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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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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

GFEChutes Lo-Fi

NASA needed to provide a software model of a parachute system for a manned re-entry vehicle. NASA has parachute codes, e.g., the Descent Simulation System (DSS), that date back to the Apollo Program. Since the space shuttle did not rely on parachutes as its primary descent control mechanism, DSS has not been maintained or incorporated into modern simulation architectures such as Osiris and Antares, which are used for new mission simulations. GFEChutes Lo-Fi is an object-oriented implementation of conventional parachute codes designed for use in modern simulation environments. The GFE (Government Furnished Equipment), low-fidelity (Lo-Fi) parachute model (GFEChutes Lo-Fi) is a software package capable of modeling the effects of multiple parachutes, deployed concurrently and/or sequentially, on a vehicle during the subsonic phase of reentry into planetary atmosphere. The term "low-fidelity" distinguishes models that represent the parachutes as simple forces acting on the vehicle, as opposed to independent aerodynamic bodies. GFEChutes Lo-Fi was created from these existing models to be clean, modular, certified as NASA Class C software, and portable, or "plug and play." The GFE Lo-Fi Chutes Model provides basic modeling capability of a sequential series of parachute activities. Actions include deploying the parachute, changing the reefing on the parachute, and cutting away the parachute. Multiple chutes can be deployed at any given time, but all chutes in that case are assumed to behave as individually isolated chutes; there is no modeling of any interactions between deployed chutes. Drag characteristics of a deployed chute are based on a coefficient of drag, the face area of the chute, and the local dynamic pressure only. The orientation of the chute is approximately modeled for purposes of obtaining torques on the vehicle, but the dynamic state of the chute as a separate entity is not integrated - the treatment is simply an approximation. The innovation in GFEChutes Lo-Fi is to use an object design that closely followed the mechanical characteristics and structure of a physical system of parachutes and their deployment mechanisms. Software objects represent the components of the system, and use of an object hierarchy allows a progression from general component outlines to specific implementations. These extra chutes were not part of the baseline deceleration sequence of drogues and mains, but still had to be simulated. The major innovation in GFEChutes Lo-Fi is the software design and architecture.

Gist, Emily↗
DOE OSTI2024

A differentiable simulation package for performing inference of synchrotron-radiation-based diagnostics

The direction of particle accelerator development is ever-increasing beam quality, currents and repetition rates. This poses a challenge to traditional diagnostics that directly intercept the beam due to the mutual destruction of both the beam and the diagnostic. An alternative approach is to infer beam parameters non-invasively from the synchrotron radiation emitted in bending magnets. However, inferring the beam distribution from a measured radiation pattern is a complex and computationally expensive task. To address this challenge we present SYRIPY ( SYnchrotron Radiation In PYthon ), a software package intended as a tool for performing inference of synchrotron-radiation-based diagnostics. SYRIPY has been developed using PyTorch , which makes it both differentiable and able to leverage the high performance of GPUs, two vital characteristics for performing statistical inference. The package consists of three modules: a particle tracker, Lienard–Wiechert solver and Fourier optics propagator, allowing start-to-end simulation of synchrotron radiation detection to be carried out. SYRIPY has been benchmarked against SRW , the prevalent numerical package in the field, showing good agreement and up to a 50× speed improvement. Finally, we have demonstrated how SYRIPY can be used to perform Bayesian inference of beam parameters using stochastic variational inference.

43 PARTICLE ACCELERATORS↗
NASA NTRS2000

Developing Conceptual Hypersonic Airbreathing Engines Using Design of Experiments Methods

