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NASA NTRS2017

Modeling Microgravity Induced Fluid Redistribution Autoregulatory and Hydrostatic Enhancements

Space flight induces a marked cephalad (headward) redistribution of blood and interstitial fluid potentially resulting in a loss of venous tone and reduction in heart muscle efficiency upon introduction into the microgravity environment. Using various types of computational models, we are investigating how this fluid redistribution may induce intracranial pressure changes, relevant to reported reductions in astronaut visual acuity, part of the Visual Impairment and Intracranial Pressure (VIIP) syndrome. Methods: We utilize a lumped parameter cardiovascular system (CVS) model, augmented by compartments comprising the cerebral spinal fluid (CSF) space, as the primary tool to describe how microgravity, and the associated lack of hydrostatic gradient, impacts fluid redistribution. Models of ocular fluid pressures and biomechanics then accept the output of the above model as boundary condition input to allow more detailed, local analysis (see IWS Abstract by Ethier et al.). Recently, we enhanced the capabilities our previously reported CVS model through the implementation of robust autoregulatory mechanisms and a more fundamental approach to the implementation of hydrostatic mechanisms. Modifying the approach of Blanco et al., we implemented auto-regulation in a quasi-static manner, as an averaged effect across the span of one heartbeat. This approach reduced the higher frequency perturbations from the regulatory mechanism and was intended to allow longer simulation times (days) than models that implement within-beat regulatory mechanisms (minutes). A more fundamental approach to hydrostatics was implemented by a quasi-1D approach, in which compartment descriptions include compartment length, orientation and relative position, allowed for modeling of body orientation, relative body positioning and, in the future, alternative gravity environments. At this time the inclusion of hydrostatic mechanisms supplies additional capabilities to train and validate the CVS model with terrestrial data. Results and Conclusions: With the implementation of auto-regulation and hydrostatic modeling capabilities, the model performs as expected in the maintaining the CA (Central Artery) compartment pressure when simulating orientations ranging from supine to standing. The model appears to generally overpredict heart rate and thus cardiac output, possibly indicating sensitivity to the nominal heart rate, which is used as an initial set point of the regulation mechanisms. Despite this sensitivity, the model performs consistently for many hours of simulation time, indicating the success of our quasi-static implementation approach.

biomechanics↗

DOE OSTI2020

Advanced High-Performance Computational Modeling of the Seismic Response of High-Hazard and/or Nuclear Facilities and Critical Infrastructure at the NNSS

New methods for predicting the amplitude and variability of ground shaking from earthquakes (and explosions) are needed for seismic hazard analysis for buildings, nuclear power plants, and critical infrastructure at the NNSS. We are comparing existing 1-D and new 3-D geophysical methods for estimating the shear-wave velocity structure in the upper 30 meters of the ground surface (Vs30), which plays a major role in ground motion amplification and seismic response of buildings. We evaluate the performance of these methodologies at the U1a Complex at the NNSS and develop simple 1-D and high-resolution 3-D Vs30 models. We then emplace these high-resolution models into a background seismic velocity model. We will collaborate with Lawrence Livermore National Laboratory (LLNL) to conduct numerical modeling of the ground shaking at the NNSS using their high-performance computing technology and state-of-the-art ground motion simulation methodology. The primary work that was completed in FY 2019 was to acquire the seismic systems and familiarize staff at the NNSS with their use. We also worked on developing a collection plan with the Device Assembly Facility (DAF) at the NNSS, but due to time constraints and other ongoing projects at the DAF, we had to use U1a as a backup. We were able to coordinate the seismic survey, and we will complete the collection of seismic data in FY 2020. Additionally, during FY 2019, we completed the geologic framework model (GFM) for the U1a Complex and modeled the Yucca fault. LLNL worked with us through FY 2019 to prepare the data files for their modeling software and tested the software for reliability. In FY 2020 we will develop the end-to-end capability so that any facility could easily be modeled and the expected shaking from a local earthquake understood. The work in FY 2020 will include building fault models from the GFM and finalizing the velocity model analysis. The final simulations will be run for multiple rupture models, and final assessments will demonstrate the seismic hazard at the U1a Complex.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1990

