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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Researchers and engineers at the National Wind Technology Center have developed a wide range of computer modeling and simulation tools to support the wind and water power industries with state-of-the-art design and analysis capabilities.
The online model is the component of Open XAL providing accelerator modeling, simulation, and dynamic synchronization to live hardware. Significant architectural changes and feature additions have been recently made in two separate areas: 1) the managing and processing of simulation data, and 2) the modeling of RF cavities. Simulation data and data processing have been completely decoupled. A single class manages all simulation data while standard tools were developed for processing the simulation results. RF accelerating cavities are now modeled as composite structures where parameter and dynamics computations are distributed. The beam and hardware models both maintain their relative phase information, which allows for dynamic phase slip and elapsed time computation.
The SPARTA package is software for simulating low-density fluids via the Direct Simulation Monte Carlo (DSMC) method, which is a particle-based method for tracking particle trajectories and collisions as a model of a multi-species gas. The main component of SPARTA is a simulation code which allows the user to specify a simulation domain, populate it with particles, embed triangulated surfaces as boundary conditions for the flow, overlay a grid for finding pairs of collision partners, and evolve the system in time via explicit timestepping. The package also includes various pre- and post-processing tools, useful for setting up simulations and analyzing the results. The simulation code runs either in serial on a single processor or desktop machine, or can be run in parallel using the MPI message-passing library, to enable faster performance on large problems.
A set of edge-gated and center-gated plaques were injection molded with long carbon fiber-reinforced thermoplastic composites, and the fiber orientation was measured at different locations of the plaques. Autodesk Simulation Moldflow Insight (ASMI) software was used to simulate the injection molding of these plaques and to predict the fiber orientation, using the anisotropic rotary diffusion and the reduced strain closure models. The phenomenological parameters of the orientation models were carefully identified by fitting to the measured orientation data. The fiber orientation predictions show very good agreement with the experimental data.
The thin-lens representation for an RF accelerating gap has been well developed and is documented by Lapostolle [5], Weiss [6], Wangler [14], and others [9], [10]. These models assume that the axial electric field is both centered and symmetric so it has a cosine expansion. Presented here is a model that considers general axial fields. Both the cosine and sine transit time factors are required plus their Hilbert transforms. The combination yields a complex Hamiltonian rotating in the complex plane with the synchronous phase. The phase and energy gains are computed in the pre-gap and post-gap regions then aligned with asymptotic values of wave number. Derivations are outlined, examples are shown, and simulations presented.
The technical and economic performance of geothermal heat pump (GHP) systems supplying year-round cooling to representative small data centers with cooling loads less than 500 kWth were analyzed and compared to air-source heat pumps (ASHPs). A numerical model was developed in TRNSYS software to simulate the operation of air-source and geothermal heat pumps with and without supplementary air cooled heat exchangers - dry coolers (DCs). The model was validated using data measured at an experimental geothermal system installed in Ithaca, NY, USA. The coefficient of performance (COP) and cooling capacity of the GHPs were calculated over a 20-year lifetime and compared to the performance of ASHPs. The total cost of ownership (TCO) of each of the cooling systems was calculated to assess its economic performance. Both the length of the geothermal borehole heat exchangers (BHEs) and the dry cooler temperature set point were optimized to minimize the TCO of the geothermal systems. Lastly, a preliminary analysis of the performance of geothermal heat pumps for cooling dominated systems was performed for other locations including Dallas, TX, Sacramento, CA, and Minneapolis, MN.
Enhanced risk monitors (ERMs) for active components in advanced reactor concepts use predictive estimates of component failure to update, in real time, predictive safety and economic risk metrics. These metrics have been shown to be capable of use in optimizing maintenance scheduling and managing plant maintenance costs. Integrating this information with plant supervisory control systems increases the potential for making control decisions that utilize real-time information on component conditions. Such decision making would limit the possibility of plant operations that increase the likelihood of degrading the functionality of one or more components while maintaining the overall functionality of the plant. ERM uses sensor data for providing real-time information about equipment condition for deriving risk monitors. This information is used to estimate the remaining useful life and probability of failure of these components. By combining this information with plant probabilistic risk assessment models, predictive estimates of risk posed by continued plant operation in the presence of detected degradation may be estimated. In this paper, we describe this methodology in greater detail, and discuss its integration with a prototypic software-based plant supervisory control platform. In order to integrate these two technologies and evaluate the integrated system, software to simulate the sensor data was developed, prognostic models for feedwater valves were developed, and several use cases defined. The full paper will describe these use cases, and the results of the initial evaluation.
