Physics-based Modeling and Simulation for Motional Cable Harness Design

The design work of motional cable in products is vital due to the difficulty in estimating the potential issues in current researches.In this paper,a physics-based modeling and simulation method for the motional cable harness design is presented.The model,based on continuum mechanics,is established by analyzing the force of microelement in equilibrium.During the analysis procedure,three coordinate systems：inertial,Frenet and main-axis coordinate systems are used.By variable substitution and dimensionless processing,the equation set is discretized by differential quadrature method and subsequently becomes an overdetermined nonlinear equation set with boundary conditions solved by Levenberg-Marquardt method.With the profile of motional cable harness obtained from the integral of arithmetic solution,a motion simulation system based on＂path＂and＂profile＂as well as the experimental equipments is built.Using the same parameters as input for the simulation and the real cable harness correspondingly,the issue in designing,such as collision,can be easily found by the simulation system.This research obtains a better result which has no potential collisions by redesign,and the proposed method can be used as an accurate and efficient way in motional cable harness design work.

Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

Due to the rapid industrialization and the development of the economy in each country,the demand for energy is increasing rapidly.The coal mines have to pace up the mining operations with large production to meet the energy demand.This requirement has led underground coal mines to go deeper with more difficult conditions,especially the mining hazards,such as large deformations,rockburst,coal burst,roof collapse,to name a few.Therefore,this study aims at investigating and predicting the stability of the roadways in underground coal mines exploited by longwall mining method,using various novel intelligent techniques based on physics-based optimization algorithms(i.e.multi-verse optimizer(MVO),equilibrium optimizer(EO),simulated annealing(SA),and Henry gas solubility optimization(HGSO)) and adaptive neuro-fuzzy inference system(ANFIS),named as MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSOANFIS models.Accordingly,162 roof displacement events were investigated based on the characteristics of surrounding rocks,such as cohesion,Young’s modulus,density,shear strength,angle of internal friction,uniaxial compressive strength,quench durability index,rock mass rating,and tensile strength.The MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSO-ANFIS models were then developed and evaluated based on this dataset for predicting roof displacements in roadways of underground mines.The results indicated that the proposed intelligent techniques could accurately predict the roof displacements in roadways of underground mines with an accuracy in the range of 83%-92%.Remarkably,the SA-ANFIS model yielded the most dominant accuracy(i.e.92%).Based on the accurate predictions from the proposed techniques,the reinforced solutions can be timely suggested to ensure the stability of roadways during exploiting coal,especially in the underground coal mines exploited by the longwall mining.

Large-scale data-driven and physics-based models offer insights into the relationships among the structures,dynamics,and functions of chromosomes

The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression.When the cell changes its identity in the cell-fate decision-making process,extensive rearrangements of chromo-some structures occur accompanied by large-scale adaptations of gene expression,underscoring the importance of chromosome dynamics in shaping genome function.Over the last two decades,rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes.In parallel,these enormous data offer valuable opportunities for developing quantitative computational models.Here,we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes.Different from the underlying modeling strategies,these approaches can be classified into data-driven(‘top-down’)and physics-based(‘bottom-up’)categories.We discuss their contributions to offering valuable insights into the relationships among the structures,dynamics,and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.

Meshless methods for physics-based modeling and simulation of deformable models

As 3D digital photographic and scanning devices produce higher resolution images, acquired geometric data sets grow more complex in terms of the modeled objects＇ size, geometry, and topology. As a consequence, point-sampled geometry is becoming ubiquitous in graphics and geometric information processing, and poses new challenges which have not been fully resolved by the state-of-art graphical techniques. In this paper, we address the challenges by proposing a meshless computational framework for dynamic modeling and simulation of solids and thin-shells represented as point sam- ples. Our meshless framework can directly compute the elastic deformation and fracture propagation for any scanned point geometry, without the need of converting them to polygonal meshes or higher order spline representations. We address the necessary computational techniques, such as Moving Least Squares, Hierarchical Discretization, and Modal Warping, to effectively and efficiently compute the physical simulation in real-time. This meahless computational framework aims to bridge the gap between the point-sampled geometry with physics-based modeling and simulation governed by partial differential equations.

