Fast Simulation Research Articles

Validation of a ray-tracing-based guided Lamb wave propagation methodology in aerostructures

Validation of a ray-tracing-based guided Lamb wave propagation methodology in aerostructures

A New Analytical Simulation Code of Acoustic-Gravity Waves of Seismic Origin and Rapid Co-Seismic Thermospheric Disturbance Energetics

A New Analytical Simulation Code of Acoustic-Gravity Waves of Seismic Origin and Rapid Co-Seismic Thermospheric Disturbance Energetics

Windblown sand around obstacles - simulation and validation of deposition patterns

Sand dunes are iconic landmarks of deserts, but can also put human infrastructures at risk, for instance by forming near buildings or roads. We present a simulator of sand erosion and deposition to predict how dunes form around and behind obstacles under wind. Inspired by both computer graphics and geo-sciences, our algorithm couples a fast wind flow simulation with physical laws of sand saltation and avalanching, which suffices to reproduce characteristic patterns of sand deposition. In particular, we validate our approach via a qualitative comparison of the erosion and deposition patterns produced by our simulator against real-world patterns measured by prior work under controlled conditions.

Fast simulation of airfoil flow field via deep neural network

Computational Fluid Dynamics (CFD) has become an indispensable tool in the optimization design, and evaluation of aircraft aerodynamics. However, solving the Navier-Stokes (NS) equations is a time-consuming, memory demanding and computationally expensive task. Artificial intelligence offers a promising avenue for flow field solving. In this work, we propose a novel deep learning framework for rapidly reconstructing airfoil flow fields. Channel attention and spatial attention modules are utilized in the downsampling stage of the UNet to enhance the feature learning capabilities of the deep learning model. Additionally, Embedding the predicted values of the deep learning model as initial values into the CFD solver to accelerate its iterative convergence. The NACA series airfoils were used to validate the prediction accuracy and generalization of the deep learning model. The experimental results represent the deep learning model achieving flow field prediction speed three orders of magnitude faster than CFD solver. Furthermore, the CFD solver integrated with deep learning model demonstrates a threefold acceleration compared to CFD solver. By extensively mining historical flow field data, an efficient solution is derived for the rapid simulation of aircraft flow fields.

An autonomous stair climbing method for the crawler robot based on a hybrid reinforcement learning controller

In response to problems in using deep reinforcement learning to perform the stair climbing task for the crawler robot, such as the lack of theoretical values for stair size range, the inaccurate force on the model using wheel to simulate track and difficulty in training, we propose an autonomous stair climbing method for the crawler robot based on a hybrid reinforcement learning controller. We propose a dynamics parameter settings method for fast simulation model by studying the distribution model of the ground pressure of the track, which improves the accuracy of the force on the fast simulation model in the stair climbing task. Our method calculates the climbing ability of the crawler robot by studying the kinematics and dynamics models of the real crawler robot in each stage of the stair climbing task, and combining with the control flow of the hybrid reinforcement learning controller. Further, we develop the training course of the hybrid reinforcement learning controller. The agent is trained in ROS-Gazebo. The course reduces the impact of poor trajectory on training, achieves higher success rates and faster convergence speed compared to random training, and effectively improves the generalization ability of the strategy.

A hybrid metaheuristic algorithm for scheduling iron ore reclaiming in ports

ABSTRACT This work deals with a scheduling problem of reclaimers. This problem involves a set of ships to be loaded with predefined stockpiles. These stockpiles are unloaded into ship holds, and each hold must store a predefined product and quantity of iron ore. Stockpiles are stored in storage yards, which have reclaimers to reclaim iron ore from stockpiles and send it to berthed ships. The objective is to schedule the operations of reclaimers to minimize the sum of ships' berthing times. A hybrid algorithm, combining the multi-start, GRASP and variable neighbourhood descent procedures, is proposed to treat instances of any scale of the problem. It was tested in real instances, and its results were compared with the hybrid simulated annealing algorithm from the literature and a fast simulation algorithm that emulates operators' decision-making. The proposed algorithm was the best in all instances, and statistical tests proved its superiority

