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At least 1,153 records · Page 64

BESTEST-GSR (Building Energy Simulation Test - Generation Simulation and Reporting) 2023 [SWR 18-23]

Building Energy Simulation Test (BESTEST) is an NREL-developed method to validate the qualitative performance of different whole building simulations engines relative to each other. https://www.nrel.gov/docs/legosti/old/6231.pdf The purpose of this repository is to generate BESTEST test case models, run simulations, and populate data for ASHRAE Standard 140 reporting spreadsheets for EnergyPlus® based whole building simulation tools. It was originally setup for 2014 version of Standard 140. In May of 2022 it was updated to the 2020 version of Standard 140. This update included updates and additions to existing test suites, the bulk of which was in Section 5.2 (Building Thermal Envelope and Fabric Load Tests). We did not add Section 5.5 Airside HVAC Equipment Performance, but we hope to add that later in 2022. At some point we also hope to add Section 5.2.4 ground modeling, which is currently excluded. Supported Tools The default IDF generation is based on the OpenStudio® CLI, but the workflow supports a 'Bring your own IDF' use case. Additionally, for non-EnergyPlus® based tools the post processing scripts can be used if simulation results are provided as a CSV file.The scripts on this repository should work on Mac, Windows, and Linux. Dependencies Install OpenStudio® 3.4.0 make sure command line can recognize the 'openstudio' command This includes EnergyPlus® 22.1 Install Ruby on your system if it isn't already setup. 2.7 is used for development but other versions may work Since OpenStudio has its own embedded Ruby, which is used for running measures, you don't necessarily have to use a version of Ruby supported by OpenStudio. Install RubyXL Ruby gem This is used to modify Microsoft Excel spreadsheets Install Parallel Ruby gem This allows the CLI to run simulations in parallel

Goldwasser, David↗

A Novel Spatio-Temporal Regime Tracking Method for Impact Simulations

In this proposal, we present a novel method of tracking the rheological regimes activated during impact cratering events that will allow researchers to gain new insights into cratering mechanics. Rheology describes the stress-strain response of rocks to different conditions. Planetary impact cratering events often occur on too large of a scale to be feasibly captured in controlled experiments. Instead, these dynamic events are primarily studied using multi-physics codes equipped with complex material models that enable calculations of the impact event at scale. However, determining which physical processes are required for the problem of interest is challenging. Because the dominant rheological regimes change with space and time during crater formation, it is difficult to link numerical simulations with observable features of craters at the end of the event. The basis of this work is the implementation of numerical flags that track the activation of each rheological regime throughout impact simulations. We demonstrate this with the ‘Rock Model’ implemented in the CTH shock-physics code. We will use this rheology tracking method to ’zoom in’ on a specific region within an event and track the conditions the rock experiences over time. This work will develop community benchmarks to validate and distribute our implemented rheological models. We will focus on improving the melt models used by the planetary impact modeling community by developing an EOS-aware rheological transition from solid to melt. Through analysis of cell and tracer-particle based tracking data, we will study the effects of different rheologies on modeled outcomes, particularly on the volume and distributions of impacts melts. We will also use this method to link observable features with the rheological mechanisms responsible for them. The deliverables (peer-reviewed papers) from the proposed work are (i) tests of the implemented rheologic processes and demonstrations of the tracking flags; (ii) the first calculations of the spatio-temporal evolution of the dominant rheologies during impact cratering events; and (iii) application to delivery of impactor iron during basin-scale impacts.

58 GEOSCIENCES↗

Integration of GOES Data for Solar Resource Assessment of the Contiguous United States

The National Solar Radiation Database (NSRDB), produced by the National Laboratory of the Rockies (NLR), provides high-resolution solar resource data for the contiguous United States (CONUS) using Geostationary Operational Environmental Satellite (GOES) East and West observations. This study evaluates the integration of multi-satellite data within the GOES-East/West overlap regions, where conventional longitude-based selection methods often produce an artificial boundary seam. Our results demonstrate that an advanced blending algorithm, which incorporates sun-satellite scattering angles and satellite viewing zenith angles, improves NSRDB accuracy and creates a spatially continuous dataset. Validation against ground-based irradiance measurements reveals reductions in both percentage error (PE) and normalized Root Mean Square Error (nRMSE), particularly in the central United States. The dynamical integration of multi-satellite data provides a robust foundation for more precise modeling of solar resource and improved spatiotemporal analysis of solar ramp across the CONUS.