Designing a hypersonic vehicle is a complicated process due to the multi-disciplinary synergy that is required. The greatest challenge involves propulsion-airframe integration. In the past, a two-dimensional flowpath was generated based on the engine performance required for a proposed mission. A three-dimensional CAD geometry was produced from the two-dimensional flowpath for aerodynamic analysis, structural design, and packaging. The aerodynamics, engine performance, and mass properties arc inputs to the vehicle performance tool to determine if the mission goals were met. If the mission goals were not met, then a flowpath and vehicle redesign would begin. This design process might have to be performed several times to produce a "closed" vehicle. This paper will describe an attempt to design a hypersonic cruise vehicle propulsion flowpath using a Design of' Experiments method to reduce the resources necessary to produce a conceptual design with fewer iterations of the design cycle. These methods also allow for more flexible mission analysis and incorporation of additional design constraints at any point. A design system was developed using an object-based software package that would quickly generate each flowpath in the study given the values of the geometric independent variables. These flowpath geometries were put into a hypersonic propulsion code and the engine performance was generated. The propulsion results were loaded into statistical software to produce regression equations that were combined with an aerodynamic database to optimize the flowpath at the vehicle performance level. For this example, the design process was executed twice. The first pass was a cursory look at the independent variables selected to determine which variables are the most important and to test all of the inputs to the optimization process. The second cycle is a more in-depth study with more cases and higher order equations representing the design space.

Ferlemann, Shelly M.↗
DOE OSTI2020

HL-LHC Computing Review: Common Tools and Community Software

Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an ambitious physics programme, notably the LHC accelerator upgrade to high-luminosity, HL-LHC, and the corresponding detector upgrades of ATLAS and CMS. In this document we address the issues for software that is used in multiple experiments (usually even more widely than ATLAS and CMS) and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity. We also give space to general considerations for future software and projects that tackle upcoming challenges, no matter who writes it, which is an area where community convergence on best practice is extremely useful.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2009

American Meteorological Society (AMS) - The Modern Era Retrospective-Analysis for Research and Applications (MERRA) Data and Accessibility

The AM Short Course on The Modern Era Retrospective-analysis for Research and Applications (MERRA) data and accessibility will be held on January 11, 2009 preceding the 89th Annual Meeting in Phoenix, Arizona. Preliminary programs, registration, hotel, and general information will be posted on the AMS Web site in mid-September 2008. Retrospective-analyses (or reanalyses) have been established as an important tool in weather and climate research over the last decade. As computer power increases, the data assimilation and modeling systems improve and become more advanced, the input data quality increases and so reanalyses become more reliable. In 2008, NASA Global Modeling and Assimilation Office began producing a new reanalysis called the Modem Era Retrospective-analysis for Research and Applications (MERRA). The initial data from the reanalysis has been made available to the community and should be complete through 30 years (1979-present) by Fall of 2009. MERRA has taken advantage of the advancement of computing resources to provide users more data than previously available. The native spatial resolution is nominally 1/2 degrees and the surface two dimensional data are one hourly frequency. In addition to the meteorological analysis data, complete mass, energy and momentum budget data and also stratospheric data are provided. The eventual data holdings will exceed 150Tb. In order to facilitate user accessibility to the data, it will be stored in online hard drives (not tape storage) and available through several portals. Subsetting tools will also be available to allow users to tailor their data requests. The goals of this short course are to provide hands on users of reanalyses instruction on MERRA systems and also interactive experience with the online data and access tools. The course is intended for students and research scientists who will be actively interested in accessing and applying MERRA data in their weather, climate or applications work. The course has three parts. There will be an overview of the MERRA system, the validation of the system and the native data format. Second, Instructors will provide examples of weather and climate data analysis using various software packages (primarily GrADS) as well as the online access tools for subsetting and download, as well as visualization (e.g. Giovanni and Google Earth). This will also include examples on changing the data format to fit user's preferences and also to regrid the data for comparisons to other reanalyses and observational data. Lastly, there will he time set aside for participants to have hands on access to the data and software while interacting with the instructors and other developers. The course convener is Dr. Michael Bosilovich, NASA GSFC Global Modeling and Assimilation Office (GMAO). He will be joined by several GMAO, Goddard Earth Science Data and information Services Center (GES DISC) and Software Integration and Visualization Office (SIVO) staff.

daSilva, Arlindo↗
NASA NTRS2010

Comparison Campaign of VLBI Data Analysis Software - First Results

During the development of the Vienna VLBI Software VieVS at the Institute of Geodesy and Geophysics at Vienna University of Technology, a special comparison setup was developed with the goal of easily finding links between deviations of results achieved with different software packages and certain parameters of the observation. The object of comparison is the computed time delay, a value calculated for each observation including all relevant models and corrections that need to be applied in geodetic VLBI analysis. Besides investigating the effects of the various models on the total delay, results of comparisons between VieVS and Occam 6.1 are shown. Using the same methods, a Comparison Campaign of VLBI data analysis software called DeDeCC is about to be launched within the IVS soon.

