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At least 1,045 records · Page 58

Microgrid Preliminary Design Specification

The following document provides guidance for developing a microgrid preliminary design specification. Development of a microgrid preliminary design specification takes previous analysis has been done in coordination with key stakeholders to scope out and perform analysis on a microgrid conceptual design to be further developed into a preliminary design. The microgrid preliminary design specification outlines the functional requirements and recommendations for the preliminary design that can be put into request for information (RFI) or request for quote (RFQ) process in order to select a microgrid integrator to oversee the final design and construction process of the microgrid to be implemented. In addition to requirements, the RFI/RFQ needs to specify of the responsibilities of the microgrid integrator as well as the microgrid owner/operator of the completed microgrid as part of the microgrid preliminary design specification as well as procurement process to evaluate the bidders applying to be the microgrid integrators.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Technical feasibility of a modular dish solar electric system

The paper presents the technical feasibility of generating electricity by using a gas turbine in conjunction with a paraboloid of revolution (dish) solar concentrator. A conceptual design of a dish concentrator is obtained by parametrically examining the significant optical parameters. The optical performance analysis was conducted using a Monte Carlo ray-trace simulation program. The performance of four candidate thermodynamic cycles utilizing two working fluids was analyzed; the cycles were the regenerative and nonregenerative Brayton cycles of both open and closed type. Air and helium were the working fluids for the open and closed type respectively. Heat transfer from the solar radiation to the gas flowing in metal tubes at the receiver was also analyzed. A paraboloid of revolution dish with a cavity receiver using an open air regenerative Brayton cycle turbine emerged as a technically feasible concept in the power range from 30 to 100 KWth per module.

Gupta, B. P.↗

Preliminary Development of an Object-Oriented Optimization Tool

The National Aeronautics and Space Administration Dryden Flight Research Center has developed a FORTRAN-based object-oriented optimization (O3) tool that leverages existing tools and practices and allows easy integration and adoption of new state-of-the-art software. The object-oriented framework can integrate the analysis codes for multiple disciplines, as opposed to relying on one code to perform analysis for all disciplines. Optimization can thus take place within each discipline module, or in a loop between the central executive module and the discipline modules, or both. Six sample optimization problems are presented. The first four sample problems are based on simple mathematical equations; the fifth and sixth problems consider a three-bar truss, which is a classical example in structural synthesis. Instructions for preparing input data for the O3 tool are presented.

Pak, Chan-gi↗

Thermal Analysis of Cryogenic Hydrogen Liquid Separator

During launch for the new Space Launch System (SLS) liquid hydrogen is bleed through the engines during replenish, pre-press, and extended pre-press to condition the engines prior to launch. The predicted bleed flow rates are larger than for the shuttle program. A consequence of the increased flow rates is having liquif hydrogen in the vent system, which the facilities was never designed to handle. To remedy the problem a liquid separator is being designed in the system to accumulated the liquid propellant and protect the facility flare stack (which can only handle gas). The attached document is a presentation of the current thermalfluid analysis performed for the separator and will be presented at the Thermal and Fluid Analysis Workshop (NASA workshop) next week in Cleveland, Ohio.

liquid separator↗

Predicting Two-Dimensional Airfoil Performance Using Graph Neural Networks

Computer simulations require the use of meshes to simulate geometries. These meshes capture important geometric features of the design and can be used in machine learning modeling. This report explores the use of graph neural networks (GNNs) to learn features from two-dimensional (2D) airfoil designs represented as a set of nodes connected using edges. This type of network is common in aerospace applications: most geometries are represented as a mesh in order to perform analysis. The objective of this work is to use GNNs to predict the performance of 2D airfoils generated using the program XFOIL. The predicted performance parameters include bulk quantities such as coefficients of lift (C L ), drag (C d , C dp ), moment (C m ), and node-specific quantities such as coefficient of pressure (C p ). In this report, a spline convolutional graph-based neural network is compared with deep learning neural networks to predict both bulk and node-specific quantities. The findings indicate the GNNs are able to predict bulk quantities quite well; however, when the number of outputs is increased, the deep neural network (DNN) proves to be better in its prediction capability. Two different normalization strategies were compared in the training of both GNNs and DNNs: minmax and standard deviation. In both types of networks, standard deviation scaling proved to be the best.