14 SOLAR ENERGY↗

Interstellar propagation and the relative spectra of cosmic ray electrons and positrons

The interstellar origin and propagation of cosmic ray electrons and positrons are discussed on the basis of radio observations and direct measurements of cosmic ray spectra. Data on the galactic nonthermal radio spectrum are indicated which imply an exponent of -2.2 for the interstellar electron spectrum below 2 GeV and suggest, together with direct cosmic ray evidence, that the spectrum steepens to -3.2 at higher energies. Comparison of the radio data for higher energies with earth-based measurements reveals that the position of the break in the spectrum is dependent on the strength of the interstellar magnetic field and thus all measured intensity values are equally valid. Analysis of the homogeneous model of cosmic ray electron and positron propagation reveals that the limits on propagation parameters are independent of the set of cosmic ray measurements considered, and predict an injection spectral index for electrons of -2.24, an energy loss parameter of 1.5 + or - 0.5 x 10 to the -16th/GeV per sec, a path length of 7.5 times the 0.33 power of the ratio of initial to measured energies (in g/sq cm), average interstellar hydrogen density of 0.22/cu cm and a cosmic ray age of 25 million years. The absence of short cosmic ray lifetimes is shown to affect the interstellar electron spectrum above 100 GeV.

Webber, W. R.↗

Mars Global Reference Atmospheric Model (Mars-GRAM) and Database for Mission Design

Mars Global Reference Atmospheric Model (Mars-GRAM 2001) is an engineering-level Mars atmosphere model widely used for many Mars mission applications. From 0-80 km, it is based on NASA Ames Mars General Circulation Model, while above 80 km it is based on Mars Thermospheric General Circulation Model. Mars-GRAM 2001 and MGCM use surface topography from Mars Global Surveyor Mars Orbiting Laser Altimeter. Validation studies are described comparing Mars-GRAM with Mars Global Surveyor Radio Science and Thermal Emission Spectrometer data. RS data from 2480 profiles were used, covering latitudes 75 deg S to 72 deg N, surface to approximately 40 km, for seasons ranging from areocentric longitude of Sun (Ls) = 70-160 deg and 265-310 deg. RS data spanned a range of local times, mostly 0-9 hours and 18-24 hours. For interests in aerocapture and precision landing, comparisons concentrated on atmospheric density. At a fixed height of 20 km, RS density varied by about a factor of 2.5 over ranges of latitudes and Ls values observed. Evaluated at matching positions and times, these figures show average RSMars-GRAM density ratios were generally 1+/-)0.05, except at heights above approximately 25 km and latitudes above approximately 50 deg N. Average standard deviation of RSMars-GRAM density ratio was 6%. TES data were used covering surface to approximately 40 km, over more than a full Mars year (February, 1999 - June, 2001, just before start of a Mars global dust storm). Depending on season, TES data covered latitudes 85 deg S to 85 deg N. Most TES data were concentrated near local times 2 hours and 14 hours. Observed average TES/Mars-GRAM density ratios were generally 1+/-0.05, except at high altitudes (15-30 km, depending on season) and high latitudes (greater than 45 deg N), or at most altitudes in the southern hemisphere at Ls approximately 90 and 180 deg. Compared to TES averages for a given latitude and season, TES data had average density standard deviation about the mean of approximately 2.5% for all data, or approximately 1-4%, depending on time of day and dust optical depth. Average standard deviation of TES/Mars-GRAM density ratio was 8.9% for local time 2 hours and 7.1% for local time 14 hours. Thus standard deviation of observed TES/Mars-GRAM density ratio, evaluated at matching positions and times, is about three times the standard deviation of TES data about the TES mean value at a given position and season.

Justus, C. G.↗

NASA Strategy to Safely Live and Work in the Space Radiation Environment

In space, astronauts are constantly bombarded with energetic particles. The goal of the National Aeronautics and Space Agency and the NASA Space Radiation Project is to ensure that astronauts can safely live and work in the space radiation environment. The space radiation environment poses both acute and chronic risks to crew health and safety, but unlike some other aspects of space travel, space radiation exposure has clinically relevant implications for the lifetime of the crew. Among the identified radiation risks are cancer, acute and late CNS damage, chronic and degenerative tissue decease, and acute radiation syndrome. The term "safely" means that risks are sufficiently understood such that acceptable limits on mission, post-mission and multi-mission consequences can be defined. The NASA Space Radiation Project strategy has several elements. The first element is to use a peer-reviewed research program to increase our mechanistic knowledge and genetic capabilities to develop tools for individual risk projection, thereby reducing our dependency on epidemiological data and population-based risk assessment. The second element is to use the NASA Space Radiation Laboratory to provide a ground-based facility to study the health effects/mechanisms of damage from space radiation exposure and the development and validation of biological models of risk, as well as methods for extrapolation to human risk. The third element is a risk modeling effort that integrates the results from research efforts into models of human risk to reduce uncertainties in predicting the identified radiation risks. To understand the biological basis for risk, we must also understand the physical aspects of the crew environment. Thus, the fourth element develops computer algorithms to predict radiation transport properties, evaluate integrated shielding technologies and provide design optimization recommendations for the design of human space systems. Understanding the risks and determining methods to mitigate the risks are keys to a successful radiation protection strategy.

