Radar Imaging Statistics of Non-Gaussian Rough Surface: A Physics-Based Simulation Study

This paper investigates the radar image statistics of rough surfaces by simulating the scattered signal’s dependence on the surface roughness. Statistically, the roughness characteristics include the height probability density (HPD) and, to the second-order, the power spectral density (PSD). We simulated the radar backscattered signal by computing the far-field scattered field from the rough surface within the antenna beam volume in the context of synthetic aperture radar (SAR) imaging. To account for the non-Gaussian height distribution, we consider microscopic details of the roughness on comparable radar wavelength scales to include specularly, singly, and multiply scatterers. We introduce surface roughness index (RSI) to distinguish the statistical characteristics of rough surfaces with different height distributions. Results suggest that increasing the RMS height does not impact the Gaussian HPD surface but significantly affects the Weibull surface. The results confirm that as the radar frequency increases, or reaches a relatively larger roughness, the surface’s HPD causes significant changes in incoherent scattering due to more frequent multiple scattering contributions. As a result, the speckle move further away from the Rayleigh model. By examining individual RSI, we see that the Gaussian HPD surface is much less sensitive to RMS height than the Weibull HPD surface. We demonstrate that to retrieve the surface parameters (both dielectric and roughness) from the estimated RCS, less accuracy is expected for the non-Gaussian surface than the Gaussian surface under the same conditions. Therefore, results drawn from this study are helpful for system performance evaluations, parameters estimation, and target detection for SAR imaging of a rough surface.

Methodology to assess wind turbine blade throw risk to vehicles on nearby roads

The public road setback distance is often an important factor that drives wind farm design. This paper outlines a methodology for assessing the risk imposed by blade throw at various road setbacks using a physics-based simulation approach. Given a road setback distance, Monte Carlo simulation is performed wherein blade throw parameters and vehicle locations are randomized. Potential collisions are determined using an “impact circle” approach which assumes that impact occurs if the vehicle is inside the impact radius of the blade fragment when it lands. This approach is exercised on several example turbines and risk levels are calculated for various road setbacks. The method is also applied to a notional wind farm with turbines located at a typical road setback distance. Results show that the blade throw risk imposed to vehicles on public roads for the example wind farm is extremely small and commensurate with risks imposed by everyday activities.

Enabling Artificial Intelligence Studies in Off-Road Mobility Through Physics-based Simulation of Multi-Agent Scenarios

We describe a simulation environment that enables the design and testing of control policies for off-road mobility of autonomous agents. The environment is demonstrated in conjunction with the training and assessment of a reinforcement learning policy that uses sensor fusion and inter-agent communication to enable the movement of mixed convoys of human-driven and autonomous vehicles. Policies learned on rigid terrain are shown to transfer to hard (silt-like) and soft (snow-like) deformable terrains. The environment described performs the following: multi-vehicle multibody dynamics co-simulation in a time/space-coherent infrastructure that relies on the Message Passing Interface standard for low-latency parallel computing; sensor simulation (e.g., camera, GPU, IMU); simulation of a virtual world that can be altered by the agents present in the simulation; training that uses reinforcement learning to 'teach' the autonomous vehicles to drive in an obstacle-riddled course. The software stack described is open source.

Creating space and time for innovation - a methodology for building adaptation design appraisal using physics-based simulation tools and interactive multi-objective optimization

PurposeIt is crucial to consider the multitude of possible building adaptation design strategies for improving the existing conditions of building stock as an alternative to demolition.Design/methodology/approachIntegration of physics-based simulation tools and decision-making tools such as Multi-Attribute Utility (MAU) and Interactive Multi-objective Optimization (IMO) in the design process enable optimized design decision-making for high-performing buildings. A methodology is presented for improving building adaptation design decision making, specifically in the early-stage design feasibility analysis. Ten residential building adaptation strategies are selected and applied to one primary building system for eight performance metrics using physics-based simulation tools. These measures include energy use, thermal comfort, daylighting, natural ventilation, systems performance, life cycle, cost-benefit and constructability. The results are processed using MAU and IMO analysis and are validated through sensitivity analysis by testing one design strategy on three building systems.FindingsQuantifiable comparison of building adaptation strategies based on multiple metrics derived from physics-based simulations can assist in the evaluation of overall environmental performance and economic feasibility for building adaptation projects.Research limitations/implicationsThe current methodology presented is limited to the analysis of one decision-maker at a time. It can be improved to include multiple decision-makers and capture varying perspectives to reflect common practices in the industry.Practical implicationsThe methodology presented supports affordable generation and analysis of a large number of design options for early-stage design optimization.Originality/valueGiven the practical implications, more space and time is created for exploration and innovation, resulting in potential for improved benefits.

