Physical Simulation Research Articles

Snow and Ice Animation Methods in Computer Graphics

AbstractSnow and ice animation methods are becoming increasingly popular in the field of computer graphics (CG). The applications of snow and ice in CG are varied, ranging from generating realistic background landscapes to avalanches and physical interaction with objects in movies, games, etc. Over the past two decades, several methods have been proposed to capture the time‐evolving physical appearance or simulation of snow and ice using different models at different scales. This state‐of‐the‐art report aims to identify existing animation methods in the field, provide an up‐to‐date summary of the research in CG, and identify gaps for promising future work. Furthermore, we also attempt to identify the primarily related work done on snow and ice in some other disciplines, such as civil or mechanical engineering, and draw a parallel with the similarities and differences in CG.

Distributions for Compositionally Differentiating Parametric Discontinuities

Computations in physical simulation, computer graphics, and probabilistic inference often require the differentiation of discontinuous processes due to contact, occlusion, and changes at a point in time. Popular differentiable programming languages, such as PyTorch and JAX, ignore discontinuities during differentiation. This is incorrect for parametric discontinuities —conditionals containing at least one real-valued parameter and at least one variable of integration. We introduce Potto, the first differentiable first-order programming language to soundly differentiate parametric discontinuities. We present a denotational semantics for programs and program derivatives and show the two accord. We describe the implementation of Potto, which enables separate compilation of programs. Our prototype implementation overcomes previous compile-time bottlenecks achieving an 88.1x and 441.2x speed up in compile time and a 2.5x and 7.9x speed up in runtime, respectively, on two increasingly large image stylization benchmarks. We showcase Potto by implementing a prototype differentiable renderer with separately compiled shaders.

2D MoS2 monolayers integration with metal oxide-based artificial synapses

In this study, we report on a memristive device structure wherein monolayers of two-dimensional (2D) molybdenum disulfide (MoS2) are integrated with an ultrathin yttrium oxide (Y2O3) layer to simulate artificial synapses functionality. The proposed physical simulation methodology is implemented in COMSOL Multiphysics tool and is based on the minimization of free energy of the used materials at the applied input voltage. The simulated device exhibits a stable bipolar resistive switching and the switching voltages is significantly reduced by increasing the number of MoS2 layers, which is key to conventional low-power computing and neuromorphic applications. The device is shown to perform synaptic functionalities under various applied bias conditions. The resulting synaptic weight decreases almost linearly with the increasing number of MoS2 layers due to the increase in the device thickness. The simulation outcomes pave the way for the development of optimised metal oxide-based memristive devices through their integration with semiconducting 2D materials. Also, the 2D MoS2 integration can enable the optoelectronic operation of this memory device.

A new method for layout layer optimization of long horizontal boreholes for gas extraction in overlying strata: A case study in Guhanshan coal mine, China

AbstractThe gases released by underground coal mining pose a threat to mine safety, lead to resource wastage, and contribute to environmental pollution. Employing long horizontal boreholes (LHB) for gas extraction proves to be an effective solution. However, accurately determining the LHB's position within the overlying rock strata is challenging due to complex mining and geological conditions. This study introduces a novel “zone‐position” method for identifying the optimal LHB placement, utilizing both physical simulation tests and theoretical analysis. The approach comprises two steps. Initially, it identifies the appropriate LHB layout zone within the overlying strata. It delineates four subzones within the fractured zone, among which the stable fractured subzone (SFSZ) is identified as the most favorable for LHB placement. The boundaries of the SFSZ are also defined. Subsequently, the method focuses on pinpointing the LHB's precise location within the SFSZ, proposing a criterion that considers gas accumulation, strata permeability, and LHB stability. Application of this method in engineering projects demonstrates that the gas extracted by LHBs accounts for about 60% of the total gas emission. Furthermore, the volume of gas extracted at the optimal LHB location reaches up to 5.52 m3 min−1, triple that of other locations.