Plank, Lucia↗
DOE OSTI2023

Hydroscanner: Instrumentation for Water Ingress Imaging in Photovoltaic Module Packaging Materials

A new iteration of the Hydroscanner prototype was designed and built during the execution of the contract. Compared to the previous designs, the communication port was changed from ethernet to USB C, since it provides a more seamless integration as a peripheral unit. An additional advantage is that one single port can be used for both communication and power up of the infrared camera. Although the power requirements are higher than the typical amount provided by a laptop computer, the camera can be run as a peripheral from a docking station when connected to a thunderbolt charging port. The software interface was modified to accommodate two imaging modes, an 8-bit and 14-bit image display. The 8-bit images use a custom colorization scheme that divides all pixels in the image into bins of equal width, such that images containing a wide range of brightness display the intermediate pixel intensities in greyscale values that enhance the contrast. The 14-bit images provide the direct readout from the focal plane array of the camera and can be saved as a text file for later processing in other software packages (MATLAB, Python).

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

A hybrid 3D/2D field response calculation for liquid argon detectors with PCB based anode plane

Liquid Argon Time Projection Chamber (LArTPC) technology is commonly utilized in neutrino detector designs. It enables detailed reconstruction of neutrino events with high spatial precision and low energy threshold. Its field response (FR) model describes the time-dependent electric currents induced in the anode-plane electrodes when ionization electrons drift nearby. An accurate and precise FR is a crucial input to LArTPC detector simulations and charge reconstruction. Established LArTPC designs have been based on parallel wire planes. It allows accurate and computationally economic two-dimensional (2D) FR models utilizing the translational symmetry along the direction of the wires. Recently, novel LArTPC designs utilize electrodes formed on printed circuit board (PCB) in the shape of strips with through holes. The translational symmetry is no longer a good approximation near the electrodes and a new FR calculation that employs regions with three dimensions (3D) has been developed. Extending the 2D models to 3D would be computationally expensive. Fortuitously, the nature of strips with through holes allows for a computationally economic approach based on the finite-difference method (FDM). In this paper, we present a new software package pochoir that calculates LArTPC field response for these new strip-based anode designs. This package combines 3D calculations in the volume near the electrodes with 2D far-field solutions to achieve fast and precise field response computation. We apply the resulting FR to simulate and reconstruct samples of cosmic-ray muons and 39 Ar decays from a Vertical Drift (VD) detector prototype operated at CERN. We find the difference between real and simulated data within 5%. Current state-of-the-art LArTPC software requires a 2D FR which we provide by averaging over one dimension and estimate that variations lost in this average are smaller than 7%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Face Recognition Oak Ridge (FaRO): A Framework for Distributed and Scalable Biometrics Applications

The facial biometrics community has seen a recent abundance of high-accuracy facial analytic models become freely available. Although these models' capabilities in facial detection, landmark detection, attribute analysis, and recognition are ever-increasing, they aren't always straightforward to deploy in a real-world environment. In reality, the use of the field's ever growing collection of models is becoming exceedingly difficult as library dependencies update and deprecate. Researchers often encounter headaches when attempting to utilize multiple models requiring different or conflicting software packages. Face Recognition Oak Ridge (FaRO) is an open-source project designed to provide a highly modular, flexible framework for unifying facial analytic models through a compartmentalized plug-and-play paradigm built on top of the gRPC (Google Remote Procedure Call) protocol. FaRO's server-client architecture and flexible portability allows easy construction of modularized and heterogeneous face analysis pipelines, distributed over many machines with differing hardware and software resources. This paper outlines FaRO's architecture and current capabilities, along with some experiments in model testing and distributed scaling through FaRO.

