Bullet Physics Engine Research Articles

Numerical construction tests to assess the feasibility of placement grids for Cubipod Homogeneous Low-Crested Structures

Placement grid design is a key phase when constructing armor layers in mound breakwaters. Homogeneous Low-Crested Structures (HLCS) are low-crested structures designed as artificial reefs for marine colonization, coral reef regeneration and beach protection. The placement grid affects the construction feasibility, hydraulic stability and wave transmission of HLCS. This study presents a numerical methodology to simulate a realistic placement procedure for armor units during the construction of HLCS. This numerical simulator is based on the open-source physics engine Bullet Physics Engine (BPE), which applies Newton's laws of motion to rigid bodies. From an initial theorical placement grid for Cubipod HLCS, a series of numerical realistic unit placement tests are carried out to emulate the placement process at prototype scale; placement velocity and spatial deviation of crawler cranes are given parameters associated with each specific project. Considering the placement velocity and spatial deviation during construction, the quality of each specific placement grid is associated with the measured placement errors and the corresponding initial construction deviation to HLCS. This numerical methodology is valuable to understand the influence and the uncertaintly of the construction procedure of armor layers in mound breakwaters.

AutoVRL: A High Fidelity Autonomous Ground Vehicle Simulator for Sim-to-Real Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle (AGV) navigation utilizing raw sensor data without a-priori maps or GPS, which is a necessity in hazardous, information poor environments such as regions where natural disasters occur, and extraterrestrial planets. The substantial training time required to learn an optimal DRL policy, which can be days or weeks for complex tasks, is a major hurdle to real-world implementation in AGV applications. Training entails repeated collisions with the surrounding environment over an extended time period, dependent on the complexity of the task, to reinforce positive exploratory, application specific behavior that is expensive, and time consuming in the real-world. Effectively bridging the simulation to real-world gap is a requisite for successful implementation of DRL in complex AGV applications, enabling learning of cost-effective policies. We present AutoVRL, an open-source high fidelity simulator built upon the Bullet physics engine utilizing OpenAI Gym and Stable Baselines3 in PyTorch to train AGV DRL agents for sim-to-real policy transfer. AutoVRL is equipped with sensor implementations of GPS, IMU, LiDAR and camera, actuators for AGV control, and realistic environments, with extensibility for new environments and AGV models. The simulator provides access to state-of-the-art DRL algorithms, utilizing a python interface for simple algorithm and environment customization, and simulation execution.

Parameterized implementation of a triple refined modeling framework for mesoscale concrete

• A refined modeling framework for mesoscale concrete is developed using the parametric method. • The refined geometric aggregate models that consider micro/meso-scale morphology are generated. • The refined aggregate placement process is performed using the 3D BPT and Bullet physics engine. • The refined material assignment scheme is applied to describe random-continuous fields of material properties. As a typical heterogeneous composite material, concrete’s complex physical and mechanical behaviors on the macroscale are controlled by its mesoscale components and material properties. This paper aims to develop a refined modeling framework for mesoscale concrete using the parametric method, including the refined geometric aggregate model, refined aggregate placement process, and refined material assignment scheme. The geometric aggregates that consider micro/meso-scale morphology are first generated procedurally. Different degrees of mesh decimating operations are applied to the aggregates, and then the aggregate surface is smoothed by the Laplacian smoothing algorithm to build aggregate libraries of different resolutions. The numerical framework proposed by Huang et al. [39] is adopted in this work to conduct 3D bin packing and simulate the compaction and vibration process through the Bullet physics engine, so as to obtain the mesoscale geometric model with a high aggregate content. Furthermore, the material assignment schemes based on the statistical distribution and spatial correlation theory are applied to describe random-continuous fields of material properties. The chloride diffusion coefficient is used to illustrate the material assignment process. The triple refined modeling framework proposed in this paper provides a reference for high-fidelity parametric modeling of mesoscale concrete.

Simulation of the Loading Density for Gun Propellants with Arbitrary Shapes Using a Discrete Element Method

AbstractThe determination of the loading density that can be achieved for a certain propellant geometry is a key factor that determines the energy content and hence the performance of propellants in a gun system. Additive manufacturing on the other hand gives charge designers a new freedom in choosing propellant geometries. Therefore, it is important to have a method that can determine the loading density achievable for a given grain geometry without actually manufacturing each possible solution in sufficient quantities for an experimental determination. For this reason, a discrete element method was adapted, and a simulation was setup with the open source 3D computer graphic software Blender and its bullet physics engine, that enables the charge designer to simulate a propellant bed for arbitrary grain and shell geometries. The method is validated by simulating the loading density of real JA2 double base propellant grains (7 perforated cylinder) and comparing it with experimental measurements. It is shown that a good agreement between the simulated and experimental loading densities can be achieved by our method. Afterwards the dependence of the loading density on the length to diameter ratio for cylindrical gun propellants was investigated with the method. It is shown how the loading density decreases with increasing L/D ratio.