CUDA-based focused Gaussian beams second-harmonic generation efficiency calculator

We present an object-oriented programming (OOP) CUDA-based package for fast and accurate simulation of second-harmonic generation (SHG) efficiency using focused Gaussian beams. The model includes linear as well as two-photon absorption that can ultimately lead to thermal lensing due to self-heating effects. Our approach speeds up calculations by nearly 40x (11x) without (with) temperature profiles with respect to an equivalent implementation using CPU. The package offers a valuable tool for experimental design and study of 3D field propagation in nonlinear three-wave interactions. It is useful for optimization of SHG-based experiments and mitigates undesired thermal effects, enabling improved oven designs and advanced device architectures, leading to stable, efficient high-power SHG. Program summaryProgram Title:cuSHGCPC Library link to program files:https://doi.org/10.17632/hn76s7x848.1Developer's repository link:https://github.com/alfredos84/cuSHGLicensing provisions: MITProgramming language:▪, CUDANature of problem: The problem which is solved in this work is that of second-harmonic generation (SHG) performance degradation in a nonlinear crystal with focused Gaussian beams due to thermal effects. By placing the nonlinear crystal in an oven that controls temperature, the package computes the involved electric fields along the medium. The implemented model includes the linear and nonlinear absorption which occasionally lead to self-heating effect, degrading the performance of the SHG.Solution method: The coupled differential equations for three-wave interactions, which describe the field evolution along the crystal, are solved using the well-known Split-Step Fourier method. The temperature profiles are estimated using the finite-elements method. The field evolution and thermal effects are embedded in a self-consistent algorithm that sequentially and separately solves the electromagnetic and thermal problems until the system reaches the steady state. Due to the eventual computational demand that some problems may have, we chose to implement the coupled equations in the ▪/CUDA programming language. This allows us to significantly speed up simulations, thanks to the computing power provided by a graphics processing unit (GPU) card. The output files obtained are the interacting electric fields and the temperature profile, which have to be analyzed during post-processing.

Application of the fast 3D simplified simulation method for the large CAP1400 nuclear island evaporator based on the coupled source term method

As for the evaporator of the large CAP1400 nuclear power plant, it has a large volume with numerous tubes along with the complex flow heat transfer phenomenon. The traditional direct simulation method usually takes a lot of time and computing resources to obtain the flow heat transfer data. To solve the problem, a fast simulation method established on the porous medium source term was herein proposed. In this simplified simulation method, the complex evaporator model is replaced with the porous medium model, where the derived heat and momentum source terms are added into the governing equation to control the heat flows. Five kinds of simplified evaporator models with different numbers of pipes were set to be the research objects, the internal flows inside the evaporator were studied with the traditional direct simulation method and the newly proposed simplified simulation method. Via comparative analysis, it can be found that: 1) The newly proposed simplified simulation method accurately captures the distribution characteristics of pressure, temperature, and entropy production in the evaporator, serving as a viable alternative to traditional direct simulation methods. Specifically, the average simulation errors for inlet-outlet pressure difference and temperature difference are both less than 5 % and 7 %, respectively. 2) The newly proposed simplified simulation method can capture the mutual interference effects between the evaporator and the nuclear main pump. When the evaporator is connected in series with the main nuclear pump, the average head of the main pump increases by 0.34 m, while the efficiency decreases by an average of 0.34 %. The average simulation errors of the simplified simulation method are 0.06 m and 0.13 %, respectively. 3) The application of the simplified simulation method leads to a significant 85 % decrease in the average computational cost for evaporator calculations and a notable 36 % decrease for combined calculations of the evaporator and main pump. The study here is expected to provide technique support for the simulation and evaluation of the large nuclear island.