Physical and numerical modelling for settling efficiency of desilting chamber

ABSTRACT In this work, a desilting chamber of the Kholongchhu Hydroelectric Project, Bhutan, was used to test the settling efficiency using physical model and Flow-3D, a three-dimensional computational fluid dynamics (CFD) software. As sediment particles greater than a particular size can cause erosion and damage to underwater machinery, the estimation of settling efficiency is essential during the planning, designing and implementation phases of hydropower projects. These studies were carried out using 12 distinct particle sizes (0.25 mm, 0.24 mm, 0.23 mm, 0.22 mm, 0.21 mm, 0.2 mm, 0.19 mm, 0.18 mm, 0.17 mm, 0.16 mm, 0.15 mm and 0.1 mm). The settling efficiency for the particle size 0.2 mm obtained by physical model and CFD simulations were 92.2% and 93.6%, respectively.

Physical information-enhanced graph neural network for predicting phase separation

Abstract Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here, we present a physical information-enhanced graph neural network (PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt numbers. The results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.

Main factors that control roadway damage when a steeply dipping coal seam crosses a fault

When the working face of a steeply dipping coal seam crosses a fault, the dynamic disturbance caused by the mining load induces fault activation, causing large deformation and failure of the roadway. In this study, a physical similarity simulation and numerical calculation method were used to determine the main factors that the deformation and failure of fault zone roadway and reveal the underlying mechanisms. The results show that when crosses a fault, the high static load formed by the coupling of mining stress and fault pillar stress induces mining stress-type fault activation, which leads to large deformation and damage to the roadway in the fault zone. The main controlling factors are the fault location and mining load intensity. In terms of fault location, the vertical stress concentration away from the roadway side of the fault surface was high, the principal stress deflection angle was large, and the surface deformation of the fault on the roadway left side was the greatest. In terms of mining load intensity, the deflection angle of the principal stress of the surrounding rock initially increased and then decreased, and tensile failure easily occurred near the fault surface. These results provide a reference for roadway support.

Physical Modeling and Simulation of Reusable Rockets for GNC Verification and Validation

Reusable rockets must rely on well-designed Guidance, Navigation and Control (GNC) algorithms. Because they are tested and verified in closed-loop, high-fidelity simulators, emphasizing the strategy to achieve such advanced models is of paramount importance. A wide spectrum of complex dynamic behaviors and their cross-couplings must be captured to achieve sufficiently representative simulations, hence a better assessment of the GNC performance and robustness. This paper focuses on of the main aspects related to the physical (acausal) modeling of reusable rockets, and the integration of these models into a suitable simulation framework oriented towards GNC Validation and Verification (V&V). Firstly, the modeling challenges and the need for physical multibody models are explained. Then, the Vertical Landing Vehicles Library (VLVLib), a Modelica-based library for the physical modeling and simulation of reusable rocket dynamics, is introduced. The VLVLib is built on specific principles that enable quick adaptations to vehicle changes and the introduction of new features during the design process, thereby enhancing project efficiency and reducing costs. Throughout the paper, we explain how these features allow for the rapid development of complex vehicle simulation models by adjusting the selected dynamic effects or changing their fidelity levels. Since the GNC algorithms are normally tested in Simulink®, we show how simulation models with a desired fidelity level can be developed, embedded and simulated within the Simulink® environment. Secondly, this work details the modeling aspects of four relevant vehicle dynamics: propellant sloshing, Thrust Vector Control (TVC), landing legs deployment and touchdown. The CALLISTO reusable rocket is taken as study case: representative simulation results are shown and analyzed to highlight the impact of the higher-fidelity models in comparison with a rigid-body model assumption.

