Open-source fiber laser and amplifier design toolbox using custom FDTD simulation engine

2014 ◽  
Author(s):  
Luke K. Rumbaugh ◽  
Ming-Cheng Cheng ◽  
Yifei Li ◽  
William D. Jemison
Keyword(s):  
Fiber Laser ◽  
Open Source ◽  
Fdtd Simulation ◽  
Simulation Engine ◽  
Amplifier Design

scholarly journals Simulating Coupled Surface-Subsurface Flows with ParFlow v3.5.0: Capabilities, applications, and ongoing developmentof an open-source, massively parallel, integrated hydrologic model

2019 ◽  
Author(s):  
Benjamin N. O. Kuffour ◽  
Nicholas B. Engdahl ◽  
Carol S. Woodward ◽  
Laura E. Condon ◽  
Stefan Kollet ◽  
...  
Keyword(s):  
Open Source ◽  
Land Surface ◽  
Subsurface Flow ◽  
Hydrologic Model ◽  
Time Step ◽  
The Core ◽  
Simulation Engine ◽  
Common Land Model ◽  
Subsurface Flows ◽  
Integrated Hydrologic Model

Abstract. Surface and subsurface flow constitute a naturally linked hydrologic continuum that has not traditionally been simulated in an integrated fashion. Recognizing the interactions between these systems has encouraged the development of integrated hydrologic models (IHMs) capable of treating surface and subsurface systems as a single integrated resource. IHMs is dynamically evolving with improvement in technology and the extent of their current capabilities are often only known to the developers and not general users. This article provides an overview of the core functionality, capability, applications, and ongoing development of one open-source IHM, ParFlow. ParFlow is a parallel, integrated, hydrologic model that simulates surface and subsurface flows. ParFlow solves Richards’ equation for three-dimensional variably saturated groundwater flow and the two-dimensional kinematic wave approximation of the shallow water equations for overland flow. The model employs a conservative centered finite difference scheme and a conservative finite volume method for subsurface flow and transport, respectively. ParFlow uses multigrid preconditioned Krylov and Newton-Krylov methods to solve the linear and nonlinear systems within each time step of the flow simulations. The code has demonstrated very efficient parallel solution capabilities. ParFlow has been coupled to geochemical reaction, land surface (e.g. Common Land Model), and atmospheric models to study the interactions among the subsurface, land surface, and the atmosphere systems across different spatial scales. This overview focuses on the current capabilities of the code, the core simulation engine, and the primary couplings of the subsurface model to other codes, taking a high-level perspective.

scholarly journals MADRaS : Multi Agent Driving Simulator

2021 ◽  
Vol 70 ◽  
pp. 1517-1555
Author(s):  
Anirban Santara ◽  
Sohan Rudra ◽  
Sree Aditya Buridi ◽  
Meha Kaushik ◽  
Abhishek Naik ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Open Source ◽  
Real World ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Machine Learning Algorithms ◽  
Control Mode ◽  
Simulation Engine ◽  
Multi Agent

Autonomous driving has emerged as one of the most active areas of research as it has the promise of making transportation safer and more efficient than ever before. Most real-world autonomous driving pipelines perform perception, motion planning and action in a loop. In this work we present MADRaS, an open-source multi-agent driving simulator for use in the design and evaluation of motion planning algorithms for autonomous driving. Given a start and a goal state, the task of motion planning is to solve for a sequence of position, orientation and speed values in order to navigate between the states while adhering to safety constraints. These constraints often involve the behaviors of other agents in the environment. MADRaS provides a platform for constructing a wide variety of highway and track driving scenarios where multiple driving agents can be trained for motion planning tasks using reinforcement learning and other machine learning algorithms. MADRaS is built on TORCS, an open-source car-racing simulator. TORCS offers a variety of cars with different dynamic properties and driving tracks with different geometries and surface.  MADRaS inherits these functionalities from TORCS and introduces support for multi-agent training, inter-vehicular communication, noisy observations, stochastic actions, and custom traffic cars whose behaviors can be programmed to simulate challenging traffic conditions encountered in the real world. MADRaS can be used to create driving tasks whose complexities can be tuned along eight axes in well-defined steps. This makes it particularly suited for curriculum and continual learning. MADRaS is lightweight and it provides a convenient OpenAI Gym interface for independent control of each car. Apart from the primitive steering-acceleration-brake control mode of TORCS, MADRaS offers a hierarchical track-position – speed control mode that can potentially be used to achieve better generalization. MADRaS uses a UDP based client server model where the simulation engine is the server and each client is a driving agent. MADRaS uses multiprocessing to run each agent as a parallel process for efficiency and integrates well with popular reinforcement learning libraries like RLLib. We show experiments on single and multi-agent reinforcement learning with and without curriculum

ODECAL, a Flexible Open Source Rag Doll Simulation Engine

2006 ◽  
pp. 680-687
Author(s):  
Pang Lih-Hern ◽  
Tan Yee Siang ◽  
Wong Chin Foo ◽  
Wong Lai Kuan
Keyword(s):  
Open Source ◽  
Simulation Engine

scholarly journals Simulating coupled surface–subsurface flows with ParFlow v3.5.0: capabilities, applications, and ongoing development of an open-source, massively parallel, integrated hydrologic model