Advances of physics-based precision modeling and simulation for manufacturing processes

The development of manufacturing process concerns precision, comprehensiveness, agileness, high efficiency and low cost. The numerical simulation has become an important method for process design and optimization. Physics-based modeling was proposed to promote simulations with a high accuracy. In this paper, three cases, on material properties, precise boundary conditions, and micro-scale physical models, have been discussed to demonstrate how physics-based modeling can improve manufacturing simulation. By using this method, manu- facturing process can be modeled precisely and optimized for getting better performance.

Physics-based data analysis for wind turbine condition monitoring

This article presents methodologies for improving wind turbine condition monitoring using physics-based data analysis techniques.The unique operating conditions of the wind turbine drivetrain are described,and the complex kinematics of the gearbox is analyzed in detail.The pros and cons of the current wind turbine condition monitoring system(CMS)are evaluated.To improve the wind turbine CMS capability,it is suggested to use linear models with unsteady excitations,instead of using nonlinear and nonstationary process models,when dealing the wind turbine dynamics response model.An analysis is undertaken of the damage excitation mechanisms cause for various components in a gearbox,especially for those associated with lower-speed shafts.Physics(mechanics)-based data analysis methods are presented for different component damage excitation mechanisms.Validation results,using the wind farm and manufacturing floor data,are reported.

A Review of physics-based and data-driven models for real-time control of polymer electrolyte membrane fuel cells

The real-time model-based control of polymer electrolyte membrane(PEM)fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various operational conditions,involving the pressure,temperature,humidity,and stoichiometry ratio.In this article,recent progress on the development of PEM fuel cell models that can be used for real-time control is reviewed.The major operational principles of PEM fuel cells and the associated mathematical description of the transport and electrochemical phenomena are described.The reduced-dimensional physics-based models(pseudo-twodimensional,one-dimensional numerical and zero dimensional analytical models)and the non-physics-based models(zero-dimensional empirical and data-driven models)have been systematically examined,and the comparison of these models has been performed.It is found that the current trends for the real-time control models are(i)to couple the single cell model with balance of plants to investigate the system performance,(ii)to incorporate aging effects to enable long-term performance prediction,(iii)to increase the computational speed(especially for one-dimensional numerical models),and(iv)to develop data-driven models with artificial intelligence/machine learning algorithms.This review will be beneficial for the development of physics or nonphysics based models with sufficient accuracy and computational speed to ensure the real-time control of PEM fuel cells.

Physics-based model for boundary layer transition prediction in a wide speed range

A new physics-based model employing three transport equations is developed for the simulation of boundary layer transitions in a wide speed range. The laminar kinetic energy is used to represent pretransitional streamwise velocity fluctuations, taking account of different instability modes. The fluctuation velocity components normal to the streamwise direction are modeled by another transport equation. Transition is triggered automatically with the development of the pretransitional velocity fluctuations. In the fully turbulent region, the model reverts to the k-ω turbulence model. Different test cases, including subsonic, supersonic and hypersonic flows around flat plates, airfoils and straight cones, are numerically simulated to validate the performance of the model. The results demonstrate the excellent predictive capabilities of the model in different paths of transition. The model can serve as a basis for the extension of additional transition mechanisms,such as rotation and curvature effects, roughness-induced transition and crossflow-induced transition.

Physics-based algorithm to simulate tree dynamics under wind load

Rapid development in the different computer science fields during the recent decades has facilitated the creation of new applications in the area of dynamic simulation of plant development.Among these new applications,simulation of trees swaying in the wind is of great importance,as those computer graphics related areas,e.g.,computer games,tree cultivation and forest management simulations,help a lot in revealing the mechanisms of tree dynamics under wind load.However,it is a big challenge to balance the effect of visualization in real time and calculation efficiency for any simulation algorithm.A physics-based algorithm to simulate tree dynamics under wind load was proposed in this study.A mechanistic model simulating the bending of a cantilever beam was used within the algorithm to simulate deformation of stems,and the algorithm was integrated with a landscape model in which different types of trees were constructed with an L-system-based formalism.Simulation results show that realistic dynamic effects can be achieved with reasonably high computational efficiency.