Motion control of a snake robot on multiple inclined planes

ABSTRACT This paper proposes a control method to realize trajectory tracking for snake robots on multiple inclined planes. By explicitly controlling the relative relationship between the robot and the environmental plane, the robot gets into contact with the environmental plane and operates while switching the grounded plane during propulsion. The robot is controlled based on a kinematic model to follow the position and orientation of the head to target trajectory along each plane. The kinematic model is derived by combining the velocity constraint indicating that the wheels do not slip laterally and the kinematic relationship between the robot and the environmental plane. By switching the constraint conditions and the controlled variables, both the head trajectory tracking and maintaining the appropriate relative relationship between the robot and the environmental plane is realized. The effectiveness of the proposed control method was verified through experiments using a physical simulator, and the proposed methods realized the head trajectory tracking on the multiple planes. However, in experiments with actual robot, errors in joint angles and position and rotation of the head accumulated in the direction of the robot body, making it impossible to maintain the appropriate relative relationship, and propulsion was not achieved.

Simulation and analysis of the transient electromagnetic method response in double-layer water-filled goaf in coal mines

Abstract The resolution ability of the transient electromagnetic method (TEM) for double-layer water-filled goaf is affected by the low resistance shielding effect of the overlying strata. Currently, there is a lack of quantitative research on how the parameter changes of the upper water-filled goaf affect its resolution capability. In this study, through numerical and physical simulations, the relative error of the TEM response between single- and double-layer water-filled goafs was regarded as the evaluation index to analyse and summarize the resolution capability of the TEM for double-layer water-filled goaf under different burial depths, thicknesses, and areas of the upper water-filled goaf. The results are summarized as follows. (ⅰ) The resolution capability of the TEM for double-layer water-filled goaf decreased with the increasing burial depth, thickness, or area of the upper water-filled goaf. (ⅱ) The rate of decrease in resolution gradually accelerated as the burial depth increased. Conversely, as the thickness increased, the decrease rate in resolution gradually reduced. Furthermore, the resolution decreased rapidly and then tended towards a fixed value as the area increased. (ⅲ) The maximum value of the relative error curve moved towards the early stage with the increasing burial depth, resulting in a detection depth of the target body smaller than the actual value. Moreover, it moved towards the later stage with the increasing thickness or area, resulting in a detection depth of the target body greater than the actual value.

Development of an Emulator for Radiation Physics Simulator Using Stochastic Variational Gaussian Process Model

This paper describes an emulator that uses a Stochastic Variational Gaussian process (SVGP) regression model to parameterize radiation in a numerical weather prediction (NWP) model that meteorologically models the Earth's weather system. The computation of radiative processes is very large, accounting for most of the total NWP model computation. Statistical emulators are surrogate models that represent simulators and can overcome the computational limitations of very complex simulators such as radiative processes. Recently, artificial neural network-based radiative transfer emulators have been developed, and in this study, a statistical model, GP, is used to develop a radiative transfer emulator. The GP model has the advantage of calculating the uncertainty of the prediction along with the prediction, so the uncertainty of the prediction can be utilized appropriately. However, the computational complexity of the conventional GP model is very high, making it difficult to apply to large data. To solve this problem, an approximate approach, the SVGP model, was utilized. To further reduce the dimensionality of the input variables, we used a combined neural network and SVGP model. As a result, the SVGP-based radiative physics emulator improved its accuracy by about 20% compared to the artificial neural network emulator. However, the computation speed was about 3 to 9 times slower than the neural network emulator, but it was faster than the computation speed of the NWP model. This suggests that statistical emulators can be used to replace NWP model simulators.

Virtual reality painting: A structured review of a decade of innovation

<p>Various studies have widely utilized virtual reality (VR) in painting creation. However, there has been few conclusive studies on VR painting. The present systematic literature review describes the current state of knowledge and practices using VR in painting and provides guidance for painters and scholars focusing on human computer interaction design by critically appraising and summarizing the existing research. It also outlines a wide range of results yielded by quantitative, qualitative and mixed-method studies to investigate potential benefits, difficulties, and effectiveness of VR painting. Overall, 44 studies published from 2013 until 2023 were analysed, with 16 articles introduced the hardware technology and interaction mode of VR painting, 17 articles introduced the beautification method of VR painting, and 11 articles focused on the application of VR painting in different fields. Implications for practice and research are discussed in detail, as the hardware selection, interaction, and physical simulation in VR painting scenes.<strong></strong></p>