Cucinotta, Francis A.↗

Validation of Finite Element Crash Test Dummy Models for Predicting Orion Crew Member Injuries During a Simulated Vehicle Landing

A series of crash tests were conducted with dummies during simulated Orion crew module landings at the Wright-Patterson Air Force Base. These tests consisted of several crew configurations with and without astronaut suits. Some test results were collected and are presented. In addition, finite element models of the tests were developed and are presented. The finite element models were validated using the experimental data, and the test responses were compared with the computed results. Occupant crash data, such as forces, moments, and accelerations, were collected from the simulations and compared with injury criteria to assess occupant survivability and injury. Some of the injury criteria published in the literature is summarized for completeness. These criteria were used to determine potential injury during crew impact events.

Tabiei, Al↗

Space Transportation System Liftoff Debris Mitigation Process Overview

Liftoff debris is a top risk to the Space Shuttle Vehicle. To manage the Liftoff debris risk, the Space Shuttle Program created a team with in the Propulsion Systems Engineering & Integration Office. The Shutt le Liftoff Debris Team harnesses the Systems Engineering process to i dentify, assess, mitigate, and communicate the Liftoff debris risk. T he Liftoff Debris Team leverages off the technical knowledge and expe rtise of engineering groups across multiple NASA centers to integrate total system solutions. These solutions connect the hardware and ana lyses to identify and characterize debris sources and zones contribut ing to the Liftoff debris risk. The solutions incorporate analyses sp anning: the definition and modeling of natural and induced environmen ts; material characterizations; statistical trending analyses, imager y based trajectory analyses; debris transport analyses, and risk asse ssments. The verification and validation of these analyses are bound by conservative assumptions and anchored by testing and flight data. The Liftoff debris risk mitigation is managed through vigilant collab orative work between the Liftoff Debris Team and Launch Pad Operation s personnel and through the management of requirements, interfaces, r isk documentation, configurations, and technical data. Furthermore, o n day of launch, decision analysis is used to apply the wealth of ana lyses to case specific identified risks. This presentation describes how the Liftoff Debris Team applies Systems Engineering in their proce sses to mitigate risk and improve the safety of the Space Shuttle Veh icle.

Mitchell, Michael↗

Error Estimates of the Ares I Computed Turbulent Ascent Longitudinal Aerodynamic Analysis

Numerical predictions of the longitudinal aerodynamic characteristics for the Ares I class of vehicles, along with the associated error estimate derived from an iterative convergence grid refinement, are presented. Computational results are based on an unstructured grid, Reynolds-averaged Navier-Stokes analysis. The validity of the approach to compute the associated error estimates, derived from a base grid to an extrapolated infinite-size grid, was first demonstrated on a sub-scaled wind tunnel model at representative ascent flow conditions for which the experimental data existed. Such analysis at the transonic flow conditions revealed a maximum deviation of about 23% between the computed longitudinal aerodynamic coefficients with the base grid and the measured data across the entire roll angles. This maximum deviation from the wind tunnel data was associated with the computed normal force coefficient at the transonic flow condition and was reduced to approximately 16% based on the infinite-size grid. However, all the computed aerodynamic coefficients with the base grid at the supersonic flow conditions showed a maximum deviation of only about 8% with that level being improved to approximately 5% for the infinite-size grid. The results and the error estimates based on the established procedure are also presented for the flight flow conditions.

Abdol-Hamid, Khaled S.↗

Semi-Empirical Validation of the Cross-Band Relative Absorption Technique for the Measurement of Molecular Mixing Ratios

Studies were performed to carry out semi-empirical validation of a new measurement approach we propose for molecular mixing ratios determination. The approach is based on relative measurements in bands of O2 and other molecules and as such may be best described as cross band relative absorption (CoBRA). . The current validation studies rely upon well verified and established theoretical and experimental databases, satellite data assimilations and modeling codes such as HITRAN, line-by-line radiative transfer model (LBLRTM), and the modern-era retrospective analysis for research and applications (MERRA). The approach holds promise for atmospheric mixing ratio measurements of CO2 and a variety of other molecules currently under investigation for several future satellite lidar missions. One of the advantages of the method is a significant reduction of the temperature sensitivity uncertainties which is illustrated with application to the ASCENDS mission for the measurement of CO2 mixing ratios (XCO2). Additional advantages of the method include the possibility to closely match cross-band weighting function combinations which is harder to achieve using conventional differential absorption techniques and the potential for additional corrections for water vapor and other interferences without using the data from numerical weather prediction (NWP) models.

