Potential Energy Field Research Articles

Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field

In order to ensure the safety of navigation in a bridge area, and based on the theory of the safety potential field, a method of ship safety assessment and early warning in an inland river bridge area is proposed. Firstly, the risk elements associated with ship collisions in a bridge area are classified. Secondly, these risks are quantified using the potential energy field, the boundary potential field and the behavioural field, and then the ship state under the influence of wind flow, predicted by the Kalman filter, is quantified using the kinetic energy field. Then, the above four potential energy fields are merged to obtain a superposition field, and the magnitude of the instantaneous risk in the bridge area is obtained based on its magnitude. Finally, the change of field strength values under different moments is used for early warning. The results of the simulation of a ship passing through the piers of the Baijusi Bridge show that the model can effectively quantify the risk of a ship–bridge collision in the inland bridge area and provide real-time warning of the risk of a ship–bridge collision in the bridge area, which is of great significance for improving the safety of the inland bridge area.

An equivalent surface model bridging intermolecular interactions and the normalization of substrate wettability

A liquid deposited on a solid surface exhibits various wetting properties, primarily influenced by solid–liquid intermolecular interactions. Achieving a well-estimation of wetting behaviors on textured surfaces remains challenging because the influence of surface textures on intermolecular interactions is unclear. Here, an equivalent surface model is proposed to unify the wettability of both plane and textured substrates through intermolecular interactions. We show that by incorporating intermolecular interactions, lattice structures, and surface topographies, the substrate wettability can be tailored in an equivalent plane with the same average Lennard–Jones potential energy field E. The wettability of one substrate (plane or patterned) is transferred from non-wetting, partial wetting to complete wetting by adjusting the potential well depth of E, εa. With enhancing εa, complete wetting is achieved under the critical potential well depth of E, εac. Before εac, the spreading radius grows as R ∼ t1/1.47 until reaches equilibrium. In this scenario, the equilibrium contact angle θ and spreading factor β are strongly dependent on εa, and cos θ is linear to εa. When exceeding εac, complete wetting is achieved, and droplets spread with a precursor film as R ∼ t1/2.3 latterly. This model builds a bridge to link intermolecular interactions and substrate wettability normalization. This strategy offers a framework for substrate surface design and wettability manipulation, catering to applications such as photoresist-drop dispensing in nanoimprint lithography, surface design for grating antifouling, and anti-icing on aircraft surfaces.

The Sun’s Non-Potential Corona over Solar Cycle 24

The global magnetic field in the solar corona is known to contain free magnetic energy and magnetic helicity above that of a current-free (potential) state. But the strength of this non-potentiality and its evolution over the solar cycle remain uncertain. Here we model the corona over Solar Cycle 24 using a simplified magneto-frictional model that retains the magnetohydrodynamic induction equation but assumes relaxation towards force-free equilibrium, driven by solar surface motions and flux emergence. The model is relatively conservative compared to some others in the literature, with free energy approximately 20 – 25% of the potential field energy. We find that unsigned helicity is about a factor 10 higher at Maximum than Minimum, while free magnetic energy shows an even greater increase. The cycle averages of these two quantities are linearly correlated, extending a result found previously for active regions. Also, we propose a practical measure of eruptivity for these simulations, and show that this increases concurrently with the sunspot number, in accordance with observed coronal mass ejection rates. Whilst shearing by surface motions generates 50%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$50\\%$\\end{document} or more of the free energy and helicity in the corona, we show that active regions must emerge with their own internal helicity otherwise the eruptivity is substantially reduced and follows the wrong pattern over time.

Vehicle group identification and evolutionary analysis using vehicle trajectory data