Memory design space exploration methods study memory systems’ performances and limitations before implementation. The computer memory design space has grown exponentially because of the enormous growth of memory types, memory controllers, and application software. Computer simulators are commonly used for memory design space exploration. However, complex memory simulations take an enormous amount of time. Hence, in this paper, we proposed a machine learning-based design space exploration method for dynamic random-access memory and non-volatile memory systems. We applied our method to the CosmoGAN and LeNet applications to predict the following six memory response parameters: (i) bandwidth, (ii) power, (iii) average latency, (iv) average total latency, (v) memory reads, and (vi) memory writes. Our experimental results show that machine learning models can predict memory response parameter values faster than simulations. We used support vector machine, random forest, and gradient boosting machine learning models. We observed that the support vector machine provides better performance for bandwidth, average latency, and average total latency. The random forest model works better for memory reads and writes. The gradient boosting model provides superior prediction performance for power. We provide a detailed discussion on learning curve characteristics, error analysis, and memory type recommendation.
The reference solution for the management of low and intermediate level short-lived waste (LILW) in France consists in their emplacement in concrete vaults built at the near-surface. The corresponding disposal facility under operation by the French waste management organization (Andra), is located in North-eastern France and planned to be closed in 2060. The safety case developed by Andra has to show the evidence that, amongst other aspects, the radionuclide transfer through the confinement barriers will occur in such a way that the human and environmental exposure will be as low as reasonably achievable (ALARA) and in accordance with the dose constraints. For this purpose, modelling is used to integrate uncertainties about the characteristics of concrete components, waste and water table and their evolution in time and space. At last, modelling is finally used to investigate the radiological impact of specific scenarios in which the failure of safety barriers occurs. In the frame of its technical review of the safety assessment, the French Institute for Radiological Protection and Nuclear Safety (IRSN) also performs such kind of numerical modelling of radionuclide fate from the waste to the hydrogeological outlets, in order to check whether the assessment performed by Andra takes into account sufficiently conservative assumptions. The present work aims to evaluate the role of components of the vaults on radionuclide transport during the post-closure period over a few thousand years. For this purpose, IRSN carries out numerical calculations with MELODIE software, which simulates water flow and transport of species, in order to simulate radionuclide migration from waste packages emplaced in a concrete vault to the surrounding groundwater. The developed transport model focuses on 3-H radionuclide and integrates Andra's main assumptions (release model from the waste packages, chemical and hydraulic properties of the vault components, shrinkage and cracks in concrete components). An adaptive mesh method, based on an a posteriori error estimator, was used to model the cracks and the consideration of a strong heterogeneity in the hydraulic and transport parameters values in the different components of the model. This method shows an improvement of results of about 40 times, whereas the mesh size was increased by only 2%. (authors)
Enhanced risk monitors (ERMs) for active components in advanced reactor concepts use predictive estimates of component failure to update, in real time, predictive safety and economic risk metrics. These metrics have been shown to be capable of use in optimizing maintenance scheduling and managing plant maintenance costs. Integrating this information with plant supervisory control systems increases the potential for making control decisions that utilize realtime information on component conditions. Such decision making would limit the possibility of plant operations that increase the likelihood of degrading the functionality of one or more components while maintaining the overall functionality of the plant. ERM uses sensor data for providing real-time information about equipment condition for deriving risk monitors. This information is used to estimate the remaining useful life and probability of failure of these components. By combining this information with plant probabilistic risk assessment models, predictive estimates of risk posed by continued plant operation in the presence of detected degradation may be estimated. In this paper, we describe this methodology in greater detail, and discuss its integration with a prototypic software-based plant supervisory control platform. In order to integrate these two technologies and evaluate the integrated system, software to simulate the sensor data was developed, prognostic models for feedwater valves were developed, and several use cases defined. The full paper will describe these use cases, and the results of the initial evaluation. (authors)