machine learning↗

On-orbit frequency stability analysis of the GPS NAVSTAR-1 quartz clock and the NAVSTARs-6 and -8 rubidium clocks

An on-orbit frequency stability performance analysis of the GPS NAVSTAR-1 quartz clock and the NAVSTARs-6 and -8 rubidium clocks is presented. The clock offsets were obtained from measurements taken at the GPS monitor stations which use high performance cesium standards as a reference. Clock performance is characterized through the use of the Allan variance, which is evaluated for sample times of 15 minutes to two hours, and from one day to 10 days. The quartz and rubidium clocks' offsets were corrected for aging rate before computing the frequency stability. The effect of small errors in aging rate is presented for the NAVSTAR-8 rubidium clock's stability analysis. The analysis includes presentation of time and frequency residuals with respect to linear and quadratic models, which aid in obtaining aging rate values and identifying systematic and random effects. The frequency stability values were further processed with a time domain noise process analysis, which is used to classify random noise process and modulation type.

Mccaskill, T. B.↗

NASA Lighting Research, Test, & Analysis

The Habitability and Human Factors Branch, at Johnson Space Center, in Houston, TX, provides technical guidance for the development of spaceflight lighting requirements, verification of light system performance, analysis of integrated environmental lighting systems, and research of lighting-related human performance issues. The Habitability & Human Factors Lighting Team maintains two physical facilities that are integrated to provide support. The Lighting Environment Test Facility (LETF) provides a controlled darkroom environment for physical verification of lighting systems with photometric and spetrographic measurement systems. The Graphics Research & Analysis Facility (GRAF) maintains the capability for computer-based analysis of operational lighting environments. The combined capabilities of the Lighting Team at Johnson Space Center have been used for a wide range of lighting-related issues.

Clark, Toni↗

2.3.4.501 - Synthesis and Analysis of Performance-Advantaged Bioproducts

This project focuses on the synthesis and analysis of performance-advantaged bioproducts (PABPs). We have established collaborations with other BETO-funded projects and academic and industrial collaborators to source new molecules that have promising manufacturing pathways and that could be serve as performance-advantaged biochemicals or biopolymers. We conduct synthesis and characterization of biochemicals and biopolymers alongside techno-economic analysis and life cycle assessment to estimate their cost and environmental impacts relative to incumbent materials. As part of the project, Profs. Linda Broadbelt and Brent Shanks are developing computational pathway prediction tools to identify optimal production pathways for bio-based compounds via biological and chemo-catalytic transformations. When coupled to the polyID tool from the Inverse Design project, these tools will ultimately enable a narrowing of design space for PABPs. From FY21-FY23, we described a framework for benchmarking PAPBs, estimated the energy and GHG emissions for commodity organic chemicals, developed performance-advantaged nylons and polyesters from beta-ketoadipic acid, and produced lignin-based plasticizers. We have shown that aromatic amines can be used in performance thermosets and that polyhydroxyalkanoates with crosslinked side chains can exhibit rubber-like properties, along with repair and degradability. We are working actively with industry partners on scale-up and validation of multiple PABPs.