Vehicles often move forward in groups on the highways, especially when speed and density are high simultaneously. Abnormal maneuvers of a vehicle in a group influence multiple vehicles surrounding it, potentially leading to traffic accidents. We propose an approach to identify vehicle groups and analyse the factors influencing their evolutions using vehicle trajectory data. The proposed approach quantifies the interactions between neighboring vehicles based on the potential energy field, represents the interactive relationships among multiple vehicles using a multi-vehicle interaction network, and adopts the process of sub-network segmentation to identify vehicle groups. A random-parameter logistic regression (RPLG) model is developed to examine the influence of vehicle group features on vehicle group split. The effectiveness of the proposed approach is demonstrated in a case study using a real-world dataset. The case study reveals that: (1) the interaction strengths between vehicles tend to increase with increasing speed, (2) the interaction strength between a vehicle and its preceding vehicle is the largest, while the interaction strengths between a vehicle and its vehicles on its sides are the lowest, and (3) higher longitudinal and lateral speeds, larger fluctuations in longitudinal speeds and accelerations, larger group size, larger distances between vehicles, more lanes occupied by a vehicle group, and higher vehicle interactions significantly increase the probability of vehicle group split. The findings of this study can potentially support the traffic management and development of autonomous driving technology in connected vehicle environments.

Research on mode adjustment control strategy of upper limb rehabilitation robot based on fuzzy recognition of interaction force

In the process of robot-assisted training for upper limb rehabilitation, a passive training strategy is usually used for stroke patients with flaccid paralysis. In order to stimulate the patient's active rehabilitation willingness, the rehabilitation therapist will use the robot-assisted training strategy for patients who gradually have the ability to generate active force. This study proposed a motor function assessment technology for human upper-limb based on fuzzy recognition on interaction force and human-robot interaction control strategy based on assistance-as-needed. A passive training mode based on the calculated torque controller and an assisted training mode combined with the potential energy field were designed, and then the interactive force information collected by the three-dimensional force sensor during the training process was imported into the fuzzy inference system, the degree of active participation σ was proposed, and the corresponding assisted strategy algorithms were designed to realize the adaptive adjustment of the two modes. The significant correlation between the degree of active participation σ and the surface electromyography signals (sEMG) was found through the experiments, and the method had a shorter response time compared to a control strategy that only adjusted the mode through the magnitude of interaction force, making the robot safer during the training process.

Parameter-Free and Electron Counting Satisfied Material Representation for Machine Learning Potential Energy and Force Fields.

We proposed a parameter-free volume element representation that satisfies the electron counting model and obtains accurate machine learning potential energy and direct force fitting of randomly perturbed hexagonal BN. Our method preserves permutational, translational, and rotational invariance and can be extended to three-dimensional systems, verified by a system of bulk Si. As a result, we obtained 0.57 meV/atom potential energy root mean squared error (RMSE) and 59 meV/Å force RMSE for perturbed bulk BN systems and 0.43 meV/atom potential energy RMSE and 36 meV/Å force RMSE for perturbed Si systems. In addition, an unbiased perturbation-based data set construction scheme is introduced and a continuous population distribution is obtained with a training data set of 4500, which is about 1 order of magnitude smaller than standard methods based on first-principles molecular dynamics simulations and saves a large amount of computing resources. General validity of our model is verified by structure optimization, molecular dynamics simulations, and extrapolations.

Investigation of the importance of criteria in potential wind farm sites via machine learning algorithms

Wind energy has received greater attention than other energy resources due to its superior economics, low greenhouse gas emissions, and limitless wind resources. As a result, wind energy capacity has significantly increased, and the selection of the best locations for wind farms is an issue that has received extensive research. A significant step toward environmentally responsible land use planning is the site suitability assessment for the placement of wind farms. This study was conducted to determine the best locations for wind farms and to prioritize different locations and alternatives in the West of Turkey by using Maximum Entropy (MaxEnt) and Logistic Regression (LR) Methods based on Geographic Information Systems (GIS). Eight criteria were selected for creating the suitability map: air density, power density, wind speed, capacity factor, elevation, slope, aspect, and land use. Both methods were effective at choosing locations for wind farms because all the results were statistically significant in the consistency tests. MaxEnt calculated the potential wind energy fields with high accuracy and reliability with 0.915 AUC and LR multiple R square values of 0.883. Compared to the current installed power values, the MaxEnt analysis results were more consistent with the recent status. İzmir has been calculated as the province with the highest potential for wind energy area of 663 km2 by MaxEnt and 620.4 km2 by LR.

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes.

We present an efficient method to compute diffusion coefficients of multiparticle systems with strong interactions directly from the geometry and topology of the potential energy field of the migrating particles. The approach is tested on Li-ion diffusion in crystalline inorganic solids, predicting Li-ion diffusion coefficients within 1 order of magnitude of molecular dynamics simulations at the same level of theory while being several orders of magnitude faster. The speed and transferability of our workflow make it well-suited for extensive and efficient screening studies of crystalline solid-state ion conductor candidates and promise to serve as a platform for diffusion prediction even up to the density functional level of theory.

