
Capacity decline is the focus of traditional battery health estimation as it is a significant external manifestation of battery aging. However, it is difficult to depict the internal aging information in depth. To achieve the goal of deeper online di...
This study developed a novel data-driven indentation computation framework to characterize the indentation response and behavior by combining the reduced-order model (ROM), optimal technology, and indentation response curve. ROM was utilized to build...
An accurate assessment of the state of charge (SOC) of electric vehicle batteries is critical for implementing frequency regulation and peak shaving. This study proposes mechanism- and data-driven SOC fusion calculation methods. First, a second-order...
In order to ensure the stable operation of blast furnace production, it is necessary to keep abreast of the trends in the gas permeability index of the blast furnace. As one of the key parameters to be monitored in the process of blast furnace smelti...
Artificial intelligence (AI) is increasingly essential for optimizing energy systems, addressing the growing complexity of energy management, and supporting the integration of diverse renewable sources. This study systematically reviews AI-enabled mo...
Lap splices are the most commonly used method worldwide because they do not require specific equipment or skilled workers. However, lap splices incur high construction costs because of the long splice lengths required for large-diameter rebars in meg...
Hydroxyl-terminated polyether (HTPE) propellants are attractive in the weapons materials and equipment industry for their insensitive properties. Storage, combustion, and explosion of solid propellants are affected by their mechanical properties, so...
The gears of helicopter transmission system have strict requirements on machining accuracy, and the accurate prediction of tooth surface grinding force is the key to its manufacturing. The existing model simplifies the micro-contact behavior of the a...
Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digi...
Establishing a high-precision passenger flow prediction model is a critical and complex task for the optimization of urban rail transit systems. With the development of artificial intelligence technology, the data-driven technology has been widely st...
This paper presents a dynamic model for full-power converter permanent magnet synchronous wind turbines based on Physics-Informed Neural Networks (PINNs). The model integrates the physical dynamics of the wind turbine directly into the loss function,...
Automated mechanical transmission (AMT) is used as a soft starter in this paper. To improve the soft starting quality, a novel data-driven method is studied. By analyzing and comparing five common soft-starting acceleration curves, a segmented accele...
In order to effectively deal with the adverse effects of the randomness of photovoltaic output on the operation of combined heat and power (CHP) microgrids, this paper proposes an adaptive robust optimal scheduling strategy for CHP microgrids based o...
This paper provides a review of models commonly used over the years in the study of microscopic models of material corrosion mechanisms, data mining methods and the corrosion-resistant performance control of structural steels. The virtual process of...
To analyze the safety behavior of electric vehicles, mechanical simulation models of their battery cells are essential. To ensure computational efficiency, the heterogeneous cell structure is represented by homogenized material models. The required p...
Conventional grain monitoring systems often rely on isolated data points (e.g., point-based temperature measurements), limiting holistic condition assessment. This study proposes a novel Mechanism and Data Driven (MDD) framework that integrates physi...
Maintenance strategies such as condition-based maintenance and predictive maintenance of machines have gained importance in industrial automation firms as key concepts in Industry 4.0. As a result, online condition monitoring of electromechanical sys...
Micro-scale positioning techniques have become essential in numerous engineering systems. In the field of fluid mechanics, particle tracking velocimetry (PTV) stands out as a key method for tracking individual particles and reconstructing flow fields...
To enhance the stable performance of wind farm (WF) equivalent models in uncertain operating scenarios, a model-data-driven equivalent modeling method for doubly-fed induction generator (DFIG)-based WFs is proposed. Firstly, the aggregation-based WF...
The steel industry serves as a cornerstone of a nation’s industrial system, with sintering playing a pivotal role in the steelmaking process. In an effort to enhance the intelligence of the sintering process and improve production efficiency, n...
In modern process industries, precise process modeling plays a vital role in intelligent manufacturing. Nevertheless, both mechanistic and data-driven modeling methods have their own limitations. To address the shortcomings of these two modeling meth...
As an important basic material for modern industry, the performance and production energy consumption of medium and thick plates have an important impact on engineering quality, industry technological progress and economic benefits. However, traditio...
Aeroengine performance diagnosis technology is essential for ensuring flight safety and reliability. The complexity of engine performance and the strong coupling of fault characteristics make it challenging to develop accurate and efficient gas path...
Accurate and efficient CFD simulations are essential for a wide range of engineering and scientific applications, from resilient structural design to environmental analysis. Traditional methods such as RANS simulations often face challenges in captur...