An efficient computational framework for generating realistic 3D mesoscale concrete models using micro X-ray computed tomography images and dynamic physics engine

This study develops a computational framework for highly efficient generation of realistic mesoscale concrete models by exploiting micro XCT 3D images, a bin packing algorithm and an open-source dynamic physics engine (“Bullet”). The aggregates extracted from an XCT image dataset are first smoothed by the HC-Laplacian algorithm and then surface-meshed with optimisation. A library of aggregates characterised with five shape indices is then built and subsequently used with a “greedy search” bin packing algorithm to generate meso-models of concrete according to specified volume fraction and size gradation. To build concrete models with high compactness, for example, with 40–60% contents of aggregates, the Bullet physics engine, based on a hard-contact discrete element method, is further applied to simulate the complicated compaction and vibration process in real casting procedures. The developed framework provides a promising basis for further mesoscale studies, such as 3D printing, multiscale homogenization, damage and fracture mechanisms, and environmental factors-induced degradation.

Calibration and extended validation of a virtual asphalt mixture compaction model using bullet physics engine

Studies involving the investigation of the influence of aggregate structure on the performance of an asphalt mixture are time and resource intensive in the laboratory. To efficiently conduct experimental research of this nature, computational models are highly beneficial due to their ability to execute broad parametric and probabilistic analyses. In this regard, previous study has examined the feasibility of using a Bullet physics engine based computational model to virtually compact asphalt mixtures using a simulated gyratory compactor and digitized aggregate particles. This study examines the effect of three parameters from this computational model that control the bonding or adhesive behavior, viscous damping, and mortar film thickness between discrete coarse aggregate particles. The model was calibrated based on the laboratory compaction of 15 different asphalt mixtures by developing prediction models to estimate parameters that drive the three aforementioned properties. The calibrated model was validated using three additional asphalt mixtures that were virtually compacted using the computational model. Results demonstrate that the compaction behavior from the calibrated computational model was very similar to the compaction behavior recorded from laboratory measurements.

Influence of Fluid Effect on Virtual Simulation of Otolith Movement

More and more physics engines are used to simulate the dynamics between two rigid bodies. However, it is still unknown whether physics engine is suitable for computing the physical information of objects with fluid effect, such as the trajectory and collision dynamics of otolith in semicircular canal in benign paroxysmal postural vertigo (BPPV). The purpose of this study is to determine whether the fluid effect in Bullet physics engine has an impact on the movement data of otoliths in semicircular canal. Based on the experimental data obtained previously, we discuss the position and trajectory of the otolith when it falls in the Dix-Hallpike test. Root Mean Square Error (RMSE) is used to evaluate the static position of otolith under two conditions, and the RMSE is 0.716. We found that the initial position of otolith is affected by fluid effects, but the final static position and trajectory of otolith is similar (x=-5.838±0.294, y=19.348±0.143, z=-9.540±0.635). The experimental results show that the fluid effect does not affect the experimental results of the Dix-Hallpike test, and it has sufficient applicability for the evaluation of BPPV diagnostic methods.

Estimation of layer coefficients of cubipod homogeneous low-crested structures using physical and numerical model placement tests

Homogeneous low-crested structures (HLCSs) on hard seabed are designed to protect beaches and regenerate coral reefs. The height of a HLCS depends on the placement grid which determines the crest freeboard, wave transmission and concrete consumption. In real seafloor conditions, it is not easy to define feasible placement grids for HLCSs on uneven sea bottoms. In this study, the parameters of the numerical model Bullet Physics Engine (BPE) are calibrated and validated using the results of small-scale physical model placement tests of five-layer Cubipod HLCSs on horizontal rigid bottom. The BPE model showed a low sensitivity to variations in the calibrated parameters; the numerical model estimated the layer coefficients with global mean relative errors of 1.04% and 1.39% in the triangular and rectangular placements grids, respectively. Once the numerical model was calibrated, new numerical and physical model tests on a 4% rigid bottom slope were compared for validation. A five-layer Cubipod HLCS on a 4% bottom slope was simulated using the BPE numerical model showing a global mean relative error of 2.75% compared to the small-scale physical model tests. A good agreement was found between numerical and physical model tests of five-layer Cubipod HLCSs on both horizontal as well as 4% rigid bottom slope. The BPE numerical model was found a suitable tool to estimate the structure height of HLCSs and to optimize placement grids of HLCSs on real cases with hard sea bottom.