The true nature of ordinary matter has always fascinated humankind, whose imagination has been pointed to the right direction since the 5th century BC with the first known atomic theory by Leucippus and Democritus. They described the matter as formed by small, invisible, indivisible, and eternal particles: the atoms. A long investigation followed them, including contributions from several of history?s greatest philosophers and scientists. The progress led to the understanding that atoms have an internal structure consisting of a central nucleus composed of protons and neutrons, surrounded by a cloud of electrons. About 50 years ago, scientists discovered that nucleons also have an internal structure. Through the Deep Inelastic Scattering (DIS) experiments, on which a lepton is scattered off a nucleon, it was possible to access the distribution of partons inside the nucleon along the longitudinal direction defined by the hard-scale probe. It was a crucial step in developing Quantum Chromodynamics (QCD), currently the best theory describing the subatomic matter. Ultimately, DIS was recognized as a limited tool to investigate the nucleon structure because it is mainly sensitive to observables at the probe?s energy scale. In recent decades, theoretical developments have allowed defining soft-scale observables by measuring more complex processes. Reactions such as Semi-Inclusive Deep Inelastic Scattering (SIDIS) with single or di-hadron production, can provide information about the momentum of parton inside the nucleon. In particular, polarized SIDIS allows access to the Transverse-Momentum-Dependent (TMD) parton distributions describing the correlations of the quark and gluon degrees of freedom in transverse momentum and spin. This study can provide three-dimensional imaging of nucleon structure and inner dynamics, as long as the hadronic component in the final state can be measured. Identifying the species of the produced particles provides information on the quark flavor and assumes a crucial role in this context. The thesis describes the work carried out by the author in preparing novel instrumentation for particle identification in the final state of DIS experiments, to support modern analysis of the parton dynamics within the basic confined object (nucleon) with flavor sensitivity. The work has been focused on the CLAS12 spectrometer in operation at Jefferson Laboratory (JLab), Newport News, VA, USA, and the ePIC experiment in preparation for the future Electron-Ion Collider (EIC) at Brookhaven National Laboratory (BNL), Long Island, NY, USA. CLAS12 is a fixed-target experiment using a 12 GeV beam of polarized electrons scattering off polarized nuclear targets. In 2018 and 2022, two Ring Imaging Cherenkov (RICH) detector modules were installed to improve the ?±/K± separation in the high-momentum region (3÷8 GeV/c) of the experiment. The author contributed to the assembly, installation, and com- missioning of the second RICH module, to the efficiency studies of the first module, and to the first SIDIS analysis with high-momentum kaons identified by the RICH. These studies led to the observation of the first spin asymmetry with high-momentum kaons from the CLAS12 experiment, and an initial assessment of the systematic error associated with the hadron identification. ePIC will be the first experiment at the future EIC, the new collider designed to expand the frontiers of QCD. The author was involved in the development of the dual-radiator Ring Imaging Cherenkov (dRICH). This detector will interpolate the measurements of the Cherenkov angles of photons produced by relativistic particles crossing two different radiators to identify charged hadrons. The author contributed to the studies conducted with the dRICH prototype, developing the reconstruction and analysis software and simulations, characterizing the aerogel radiator samples, and being responsible for the tracking system used during the beam tests. The performance obtained by the prototype has been progressively improved and the results are now comparable with the expectation derived from the simulation and satisfy the requirements of the experiment. In Chapter 1, the SIDIS theory is briefly introduced, outlying the connection with the experimental measurement. Chapter 2 includes the description of the CLAS12 RICH, its assembly and installation, and the efficiency study. In Chapter 3, the analysis of the Beam-Spin Asymmetry associated with SIDIS kaons is described. Chapter 4 describes the contribution to the dRICH for EIC, starting from a description of the ePIC experiment and focusing on the studies performed for the dRICH prototype. The conclusions of this work are summarized at the end.
The primary purpose of the software is to optimize shuttle routes to and from terminals, given some airport campus location like the rental car center. This code was developed as part of the DOE funded Athena project. One aspect of the Athena project was to investigate if the optimization of shuttle routes that move passengers to and from the five DFW terminals and the rental car center could result in lower energy consumption from the shuttle fleet while still maintaining an acceptable level of service to passengers. Hence, the optimization done by the code determines a set of shuttle routes, the type of shuttle on each route, and the number of shuttles servicing each route. The optimization aims to make these choices in a way that the energy consumption per hour by the fleet is minimized. Additionally, the software can fast simulate airport shuttle operations with collected data. The simulation inputs are passenger arrival rates, shuttle routes and frequencies, and simulation configurations. The outputs include: time dependent shuttle energy level, time dependent charging station usage, time dependent number of passengers on each bus, time dependent number of passengers at each bus stop, history of number of passengers left at each bus stop, route history of fleet shuttle buses and on-demand buses, and time dependent bus distance traveled.