Bolme, David↗
DOE OSTI2024

CMLM (Co-Optimized Machine-Learned Manifolds) [SWR-23-41]

Co-optimized Machine-Learned Manifolds (CMLM) is a data-driven approach for developing reduced-order manifold models for high-dimensional chemically reacting systems. It involves a specially designed neural network, the training of which simultaneously optimizes linear combinations of species that define the manifold, nonlinear mapping to outputs of interest such as reaction rates, and (optionally) subfilter closure for large eddy simulation. This software package provides an implementation of the CMLM approach in Python using the PyTorch machine learning library. A few example cases are included, showing how the tool can be applied to different types of data from 0D and 1D reacting simulations performed using Cantera. The neural networks can be saved in a format that is readable by the Pele suite of combustion solvers for use in reacting computational fluid dynamics simulations. This software repository contains several python scripts to perform various tasks associated with the Co-optimized Machine Learned Manifolds (CMLM) model, which is described in Perry, Henry de Frahan, and Yellapantula, CNF, 2022 (https://doi.org/10.1016/j.combustflame.2022.112286). This includes not only the code that defines the CMLM model, but also scripts to generate suitable training data, scripts to pre-process the data, scripts to train the CMLM model, and scripts to plot the output, as well as various other helper files. The scripts depend on several commonly used python libraries for data analysis and chemical reaction computations. The trained models that result from this tool are designed to work with the an interface being implemented in the Pele suite of reacting flow solvers (https://github.com/AMReX-Combustion).

Perry, Bruce↗
NASA NTRSDate not supplied

Modeling the Crystallization of Martian Igneous Compositions

The martian crust is dominated by basalts and ultramafic rocks [1]. Understanding the crystallization sequence of martian igneous rocks is critical for constraining the petrologic and geochemical evolution of the crust and mantle. Experimental studies have investigated the parental liquid compositions and crystallization behavior using martian meteorite compositions [2-7], as well as compositions measured by rovers on the martian surface [8,9]. While experiments deliver crucial results, they are often limited by time and resources. Therefore, modeling approaches are an ideal tool to complement experimental results. The application of the MELTS software package [10, 11], to calculate the crystallization of martian compositions, reproduces important compositional details of magma crystallization but significant uncertainties regarding the pressure of the olivine-pyroxene multiple saturation point have been reported [12], and in the temperatures of initial crystallization [12, 13]. Recently, a new melting model, MAGMARS, has been shown to reproduce martian primary melts more accurately [14]. Here, we constrain the applicability of Gibbs free energy minimization software Perple_X [15] for martian compositions by comparing our modeled results to experimentally derived crystallization paths for two martian compositions.

J Semprich↗
NASA NTRS2018

Implementation of J-A Methodology Elastic-Plastic Crack Instability Analysis Capability into the WARP-3D Code