Strategies for concurrent processing of complex algorithms in data driven architectures

The performance modeling and enhancement for periodic execution of large-grain, decision-free algorithms in data flow architectures is examined. Applications include real-time implementation of control and signal processing algorithms where performance is required to be highly predictable. The mapping of algorithms onto the specified class of data flow architectures is realized by a marked graph model called ATAMM (Algorithm To Architecture Mapping Model). Performance measures and bounds are established. Algorithm transformation techniques are identified for performance enhancement and reduction of resource (computing element) requirements. A systematic design procedure is described for generating operating conditions for predictable performance both with and without resource constraints. An ATAMM simulator is used to test and validate the performance prediction by the design procedure. Experiments on a three resource testbed provide verification of the ATAMM model and the design procedure.

Stoughton, John W.↗

DOE OSTI2023

Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia

Abstract Background and hypothesis Neuroimaging-based machine learning (ML) algorithms have the potential to aid the clinical diagnosis of schizophrenia. However, literature on the effect of prevalent comorbidities such as substance use disorder (SUD) and antisocial personality (ASPD) on these models’ performance has remained unexplored. We investigated whether the presence of SUD or ASPD affects the performance of neuroimaging-based ML models trained to discern patients with schizophrenia (SCH) from controls. Study design We trained an ML model on structural MRI data from public datasets to distinguish between SCH and controls (SCH = 347, controls = 341). We then investigated the model’s performance in two independent samples of individuals undergoing forensic psychiatric examination: sample 1 was used for sensitivity analysis to discern ASPD (N = 52) from SCH (N = 66), and sample 2 was used for specificity analysis to discern ASPD (N = 26) from controls (N = 25). Both samples included individuals with SUD. Study results In sample 1, 94.4% of SCH with comorbid ASPD and SUD were classified as SCH, followed by patients with SCH + SUD (78.8% classified as SCH) and patients with SCH (60.0% classified as SCH). The model failed to discern SCH without comorbidities from ASPD + SUD (AUC = 0.562, 95%CI = 0.400–0.723). In sample 2, the model’s specificity to predict controls was 84.0%. In both samples, about half of the ASPD + SUD were misclassified as SCH. Data-driven functional characterization revealed associations between the classification as SCH and cognition-related brain regions. Conclusion Altogether, ASPD and SUD appear to have effects on ML prediction performance, which potentially results from converging cognition-related brain abnormalities between SCH, ASPD, and SUD.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Modeling the performance and faradaic efficiency of solid oxide electrolysis cells using doped barium zirconate perovskite electrolytes

Y-doped BaZrO 3 (BaZr 1 x Y x O 3 δ , or “BZY”), a proton-conducting ceramic featuring high bulk conductivity and good chemical stability, is a promising electrolyte material for solid oxide electrolysis cells. Further doping with Ce and/or Yb (creating materials “BCZY” and “BCZYYb”) can improve conductivity and sintering properties, but at significant penalty to cells’ faradaic efficiency (FE). Studies have proposed that reduction of lattice Ce can occur in the hydrogen electrode, which consumes some hydrogen produced by the hydrogen evolution reaction, leading to decreased FE. Despite studies suggesting this phenomenon, the mechanism is largely unknown. In this work, we developed a multiphysics model to study the transport of multiple defect species and the performance of BZY, BCZY, and BCZYYb, capturing the tradeoff between enhanced performance at the cost of FE for BCZY and BCZYYb electrolytes compared to BZY. We also found that increasing the water content of the anode gas supply lowers the current output of the cell but results in better FE. The model, which uses several parameters previously unavailable in the literature, was validated to experiments varying temperature, steam water content, and electrolyte material, as well as two performance metrics (performance curves and FE). Results verify and explain observed trends, informing future work on Ce-doped BZY electrolytes.