2D Analytical Modelling for Ultrasonic Inspection of Concrete Structures: Effects of Scatterers Position Correlation

Ultrasonic Non-Destructive Testing (NDT) simulation plays an important role in designing inspections of concrete structures. The concrete structural complexity (presence of different heterogeneities: aggregates, porosities, microcracks …) leads to dispersion and attenuation of the ultrasonic waves, which requires an accurate modelling of the waves scattered from heterogeneities. For that purpose, studying the mean coherent wave characterized by its attenuation and phase velocity is of great interest. Two major approaches (numerical or analytical) exist for modelling the coherent wave. Numerical methods are more efficient but still limited in the 3D case by their computation time. Therefore, semi-analytical homogenization methods are considered a practical solution for a fast 3D simulation. Nevertheless, they may encounter several limitations in estimating the attenuation in dense heterogeneous materials such as concrete. Our goal is to explain and handle these prediction errors. Since the scattered ultrasonic field depends strongly on the obstacles’ positions in concrete, a particular focus is put on the pair correlation function describing the radial distribution of scatterers’ positions around a fixed scatterer (mean concentration at a distance r from this scatterer). Existing homogenization models are employed for simulating coherent wave propagation in concrete use by simplification of a uniform distribution. In order to improve the analytical models, the effects of scatterers’ positions correlation are investigated. In the present work, we propose to evaluate precisely the existing homogenization model that is the most suitable for high scatterers concentration. A comparison between this model and a 2D finite element code under development in the CIVA NDT simulation software is presented.

Harmonic balance for differential constitutive models under oscillatory shear

Harmonic balance (HB) is a popular Fourier–Galerkin method used in the analysis of nonlinear vibration problems where dynamical systems are subjected to periodic forcing. We adapt HB to find the periodic steady-state response of nonlinear differential constitutive models subjected to large-amplitude oscillatory shear flow. By incorporating the alternating-frequency-time scheme into HB, we develop a computer program called FLASH (acronym for Fast Large Amplitude Simulation using Harmonic balance), which makes it convenient to apply HB to any differential constitutive model. We validate FLASH by considering two representative constitutive models, viz., the exponential Phan-Thien–Tanner model and a nonlinear temporary network model. In terms of accuracy and speed, FLASH typically outperforms the conventional approach of solving initial value problems by numerical integration via time-stepping methods often by several orders of magnitude. Exceptions to this rule are sometimes encountered for materials that are strongly shear thinning or described by constitutive models with discontinuous derivatives. We discuss how FLASH can be conveniently extended for other nonlinear constitutive models, which opens up potential applications in model calibration and selection, and stability analysis.

Sesame: A power spectrum emulator pipeline for beyond-ΛCDM models

The mysterious nature of the dark sector of the Λ-cold-dark-matter (ΛCDM) model is one of the main motivators behind the study of alternative cosmological models. A central quantity of interest for these models is the matter power spectrum, which quantifies structure formation on various scales and can be cross-validated through theory, simulations, and observations. Here, we present a tool that can be used to create emulators for the non-linear matter power spectrum, and similar global clustering statistics, for models beyond ΛCDM with very little computation effort and without the need for supercomputers. We use fast approximate N-body simulations to emulate the boost, B(k, z) = Pbeyond − ΛCDM(k, z)/PΛCDM(k, z), and then rely on existing high-quality emulators made for ΛCDM to isolate Pbeyond − ΛCDM(k, z). Since both the ΛCDM and beyond-ΛCDM models are simulated in our approach, some of the lack of power on small scales due to the low force-resolution in the simulations is factored out, allowing us to extend the emulator to k ∼ 3 − 5 h Mpc−1 and still maintain good accuracy. In addition, errors from the simulation and emulation process can easily be estimated and factored into the covariance when using the emulator on data. As an example of using the pipeline, we create an emulator for the well-studied f(R) model with massive neutrinos, using approximately 3000 CPU hours of computation time. Provided with the paper is a fully functioning pipeline that generates parameter samples, runs a Boltzmann solver to produce initial conditions, runs the simulations, and then gathers all the data and runs it through a machine learning module to develop the emulator. This tool, named Sesame, can be used by anyone to generate a power spectrum emulator for the cosmological model of their choice.