BIOMASS FUELS↗

An Effective Purification Process for the Nuclear Radiation Detector Tl 6 SeI 4

The semiconductor Tl6SeI4 was previously identified as a promising semiconductor for room temperature nuclear radiation detection. As the detection performance and carrier transport strongly depend on the concentration of impurity energy levels acting as scattering centers and carrier trapping, material purification is a crucial prerequisite step to obtain spectroscopic-grade detector performance. In this contribution, we present a highly efficient purification method using a bent ampule for evaporating Se, Tl2Se, and TlI precursors for Tl6Se4. On the basis of impurity analysis performed by glow discharge mass spectroscopy, the main impurities in Tl2Se were identified to be Pb, Bi, and Al, while in TlI the main impurities are Al and Sn. The bent-ampule method successfully reduces or removes the Cl, Pb, and Te impurities from the Se precursor, the Pb, Bi, and Al impurities from the Tl2Se precursor, and removes Sn from TU. Informed by the analysis results, density functional theory calculations were performed to study the identified impurities and related defects. The calculation results show that Bi and Al act as deep defect levels, which can be detrimental to the detector performance of Tl6SeI4. If the growth condition of Tl6SeI4 is Tl-rich/Se-poor, impurity of Si can introduce deep donors. However, it becomes electrically benign if growth conditions are Tl-poor/Se-rich, while Sn and Pb impurities are shallow donors. Centimeter-size Tl6SeI4 crystals were grown by the two-zone vertical Bridgman method using the purified precursors. The detector made of Tl6SeI4 crystal maintains the high resistivity on the order of 10(11) Omega.cm after purification, ideal for suppressing leakage current. The detector exhibits both full-energy and Tl escape photopeaks upon 122 keV gamma-ray from Co-57 radiation source. The electron mobility-lifetime product mu(e)tau(e) for TI6SeI4 detector is 8.1 X 10(-5) cm(2).V-1. On the basis of the carrier rise time measured from output pulses induced by 5.5 MeV alpha-particles from Am-241, the electron and hole mobilities were estimated to be 112 +/- 22 and 81 +/- 16 cm(2).V-1.s(-1), respectively, comparable to those of the leading detector materials HgI2 and TlBr. These results validate the potential of this compound for hard radiation detection, and the impurity analysis presented here allows future efforts to focus on reducing the concentration of the identified impurities.

Lin, Wenwen (ORCID:0000000216279558)↗

Thermal Performance Testing of Order Dependancy of Aerogels Multilayered Insulation

Robust multilayer insulation systems have long been a goal of many research projects. Such insulation systems must provide some degree of structural support and also mechanical integrity during loss of vacuum scenarios while continuing to provide insulative value to the vessel. Aerogel composite blankets can be the best insulation materials in ambient pressure environments; in high vacuum, the thermal performance of aerogel improves by about one order of magnitude. Standard multilayer insulation (MU) is typically 50% worse at ambient pressure and at soft vacuum, but as much as two or three orders of magnitude better at high vacuum. Different combinations of aerogel and multilayer insulation systems have been tested at Cryogenics Test Laboratory of NASA Kennedy Space Center. Analysis performed at Oak Ridge National Laboratory showed an importance to the relative location of the MU and aerogel blankets. Apparent thermal conductivity testing under cryogenic-vacuum conditions was performed to verify the analytical conclusion. Tests results are shown to be in agreement with the analysis which indicated that the best performance is obtained with aerogel layers located in the middle of the blanket insulation system.

Johnson, Wesley L.↗

SEE Rate Estimation: Model Complexity and Data Requirements

Statistical Methods outlined in [Ladbury, TNS20071 can be generalized for Monte Carlo Rate Calculation Methods Two Monte Carlo Approaches: a) Rate based on vendor-supplied (or reverse-engineered) model SEE testing and statistical analysis performed to validate model; b) Rate calculated based on model fit to SEE data Statistical analysis very similar to case for CREME96. Information Theory allows simultaneous consideration of multiple models with different complexities: a) Model with lowest AIC usually has greatest predictive power; b) Model averaging using AIC weights may give better performance if several models have similar good performance; and c) Rates can be bounded for a given confidence level over multiple models, as well as over the parameter space of a model.

Ladbury, Ray↗

Moderator Optimization for the Second Target Station Final Design

This report details the results from the optimization simulations performed for the final design of the Second Target Station (STS). This is a continuation of the analysis performed in 2022 for the preliminary design. To evaluate the impact of the design changes since 2022, the analysis was repeated with updated target and moderator models. Additionally, more degrees of freedom have been considered in the premoderator dimensions which provide a refinement compared to the previous optimization analysis.