A physics-based approach to motion capture data processing for virtual plant modeling and simulation

Dynamic virtual plant simulation is an attractive research issue in both botany and computer graphics.Data-driven method is an efficient way for motion analysis and animation synthesis.As a widely used tool,motion capture has been used in plant motion data acquisition and analysis.The most prominent and important problem in motion capture for plants is primary data processing such as missing markers reconstruction.This paper presents a novel physics-based approach to motion capture data processing of plants.Firstly,a physics-based mechanics model is found by Lagrangian mechanics for a motion captured plant organ such as a leaf,and then its dynamic mechanical properties are analyzed and relevant model parameters are evaluated.Further,by using the physical model with evaluated parameters,we can calculate the next positions of a maker to reconstruct the missing makers in motion capture sequence.We take an example of a maize leaf and pachira leaf to examine the proposed approach,and the results show that the physics-based method is feasible and effective for plant motion data processing.

Fully-Coupled Multi-Physical Simulation with Physics-Based Nonlinearity-Elimination Preconditioned Inexact Newton Method for Enhanced Oil Recovery

In this paper,we introduce a physics-based nonlinear preconditioned Inexact Newton Method(INB)for the multiphysical simulation of fractured reservoirs.Instead of solving the partial differential equations(PDE)exactly,Inexact Newton method finds a direction for the iteration and solves the equations inexactly with fewer iterations.However,when the equations are not smooth enough,especially when lo-cal discontinuities exits,and when proper preconditioning operations are not adopted,the Inexact Newton method may be slow or even stagnant.As pointed out by Keyes et al.[1],multi-physical numerical simulation faces several challenges,one of which is the local-scale nonlinearity and discontinuity.In this work,we have proposed and studied a nonlinear preconditioner to improve the performance of Inexact Newton Method.The nonlinear preconditioner is essentially a physics-based strategy to adaptively identify and eliminate the highly nonlinear zones.The proposed algorithm has been implemented into our fully coupled,fully implicit THM reservoir simulator(Wang et al.[2,3])to study the effects of cold water injection on fractured petroleum reservoirs.The results of this work show that after the implementation of this nonlinear preconditioner,the iterative solver has become significantly more robust and efficient.

用于重建物理和虚拟抓取的可重构数据手套

In this work,we present a reconfigurable data glove design to capture different modes of human hand-object interactions,which are critical in training embodied artificial intelligence(AI)agents for fine manipulation tasks.To achieve various downstream tasks with distinct features,our reconfigurable data glove operates in three modes sharing a unified backbone design that reconstructs hand gestures in real time.In the tactile-sensing mode,the glove system aggregates manipulation force via customized force sensors made from a soft and thin piezoresistive material;this design minimizes interference during complex hand movements.The virtual reality(VR)mode enables real-time interaction in a physically plausible fashion:A caging-based approach is devised to determine stable grasps by detecting collision events.Leveraging a state-of-the-art finite element method,the simulation mode collects data on fine-grained four-dimensionalmanipulation events comprising hand and object motions in three-dimensional space and how the object's physical properties(e.g.,stress and energy)change in accordance with manipulation over time.Notably,the glove system presented here is the first to use high-fidelity simulation to investigate the unobservable physical and causal factors behind manipulation actions.In a series of experiments,we characterize our data glove in terms of individual sensors and the overall system.More specifically,we evaluate the system's three modes by①recording hand gestures and associated forces,②improving manipulation fluency in VR,and③producing realistic simulation effects of various tool uses,respectively.Based on these three modes,our reconfigurable data glove collects and reconstructs fine-grained human grasp data in both physical and virtual environments,thereby opening up new avenues for the learning of manipulation skills for embodied AI agents.