Characterization of the near crack-tip stress/strain fields is the foundation of fracture mechanics. The description of the near tip stress field and the prediction of when fracture occurs is well established for brittle materials that exhibit linear elastic behavior. However, in ductile materials or conditions that violate linear elastic assumptions (Aluminum alloys, Al 2024-T3, Al 2024- T351 etc.), the elastic-plastic crack-tip stress fields are characterized by the Hutchison-Rice-Rosengren (HRR) field. The J-integral is commonly used to characterize amplitude of the HRR field under elastic-plastic conditions. The J-integral has been demonstrated for crack-tip fields that are under high constraint conditions (i.e., small-scale plasticity where the J-dominance is maintained). However, as the external load increases, yielding changes from small- to largescale plasticity and usually a loss of constraint (i.e., reduction in the triaxial stress field along the crack front). The loss of constraint leads to the deviation of the crack-tip stress fields from that given by the HRR field. Hence, the J-dominance will be gradually lost and additional parameter(s) are required to quantify the crack-tip stress fields and predict fracture behavior. The assessment objectives were to: 1) implement a two-parameter (i.e., J-A) fracture criterion into an elastic-plastic three-dimensional (3D) finite element analysis (FEA), 2) validate the implementation by comparison with the A parameter from literature data, 3) conduct material characterization tests to quantify the material behavior and provide fracture data for validation of the J-A fracture criteria, and (4) perform evaluations to establish if the J-A criteria can be used to predict fracture in a ductile metallic material (e.g., aluminum alloys). The A parameter in these criteria is the second parameter in a three-term elastic-plastic asymptotic expansion of the neartip stress behavior. A series of extensive FEAs were performed using WARP3D software package to obtain solutions for the A parameter for different specimen configurations. The methodology needed for the estimation of the A parameter in the asymptotic expansion was developed and implemented using Matlab®. A user material (UMAT) routine was used to model the material stress-strain response using a Ramberg-Osgood power law with a hardening exponent (n) and a material coefficient (alpha). This UMAT routine was successfully implemented in WARP3D software and validated through comparison with the experimental data. Three configurations were extracted from published results: 1) center cracked plate (CCP), 2) single edge-cracked plate (SECP), and 3) double edge-cracked plate (DECP). These configurations and four other configurations (three-hole tension (THT)), three-point bend (3PTB), three-hole compact tension (3PCT), and compact tension (CT)) were analyzed to verify the methodology that was developed and implemented into WARP3D. Solutions of the A parameter were obtained for remote tension loading conditions that started with small-scale yielding and continued into the large-scale plasticity regime. The results indicate that the methodology developed can be used to calculate the elastic-plastic J-A parameters for test specimens with a range of crack geometries, material strain hardening behaviors, and loading conditions. The J-A parameters were implemented as fracture criteria and used to predict the test results. For comparison, other fracture criteria were used to predict the same test results. Major findings include: The A constraint parameter A varies with specimen type and applied load thus accurate determination is crucial in predicting the failure load, and the A parameter is asymptotic as the failure load is approached, making an accurate determination difficult (i.e., small differences in the A parameter can cause large variations in failure load) for materials exhibiting elastic-plastic behavior. The failure predictions from J-A methodology were more accurate than the traditionally used KC and J methods, and have comparable scatter to that observed when using the crack-tip opening angle (CTOA) method. However, the J-A methodology requires considerable effort (expertise level and labor) to implement and to evaluate the A parameter for different specimen types and materials, or to apply this methodology to part-through crack (e.g., 3D problems) structural applications.

Hamm, Kenneth R., Jr.↗
NASA NTRS1990

Solar Astronomy Data Base: Packaged Information on Diskette

In its role as a library, the National Geophysical Data Center has transferred to diskette a collection of small, digital files of routinely measured solar indices for use on an IBM-compatible desktop computer. Recording these observations on diskette allows the distribution of specialized information to researchers with a wide range of expertise in computer science and solar astronomy. Every data set was made self-contained by including formats, extraction utilities, and plain-language descriptive text. Moreover, for several archives, two versions of the observations are provided - one suitable for display, the other for analysis with popular software packages. Since the files contain no control characters, each one can be modified with any text editor.

Mckinnon, John A.↗
NASA NTRS1995

Commercial Earth Observation

Through the Earth Observation Commercial Applications Program (EOCAP) at Stennis Space Center, Applied Analysis, Inc. developed a new tool for analyzing remotely sensed data. The Applied Analysis Spectral Analytical Process (AASAP) detects or classifies objects smaller than a pixel and removes the background. This significantly enhances the discrimination among surface features in imagery. ERDAS, Inc. offers the system as a modular addition to its ERDAS IMAGINE software package for remote sensing applications. EOCAP is a government/industry cooperative program designed to encourage commercial applications of remote sensing. Projects can run three years or more and funding is shared by NASA and the private sector participant. Through the Earth Observation Commercial Applications Program (EOCAP), Ocean and Coastal Environmental Sensing (OCENS) developed SeaStation for marine users. SeaStation is a low-cost, portable, shipboard satellite groundstation integrated with vessel catch and product monitoring software. Linked to the Global Positioning System, SeaStation provides real time relationships between vessel position and data such as sea surface temperature, weather conditions and ice edge location. This allows the user to increase fishing productivity and improve vessel safety. EOCAP is a government/industry cooperative program designed to encourage commercial applications of remote sensing. Projects can run three years or more and funding is shared by NASA and the private sector participant.