Integrating physical model and image simulations to correct topographic effects on surface reflectance

Topography complicates the illumination distribution over rugged terrains and hinders the applications of surface reflectance data over mountainous areas. Topographic correction is an essential process to remove the topographic effects in surface reflectance data. This study proposed a Physical model and image Simulation-based topographic Correction method (PSC) for atmospherically corrected surface reflectance images. In contrast with traditional methods, the proposed approach explicitly estimates the illumination distribution over rugged terrains based on image information and then corrects the topographic effects following physical laws. The proposed method was validated and compared with existing well-performed topographic correction methods, including path length correction (PLC), sun-canopy-sensor correction with c factor (SCSC), C correction (CC), and statistical empirical correction (SE). Simulated and satellite data obtained at different times and geolocations are used for evaluation and comparison. The results demonstrated that most existing methods face challenges in removing the biases induced by topography in surface reflectance images. The PLC method failed to obtain reasonable results for faint illumination conditions when the sun zenith angle is high. SE showed relatively poor performance in terms of physical consistency and outlier percentage. Besides, all the traditional methods failed in the correction of cast shadow regions. Comparably, our method consistently demonstrated superior performance in physical consistency, cast shadow correction, and outlier percentage across various regions, encompassing different sun zenith angles and illumination conditions. The proposed method offers an effective and physically consistent approach for the topographic correction of surface reflectance images, facilitating multi-temporal applications over mountainous areas.

A fast fluid–solid coupled heat transfer algorithm based on dividing the development stages of the flow field

In this study, a fast numerical method for solving the forced convection conjugate heat transfer problem was developed. The method first proposes a new dimensionless number (Fs) that represents the degree of influence of convection on the temperature field in the flow field. It delineates the stage of development of the flow field by monitoring the change in Fs to determine the moment when the flow field suspends updating and improve the computational efficiency of the transient temperature field. The accuracy of the algorithm is verified by taking the fluid–solid conjugate heat transfer under forced convection conditions as an example, which can accurately capture the changes of the flow field for a given monitoring step number of 100, and classify the flow field into E3, E4 and E5 development stages according to the judgment criteria. The results show that the higher development stages correspond to smaller levels of root mean square error (RMSE) of monitoring point temperatures within 3600 s of physical simulation time, and stages E3, E4, and E5 can reach the levels of E-2, E-3, and E-4, which are 3.4, 3.3, and 3.1 times faster than the traditional coupled calculations, respectively. The algorithm is still applicable at variable time steps, but it will require a higher number of determinations compared to a fixed step. The initial error of the quasi-steady algorithm can be reduced from the E-1 level to E-4 by choosing a higher stage of development. Finally the algorithm is tested under a variety of conditions by varying the inlet temperature and flow rate and is found to be robust to both device warming and cooling.

The method for precise seismic detection of geological structures in underground coal mines and application

The occurrence of major accidents such as water inrush in coal mines and coal-gas outbursts is closely correlated with the unclear exploration of underground geological structures. Seismic exploration in underground coal mines is not limited by ground conditions and close to the detected target, so it has advantages with respect to the detection of underground geological structures. However, the geological structures leading to accidents are usually characterized by small size and diverse combinations, making it difficult to detect. In coal mine working faces, the use of transmission observation systems, combined with full waveform inversion methods, represents a new attempt at finely detecting geological structures. Therefore, a three-dimensional numerical model of an abnormal combination of faults and collapse columns was established, a physical simulation model of a multi-collapse column combination was built, and transmission and detection experiments were carried out. The results show that the morphology and locations of 1 fault and 4 collapse columns obtained by the FWI algorithm are consistent with the data from the actual numerical model. According to physical simulation results, the background velocity of the model after inversions and the velocity of 5 anomalies are consistent with the actual values. Field experiments were conducted in the actual underground coal mines. After verification, the average error ratios of the long axis, short axis and intersection angle of the 4 collapse columns were 0.19, 0.15 and 0.02. The detection findings and comparison results confirm the feasibility of precise detection of geological structures by transmissive seismic waves based on the FWI method.

