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At least 919 records · Page 51

Advances in dynamics and control of flexible spacecraft and space-based manipulations; Proceedings of the Symposium, ASME Winter Annual Meeting, Dallas, TX, Nov. 25-30, 1990

Consideration is given to control formulations for vibration suppression of an active structure in slewing motions, the use if distributed sensing in control of large flexible spacecraft, an improved shooting method for solving minimum-time maneuver problems, and nonlinear slew maneuver dynamics of large flexible spacecrafts. Attention is also given to initial experiments in trusterless locomotion control of a free-flying robot, dynamic analysis to evaluate viscoelastic passive damping augmentation for the Space Shuttle Remote Manipulator System, initial experiments in cooperative manipulation from a moving platform, and intelligent pipelined control architecture for remote robotic applications.

Joshi, Suresh M.↗

A Novel 3D DRAM Memory Cube Architecture for Space Applications

The first mainstream products in 3D IC design are memory devices where multiple memory tiers are horizontally integrated to offer manifold improvements compared with their 2D counterparts. Unfortunately, none of these existing 3D memory cubes are ready for harsh space environments. This paper presents a new memory cube architecture for space, based on vertical integration of Commercial Off-The-Shelf (COTS), 3D stacked, DRAM memory devices with a custom Radiation-Hardened-By-Design (RHBD) controller offering high memory capacity, robust reliability and low latency.Validation and evaluation of the ASIC controller will be conducted prior to tape-out on a custom FPGA-based emulator platform integrating the 3D-stack.

Yang-Scharlotta, Jean↗

Microstructural Characterization of Laser Powder Bed Fusion (L-PBF) Additively Manufactured Inconel 718 for Aerospace Application

Alloy 718 (Inconel 718) is used for aerospace applications because of its excellent corrosion resistance and mechanical properties. This alloy is particularly applicable in manufacturing components subjected to high temperatures in rocket engines, aero-engines, and gas turbines. Properties for this alloy when processing on systems from similar and different Laser Powder Bed Fusion (L-PBF) machines provide subtle differences due to process parameters, feedstock, and machine configurations. A series of sixteen L-PBF AM Inconel 718 geometric feature build plates have been evaluated for microstructure using optical microscopy. This study presents the details of the microstructure analysis concerning geometry and different machine platforms. Microstructural investigations of these samples included average grain width measurement for all the X-Y and Y-Z build layers and are accompanied by process parameters and powder characterization. The present work concludes with a discussion on the importance of captured differences among builds to understand the practical limitations among AM platforms.

Additive Manufacturing↗

Supporting Space Weather Modelling at the Community Coordinated Modeling Center (CCMC)

Space weather models are essential to our ability to understand and predict space weather events. Nonetheless, some of the most cutting-edge models may struggle to move past the initial research stage, remaining unknown and inaccessible to a wider research audience, thereby hindering validation, intercomparison and adoption of the models. The Community Coordinated Modeling Center (CCMC, https://ccmc.gsfc.nasa.gov) closes this gap by providing a convenient platform for hosting space weather models and associated services. Using these services, researchers and other end-users may exercise, evaluate, and intercompare contributed models, as well as collaborate on a growing archive of model run results. In this presentation, we will discuss current and planned capabilities in some of the model services at CCMC, including Runs-on-Request, Instant Runs, and Real-Time Continuous Runs. We will also review new models added to the extensive collection of space weather models hosted at CCMC. Finally, we will talk about our efforts at streamlining model delivery to CCMC, including support for containerized models and establishment of an open collaborative environment based on Amazon Web Services (AWS).

space weather↗

Enabling Collaborative Space Weather Research at the Community Coordinated Modeling Center (CCMC)

Space weather models have been actively developed by the international research community, covering extensive spatial and physical domains. Still, many of the ground-breaking models remain a granular effort, insulated from a wider research audience, particularly that in different domains, and possible end users. The Community Coordinated Modeling Center (CCMC, https://ccmc.gsfc.nasa.gov) seeks to remove such barriers to collaboration and coordination by providing a convenient platform for hosting space weather models and associated services. Using these services, researchers and other end-users may exercise, evaluate, and intercompare contributed models, as well as collaborate on a continuously updated archive of model run results. Moreover, the multi-disciplinary science support team at CCMC facilitates and enables collaboration across domains. In our presentation, we will discuss the space weather model services at CCMC, including Runs-on-Request, Real-Time Continuous Runs, and Instant Runs. We will also review new and updated models added to the extensive collection of space weather models hosted at CCMC. We will focus on trends in model, service, and science support utilization at CCMC as a proxy to the most pressing needs of the collaborative modeling community.