The effects of abrasive moving speed and motion mode on the thinning mechanism of SiC in three-body contact

Three-body contact is the main contact type in polishing process and leads to a different thinning mechanism than the two-body contact. Molecular dynamics simulation is employed to investigate the thinning mechanism of 3C-SiC substrate in three-body contact. The thinning mechanisms of 3C-SiC under different moving speeds and motion modes of diamond abrasive are compared. Through the analysis of force, temperature, potential energy, stress distribution and atomic flow field, the causes of different thinning mechanisms are explained. It is found that the influence of moving speed is mainly reflected in the temperature rise of substrate when the motion mode of abrasives is the same. The changes of motion mode will significantly alter the stress distribution, which is closely related to the damage depth and atomic flow field. When the feed speed is the same, increasing the self-rotation speed of abrasives can reduce concentration zone of hydrostatic stress and then reduce the damage depth. The self-rotation of abrasives will also change the distribution of von Mises stress, resulting in the different displacement directions of 3C-SiC atoms. Dislocations are more easily generated when the displacement direction of SiC atoms is the same. The moving speed of abrasive is found to have little effect on the thinning mechanism, while the motion mode of abrasive will significantly change the thinning mechanism.

Traffic Flow Prediction Based on Spatiotemporal Potential Energy Fields

Traffic flow prediction is a fundamental problem in spatiotemporal data mining. Most of the existing studies focuses on designing statistical models to fit historical traffic data, which are purely data-driven approaches and fail to reveal the underlying mechanisms of urban traffic. To address this issue, we propose the spatiotemporal potential energy field model (ST-PEF+), which applies the field theory for human mobility to interpret the underlying mechanisms of urban traffic, and introduces the theory into data-driven deep learning models. ST-PEF+ consists of a PEF extraction module and a data-driven module. Inspired by the field theory for human mobility, the PEF extraction module adopts an algorithm to decompose the grid-based traffic flow graph into several polytree-based potential energy fields (PEFs), where traffic flows from high potential locations to low potential locations, just as water is driven by the gravity field. We also provide a theoretical analysis to ensure that the polytree decomposition algorithm can decompose any traffic flow graph. In the data-driven module, ST-PEF+ learns a spatiotemporal deep learning model to predict the dynamics of PEFs. The model adopts correlation-adaptive neural network structures, which consists of a temporal component for temporal correlations and a spatial component for spatial correlations. The temporal component employs a GRU and DCN combined structure to capture both short-term autocorrelation and long-term repeating patterns of PEFs. The spatial component extends the GAT using weighted directed attention to model the asymmetric spatial structure in PEFs. The prediction results of traffic flow are finally derived from PEFs that are predicted by the spatiotemporal deep learning model. We conduct extensive evaluations on three real-world traffic datasets. The results show that our model outperforms the state-of-the-art baselines. In addition, case studies confirm that the PEFs learned in our framework can reveal the underlying mechanisms of urban traffic, thus improving the model interpretability.

Insights into influence of CH4 on CO2/N2 injection into bituminous coal matrix with biaxial compression strain in molecular terms

Under the influence of strong mining disturbances in coal mines, the coal gradually deforms, which affects the extraction of coalbed methane (CBM). In this study, the microscopic mechanism of CH4 affecting CO2/N2 injection into a bituminous coal matrix with biaxial compression strain was investigated by analyzing its microporous structure, adsorption isotherm, CH4 content effect, strain effect, adsorption density, intermolecular adsorption potential energy and adsorption energy using grand canonical Monte Carlo simulation, molecular dynamic simulation and density functional theory. The results show that with increasing (negative) compression strain (0.00 → −0.20), the distribution range of the gas adsorption potential energy field decreases, with a deviation order of CO2 > CH4 > N2. Also the Henry constant of CO2 increases while those of CH4 and N2 stabilize. The orders of influence of CH4 content and strain on adsorption are N2 > CO2 > CO2/N2 and CO2 > CO2/N2 > N2, respectively. Additionally, the potential energy field distribution ranges of CO2 and N2 injections decrease with increasing CH4 content. More importantly, CH4 is adsorbed in the Up orientation, while CO2 and N2 are adsorbed parallel to the aromatic groups on the pore surface of coal matrix. Also, pre-adsorbed CH4 increases the negative adsorption energy of CO2 and N2 by 45.37–50.26%, thereby effectively inhibiting their injections. This research lays the foundation for improving CBM recovery efficiency in the deformation zone of CBM reservoirs with realistic exploitation.

DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations

Molecular dynamics (MD) has long been the de facto choice for simulating complex atomistic systems from first principles. Recently deep learning models become a popular way to accelerate MD. Notwithstanding, existing models depend on intermediate variables such as the potential energy or force fields to update atomic positions, which requires additional computations to perform back-propagation. To waive this requirement, we propose a novel model called DiffMD by directly estimating the gradient of the log density of molecular conformations. DiffMD relies on a score-based denoising diffusion generative model that perturbs the molecular structure with a conditional noise depending on atomic accelerations and treats conformations at previous timeframes as the prior distribution for sampling. Another challenge of modeling such a conformation generation process is that a molecule is kinetic instead of static, which no prior works have strictly studied. To solve this challenge, we propose an equivariant geometric Transformer as the score function in the diffusion process to calculate corresponding gradients. It incorporates the directions and velocities of atomic motions via 3D spherical Fourier-Bessel representations. With multiple architectural improvements, we outperform state-of-the-art baselines on MD17 and isomers of C7O2H10 datasets. This work contributes to accelerating material and drug discovery.

Quantum Physisorption of Gas in Nanoporous Media: A New Perspective

Although numerous investigations have revealed the gas physisorption characteristics of porous media, the essence of physisorption behavior of gas within nanoscale space is still indistinct. We speculated that the physisorption behavior of a complex molecular system (e.g., CH4 and CO2) exhibits a quantum effect due to the confinement effect of nanopores. Gas molecules occur in varied orbitals following certain probabilities and, therefore, have separate energy levels inside a nanoscale space. Energy level transition of molecules from excited state to ground state triggers gas physisorption, while non-uniform spatial distribution of energy-quantized molecules within nanopores dominates the gas physisorption behavior. The spatial distribution of gas molecules can be adjusted by temperature, pressure and potential energy field. Based on the quantum effect, we developed a physisorption equation from the perspective of quantum mechanics to re-understand the basic principles of gas physisorption within nanopores.

An RRT-Dijkstra-Based Path Planning Strategy for Autonomous Vehicles

It is challenging to plan paths for autonomous vehicles on half-structured roads because of the vast planning area and complex environmental constraints. This work aims to plan optimized paths with high accuracy and efficiency. A two-step path planning strategy is proposed. The classic planning problem is divided into two simpler planning problems: reduction problems for a vast planning area and solving problems for weighted directed graphs. The original planning area is first reduced using an RRT (Rapidly Exploring Random Tree) based guideline planner. Second, the path planning problem in the smaller planning region is expanded into a weighted directed graph and transformed into a discrete multi-source cost optimization problem, in which a potential energy field based discrete cost assessment function was designed considering obstacles, lanes, vehicle kinematics, and collision avoidance performances, etc. The output path is then obtained by applying a Dijkstra optimizer. Comparative simulations are conducted to assess the effectiveness of the proposed strategy. The results shows that the designed strategy balances efficiency and accuracy with enough planning flexibility and a 22% improvement in real-time performance compared to the classic Lattice planner, without significant loss of accuracy.

A microscopic computational model based on particle dynamics and evolutionary algorithm for the prediction of gas solubility in polymers

Solubility is one of the key physicochemical properties of foaming materials and plastic membrane materials, which are commonly used in the processing, preparation, and modification of various new materials. It is challenging to develop a good dissolution behavior calculation model. A novel particle dynamics evolutionary algorithm (DP-PD-EA) is developed to simulate the movement of molecules in the process of dissolution at the microscopic scale. DP-PD-EA deeply combines particle dynamics with evolutionary algorithm so that particles evolve iteratively under the action of the potential energy field. The prediction experiments of the solubility of supercritical carbon dioxide (SCCO2) in three polymers confirmed that DP-PD-EA had the better prediction performances than other models. The proposed model can be used in the solubility calculation of foaming materials and has theoretical and practical values in many fields, such as the prediction of physical/chemical properties and the calculation of multi-scale theory.