Finite Element Analysis of UHV Transmission Line Stringing System Based on APDL

Nowadays, the tension stringing construction is used in UHV transmission lines. However, in the tension stringing system of UHV transmission lines, due to the flexibility of cables, such as guide ropes, ground wires, etc., in systems, the vibration will be occurred during the construction process. In order to analyze the vibration, the finite element analysis method is required for the system. In this paper, a certain position in the stringing process of the first and the last electric tower of the third section of a 1000kV UHV project was taken as the research object, the setting of constraint conditions, modal analysis and power spectrum analysis were carried out for the fast simulation calculation using APDL, the ANSYS parametric design language. It can be concluded that this method can improve efficiency of modeling and simulation and realize the rapid finite element analysis.

Comparison of point cloud and image-based models for calorimeter fast simulation

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared.

Assessment on fast simulation of wind-driven pollutant dispersion around a street canyon with regime-switching Markov chain

High-density cities shorten commutes but also create proximity to air pollution sources. Sensors coupled with simulations can achieve real-time monitoring and risk assessment, and the recently emerging data-driven models provide opportunities that allow minimizing the computation burden of physical models. This study investigates the use of a time inhomogeneous Markov chain (CLF-MC) to fast simulation of wind-driven pollutant dispersion around a street canyon. The CLF-MC model is trained and tested using synthetic datasets obtained from large eddy simulations (LES). The LES model is validated using a benchmark wind tunnel test, which yields correlation coefficients above 0.95. The results show that the CLF-MC can faithfully reproduce the cumulative distribution of air pollution concentrations at the receptor site using the optimal model configuration identified in this study. For the hold-out test dataset, the release condition is a time-varying release strength, and the CLF-MC shows improved prediction results compared to the baseline models, i.e. the weighted average deviation is 15 % and 51 % smaller, respectively. In comparison to LES, the CLF-MC model is 4.8 × 106 times faster. These findings provide scientific references for fast and accurate wind-driven pollutant dispersion simulation using the probabilistic modelling approach.

Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode

A major challenge for the Air Traffic Management system is the need to boost airspace capacity, which is near saturation in some situations. Separation minima are one of the factors related to airspace capacity, and the SESAR program promotes research into advanced separation modes. Ad Hoc separation, a novel separation mode, involves applying different pairwise separation minima in the same volume of airspace depending on a set of factors, such as encounter geometry, aircraft models, and flight level, among others. This research examines the impact of implementing this concept in different en-route scenarios. The goal is to determine whether applying this concept proves advantageous or, conversely, results in an increase in the complexity of the system without significant benefits in the key performance areas of capacity, environment, and cost-efficiency. Fast Time Simulations are conducted in RAMS software, with the concept being implemented in the LECMZMU, LECMTLU, and LECMDGU sectors of the Madrid ACC. The results reveal favorable capacity outcomes with increases of around 2% and the LECMZMU sector exhibits the most significant environmental and cost-efficiency benefits. Furthermore, implementing the Ad Hoc concept in a larger scenario could yield even greater environmental and cost-efficiency benefits.

Weak-form latent space dynamics identification

Recent work in data-driven modeling has demonstrated that a weak formulation of model equations enhances the noise robustness of a wide range of computational methods. In this paper, we demonstrate the power of the weak form to enhance the LaSDI (Latent Space Dynamics Identification) algorithm, a recently developed data-driven reduced order modeling technique.We introduce a weak form-based version WLaSDI (Weak-form Latent Space Dynamics Identification). WLaSDI first compresses data, then projects onto the test functions and learns the local latent space models. Notably, WLaSDI demonstrates significantly enhanced robustness to noise. With WLaSDI, the local latent space is obtained using weak-form equation learning techniques. Compared to the standard sparse identification of nonlinear dynamics (SINDy) used in LaSDI, the variance reduction of the weak form guarantees a robust and precise latent space recovery, hence allowing for a fast, robust, and accurate simulation. We demonstrate the efficacy of WLaSDI vs. LaSDI on several common benchmark examples including viscid and inviscid Burgers’, radial advection, and heat conduction. For instance, in the case of 1D inviscid Burgers’ simulations with the addition of up to 100% Gaussian white noise, the relative error remains consistently below 6% for WLaSDI, while it can exceed 10,000% for LaSDI. Similarly, for radial advection simulations, the relative errors stay below 16% for WLaSDI, in stark contrast to the potential errors of up to 10,000% with LaSDI. Moreover, speedups of several orders of magnitude can be obtained with WLaSDI. For example applying WLaSDI to 1D Burgers’ yields a 140X speedup compared to the corresponding full order model.Python code to reproduce the results in this work is available at (https://github.com/MathBioCU/PyWSINDy_ODE) and (https://github.com/MathBioCU/PyWLaSDI).