Modeling of effect of powder spreading on green body dimensional accuracy in additive manufacturing by binder jetting

It is shown that Bullet Physics Engine can be used for Discrete Element modeling of powder spreading during additive manufacturing by binder jetting. It is known that, due to technological restrictions, the thickness of deposited layers during binder jetting should not exceed several powder particle diameters making the application of continuum models of powder medium behavior to be problematic. Discrete element modeling provides the detailed information on powder behavior during powder spreading. The conducted modeling shows that dilation of the deposited powder and distortion of previously deposited layers depend on the amount of powder that is removed during spreading, on the thicknesses of the deposited layers, and on the dimensions of the manufactured components. An empirical equation describing the distortion strain as a function of powder spreading parameters is suggested as an approximation of the numerical modeling results. The main trends in green body distortion under powder spreading are experimentally validated.

Simulation of Realistic Particles with Bullet Physics Engine

The traditional discrete element method (DEM) uses clumps to approximate realistic particles, which is computationally demanding when simulating many particles. In this paper, the Bullet physics engine is applied as an alternative to simulate realistic particles. Bullet was originally developed for computer games to simulate physical and mechanical processes that occur in the real world to produce realistic game experiences. Physics engines integrate a variety of techniques to simulate complex physical processes in games, such as rigid bodies (e.g., rocks, and soil particles), soft bodies (e.g., clothes), and their interactions. Therefore, physics engines have the capabilities to simulate realistic particles. This paper integrates three-dimensional laser scanner and Bullet to form a realistic particle simulation framework. The soil specimen collapse process is simulated to demonstrate the capability of the proposed framework to simulate realistic particles.

Physics engine application to overturning dynamics analysis on banks and uniform slopes for an agricultural tractor with a rollover protective structure

Physics engines have become popular for the realistic animation of video games and movies to model the contact dynamics between two rigid bodies. However, the applicability of a physics engine to collect important physical information such as velocity during contact or impact dynamics of overturning vehicles remains unknown. This study is intended to ascertain whether or not the Bullet physics engine can simulate overturning behaviour of agricultural tractors with a roll over protective structure (ROPS) on a bank slope and on a uniform slope. Visual behaviour data and velocity transition during overturning processes are discussed based on the earlier reported experimental data. The results of vertical and lateral linear velocities, and roll angular velocity were evaluated in terms of Root Mean Square Error (RMSE), and the estimated average error was approximately 0.7. The behaviour time response was short in the simulation results because of the assumption of rigid-body contact. Moreover, the simulation results became smaller, but it is sufficiently accurate to predict the critical ROPS height (CRH) for various agricultural tractors. Therefore, results show that this open source physics engine has sufficient applicability to the overturning dynamics of agricultural tractors with ROPS on banks and uniform slopes.

Lightweight particle-based real-time fluid simulation for mobile environment

This paper presents a real-time lightweight fluid simulation based on a particle fluid technique developed for mobile environment . The Bullet physics engine and smoothed particle hydrodynamic (SPH) fluid algorithm will be used for our lightweight fluid simulation. First, we describe an advanced collision detection mechanism that will be used. By using this method, less computational resources are required. Secondly, we present a simplified SPH algorithm where nearby particles are grouped together to minimize the number of calculations. By decreasing the number of particles, an improved computational performance is expected. Finally, the ARM NEON based parallel computing technique was enabled to reduce execution time by lowering the number of arithmetic instructions. Several experiments are carried out where the experimental results indicate the first technique led to a 50% improvement in performance. The second technique provided a 17% overall improvement. The third technique delivered a performance improvement within the range of 26%–40%. Overall, the experimental results show that the proposed techniques provided an accumulative performance improvement of approximately 120% for all applied methodologies.

Automated Shape Optimization of Orienting Devices for Vibratory Bowl Feeders

Orienting devices for vibratory bowl feeders are still the most widely used system for the automated sorting and feeding of small parts. The design process of these orienting devices has recently been supported by simulation methods. However, this merely shifts the well-known trial-and-error-based adaption of the orienting device's geometry into virtual world. Yet, this does not provide optimal design and, furthermore, requires strong involvement of the developer due to manual shape variation. This paper proposes an optimization algorithm for the automated simulation-based shape optimization of orienting devices for vibratory bowl feeders. First, general formalisms to state the multiobjective optimization problem for arbitrary types of orienting devices and feeding parts are provided. Then, the implementation of the algorithm is described based on Bullet Physics Engine and random search optimization technique. Finally, comparison of simulation results with experimental data point out good accuracy and, thus, great potential of the developed shape optimization software.