08 HYDROGEN↗

NASA NTRS2005

Experimental and analytical investigation of a modified ring cusp NSTAR engine

A series of experimental measurements on a modified laboratory NSTAR engine were used to validate a zero dimensional analytical discharge performance model of a ring cusp ion thruster. The model predicts the discharge performance of a ring cusp NSTAR thruster as a function the magnetic field configuration, thruster geometry, and throttle level. Analytical formalisms for electron and ion confinement are used to predict the ionization efficiency for a given thruster design. Explicit determination of discharge loss and volume averaged plasma parameters are also obtained. The model was used to predict the performance of the nominal and modified three and four ring cusp 30-cm ion thruster configurations operating at the full power (2.3 kW) NSTAR throttle level. Experimental measurements of the modified engine configuration discharge loss compare well with the predicted value for propellant utilizations from 80 to 95%. The theory, as validated by experiment, indicates that increasing the magnetic strength of the minimum closed reduces maxwellian electron diffusion and electrostatically confines the ion population and subsequent loss to the anode wall. The theory also indicates that increasing the cusp strength and minimizing the cusp area improves primary electron confinement increasing the probability of an ionization collision prior to loss at the cusp.

cathodes↗

DOE OSTI2020

Dynamics of Fungal and Bacterial Biomass Carbon in Natural Ecosystems: Site-level Applications of the CLM-Microbe Model

Explicitly representing microbial processes has been recognized as a key improvement to Earth system models for the realistic projections of soil carbon (C) and climate dynamics. The CLM-Microbe model builds upon the CLM4.5 and explicitly represents two major soil microbial groups, fungi and bacteria. Based on the compiled time-series data of fungal (FBC) and bacterial (BBC) biomass C from nine biomes, we parameterized and validated the CLM-Microbe model, and further conducted sensitivity analysis and uncertainty analysis for simulating C cycling. The model performance was evaluated with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) for relative change in FBC and BBC. The CLM-Microbe model is able to reasonably capture the seasonal dynamics of FBC and BBC across biomes, particularly for tropical/subtropical forest, temperate broadleaf forest, and grassland, with MAE < 0.49 for FBC and <0.36 for BBC and RMSE <0.52 FBC and <0.39 for BBC, while R2 values are relatively smaller in some biomes (e.g., shrub) due to small sample sizes. We found good consistencies between simulated and observed FBC (R 2 =0.70, P<0.001) and BBC (R 2 =0.26, P<0.05) on average across biomes, but the model is not able to fully capture the large variation in observed FBC and BBC. Sensitivity analysis shows the most critical parameters are turnover rate, carbon-to-nitrogen ratio of fungi and bacteria, and microbial assimilation efficiency. This study confirms that the explicit representation of soil microbial mechanisms enhances model performance in simulating C variables such as heterotrophic respiration and soil organic C density. The further application of the CLM-Microbe model would deepen our understanding of microbial contributions to the global C cycle.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Error analysis of a hybrid control drum worth model

This paper presents a perturbation-based model for control drum worth prediction which employs both physics-based and statistics-based components. Control drums, or control shims, are cylindrical in shape and span the axial length of the core. A portion of the cylinder is coated in neutron absorbing material and the drum can rotate to introduce the absorbing material to the body of the core to reduce reactivity. The model can be expensive to create due to the requirement for full-core Monte Carlo eigenvalue calculations. Therefore, it is important to analyze how the errors in Monte Carlo calculated k{sub eff} used for model training affect model performance. It was found that the error in predicted criticalities could average to 70 pcm in the most complex form of the model and 215 pcm in the simplest form of the model. Furthermore, it was found that the Monte Carlo uncertainty in quantities calculated with Serpent used to train the models had minimal impact on the error observed from the model. Lastly, one of the forms of the hybrid model could be trained in considerably less computational time if the Monte Carlo calculations were run to higher uncertainty in k{sub eff} with a small penalty to model performance.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1987

Solving the Cauchy-Riemann equations on parallel computers

Discussed is the implementation of a single algorithm on three parallel-vector computers. The algorithm is a relaxation scheme for the solution of the Cauchy-Riemann equations; a set of coupled first order partial differential equations. The computers were chosen so as to encompass a variety of architectures. They are: the MPP, and SIMD machine with 16K bit serial processors; FLEX/32, an MIMD machine with 20 processors; and CRAY/2, an MIMD machine with four vector processors. The machine architectures are briefly described. The implementation of the algorithm is discussed in relation to these architectures and measures of the performance on each machine are given. Simple performance models are used to describe the performance. These models highlight the bottlenecks and limiting factors for this algorithm on these architectures. Conclusions are presented.