Source record↗
DOE OSTI2022

INN Airfoil Tools

This software package contains code to perform airfoil inverse shape design using a sparsified invertible neural network model. This is a specialized deep learning architecture that enables both network weights to be shared to evaluate forward or inverse passes through the network. As a design tool, this network allows practitioners to specify desired aerodynamic and structural properties and generate shapes that satisfy those properties. The invertible neural network is accompanied with tuned parameters as well as software tools to train new models as needed.

Glaws, Andrew↗
DOE OSTI2021

Concurrent Optimization of Capital Cost and Expected O&M

Concentrating solar power (CSP) technologies can utilize heat from concentrated sunlight from a field of tracking mirrors to generate electricity, reform fuel, provide process heat, or augment fossil plant heat sources. Electricity-generating power tower systems focus light from thousands of independent heliostats onto a thermal receiver, which uses the focused light to warm a heat transfer fluid (HTF), typically, a molten nitrate salt. The HTF is then sent to a power generation cycle or diverted into thermal energy storage (TES) for later use. Thermal storage is – in principle – a straightforward proposition. However, optimal utilization of a TES resource is complex and multi-faceted: thermal energy may be dispatched to produce electricity immediately upon first availability, or thermal energy may be reserved for next-day peak periods at risk of filling storage and dumping energy, or a portion of the thermal energy can be reserved to maintain equipment temperatures, reducing power cycle startup time, etc. Many possible dispatch permutations variously emphasize producing peak power, operating through transients, expediting daily startup, etc. The best operation strategy can change day-to-day throughout the year, depending on the weather and market pricing forecasts. The project we describe in this report develops a software package that allows users to explore design optimization, operations decisions, and performance characterization of concentrating solar power tower plants. Users interface with the tool through a scripting language, and results are reported in time series tables, plots, runtime logs, and design outputs. Users choose from a list of variables such as tower height, solar multiple, design-point irradiance, thermal storage size, etc., and specify information about the system using a list of parameters. The software can then optimize the specified variables to reduce the cost of energy produced by the system while meeting certain production requirements, accounting for uncertain weather and electricity price forecasts, and correcting for equipment failures or repair time. The software we develop is the first comprehensive design tool of its kind to incorporate all of these aspects while being deployed as open source.

14 SOLAR ENERGY↗
NASA NTRS2009

Geospatial Authentication

A software package that has been designed to allow authentication for determining if the rover(s) is/are within a set of boundaries or a specific area to access critical geospatial information by using GPS signal structures as a means to authenticate mobile devices into a network wirelessly and in real-time has been developed. The advantage lies in that the system only allows those with designated geospatial boundaries or areas into the server. The Geospatial Authentication software has two parts Server and Client. The server software is a virtual private network (VPN) developed in Linux operating system using Perl programming language. The server can be a stand-alone VPN server or can be combined with other applications and services. The client software is a GUI Windows CE software, or Mobile Graphical Software, that allows users to authenticate into a network. The purpose of the client software is to pass the needed satellite information to the server for authentication.

Lyle, Stacey D.↗
NASA NTRS2018

Stochastic Reduced Order Models with Python (SROMPy)

Stochastic Reduced Order Models with Python (SROMPy) is a software package developed to enable user-friendly utilization of the stochastic reduced order model (SROM) approach for uncertainty quantification. A SROM is a low dimensional, discrete approximation to a random quantity that enables efficient and non-intrusive stochastic computations. With SROMPy, a user can easily generate a SROM to approximate a random variable or vector described by several different types of probability distributions using the Python programming language. Once a SROM is constructed, the software can be used to propagate uncertainty through a user-defined computational model to estimate statistics of a given quantity of interest. This report is meant to introduce the SROMPy module and brie y demonstrate its capabilities. A simple example of a spring-mass system with a random input is included to illustrate the practicality of the SROM approach to uncertainty quantification and relative ease of applying it with SROMPy. The example includes a comparison with a solution obtained using classical Monte Carlo simulation, demonstrating the similarities and advantages of using the SROM approach.

Warner, James E.↗