Pliutau, Denis↗

Near Real Time Tools for ISS Plasma Science and Engineering Applications

The International Space Station (ISS) program utilizes a plasma environment forecast for estimating electrical charging hazards for crews during extravehicular activity (EVA). The process uses ionospheric electron density (Ne) and temperature (Te) measurements from the ISS Floating Potential Measurement Unit (FPMU) instrument suite with the assumption that the plasma conditions will remain constant for one to fourteen days with a low probability for a space weather event which would significantly change the environment before an EVA. FPMU data is typically not available during EVA's, therefore, the most recent FPMU data available for characterizing the state of the ionosphere during EVA is typically a day or two before the start of an EVA or after the EVA has been completed. Three near real time space weather tools under development for ISS applications are described here including: (a) Ne from ground based ionosonde measurements of foF2 (b) Ne from near real time satellite radio occultation measurements of electron density profiles (c) Ne, Te from a physics based ionosphere model These applications are used to characterize the ISS space plasma environment during EVA periods when FPMU data is not available, monitor for large changes in ionosphere density that could render the ionosphere forecast and plasma hazard assessment invalid, and validate the "persistence of conditions" forecast assumption. In addition, the tools are useful for providing space environment input to science payloads on ISS and anomaly investigations during periods the FPMU is not operating.

Minow, Joseph I.↗

Computational Fluid Dynamics Based Extraction of Heat Transfer Coefficient in Cryogenic Propellant Tanks

Current reduced-order thermal model for cryogenic propellant tanks is based on correlations built for flat plates collected in the 1950's. The use of these correlations suffers from: inaccurate geometry representation; inaccurate gravity orientation; ambiguous length scale; and lack of detailed validation. The work presented under this task uses the first-principles based Computational Fluid Dynamics (CFD) technique to compute heat transfer from tank wall to the cryogenic fluids, and extracts and correlates the equivalent heat transfer coefficient to support reduced-order thermal model. The CFD tool was first validated against available experimental data and commonly used correlations for natural convection along a vertically heated wall. Good agreements between the present prediction and experimental data have been found for flows in laminar as well turbulent regimes. The convective heat transfer between tank wall and cryogenic propellant, and that between tank wall and ullage gas were then simulated. The results showed that commonly used heat transfer correlations for either vertical or horizontal plate over predict heat transfer rate for the cryogenic tank, in some cases by as much as one order of magnitude. A characteristic length scale has been defined that can correlate all heat transfer coefficients for different fill levels into a single curve. This curve can be used for the reduced-order heat transfer model analysis.

Yang, H. Q.↗

Improving the Representation of Snow Crystal Properties with a Single-Moment Mircophysics Scheme

Single-moment microphysics schemes are utilized in an increasing number of applications and are widely available within numerical modeling packages, often executed in near real-time to aid in the issuance of weather forecasts and advisories. In order to simulate cloud microphysical and precipitation processes, a number of assumptions are made within these schemes. Snow crystals are often assumed to be spherical and of uniform density, and their size distribution intercept may be fixed to simplify calculation of the remaining parameters. Recently, the Canadian CloudSat/CALIPSO Validation Project (C3VP) provided aircraft observations of snow crystal size distributions and environmental state variables, sampling widespread snowfall associated with a passing extratropical cyclone on 22 January 2007. Aircraft instrumentation was supplemented by comparable surface estimations and sampling by two radars: the C-band, dual-polarimetric radar in King City, Ontario and the NASA CloudSat 94 GHz Cloud Profiling Radar. As radar systems respond to both hydrometeor mass and size distribution, they provide value when assessing the accuracy of cloud characteristics as simulated by a forecast model. However, simulation of the 94 GHz radar signal requires special attention, as radar backscatter is sensitive to the assumed crystal shape. Observations obtained during the 22 January 2007 event are used to validate assumptions of density and size distribution within the NASA Goddard six-class single-moment microphysics scheme. Two high resolution forecasts are performed on a 9-3-1 km grid, with C3VP-based alternative parameterizations incorporated and examined for improvement. In order to apply the CloudSat 94 GHz radar to model validation, the single scattering characteristics of various crystal types are used and demonstrate that the assumption of Mie spheres is insufficient for representing CloudSat reflectivity derived from winter precipitation. Furthermore, snow density and size distribution characteristics are allowed to vary with height, based upon direct aircraft estimates obtained from C3VP data. These combinations improve the representation of modeled clouds versus their radar-observed counterparts, based on profiles and vertical distributions of reflectivity. These meteorological events are commonplace within the mid-latitude cold season and present a challenge to operational forecasters. This study focuses on one event, likely representative of others during the winter season, and aims to improve the representation of snow for use in future operational forecasts.