space weather↗

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential while Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗

A Bayesian Learning Approach to Wireless Outdoor Heatmap Construction using Deep Gaussian Process

We present a novel Bayesian learning approach to outdoor radio heatmap construction utilizing deep Gaussian process (GP). The proposed approach employs a two-layer hierarchy which consists of two cascaded Gaussian processes that are capable of modeling more complex input-output relations than standard single-layer Gaussian processes. Since deriving the exact model likelihood is challenging, a lower bound is optimized instead so that gradient descent-based methods can be performed to find out the optimal model parameters. Typically, inducing points are used in GPs to facilitate low-rank approximation of covariance (kernel) matrices for computation speedup. However, the inaccuracy induced by inducing points can accumulate when stacking multiple layers of GP which may hinder the performance of deep GP. Moreover, since inducing points need to be learned, having them at all layers of deep GP also incurs computational burden. To overcome the above challenges, in contrast to the canonical deep GP model, we use a modified architecture where a full standard GP resides in the first layer and inducing points are only introduced for the second layer. This modified architecture strikes a balance between model accuracy and training complexity. In the proposed model, the noise parameter of the first GP layer is also eliminated to improve the training efficiency as the noise parameter at the output of the second layer suffices to model the uncertainty in the output. The proposed approach is evaluated on real-world datasets, in the form of location-Received Signal Strength (RSS) pairs, collected from the Platform for Open Wireless Data-driven Experimental Research (POWDER) located at the campus of the University of Utah. Experiment results show that the proposed approach can achieve smaller prediction errors on various training and testing data configurations than DNN-based and GP-based methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

An Open-Source Decarbonization Analytics Framework: Designing for Low-Carbon Emission Districts and Communities: Preprint

This paper introduces an open-source analytics framework designed to assist in creating low or net-zero carbon buildings and urban districts. Integrated within URBANopt, an open-source platform for energy analysis in districts and communities, this framework equips researchers, architects, engineers, and other stakeholders with tools to evaluate the carbon footprint implications of their design choices. The framework enables the analysis of various scenarios, incorporating both historical and future emission factors, and can span across different climate zones, each with distinct grid and emissions characteristics. The results showcase the framework's capability to evaluate the impact of design upgrades and control strategies on carbon emissions in districts and communities. An illustrative analysis using a hypothetical district in Denver, Colorado, shows reduced emissions from energy efficiency upgrades and control strategies, highlighting the sensitivity in their effects on emissions and energy use.

buildings energy efficiency↗

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential While Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics: Preprint

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗

Comparing Control Performance Between Simulation and Experiment using the Microreactor Automated Control System Testbed

In the advanced reactor domain, a flexible and scalable software/hardware infrastructure is crucial for integrating and validating various control technologies. This study used the Microreactor Automated Control System (MACS) hardware platform as a testbed. MACS was originally designed to mirror Idaho National Laboratory (INL)'s Microreactor Applications Research Validation and Evaluation (MARVEL), a 85-kW thermal fission microreactor. It features control drums for simulated reactivity control; lights that function as a surrogate reactor core, with the brightness being proportional to the reactor power; and light sensors that emulate neutron detectors. To transform MACS into a physical twin of MARVEL for evaluating control methods, the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) software was employed. This software integrated the hardware with two models of the MARVEL core, based on Reactor Excursion and Leak Analysis Program (RELAP5-3D) and Monte Carlo N-Particle (MCNP) models. The study aimed to demonstrate the gap between control theory and actual practice—a gap that often necessitates empirical adjustments such as control gain retuning, filters, time discretization, and integrator anti-windup measures. Controllers were developed based on increasingly complex simulations without hardware, starting from the base MARVEL model and then introducing actuator saturation constraints and sensor noise. The final control strategy was then tested using MACS, and a comparative performance analysis was conducted.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

Modelling and Optimizing CSP-PV Hybrid Systems Using the Hybrid Optimization and Performance Platform (HOPP)