Synapse (SYNergistic software platform for AI, Physics Simulations, and Experiments) is a software package meant to deploy real-time guidance from simulations during experimental campaigns, The software package contains functionalities to collect data from simulations (e.g. running at NERSC) and experiments (e.g. from the BELLA facility at LBNL) into a database, train ML surrogate models from this data, and display the predictions of the surrogate model in the control room of an experimental facility, so as to guide on-going experimental campaign. This software was developed as part of an on-going LDRD.
This document provides the software requirements for a computer code to perform point kinetics-based simulations of nuclear reactor transients. These requirements address all aspects of code development and use, including input and output parameters and structure, software capabilities, and supporting documentation. This software is intended to replace the existing point kinetics code TREKIN, which is currently used for the analysis of the Transient Reactor Test Facility (TREAT).
The Muon Ionization Cooling Experiment (MICE) collaboration has developed the MICE Analysis User Software (MAUS) to simulate and analyze experimental data. It serves as the primary codebase for the experiment, providing for offline batch simulation and reconstruction as well as online data quality checks. The software provides both traditional particle-physics functionalities such as track reconstruction and particle identification, and accelerator physics functions, such as calculating transfer matrices and emittances. The code design is object orientated, but has a top-level structure based on the Map-Reduce model. This allows for parallelization to support live data reconstruction during data-taking operations. MAUS allows users to develop in either Python or C++ and provides APIs for both. Various software engineering practices from industry are also used to ensure correct and maintainable code, including style, unit and integration tests, continuous integration and load testing, code reviews, and distributed version control. The software framework and the simulation and reconstruction capabilities are described.
There is a growing need in the simulation community for software that provides a transparent, reproducible, usable, and extensible (TRUE) Monte Carlo (MC) simulation framework employing energies from ab initio methods and machine-learning interatomic potentials (MLIPs). We introduce a Python library (ASE-MC) that adds Monte Carlo functionality to the Atomic Simulation Environment (ASE) package. Now, we can combine the powerful tools used to build systems and perform ab initio and MLIP in ASE with MC simulation algorithms to sample the configurational space with a concise Python script. After presenting the design philosophy, we demonstrate the flexibility of our approach using selected examples. These example simulations include liquid water described with a message-passing MLIP in the canonical and isothermal–isobaric ensembles, sampling the characteristic dihedral angle of biphenyl and comparing an MLIP to first-principles calculations, and a grand canonical Monte Carlo simulation of ammonia adsorption on Pt(111). These examples showcase the main features of the software, which include flexibility in the choice of ab initio or MLIP engine, ab initio or MLIP grand canonical MC with cavity bias insertions and deletions, the ability to add custom MC moves to the move set, and how users can condense complex MC workflows into a single Python script. Finally, this library serves as a framework for reproducible Monte Carlo simulations, facilitating easy reproduction of the work and application to new systems.
We have developed a software library for chemical evolution simulations of galaxy formation under the simple stellar population (SSP) approximation. In this library, all of the necessary components concerning chemical evolution, such as initial mass functions, stellar lifetimes, yields from Type II and Type Ia supernovae, asymptotic giant branch stars, and neutron star mergers, are compiled from the literature. Various models are pre-implemented in this library so that users can choose their favorite combination of models. Subroutines of this library return released energy and masses of individual elements depending on a given event type. Since the redistribution manner of these quantities depends on the implementation of users’ simulation codes, this library leaves it up to the simulation code. As demonstrations, we carry out both one-zone, closed-box simulations and 3D simulations of a collapsing gas and dark matter system using this library. In these simulations, we can easily compare the impact of individual models on the chemical evolution of galaxies, just by changing the control flags and parameters of the library. Since this library only deals with the part of chemical evolution under the SSP approximation, any simulation codes that use the SSP approximation—namely, particle-base and mesh codes, as well as semianalytical models—can use it. This library is named “CELib” after the term “Chemical Evolution Library” and is made available to the community.
Software is an integral part of almost every non-trivial system. Yet simulation technologies to understand how software affects system performance lag behind what is available to understand physical behavior. This is not surprising. An abstract model of software tells us what it is supposed to do, not what it actually does. In this respect software is very different from a physical system. Kirchhoff’s laws predict the behavior of an electrical circuit with great precision. The mathematics of fluids do an adequate job of anticipating how a wing will perform in flight. Only the software itself, observed in operation, can tell us how it behaves.