Physics Based Animation Using Computer Graphics

Physics-based animation is a multidisciplinary area that uses ideas from physics, computer science, and mathematics to create realistic and dynamic movements in virtual settings. The basic ideas and methods of physics-based animation are introduced in this research paper, with an emphasis on how mathematical models and physical laws are used to produce realistic motion in computer-generated images. To construct realistic virtual environments, key disciplines covered include fluid simulation, fabric simulation, rigid body dynamics, and soft body dynamics. The report highlights recent developments in real-time physics simulations and addresses other topics such as processing economy, accuracy, and scalability. Applications for physics-based animation can be found in many different fields, such as virtual reality, simulation training, movies, and video games. The pursuit of more precise and effective physics-based animation is essential as technology develops to produce captivating and realistic virtual worlds. Keywords— Physics-based animation(PBA), virtual environment, Physics simulation, Dynamics, Animation software, Real-time physics, Collision detection, Fluid dynamics, Particle systems, Cloth simulation, Smooth particle hydrodynamics, finite element method, Navier-Stokes, Rigid body dynamics, Soft body dynamics, Deformation, Motion capture, Character animation, Kinematics, Rendering, Game development, Virtual reality, Augmented reality, Computer graphics, Simulation accuracy

OkayPlan: Obstacle Kinematics Augmented Dynamic real-time path Planning via particle swarm optimization

Existing Global Path Planning (GPP) algorithms predominantly presume planning in static environments. This assumption immensely limits their applications to Unmanned Surface Vehicles (USVs) that typically navigate in dynamic environments. To address this limitation, we present OkayPlan, a GPP algorithm capable of generating safe and short paths in dynamic scenarios at a real-time executing speed (125 Hz on a desktop-class computer). Specifically, we approach the challenge of dynamic obstacle avoidance by formulating the path planning problem as an Obstacle Kinematics Augmented Optimization Problem (OKAOP), which can be efficiently resolved through a PSO-based optimizer at a real-time speed. Meanwhile, a Dynamic Prioritized Initialization (DPI) mechanism that adaptively initializes potential solutions for the optimization problem is established to further ameliorate the solution quality. Additionally, a relaxation strategy that facilitates the autonomous tuning of OkayPlan’s hyperparameters in dynamic environments is devised. Comprehensive experiments, including comparative evaluations, ablation studies, and applications to 3D physical simulation platforms, have been conducted to substantiate the efficacy of our approach. Results indicate that OkayPlan outstrips existing methods in terms of path safety, length optimality, and computational efficiency, establishing it as a potent GPP technique for dynamic environments. The video and code associated with this paper are accessible at https://github.com/XinJingHao/OkayPlan.

Study on the Spatial Damage Characteristics of Working Fluid in a Sandstone Reservoir and an Unplugged Fluid System.

Due to its good physical properties and low pressure coefficient, the K75 reservoir is prone to leakage and intrusion of drilling and completion fluids during well construction, resulting in plugging of the borehole throat, which will inevitably affect the injection and recovery capacity of the gas well. In order to ensure the strong injection and production capacity of the K75 reservoir, this paper clarifies the reservoir space damage characteristics under different pressure differences and different entry fluids by establishing physical simulation of the damage, selecting the optimal system of the decongestant, and conducting dynamic flow decongestion experiments, as well as combining with the characterization means of the continuous scanning system of the core and the permeability test of the gas measurement. The test results show that the degree of damage of the same working fluid increases with the increase of differential pressure of repulsion, and the degree of damage of sequential working fluids (drilling fluid, completion fluid, and perforating fluid) is greater than that of drilling fluid damage under the same differential pressure of repulsion. In order to relieve the damage of wellbore working fluids, a set of multifunctional, low-corrosion composite acid system formulas was selected through rock powder, mud cake dissolution experiments, and dynamic flow unblocking experiments; the permeability recovery rate of this acid system after unblocking was more than 90%, which is widely applicable, and it is useful for the acidizing and unblocking practice in similar reservoirs.