2020 ◽  
Vol 13 (3) ◽  
pp. 1373-1397 ◽  
Author(s):  
Benjamin N. O. Kuffour ◽  
Nicholas B. Engdahl ◽  
Carol S. Woodward ◽  
Laura E. Condon ◽  
Stefan Kollet ◽  
...  
Keyword(s):  
Open Source ◽  
Land Surface ◽  
Subsurface Flow ◽  
Hydrologic Model ◽  
The Core ◽  
Simulation Engine ◽  
Common Land Model ◽  
Subsurface Flows ◽  
Ongoing Development ◽  
Integrated Hydrologic Model

Abstract. Surface flow and subsurface flow constitute a naturally linked hydrologic continuum that has not traditionally been simulated in an integrated fashion. Recognizing the interactions between these systems has encouraged the development of integrated hydrologic models (IHMs) capable of treating surface and subsurface systems as a single integrated resource. IHMs are dynamically evolving with improvements in technology, and the extent of their current capabilities are often only known to the developers and not general users. This article provides an overview of the core functionality, capability, applications, and ongoing development of one open-source IHM, ParFlow. ParFlow is a parallel, integrated, hydrologic model that simulates surface and subsurface flows. ParFlow solves the Richards equation for three-dimensional variably saturated groundwater flow and the two-dimensional kinematic wave approximation of the shallow water equations for overland flow. The model employs a conservative centered finite-difference scheme and a conservative finite-volume method for subsurface flow and transport, respectively. ParFlow uses multigrid-preconditioned Krylov and Newton–Krylov methods to solve the linear and nonlinear systems within each time step of the flow simulations. The code has demonstrated very efficient parallel solution capabilities. ParFlow has been coupled to geochemical reaction, land surface (e.g., the Common Land Model), and atmospheric models to study the interactions among the subsurface, land surface, and atmosphere systems across different spatial scales. This overview focuses on the current capabilities of the code, the core simulation engine, and the primary couplings of the subsurface model to other codes, taking a high-level perspective.

scholarly journals Chrono::GPU: An Open-Source Simulation Package for Granular Dynamics Using the Discrete Element Method

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1813
Author(s):  
Luning Fang ◽  
Ruochun Zhang ◽  
Colin Vanden Vanden Heuvel ◽  
Radu Serban ◽  
Dan Negrut
Keyword(s):  
Discrete Element Method ◽  
Open Source ◽  
Discrete Element ◽  
Mixed Data ◽  
Tangential Displacement ◽  
Force Model ◽  
Processing Unit ◽  
Granular Dynamics ◽  
Simulation Engine ◽  
Element Method

We report on an open-source, publicly available C++ software module called Chrono::GPU, which uses the Discrete Element Method (DEM) to simulate large granular systems on Graphics Processing Unit (GPU) cards. The solver supports the integration of granular material with geometries defined by triangle meshes, as well as co-simulation with the multi-physics simulation engine Chrono. Chrono::GPU adopts a smooth contact formulation and implements various common contact force models, such as the Hertzian model for normal force and the Mindlin friction force model, which takes into account the history of tangential displacement, rolling frictional torques, and cohesion. We report on the code structure and highlight its use of mixed data types for reducing the memory footprint and increasing simulation speed. We discuss several validation tests (wave propagation, rotating drum, direct shear test, crater test) that compare the simulation results against experimental data or results reported in the literature. In another benchmark test, we demonstrate linear scaling with a problem size up to the GPU memory capacity; specifically, for systems with 130 million DEM elements. The simulation infrastructure is demonstrated in conjunction with simulations of the NASA Curiosity rover, which is currently active on Mars.

scholarly journals SLALOM: Open-source, portable, and easy-to-use solar cell optimizer. Application to the design of InGaN solar cells

2018 ◽  
Vol 9 ◽  
pp. 13 ◽  
Author(s):  
Sidi Ould Saad Hamady ◽  
Nicolas Fressengeas
Keyword(s):  
Solar Cells ◽  
Solar Cell ◽  
Open Source ◽  
High Performance ◽  
Geometrical Parameters ◽  
Multivariate Approach ◽  
Design And Optimization ◽  
Simulation Engine ◽  
Code Structure ◽  
The One

The design and optimization of novel structures is an essential part of the next-generation solar cells development. Indeed, the technological steps involved in the development of high-performance solar cells involve a huge set of interdependent physical and geometrical parameters: layers thicknesses, dopings, compositions, and defect characteristics. In this work, we propose a new open-source and free solar cell optimizer: SLALOM − for SoLAr ceLl multivariate OptiMizer − that implements a rigorous multivariate approach, which improves from the one-parameter-at-a-time procedure that is traditionally used in the field to a state-of-the-art multivariate approach. Applied to indium gallium nitride (InGaN) solar cells, it shows its potential to become a useful tool for the development of novel solar cells. SLALOM is implemented to be extended to any semiconductor simulation engine. Several models for solar cells have been implemented in SLALOM, including, for instance, InGaN. One can adapt these models to any solar cell technology by changing the parameter set, the here proposed generic code structure remaining unchanged.

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