The hybridization of concentrating solar power (CSP) with thermal energy storage (TES), photovoltaics (PV), and electrochemical battery energy storage systems (BESS) has the potential to provide continuous, high-capacity-factor energy production at a lower cost than a PV-BESS or CSP with TES alone. Because of the system complexity of CSP technology, it is challenging to evaluate the technological and financial performance of a CSP-PV hybrid system without detailed modeling of annual operations. To address this challenge, we have developed a modeling framework for evaluating the performance and financial viability of CSP systems hybridized with PV and BESS technologies. This modeling effort incorporates CSP tower and trough configurations into an existing modeling tool recently developed by NREL, the Hybrid Optimization and Performance Platform (HOPP). This paper provides a brief overview of our methodology, as well as an example case study. CSP with TES hybridized with PV provides the best benefit-to-cost ratio compared to the other simulated technology combinations. However, for the conditions considered, this configuration only increases the benefit-to-cost ratio by about 1% compared to the CSP with TES configuration. The PV-BESS system provides the lowest benefit-to-cost ratio compared to the other configurations explored because of the relatively low capacity credit received by the system.

Hamilton, William (ORCID:0000000244654239)↗

Evaluation of DVD-R for Archival Applications

For more than a decade, CD-ROM and CD-R have provided an unprecedented level of reliability, low cost and cross-platform compatibility to support federal data archiving and distribution efforts. However, it should be remembered that years of effort were required to achieve the standardization that has supported the growth of the CD industry. Incompatibilities in the interpretation of the ISO-9660 standard on different operating systems had to be dealt with, and the imprecise specifications in the Orange Book Part n and Part Hi led to incompatibilities between CD-R media and CD-R recorders. Some of these issues were presented by the authors at Optical Data Storage '95. The major current problem with the use of CD technology is the growing volume of digital data that needs to be stored. CD-ROM collections of hundreds of volumes and CD-R collections of several thousand volumes are becoming almost too cumbersome to be useful. The emergence of Digital Video Disks Recorder (DVD-R) technology promises to reduce the number of discs required for archive applications by a factor of seven while providing improved reliability. It is important to identify problem areas for DVD-R media and provide guidelines to manufacturers, file system developers and users in order to provide reliable data storage and interchange. The Data Distribution Laboratory (DDL) at NASA's Jet Propulsion Laboratory began its evaluation of DVD-R technology in early 1998. The initial plan was to obtain a DVD-Recorder for preliminary testing, deploy reader hardware to user sites for compatibility testing, evaluate the quality and longevity of DVD-R media and develop proof-of-concept archive collections to test the reliability and usability of DVD-R media and jukebox hardware.

Martin, Michael D.↗

Evaluation of postural mechanisms under dynamic conditions

A stimulus delivery and data acquisition system for assessment of human posture was developed based on a digital computer and a translating platform. The movement of the platform acts to displace the subject's base of support while the computer tracks the corrections which are made by the subject to maintain balance. Various stimuli are used ranging from fast transients to sine waves.

Anderson, D. J.↗

Airborne lidar/radiometric measurements of cirrus cloud parameters and their application to LOWTRAN radiance evaluations

SRI has assembled an airborne lidar/radiometric instrumentation suite for mapping cirrus cloud distribution and analyzing cirrus cloud optical properties. Operation of upward viewing infrared radiometers from an airborne platform provides the optimum method of measuring high altitude cold cloud radiative properties with minimum interference from the thermal emission by the earth's surface and lower atmospheric components. Airborne installed sensors can also operate over large regional areas including water, urban, and mountain surfaces and above lower atmospheric convective clouds and haze layers. Currently available sensors installed on the SRI Queen Air aircraft are illustrated. Lidar and radiometric data records are processed for real time viewing on a color video screen. A cirrus cloud data example is presented as a black and white reproduction of a color display of data at the aircraft altitude of 12,000 ft, the 8 to 14 micron atmospheric radiation background was equivalent to a blackbody temperature of about -60 C and, therefore, the radiometer did not respond strongly to low density cirrus cloud concentrations detected by the lidar. Cloud blackbody temperatures (observed by radiometer) are shown plotted against midcloud temperatures (derived from lidar observed cloud heights and supporting temperature profiles) for data collected on 30 June and 28 July.