Fatoohi, Raad A.↗

NASA NTRS1987

Implementation of an ADI method on parallel computers

The implementation of an ADI method for solving the diffusion equation on three parallel/vector computers is discussed. The computers were chosen so as to encompass a variety of architectures. They are: the MPP, an SIMD machine with 16K bit serial processors; FLEX/32, an MIMD machine with 20 processors; and CRAY/2, an MIMD machine with four vector processors. The Gaussian elimination algorithm is used to solve a set of tridiagonal systems on the FLEX/32 and CRAY/2 while the cyclic elimination algorithm is used to solve these systems on the MPP. The implementation of the method is discussed in relation to these architectures and measures of the performance on each machine are given. Simple performance models are used to describe the performance. These models highlight the bottlenecks and limiting factors for this algorithm on these architectures. Finally, conclusions are presented.

Fatoohi, Raad A.↗

NASA NTRS1988

Implementation and analysis of a Navier-Stokes algorithm on parallel computers

The results of the implementation of a Navier-Stokes algorithm on three parallel/vector computers are presented. The object of this research is to determine how well, or poorly, a single numerical algorithm would map onto three different architectures. The algorithm is a compact difference scheme for the solution of the incompressible, two-dimensional, time-dependent Navier-Stokes equations. The computers were chosen so as to encompass a variety of architectures. They are the following: the MPP, an SIMD machine with 16K bit serial processors; Flex/32, an MIMD machine with 20 processors; and Cray/2. The implementation of the algorithm is discussed in relation to these architectures and measures of the performance on each machine are given. The basic comparison is among SIMD instruction parallelism on the MPP, MIMD process parallelism on the Flex/32, and vectorization of a serial code on the Cray/2. Simple performance models are used to describe the performance. These models highlight the bottlenecks and limiting factors for this algorithm on these architectures. Finally, conclusions are presented.

Fatoohi, Raad A.↗

NASA NTRS1987

Implementation of an ADI method on parallel computers

In this paper the implementation of an ADI method for solving the diffusion equation on three parallel/vector computers is discussed. The computers were chosen so as to encompass a variety of architectures. They are the MPP, an SIMD machine with 16-Kbit serial processors; Flex/32, an MIMD machine with 20 processors; and Cray/2, an MIMD machine with four vector processors. The Gaussian elimination algorithm is used to solve a set of tridiagonal systems on the Flex/32 and Cray/2 while the cyclic elimination algorithm is used to solve these systems on the MPP. The implementation of the method is discussed in relation to these architectures and measures of the performance on each machine are given. Simple performance models are used to describe the performance. These models highlight the bottlenecks and limiting factors for this algorithm on these architectures. Finally conclusions are presented.

Fatoohi, Raad A.↗

NASA NTRS2001

Development of Numerical Models for Performance Predictions of Single-Photon Avalanche Photodetectors (SPAP) for the 2-Micron Regime

Field dependent drift velocity results are presented for electron transport in bulk Indium Arsenide (InAs) material based on a Monte Carlo model, which includes an analytical treatment of band-to-band impact ionization. Avalanche multiplication and related excess noise factor (F) are computed as a function of device length and applied voltage. A decrease in F with increases in device length is obtained. The results suggest an inherent utility for InAs-based single-photon avalanche detectors, particularly around the 2 microns region of interest for atmospheric remote sensing applications. The dark current response was also evaluated. The role of the various components has been analyzed. For shorter devices, the tunneling component is shown to dominate at low temperatures. Finally, possible structures for enhanced photodetection are proposed for future research.

