scholarly journals CogSim: Integrate ML-based Library (LAGER) into Multi-Physics Simulation Code

2019 ◽  
Author(s):  
M Jiang ◽  
A Maguire
Keyword(s):  
Simulation Code ◽  
Physics Simulation

scholarly journals LHC@Home: a BOINC-based volunteer computing infrastructure for physics studies at CERN

2017 ◽  
Vol 7 (1) ◽  
pp. 379-393 ◽  
Author(s):  
Javier Barranco ◽  
Yunhai Cai ◽  
David Cameron ◽  
Matthew Crouch ◽  
Riccardo De Maria ◽  
...  
Keyword(s):  
Hadron Collider ◽  
Particle Dynamics ◽  
Volunteer Computing ◽  
Accelerator Physics ◽  
Simulation Code ◽  
Beam Physics ◽  
Computing Power ◽  
Cern Large Hadron Collider ◽  
Home Service ◽  
Physics Simulation

AbstractThe LHC@Home BOINC project has provided computing capacity for numerical simulations to researchers at CERN since 2004, and has since 2011 been expanded with a wider range of applications. The traditional CERN accelerator physics simulation code SixTrack enjoys continuing volunteers support, and thanks to virtualisation a number of applications from the LHC experiment collaborations and particle theory groups have joined the consolidated LHC@Home BOINC project. This paper addresses the challenges related to traditional and virtualized applications in the BOINC environment, and how volunteer computing has been integrated into the overall computing strategy of the laboratory through the consolidated LHC@Home service. Thanks to the computing power provided by volunteers joining LHC@Home, numerous accelerator beam physics studies have been carried out, yielding an improved understanding of charged particle dynamics in the CERN Large Hadron Collider (LHC) and its future upgrades. The main results are highlighted in this paper.

scholarly journals 3-D COUPLED SIMULATION OF A VVER 1000 WITH PARCS/ATHLET

2021 ◽  
Vol 247 ◽  
pp. 06015
Author(s):  
Romain Henry ◽  
Yann Périn ◽  
Kiril Velkov ◽  
Sergei Pavlovich Nikonov
Keyword(s):  
Cross Section ◽  
Cross Sections ◽  
Nuclear Data ◽  
Monte Carlo Code ◽  
Simulation Code ◽  
Control Rod ◽  
Lattice Codes ◽  
Physics Simulation ◽  
Two Phases ◽  
Effective Multiplication

A new OECD/NEA benchmark entitled “Reactivity compensation with diluted boron by stepwise insertion of control rod cluster” is starting. This benchmark, based on high quality measurements performed at the NPP Rostov Unit 2, aims to validate and assess high fidelity multi-physics simulation code capabilities. The Benchmark is divided in two phases: assembly wise and pin-by-pin resolution of steady-state and transient multi-physics problems. Multi-physics simulation requires the generation of parametrized few-group cross-sections. This task used to be done with deterministic (2-D) lattice codes, but in the past few years the Monte-Carlo code SERPENT has demonstrate its ability to generate accurate few-group homogenized cross-section without approximations, neither on the geometry nor in the nuclear data. Since the whole core SERPENT models for production of such cross-section libraries would be computationally costly (and the standard 2-D approach may introduce unnecessary large approximations), 3-D models of each assembly type in infinite radial lattice configurations have been created. These cross-sections are then used to evaluate effective multiplication factors for different core configurations with the diffusion code PARCS. The results are compared with the reference SERPENT calculations. In the next step, a thermal-hydraulic model with the system code ATHLET applying an assembly-wise description of the core (i.e. one channel per fuel assembly) has been developed for coupled PARCS/ATHLET transient test calculations. This paper describes in detail the models and techniques used for the generation of the few-group parameterized cross section libraries, the PARCS model and the ATHLET model. Additionally, a simple exercise with coupled code system PARCS/ATHLET is presented and analysed.

Design Support for 3D System Integration by Multi Physics Simulation

2008 ◽  
Author(s):  
Peter Schneider ◽  
Sven Reitz ◽  
Joern Stolle ◽  
Roland Martin ◽  
Andreas Wilde ◽  
...  
Keyword(s):  
System Integration ◽  
Design Support ◽  
Physics Simulation

scholarly journals Building the Foundation of Robot Explanation Generation Using Behavior Trees

2021 ◽  
Vol 10 (3) ◽  
pp. 1-31
Author(s):  
Zhao Han ◽  
Daniel Giger ◽  
Jordan Allspaw ◽  
Michael S. Lee ◽  
Henny Admoni ◽  
...  
Keyword(s):  
Autonomous Robots ◽  
Free Form ◽  
Static Structure ◽  
Simulation Code ◽  
Causal Information ◽  
Robot Behavior ◽  
Behavior Trees ◽  
Robot Task ◽  
Task Specification ◽  
Explanation Generation

As autonomous robots continue to be deployed near people, robots need to be able to explain their actions. In this article, we focus on organizing and representing complex tasks in a way that makes them readily explainable. Many actions consist of sub-actions, each of which may have several sub-actions of their own, and the robot must be able to represent these complex actions before it can explain them. To generate explanations for robot behavior, we propose using Behavior Trees (BTs), which are a powerful and rich tool for robot task specification and execution. However, for BTs to be used for robot explanations, their free-form, static structure must be adapted. In this work, we add structure to previously free-form BTs by framing them as a set of semantic sets {goal, subgoals, steps, actions} and subsequently build explanation generation algorithms that answer questions seeking causal information about robot behavior. We make BTs less static with an algorithm that inserts a subgoal that satisfies all dependencies. We evaluate our BTs for robot explanation generation in two domains: a kitting task to assemble a gearbox, and a taxi simulation. Code for the behavior trees (in XML) and all the algorithms is available at github.com/uml-robotics/robot-explanation-BTs.

McChasy2: New Monte Carlo RBS/C simulation code designed for use with large crystalline structures

2021 ◽  
Vol 498 ◽  
pp. 9-14
Author(s):  
Lech Nowicki ◽  
Jacek Jagielski ◽  
Cyprian Mieszczyński ◽  
Kazimierz Skrobas ◽  
Przemysław Jóźwik ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Simulation Code ◽  
Crystalline Structures
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