Uthe, Edward E.↗

Evaluating the pressure dependence of PZT structures using a virtual reality environment

Pb–Zr–Ti–O (PZT) perovskites span a large solid-solution range and have found widespread use due to their piezoelectric and ferroelectric properties that also span a large range. Crystal structure analysis via Rietveld refinement facilitates materials analysis via the extraction of the structural parameters. These parameters, often obtained as a function of an additional dimension (e.g., pressure), can help to diagnose materials response within a use environment. Often referred to as “ in-situ ” studies, these experiments provide an abundance of data. Viewing structural changes due to applied pressure conditions can give much-needed insight into materials performance. However, challenges exist for viewing/presenting results when the details are inherently three-dimensional (3D) in nature. For PZT perovskites, the use of polyhedra (e.g., Zr/Ti–O 6 octahedra) to view bonding/connectivity is beneficial; however, the visualization of the octahedra behavior with pressure dependence is less easily demonstrated due to the complexity of the added pressure dimension. We present a more intuitive visualization by projecting structural data into virtual reality (VR). We employ previously published structural data for Pb 0.99 (Zr 0.95 Ti 0.05 ) 0.98 Nb 0.02 O 3 as an exemplar for VR visualization of the PZT R3c crystal structure between ambient and 0.62 GPa pressure. This is accomplished via our in-house CAD2VR™ software platform and the new CrystalVR plugin. The use of the VR environment enables a more intuitive viewing experience, while enabling on-the-fly evaluation of crystal data, to form a detailed and comprehensive understanding of in-situ datasets. Discussion of methodology and tools for viewing are given, along with how recording results in video form can enable the viewing experience.

Rodriguez, Mark A. (ORCID:0000000269405044)↗

3D-ReG: A 3D ReRAM-based Heterogeneous Architecture for Training Deep Neural Networks

Deep neural network (DNN) models are being expanded to a broader range of applications. The computational capability of traditional hardware platforms cannot accommodate the growth of model complexity. Among recent technologies to accelerate DNN, resistive memory (ReRAM)-based processing-in-memory (PIM) emerged as a promising solution for DNN inference due to its high efficiency for matrix-based computation. We face two major technical challenges in extending the use of ReRAM-based accelerators for training: (1) full-precision data is essential in back-propagation; (2) the need to support both feed-forward and back-propagation aggravates the data-movement burden. We propose a heterogeneous architecture named as 3D-ReG, which leverages full-precision GPU to ensure training accuracy and low-overhead 3D integration to provide low-cost data movements. Moreover, we introduce conservative and aggressive task-mapping schemes, which partition the computation phases in different ways to balance execution efficiency and training accuracy. We evaluate 3D-ReG implemented with two 3D integration technologies, through-silicon vias (TSVs) and monolithic inter-tier vias (MIVs), and compare them with GPU-only and PIM-only counterparts. Various GPU-only platforms using two main-memory technologies (DRAM, ReRAM) and three interconnect technologies (2D, TSV, MIV) are evaluated as well. Experimental results show that 3D-ReG can achieve on average 5.64× training speedup and 3.56× higher energy efficiency compared with the GPU with DRAM as main memory, at the cost of 0.05%–3.39% accuracy drop. We define a new metric, gain-loss ratio (GLR), which quantitatively evaluates the capability of a DNN training hardware in terms of the model accuracy and hardware efficiency. The results of our comparison show that the aggressive task-mapping scheme on MIV-based 3D-ReG outperforms the other methods.

Computer Science↗

Extrusion‐Based Printing of Nanostructured Fatty Acid Gels Incorporated in Hydrogels

Soft materials with unique nanostructures such as lamellar, hexagonal, and cubic morphologies can replicate complex structures that have potential in various fields, including biomedical and industrial applications. However, a key challenge in advancing the broader applications of 3D printing for these nanostructured soft materials is insufficient mechanical properties that hinder their printability and compromise structural stability in the final product. In this study, the suitability of a fatty acid‐based lamellar gel is evaluated for direct extrusion‐based 3D printing. Here, the lamellar gel with varying water content is integrated with a photocurable hydrogel to preserve the shape and stability of the final prints. Complex 2D and 3D design patterns are used to assess extrusion behavior, structural stability, and print precision under varying pressures. Small‐angle X‐ray Scattering (SAXS) measurements reveal the formation of lamellar nanostructures and confirm their retention after photocuring in various gels. Rheological analysis confirms that these gels exhibit key properties suitable for extrusion‐based 3D printing, such as shear‐thinning behavior. Additionally, tensile testing is conducted to evaluate the mechanical properties across cured print samples. This study underscores the potential of nanostructured gels as a robust and versatile platform, facilitating the development of materials engineered for various applications.

36 MATERIALS SCIENCE↗