Multi-Vehicle Interactive Lane-Changing Velocity Change Model Based on Potential Energy Field

To quantitatively analyze velocity change in the interaction of multiple vehicles during the lane-changing process, this study defines the concepts of driving constraint region and multi-vehicle interaction region (MVIR) by analyzing the potential energy field. The attraction and repulsion effects of the target vehicle and surrounding vehicles are attributed to the area change in the overlapping parts of their MVIRs. A multi-vehicle interactive lane-changing method considering MVIR is proposed. A velocity change model of multi-vehicle interactive lane-changing is established to reveal the change regularity of velocity under multi-vehicle interactive lane-changing. The model parameters are calibrated using a P3-DT Beidou high-precision positioning direction finding receiver to collect vehicle coordinates and velocity. The error of vehicle velocity variation was less than 11%, which verified the validity of the velocity change model. The research results can guide drivers in completing the lane-changing process safely and quickly, and provide theoretical support for micro-traffic flow simulation, road traffic safety proactive prevention and control, multi-vehicle lane-changing rules, and multi-agent simulation platform construction.

Study on sintering mechanism and mechanical properties of Fe–Ni elastocaloric refrigeration alloy through molecular dynamics simulation

In this paper, the embedded atom method (EAM) potential function was introduced to study the sintering mechanism and the mechanical properties of Fe14.6Ni (at%) elastocaloric refrigeration alloys through molecular dynamics (MD) simulations. The analysis of the energy field showed that sintering mechanism was atoms behaviors of vibration, diffusion and recombination when affected by different potential energy fields and kinetic energy fields. In addition, the analysis of the sintering process parameters further confirmed the sintering mechanism. The evaluation of the mechanical properties of the alloy showed that mechanical properties of the Fe14.6Ni (at%) alloy were greatly affected by the strain rate and temperature. This work provided theoretical guidance for the preparation of Fe14.6Ni (at%) elastocaloric refrigeration alloy and laid a foundation for the study of elastocaloric effects.

STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio-Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications.

Combining Geological Conditions and Geochemical Techniques to Analyze the Controlling Effect of Lateral Migration Paths of Sand Bodies on Reservoir Distribution

The lateral migration of oil and gas in the sandstone transport layer is continuously distributed, which is controlled by the distribution of the oil and gas potential energy field in the formation. The parameter ratio traces the migration path of oil and gas in the sandstone transport layer, and the results show that the lateral migration of oil and gas in the sand body is mainly concentrated in the vicinity of the paleostructural ridge and the dominant migration channel.

Real Time Building Evacuation Modeling with an Improved Cellular Automata Method and Corresponding IoT System Implementation

Facility emergence evacuation is often a complicated process under extreme conditions. Most of the buildings today use pre-installed signages to guide the emergence evacuation. However, these guidances are sometimes insufficient or misleading, particularly for evacuating from high-rise buildings or complex buildings, such as schools, hospitals, and stadiums. Following a planned route may lead the crowd to move towards dangers, such as smoke and fire. The future emergency guidance system should be more intelligent and be able to guide people to evacuate with a higher survival possibility. This study proposes a real-time building evacuation model with an improved cellular automata (CA) method. This algorithm combines cellular automata with the potential energy field (PEF) model in fluid dynamic theory (FDT) to choose safe paths for the crowd and reduce the possibility of stampedes. Custom-designed wireless sensors, artificial intelligence (A.I.) enhanced surveillance cameras, intelligent emergency signage systems, and edge computing servers are used to sample fire and crowd data, operate the intelligent evacuation algorithm, and guide the crowd with the signage system in real-time conditions. In addition, we performed the algorithm simulation on a two-dimensional plane generated based on the building structure of the Beijing Capital Airport Hospital. The evacuation drill simulations show that the average escape time is significantly shortened with optimal real-time guidance. In one case, a 72% reduction in evacuation time is achieved compared with evacuation using pre-installed signages. The results also demonstrated that the proposed model and system’s evacuation time reduction performance is particularly good in crowded buildings, such as schools or stadiums.