Approaches for fast Brownian dynamics simulation with constraints

Constraints can be used to eliminate quickly fluctuating degrees of freedom in dynamical systems enabling solutions that resolve the behavior of the system with fewer time steps over long time scales. In Brownian dynamics models, these constraints lead to linear equations which represent saddle point problems describing the velocity tangent to the constraint manifold and the forces resulting from imposition of the constraint on the dynamics. Simulations of these constrained systems must solve these saddle point problems, which are often ill-conditioned and require clever preconditioning in order to facilitate large scale simulation using iterative methods like GMRES. In this work, we show that the projected conjugate gradient (PrCG) method, which applies conjugate gradients projected onto the tangent space of the constraint manifold, has certain advantages compared to preconditioned iterative methods like GMRES, without the need to devise and compute a preconditioner. Specifically, for simulations of beads interacting hydrodynamically with commonly encountered holonomic constraints, PrCG exhibits linear scaling with the number of beads similar to GMRES. However, because PrCG does not require a preconditioner it often requires less precomputational effort both in terms of computational complexity and storage, depending on the nature of the constraints. Furthermore, PrCG produces iterates which are always feasible, allowing for independent tuning of the error tolerance on the bead velocities from the error tolerance on the constraint equations. We demonstrate PrCG's efficiency by solving several example low Reynolds number systems involving rigid bodies, freely jointed chains, and immobile particles, as well as a mixed constraint problem with both a rigid body and immobile particles, comparing to GMRES and analytical solutions, when available.

Wave optical propagation in realistic lens systems through multi-slice decomposition with phase compensation

A fast and accurate optical simulation is essential in the design and analysis of optical systems. We propose a wave propagation method through multi-slice decomposition with phase compensation (MSDP) to calculate the wave propagation in realistic lens systems. An estimation of the phase compensation term for the refraction effect and diffraction effect is given, and a modified phase compensation term is presented for calculation through optical systems with a high numerical aperture or an incident optical field featuring a broad spectrum. The proposed method yields accurate and fast results for micro-optical lens systems with the radius of curvature comparable to an illumination wavelength. In a macroscopic optical system with a high numerical aperture, an object with abundant spatial frequencies is reconstructed from the diffraction patterns produced by a single aspherical lens, further demonstrating the effectiveness and applicability of the method in realistic lens systems.

Accelerating the design of gerotor pumps using interactive tools and fast simulation

Accelerating the design of gerotor pumps using interactive tools and fast simulation

New semi-analytical method for fast transcranial ultrasonic field simulation

Objective. To optimize and ensure the safety of ultrasound brain therapy, personalized transcranial ultrasound simulations are very useful. They allow to predict the pressure field, depending on the patient skull and probe position. Most transcranial ultrasound simulations are based on numerical methods which have a long computation time and a high memory usage. The goal of this study is to develop a new semi-analytical field computation method that combines realism and computation speed. Approach. Instead of the classic ray tracing, the ultrasonic paths are computed by time of flight minimization. Then the pressure field is computed using the pencil method. This method requires a smooth and homogeneous skull model. The simulation algorithm, so-called SplineBeam, was numerically validated, by comparison with existing solvers, and experimentally validated by comparison with hydrophone measured pressure fields through an ex vivo human skull. Main results. SplineBeam simulated pressure fields were close to the experimentally measured ones, with a focus position difference of the order of the positioning error and a maximum pressure difference lower than 6.02%. In addition, for those configurations, SplineBeam computation time was lower than another simulation software, k-Wave’s, by two orders of magnitude, thanks to its capacity to compute the field only at the focal spot. Significance. These results show the potential of this new method to compute fast and realistic transcranial pressure fields. The combination of this two assets makes it a promising tool for real time transcranial pressure field prediction during ultrasound brain therapy interventions.