Model-based Damage Detection through Physics Guided Learning

Data-driven learning approaches have gained a lot of interest in evaluating and validating complex dynamic systems. One of the main challenges for developing a reliable learning model is the lack of training data covering a large range of various operational conditions. Extensive training data can be generated using a physics-based simulation model. On the other hand, the learning algorithm should be still tested with experimental data obtained from the actual system. Modeling errors may lead to a statistical divergence between the simulation training data and the experimental testing data, causing poor performance, especially for domain-agnostic black-box learning methods. To close the gap between the simulation and experimental domains, this paper proposes a physics-guided learning approach that combines the power of the neural network with domain-specific physics knowledge. Specifically, the proposed architecture integrates physical parameters extracted from the physics-based simulation model into the intermediate layers of the neural network to constrain the learning process. To demonstrate the effectiveness of the proposed approach, the architecture is adopted to a damage classification problem for a three-story structure. Our results show that the accuracy for localizing the damage correctly based on experimental data improves significantly over black-box models, especially under large modeling errors. In this paper, we also use the physics-based intermediate layers to analyze the interpretability of the classification results.

Physics-Based Simulation of Spatiotemporal Patterns of Earthquakes in the Corinth Gulf, Greece, Fault System

ABSTRACT A physics-based earthquake simulation algorithm for modeling the long-term spatiotemporal process of strong (M ≥ 6.0) earthquakes in Corinth Gulf area, Greece, is employed and its performance is explored. The underlying physical model includes the rate- and state-dependent frictional formulation, along with the slow tectonic loading and coseismic static stress transfer. The study area constitutes a rapidly extending rift about 100 km long, where the deformation is taken up by eight major fault segments aligned along its southern coastline, and which is associated with several strong (M ≥ 6.0) earthquakes in the last three centuries, since when the historical earthquake catalog is complete. The recurrence time of these earthquakes and their spatial relation are studied, and the simulator results reveal spatiotemporal properties of the regional seismicity such as pseudoperiodicity as well as multisegment ruptures of strong earthquakes. As the simulator algorithm allows the display of the stress pattern on all the single elements of the fault, we are focusing on the time evolution of the stress level before, during, and after these earthquakes occur. In this respect, the spatiotemporal variation of the stress and its heterogeneity appear to be correlated with the process of preparation of strong earthquakes in a quantitative way.

Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.

Enabling Artificial Intelligence Studies in Off-Road Mobility Through Physics-Based Simulation of Multi-Agent Scenarios

We describe a simulation environment that enables the development and testing of control policies for off-road mobility of autonomous agents. The environment is demonstrated in conjunction with the design and assessment of a reinforcement learning policy that uses sensor fusion and inter-agent communication to enable the movement of mixed convoys of human-driven and autonomous vehicles. Policies are learned on rigid terrain and are subsequently shown to transfer successfully to hard (silt-like) and soft (snow-like) deformable terrains. The enabling simulation environment is developed from the high fidelity, physics-based simulation engine Chrono. Five Chrono modules are employed herein: Chrono::Engine, Chrono::Vehicle, PyChrono, SynChrono and Chrono::Sensor. Vehicle’s are modeled using Chrono::Engine and Chrono::Vehicle and deployed on deformable terrain within the training/testing environment. Utilizing the Python interface to the C++ Chrono API called PyChrono and OpenAI Gym’s supporting infrastructure, training is conducted in a GymChrono learning environment. The GymChrono-generated policy is subsequently deployed for testing in SynChrono, a scalable, cluster-deployable multi-agent testing infrastructure built on MPI. SynChrono facilitates inter-agent communication and maintains time and space coherence between agents. A sensor modeling tool, Chrono::Sensor, supplies sensing data that is used to inform agents during the learning and inference processes. The software stack and the Chrono simulator are both open source. Relevant movies: [1].