Molthan, Andrew L.↗

Computational Framework for Machine-Learning-Enabled 13 C Fluxomics

13 C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for 13 C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical 13 C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.

13C metabolic flux analysis↗

Direct numerical simulations of three-component Rayleigh–Taylor mixing and an improved model for multicomponent reacting mixtures

We present direct numerical simulations of a three-layer Rayleigh–Taylor instability (RTI) problem with a configuration based on the experiments of Suchandra & Ranjan ( J. Fluid Mech. , vol. 974, 2023, A35) and Jacobs & Dalziel ( J. Fluid Mech. , vol. 542, 2005, pp. 251–279). The problem consists of a layer of light fluid between two layers of heavy fluid with an Atwood number of 0.3. These simulations are first validated through comparison with available experimental data. The validated simulations are then utilized to analyse statistics in this three-component flow. First, length scales are examined utilizing spectra and two-point spatial correlations of velocity and species concentration fluctuations. Next, joint probability density functions (p.d.f.s) of species concentration are compared against several model p.d.f.s representing generalizations of the bivariate beta distribution. Notably, the joint p.d.f.s do not appear to be accurately described by a Dirichlet distribution, indicating the marginal distributions do not conform to a beta distribution. Finally, similarity of the present configuration to three-component mixing found in inertial confinement fusion (ICF) applications is exploited to develop and validate an improved model for the impact of multicomponent mixing on thermonuclear (TN) reaction rates. A single time instant from the present simulations is chosen for a TN burn calculation under the hypothetical assumption of ICF materials and temperatures. Total TN output from this second calculation is then compared against the prediction of the improved model. The new model is found to accurately predict TN reaction rates in both premixed and non-premixed configurations.

42 ENGINEERING↗

Adaptive Reinforcement Learning (ARL) Control of a Multi-port Resonant Converter in UAV Systems

This study presents an adaptive reinforcement learning (ARL) control framework for a multi-port resonant converter used in hybrid unmanned aerial vehicle (UAV) power systems. The converter integrates high-frequency half-bridge input ports connected to a rectified engine–generator set and a battery energy storage system, along with a semi-bridgeless active rectifier supplying the propulsion load. A deep RL agent is trained to dynamically regulate inter-port phase-shift commands in real time based on flight conditions and load power demand. The ARL controller autonomously identifies phase-shift combinations that maximize conversion efficiency while maintaining stable and coordinated power flow, even under rapidly varying operating scenarios. This data-driven approach eliminates the need for explicit system modeling or extensive manual tuning and enables coordinated control among multiple power ports without inter-port communication. Experimental results validate that the ARL based strategy achieves reliable power sharing and consistently high-efficiency operation across diverse UAV operating conditions.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗

Comparison Between Numerically Simulated and Experimentally Measured Flowfield Quantities Behind a Pulsejet

Pulsed combustion is receiving renewed interest as a potential route to higher performance in air breathing propulsion systems. Pulsejets offer a simple experimental device with which to study unsteady combustion phenomena and validate simulations. Previous computational fluid dynamic (CFD) simulation work focused primarily on the pulsejet combustion and exhaust processes. This paper describes a new inlet sub-model which simulates the fluidic and mechanical operation of a valved pulsejet head. The governing equations for this sub-model are described. Sub-model validation is provided through comparisons of simulated and experimentally measured reed valve motion, and time averaged inlet mass flow rate. The updated pulsejet simulation, with the inlet sub-model implemented, is validated through comparison with experimentally measured combustion chamber pressure, inlet mass flow rate, operational frequency, and thrust. Additionally, the simulated pulsejet exhaust flowfield, which is dominated by a starting vortex ring, is compared with particle imaging velocimetry (PIV) measurements on the bases of velocity, vorticity, and vortex location. The results show good agreement between simulated and experimental data. The inlet sub-model is shown to be critical for the successful modeling of pulsejet operation. This sub-model correctly predicts both the inlet mass flow rate and its phase relationship with the combustion chamber pressure. As a result, the predicted pulsejet thrust agrees very well with experimental data.

Geng, Tao↗

Mapping 2000 2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 200-02010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approach An independent validation dataset was also developed using Google Earth imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 +/- 7.84 sq km.

Wang, Panshi↗