Machine learning (ML) is becoming an established part of alloy research, offering new ways to link composition, processing routes, and microstructure with measured properties. In this work, recent studies using ML for predicting or optimizing alloy b...
As a fundamental element in industrial fluid transportation, the main pump fulfills an irreplaceable function in critical infrastructure, including the energy, water conservancy, petrochemical, and sewage treatment industries. As the core component o...
Drilling more efficiently and with less non-productive time (NPT) is one of the key enablers to reduce field development costs. In this work, we investigate the application of a data-driven optimization method called extremum seeking (ES) to achieve...
The growing use of lithium-ion batteries (LIBs) in electric vehicles has accelerated the need for efficient strategies to extend their lifespan through second-life applications, where retired batteries are repurposed for stationary storage and other...
Accurate prediction of endpoint carbon at the dynamic control stage in the converter is crucial for achieving smelting targets. Currently, there are two main methods for converter endpoint prediction: the data-driven method and the mechanism-based me...
To be able to provide appropriate services in social and human application contexts, smart cyber-physical systems (S-CPSs) need ampliative reasoning and decision-making (ARDM) mechanisms. As one option, procedural abduction (PA) is suggested for self...
Internet of Things (IoT) applications have led to exploding contextual data for predictive analytics and exploration tasks. Consequently, computationally data-driven tasks at the network edge, such as machine learning models’ training and infer...
Against the backdrop of digital transformation, this study constructs an analytical framework for the influence mechanism of the data-driven dynamic capabilities of foreign trade SMEs from the perspective of digital intelligence immunity, aiming to c...
To obtain the mechanical behavior response of the material under loading, a data-driven construction method of material mechanical behavior model is proposed, which is universal for predicting the mechanical behavior of any material under different l...
Digital transformation of bridge engineering utilizes distinct modeling techniques to combine domain knowledge with digital information modeling. In particular, a long-span bridge is a key link in a transportation network, with more than 100 years of...
The construction of mega infrastructure projects has the characteristics of repeatability, long duration, and high complexity. Therefore, it is particularly important to implement dynamic decision-making in projects. This study takes data-driven deci...
Toward the prognostic and health management of mechanical systems, we propose and validate a novel effective, data-driven fault diagnosis method. In this method, we develop a trained subtracted spectrogram, the so called critical information map (CIM...
A one-size-fits-all approach to fall risk assessment in older adults has critical limitations. This study aimed to overcome this by identifying distinct gait phenotypes and their specific fall mechanisms using foot-mounted IMU waveform data from 146...
Ultrasonic flow meters are crucial measuring instruments in natural gas transportation pipeline scenarios. The collected flow velocity data, along with the operational conditions data, are vital for the analysis of the metering performance of ultraso...
The advancement of the Internet of Things (IoT) as a solution in diverse application domains has nurtured the expansion in the number of devices and data volume. Multiple platforms and protocols have been introduced and resulted in high device ubiqui...
Real-time decision making needs evaluating quantities of interest (QoI) in almost real time. When these QoI are related to models based on physics, the use of Model Order Reduction techniques allows speeding-up calculations, enabling fast and accurat...
The classical thermal cycle in a Hot Dip Galvanizing (HDG) line has four steps: heating, soaking, cooling, and aging. The furnace of an ArcelorMittal HDG line was revamped to increase its heating capacity. This new configuration without the soaking s...
A recently proposed, game-changing transformative energetics concept based on predictive synthesis and preprocessing at the nanoscale is considered as a pathway towards the development of the next generation of high-end nanoenergetic materials for fu...
In the deep well drilling process in the Fukang Depression of the Eastern Junggar Basin, rock fracturing issues and low rate of penetration (ROP) have posed significant challenges to drilling efficiency. Accurate predictions of ROP prior to drilling...
To maintain the life of building materials, it is critical to understand the hygrothermal transfer mechanisms (HTM) between the walls and the layers inside the walls. Due to the extreme instability of weather data, the actual data models of the HTM&m...
Physics-informed neural networks (PINNs) are gaining traction as surrogate models for fluid dynamics problems, combining machine learning with physics-based constraints. This study investigates the impact of labeled data on the performance of paramet...
The assembly, as the core part of low-pressure rotor manufacturing, is of great importance to ensure its unbalance. Low-voltage rotor assembly is a multi-process process influenced by the quality of part machining, assembly process, and assembly qual...
Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic prop...
Product quality control in chemical processes faces challenges from dynamic non-stationary data, underutilized variable spatial correlations, and overreliance on prior knowledge. This paper addresses these issues by proposing an enhanced Spatio-Tempo...
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