Joshi, Ravindra P.↗

NASA NTRS2022

Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery

In mountainous environments, topography strongly affects the reflectance due to illumination effects and cast shadows, which introduce errors in land cover classifications. However, topographic correction is not routinely implemented in standard data pre-processing chains (e.g., Landsat Analysis Ready Data), and there is a lack of consensus whether topographic correction is necessary, and if so, how to conduct it. Furthermore, methods that correct simultaneously for atmospheric and topographic effects are becoming available, but they have not been compared directly. Our objects were to investigate (1) the effectiveness of two topographic correction approaches that integrate atmospheric and topographic correction, (2) improvements in classification accuracy when analyzing topographically corrected single-date imagery (14 July 2016 and 2 October 2016), versus a full Landsat time series from 2014 to 2016, and 3) improvements in classification accuracy when including additional terrain information (i.e., topographic slope, elevation, and aspect). We developed a physical based model and compared it with an enhanced C-correction, both of which integrate atmospheric and topographic correction. We compared classification accuracies with and without topographic correction using combinations of single-date imagery, image composites and spectral-temporal metrics generated from the full Landsat time series, and additional terrain information in the Caucasus Mountains. We found that both the enhanced C-correction and the physical model performed very well and largely eliminated the correlation (Pearson’s correlation coefficient r ranges from 0.06 to 0.24) between surface reflectance and illumination condition, but the physical model performed best (r ranges from 0.05 to 0.11). Both image composites, and spectral-temporal metrics generated from corrected imagery, resulted in significantly (p ≤ 0.05) higher classification accuracies and better forest classifications, especially for the mixed forests. Adding terrain information reduced classification error significantly, but not as much as topographic correction. In summary, topographic correction remains necessary, even when analyzing a full Landsat time series and including a digital elevation model in the classification. We recommend that topographic correction should be applied when analyzing Landsat satellite imagery in mountainous region for forest cover classification.

Atmospheric correction↗

NASA NTRS1985

Iterative solution of large, sparse linear systems on a static data flow architecture - Performance studies

The applicability of static data flow architectures to the iterative solution of sparse linear systems of equations is investigated. An analytic performance model of a static data flow computation is developed. This model includes both spatial parallelism, concurrent execution in multiple PE's, and pipelining, the streaming of data from array memories through the PE's. The performance model is used to analyze a row partitioned iterative algorithm for solving sparse linear systems of algebraic equations. Based on this analysis, design parameters for the static data flow architecture as a function of matrix sparsity and dimension are proposed.

Reed, D. A.↗

NASA NTRS1993

CFD simulation of coaxial injectors

The development of improved performance models for the Space Shuttle Main Engine (SSME) is an important, ongoing program at NASA MSFC. These models allow prediction of overall system performance, as well as analysis of run-time anomalies which might adversely affect engine performance or safety. Due to the complexity of the flow fields associated with the SSME, NASA has increasingly turned to Computational Fluid Dynamics (CFD) techniques as modeling tools. An important component of the SSME system is the fuel preburner, which consists of a cylindrical chamber with a plate containing 264 coaxial injector elements at one end. A fuel rich mixture of gaseous hydrogen and liquid oxygen is injected and combusted in the chamber. This process preheats the hydrogen fuel before it enters the main combustion chamber, powers the hydrogen turbo-pump, and provides a heat dump for nozzle cooling. Issues of interest include the temperature and pressure fields at the turbine inlet and the thermal compatibility between the preburner chamber and injector plate. Performance anomalies can occur due to incomplete combustion, blocked injector ports, etc. The performance model should include the capability to simulate the effects of these anomalies. The current approach to the numerical simulation of the SSME fuel preburner flow field is to use a global model based on the MSFC sponsored FNDS code. This code does not have the capabilities of modeling several aspects of the problem such as detailed modeling of the coaxial injectors. Therefore, an effort has been initiated to develop a detailed simulation of the preburner coaxial injectors and provide gas phase boundary conditions just downstream of the injector face as input to the FDNS code. This simulation should include three-dimensional geometric effects such as proximity of injectors to baffles and chamber walls and interaction between injectors. This report describes an investigation into the numerical simulation of GH2/LOX coaxial injectors. The following sections will discuss the physical aspects of injectors, the CFD code employed, and preliminary results of a simulation of a single coaxial injector for which experimental data is available. It is hoped that this work will lay the foundation for the development of a unique and useful tool to support the SSME program.