Numerical simulation of materials-oriented ultra-precision diamond cutting:review and outlook

Ultra-precision diamond cutting is a promising machining technique for realizing ultra-smooth surface of different kinds of materials.While fundamental understanding of the impact of workpiece material properties on cutting mechanisms is crucial for promoting the capability of the machining technique,numerical simulation methods at different length and time scales act as important supplements to experimental investigations.In this work,we present a compact review on recent advancements in the numerical simulations of material-oriented diamond cutting,in which representative machining phenomena are systematically summarized and discussed by multiscale simulations such as molecular dynamics simulation and finite element simulation:the anisotropy cutting behavior of polycrystalline material,the thermo-mechanical coupling tool-chip friction states,the synergetic cutting responses of individual phase in composite materials,and the impact of various external energetic fields on cutting processes.In particular,the novel physics-based numerical models,which involve the high precision constitutive law associated with heterogeneous deformation behavior,the thermo-mechanical coupling algorithm associated with tool-chip friction,the configurations of individual phases in line with real microstructural characteristics of composite materials,and the integration of external energetic fields into cutting models,are highlighted.Finally,insights into the future development of advanced numerical simulation techniques for diamond cutting of advanced structured materials are also provided.The aspects reported in this review present guidelines for the numerical simulations of ultra-precision mechanical machining responses for a variety of materials.

Development of a building information model-guided post-earthquake building inspection framework using 3D synthetic environments

Computer vision-based inspection methods show promise for automating post-earthquake building inspections.These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected images.Nevertheless,assessing the damage′s impact on structural safety requires localizing damage to specific building components with known design and function.This paper proposes a BIM-based automated inspection framework to provide context for visual surveys.A deep learning-based semantic segmentation algorithm is trained to automatically identify damage in images.The BIM automatically associates any identified damage with specific building components.Then,components are classified into damage states consistent with component fragility models for integration with a structural analysis.To demonstrate the framework,methods are developed to photorealistically simulate severe structural damage in a synthetic computer graphics environment.A graphics model of a real building in Urbana,Illinois,is generated to test the framework;the model is integrated with a structural analysis to apply earthquake damage in a physically realistic manner.A simulated UAV survey is flown of the graphics model and the framework is applied.The method achieves high accuracy in assigning damage states to visible structural components.This assignment enables integration with a performance-based earthquake assessment to classify building safety.

A framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations

Computer vision(CV)methods for measurement of structural vibration are less expensive,and their application is more straightforward than methods based on sensors that measure physical quantities at particular points of a structure.However,CV methods produce significantly more measurement errors.Thus,computer vision-based structural health monitoring(CVSHM)requires appropriate methods of damage assessment that are robust with respect to highly contaminated measurement data.In this paper a complete CVSHM framework is proposed,and three damage assessment methods are tested.The first is the augmented inverse estimate(AIE),proposed by Peng et al.in 2021.This method is designed to work with highly contaminated measurement data,but it fails with a large noise provided by CV measurement.The second method,as proposed in this paper,is based on the AIE,but it introduces a weighting matrix that enhances the conditioning of the problem.The third method,also proposed in this paper,introduces additional constraints in the optimization process;these constraints ensure that the stiffness of structural elements can only decrease.Both proposed methods perform better than the original AIE.The latter of the two proposed methods gives the best results,and it is robust with respect to the selected coefficients,as required by the algorithm.

A review of compact modeling for phase change memory

Phase change memory(PCM)attracts wide attention for the memory-centric computing and neuromorphic comput-ing.For circuit and system designs,PCM compact models are mandatory and their status are reviewed in this work.Macro mod-els and physics-based models have been proposed in different stages of the PCM technology developments.Compact model-ing of PCM is indeed more complex than the transistor modeling due to their multi-physics nature including electrical,thermal and phase transition dynamics as well as their interactions.Realizations of the PCM operations including threshold switching,set and reset programming in these models are diverse,which also differs from the perspective of circuit simulations.For the purpose of efficient and reliable designs of the PCM technology,open issues and challenges of the compact modeling are also discussed.