Study on Characteristics of Steam Chamber and Factors Influencing Nitrogen-Assisted Vertical–Horizontal Steam Drainage Development

With the notable achievements attained through the implementation of steam-assisted gravity drainage (SAGD), the vertical–horizontal steam drive (VHSD) emerges as a pivotal technological advancement aimed at significantly enhancing the efficiency of thin reservoir heavy oil recovery subsequent to steam cyclic stimulation. The inclusion of nitrogen assistance has proven effective in enhancing the efficacy of gravity drainage techniques in reservoir development. However, it is noteworthy that this method has only led to improvements in approximately 50% of the well groups within the observed field. The comprehensive evaluation index of VHSD was proposed, and as the objective function, it was determined that the greatest contribution to the VHSD technique lies in oil saturation, accounting for 40% of the overall evaluations. This differs from conventional SAGD operations, where reservoir thickness serves as the primary determinant. Building upon an enhanced physical simulation similarity criterion, two comparative injection scheme experiments were conducted to explore the impact of nitrogen injection on the performance of VHSD and the characteristics of the steam chamber. Nitrogen is distributed in the vicinity of the steam chamber, leading to the formation of a dual mechanism characterized by ‘top heat insulation and lateral traction’ on the steam chamber. The lateral traction accounts for approximately 25% of the team chamber volume. Additionally, the inducement of nitrogen causes a downward displacement of crude oil, resulting in its accumulation within the high-temperature region of the steam chamber. This, in turn, enhances the contact area between the high-temperature steam and the crude oil, ultimately leading to improvement in production efficiency. Further validation of the impact of nitrogen on steam lateral traction and interlayer steam drainage within the reservoir was confirmed using Xinjiang oilfield testing. The well temperature increased from 75 °C to 130 °C.

A comprehensive evaluation of marker-based, markerless methods for loose garment scenarios in varying camera configurations

In support of smart wearable researchers striving to select optimal ground truth methods for motion capture across a spectrum of loose garment types, we present an extended benchmark named DrapeMoCapBench (DMCB+). This augmented benchmark incorporates a more intricate limb-wise Motion Capture (MoCap) accuracy analysis, and enhanced drape calculation, and introduces a novel benchmarking tool that encompasses multicamera deep learning MoCap methods. DMCB+ is specifically designed to evaluate the performance of both optical marker-based and markerless MoCap techniques, taking into account the challenges posed by various loose garment types. While high-cost marker-based systems are acknowledged for their precision, they often require skin-tight markers on bony areas, which can be impractical with loose garments. On the other hand, markerless MoCap methods driven by computer vision models have evolved to be more cost-effective, utilizing smartphone cameras and exhibiting promising results. Utilizing real-world MoCap datasets, DMCB+ conducts 3D physics simulations with a comprehensive set of variables, including six drape levels, three motion intensities, and six body-gender combinations. The extended benchmark provides a nuanced analysis of advanced marker-based and markerless MoCap techniques, highlighting their strengths and weaknesses across distinct scenarios. In particular, DMCB+ reveals that when evaluating casual loose garments, both marker-based and markerless methods exhibit notable performance degradation (>10 cm). However, in scenarios involving everyday activities with basic and swift motions, markerless MoCap outperforms marker-based alternatives. This positions markerless MoCap as an advantageous and economical choice for wearable studies. The inclusion of a multicamera deep learning MoCap method in the benchmarking tool further expands the scope, allowing researchers to assess the capabilities of cutting-edge technologies in diverse motion capture scenarios.