Landrum, D. Brian↗

NASA NTRS2000

FACET: Future ATM Concepts Evaluation Tool

FACET (Future ATM Concepts Evaluation Tool) is an Air Traffic Management research tool being developed at the NASA Ames Research Center. This paper describes the design, architecture and functionalities of FACET. The purpose of FACET is to provide E simulation environment for exploration, development and evaluation of advanced ATM concepts. Examples of these concepts include new ATM paradigms such as Distributed Air-Ground Traffic Management, airspace redesign and new Decision Support Tools (DSTs) for controllers working within the operational procedures of the existing air traffic control system. FACET is currently capable of modeling system-wide en route airspace operations over the contiguous United States. Airspace models (e.g., Center/sector boundaries, airways, locations of navigation aids and airports) are available from databases. A core capability of FACET is the modeling of aircraft trajectories. Using round-earth kinematic equations, aircraft can be flown along flight plan routes or great circle routes as they climb, cruise and descend according to their individual aircraft-type performance models. Performance parameters (e.g., climb/descent rates and speeds, cruise speeds) are obtained from data table lookups. Heading, airspeed and altitude-rate dynamics are also modeled. Additional functionalities will be added as necessary for specific applications. FACET software is written in Java and C programming languages. It is platform-independent, and can be run on a variety of computers. FACET has been designed with a modular software architecture to enable rapid integration of research prototype implementations of new ATM concepts. There are several advanced ATM concepts that are currently being implemented in FACET airborne separation assurance, dynamic density predictions, airspace redesign (re-sectorization), benefits of a controller DST for direct-routing, and the integration of commercial space transportation system operations into the U.S. National Airspace System (NAS).

Bilmoria, Karl D.↗

DOE OSTI2025

Hydrologic Model Data for the East Fork Poplar Creek Watershed Simulated with the Advanced Terrestrial Simulator (ATS): Streamflow and Network Expansion–Contraction Dynamics

This dataset supports hydrologic modeling and stream network expansion–contraction analysis for the East Fork Poplar Creek (EFPC) Watershed in Tennessee. It includes a Jupyter notebook for model setup, model configuration files, simulation outputs, and derived products used to evaluate model performance and investigate stream dynamics under varying hydrologic conditions. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations using a stream-aligned mesh. Outputs include high-resolution time series of streamflow, active network length, water table depth, and related hydrologic variables. Also included are spatially explicit stream persistency indices and classifications of reaches as perennial or non-perennial. These data facilitate reproducibility and support further research on stream intermittency and variability in network extent.The model data archive is organized in following directories:1) model_setup_inputsContains the Watershed Workflow Jupyter notebooks (accessed through any open source code editor), selected input datasets, and resulting ATS input files, including XML files (access through any open source code editor), computational mesh (.exo files can be viewed using Paraview), and meteorological forcing files (.h5 files can be accessed through h5py python package and HDFView open source software). 2) model_outputsIncludes ATS simulation outputs relevant to this study. Time series of spatially integrated or averaged variables (e.g., streamflow, water table depth) are provided as CSV files. Select spatial fields (e.g., ponded depth and water table depth) are saved as pickled Python objects to reduce file size, and can be accessed through pickle package in Python. Key geometry objects from Watershed Workflow—such as the surface mesh and river tree—are also included to support analysis of streamflow persistency and expansion–contraction dynamics. These files can also be accessed through Watershed Workflow Python package.3) model_evaluationProvides observed streamflow time series and field survey-based flow regime classifications used to evaluate model performance. Jupyter notebooks for processing ATS outputs and comparing model predictions with observations to build confidence in the model prior to scientific analysis are also included.4) Q_L_relationshipsContains workflows for generating time series of discharge, active network length, and related hydrologic variables used in the stream network expansion–contraction analysis. Includes routines for delineating baseflow-dominated periods. For each catchment, notebooks and processed data (as pickled DataFrames accessed through Pandas Python package) are provided. 5) figure_scriptsProvides the Jupyter notebooks used to generate the figures presented in the paper.

54 ENVIRONMENTAL SCIENCES↗