Optimization of terahertz monolithic integrated frequency multiplier based on trap-assisted physics model of THz Schottky barrier varactor

The optimization of high power terahertz monolithic integrated circuit (TMIC) is systemically studied based on the physical model of the Schottky barrier varactor (SBV) with interface defects and tunneling effect. An ultra-thin dielectric layer is added to describe the extra tunneling effect and the damping of thermionic emission current induced by the interface defects. Power consumption of the dielectric layer results in the decrease of capacitance modulation ration (Cmax/Cmin), and thus leads to poor nonlinear C–V characteristics. The proposed Schottky metal-brim (SMB) terminal structure could improve the capacitance modulation ration by reducing the influence of the interface charge and eliminating the fringing capacitance effect. Finally, a 215 GHz tripler TMIC is fabricated based on the SMB terminal structure. The output power is above 5 mW at 210–218 GHz and the maximum could exceed 10 mW at 216 GHz, which could be widely used in terahertz imaging, radiometers, and so on. This paper also provides theoretical support for the SMB structure to optimize the TMIC performance.

Thermo-kinetic characteristics on stabilizing hetero-phase interface of metal matrix composites by crystal plasticity finite element method

Using dislocation-based constitutive modeling in three-dimension crystal plasticity finite element(3D CPFE)simulations,co-deformation and instability of hetero-phase interface in different material systems were herein studied for polycrystalline metal matrix composites(MMCs).Local stress and strain fields in two types of 3layer MMCs such as fcc/fcc Cu-Ag and fcc/bcc Cu-Nb have been predicted under simple compressive deformations.Accordingly,more severe strain-induced interface instability can be observed in the fcc/bcc systems than in the fcc/fcc systems upon refining to metallic nanolayered composites(MNCs).By detailed analysis of stress and strain localization,it has been demonstrated that the interface instability is always accompanied by high-stress concentration,i.e.,thermodynamic characteristics,or high strain prevention i.e.,kinetic characteristics,at the hetero-phase interface.It then follows that the thermodynamic driving forceG and the kinetic energy barrier Q during dislocation and shear banding can be adopted to classify the deformation modes,following the so-called thermo-kinetic correlation.Then by inserting a high density of high-energy interfaces into the Cu-Nb composites,such thermo-kinetic integration at the hetero-phase interface allows a successful establishment of MMCs with the high△G-high Q deformation mode,which ensures high hardening and uniform strain distri-bution,thus efficiently suppressing the shear band,stabilizing the hetero-phase interface,and obtaining an exceptional combination in strength and ductility.Such hetero-phase interface chosen by a couple of thermodynamics and kinetics can be defined as breaking the thermo-kinetic correlation and has been proposed for artificially designing MNCs.

A Physics‑Based Seismic Risk Assessment of the Qujiang Fault:From Dynamic Rupture to Disaster Estimation

This study achieved the construction of earthquake disaster scenarios based on physics-based methods-from fault dynamic rupture to seismic wave propagation-and then population and economic loss estimations.The physics-based dynamic rupture and strong ground motion simulations can fully consider the three-dimensional complexity of physical parameters such as fault geometry,stress field,rock properties,and terrain.Quantitative analysis of multiple seismic disaster scenarios along the Qujiang Fault in western Yunnan Province in southwestern China based on different nucleation locations was achieved.The results indicate that the northwestern segment of the Qujiang Fault is expected to experience significantly higher levels of damage compared to the southeastern segment.Additionally,there are significant variations in human losses,even though the economic losses are similar across different scenarios.Dali Bai Autonomous Prefecture,Chuxiong Yi Autonomous Prefecture,Yuxi City,Honghe Hani and Yi Autonomous Prefecture,and Wenshan Zhuang and Miao Autonomous Prefecture were identified as at medium to high seismic risks,with Yuxi and Honghe being particularly vulnerable.Implementing targeted earthquake prevention measures in Yuxi and Honghe will significantly mitigate the potential risks posed by the Qujiang Fault.Notably,although the fault is within Yuxi,Honghe is likely to suffer the most severe damage.These findings emphasize the importance of considering rupture directivity and its influence on ground motion distribution when assessing seismic risk.