43 PARTICLE ACCELERATORS↗

Ceramic nuclear fuel fracture modeling with the extended finite element method

Ceramic fuel pellets used in nuclear light water reactors experience significant fracture due to the high thermal gradients experienced under normal operating conditions. This has important effects on the performance of the fuel system. Because of this, a realistic, physically based fracture modeling capability is essential to predict fuel behavior in a wide variety of normal and off-normal conditions. The extended finite element method (X-FEM) is a powerful method to represent arbitrary propagating discrete cracks in finite element models, and has many characteristics that make it attractive for nuclear fuel performance analysis. This paper describes the implementation of X-FEM in a multiphysics fuel performance code and presents applications of that capability. These applications include several thermal mechanics fracture benchmark problems, which demonstrate the accuracy of this approach. It also includes application of this capability to study nuclear fuel fracture, both on stationary and propagating cracks. The study on stationary cracks shows the effects that varying the number of radial cracks and the length of those cracks have on the energy release rate. Here, the propagating crack case demonstrates random initiation and subsequent propagation of interacting thermally induced cracks during an initial ramp to full power with fresh fuel.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Pretraining Billion-Scale Geospatial Foundational Models on Frontier

As AI workloads increase in scope, generalization capability becomes challenging for small task-specific models and their demand for large amounts of labeled training samples increases. On the contrary, Foundation Models (FMs) are trained with internet-scale unlabeled data via self-supervised learning and have been shown to adapt to various tasks with minimal fine-tuning. Although large FMs have demonstrated significant impact in natural language processing and computer vision, efforts toward FMs for geospatial applications have been restricted to smaller size models, as pretraining larger models requires very large computing resources equipped with state-of-the-art hardware accelerators. Current satellite constellations collect 100+TBs of data a day, resulting in images that are billions of pixels and multimodal in nature. Such geospatial data poses unique challenges opening up new opportunities to develop FMs. We investigate billion scale FMs and HPC training profiles for geospatial applications by pretraining on publicly available data. We studied from end-to-end the performance and impact in the solution by scaling the model size. Our larger 3B parameter size model achieves up to 30% improvement in top1 scene classification accuracy when comparing a 100M parameter model. Moreover, we detail performance experiments on the Frontier supercomputer, America's first exascale system, where we study different model and data parallel approaches using PyTorch's Fully Sharded Data Parallel library. Specifically, we study variants of the Vision Transformer architecture (ViT), conducting performance analysis for ViT models with size up to 15B parameters. By discussing throughput and performance bottlenecks under different parallelism configurations, we offer insights on how to leverage such leadership-class HPC resources when developing large models for geospatial imagery applications.

Tsaris, Aristeidis (aris)↗

Communications Reliability for Vehicle Grid Integration

Electric Vehicles (EVs) adoption rate has been steadily increasing in the US leading to a growing number of charging stations including faster DC (Direct Current) chargers and slower Level 1 and Level 2 AC (Alternating Current) chargers. This increase in demand for electricity is further exacerbated by recent developments in Artificial Intelligence (AI) technology, advanced manufacturing, and digitization. These factors will require electric utilities to upgrade their infrastructure to keep up with the increasing electrical demand (especially during peak hours). An easy way to counteract the need for these upgrades is to shift a major chunk of active charge sessions (durations where there is energy transfer from charger to EV's propulsion battery) to off-peak hours thereby flattening the load curve and making the infrastructure more resilient. This concept is known as Smart Charge Management (SCM). EV owners also benefit from SCM since it lowers their charging costs and consequently their transportation costs by prioritizing charging during off-peak hours. SCM takes advantage of EV's capability to act as a controllable load or DER (Distributed Energy Resource). This report summarizes the reliability analysis performed on the communication required for two of these SCM use-cases. This analysis only focuses on SCM strategies for unidirectional charging (energy transfer from EVSE to EV or V1G) and not bidirectional charging.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Extension of a simplified computer program for analysis of solid-propellant rocket motors

A research project to develop a computer program for the preliminary design and performance analysis of solid propellant rocket engines is discussed. The following capabilities are included as computer program options: (1) treatment of wagon wheel cross sectional propellant configurations alone or in combination with circular perforated grains, (2) calculation of ignition transients with the igniter treated as a small rocket engine, (3) representation of spherical circular perforated grain ends as an alternative to the conical end surface approximation used in the original program, and (4) graphical presentation of program results using a digital plotter.

Sforzini, R. H.↗