Search NASA⌕ Search

SEARCH · Search NASA

Results for “Computing, Instrumentation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 991 records · Page 55

Presentation: Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning: An overview of methods and results

This presentation is a culmination of work which has occurred over the course of a 10-week internship. Anomaly detection methods must be both robust enough to detect subtle anomalies yet not so sensitive to report false positives, which would result significant loss of revenue. Methods currently being developed for autonomous systems are often pursuing a Digital Twin method, which will look at the entire system and model it as a whole. This presentation, however, focuses less on direct application to an NPP, rather acting as a proof of concept for the methods developed. For the project, we look to develop methods to analyze steady-state data and report anomalies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

AI-based Detection and Defense Against Cyberattacks in Distributed Energy Resources

This study will provide comprehensive artificial intelligence (AI)-based solution tools for network security, malware prevention, and sensor data anomaly detection for distributed energy resource (DER) research, development, and demonstration. DER technologies are energy systems (e.g., solar panels, wind turbines, and energy storage systems) that are often connected to the internet and thus vulnerable to cyberattacks. Cybersecurity should be of primary concern for DERs, which is why we propose an integrated multi-layer cyber-defense system for DERs. This system encompasses risk assessments, network security, malware prevention, and detection of anomalies in the sensor data. Implementation of a comprehensive risk assessment with an overview of the model architecture should be the primary step, and should include the potential impact of experiencing, at a given time, one or more cyberattacks on the system. The second step is to ensure that the network security includes firewalls, intrusion detection, and malware prevention. The third step is to provide solution tools that enable sensor data anomaly detection for DERs. By incorporating these considerations into DER research, development, and demonstration, organizations can help ensure the safety and security of their systems and protect against potential cyberattacks.

20 FOSSIL-FUELED POWER PLANTS↗

Presentation: Reactor Containment Passive Safety Analysis: Steam Condensation in Presence of Non-condensable Gas Scaled Experiment and Modeling

This study presents steam condensation scaled experiments and semi-empirical models in presence of nitrogen--a noncondensable gas (NCG), simulating air in the reactor containment--to support water-cooled small modular reactors (SMRs) passive containment cooling system (PCCS) design and analysis. Previous experimental studies on PCCS are focused on fixed and smaller tube (mostly 2-in.) geometries and specific test condition variations, bringing challenges with geometric scaling and mismatching with SMR prototypic design. To address these challenges, this study presents steam condensation test dataset obtained from three scaled test sections of 1-, 2-, and 4-in.-diameter steam condensers with an annular/jacket cooling of 2-, 3-, and 6 in.-diameter tubes, respectively. Test data were collected for steam ranges from 58 to 63 kg/hr., and NCG flow of 4.4 to 13.3 kg/hr. Annular cooling water flow was varied to obtain required testing conditions of saturated steam inlet and fully condensed outlet. Axial temperature test data of bulk cooling water, steam and condensate were collected by thermocouples for three test sections and various steam-NCG mixing/testing conditions. A standard data reduction method was adopted--utilizing iterative and nodalized mass and heat transfer calculation to estimate axial local heat fluxes, heat transfer coefficients (HTCs), condensation rates, film thickness, and Nusselt number. Based on the obtained dataset semi-empirical model results--a ratio of experimental and Nusselt's theoretical HTC are presented. Such results and findings are supportive of developing scaled-up testing facility, to enable model validations and accelerate next generation of reactors development and deployment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Analysis of Waste Material Feedstocks Using Laser-Induced Breakdown Spectroscopy and Machine Learning

Predicting properties such as heating value, ash fusion temperature, and mineral ash composition from Laser-Induced Breakdown Spectroscopy (LIBS) data can make gasifiers more flexible to different feedstocks. Understanding these feedstock properties in-situ improves feedstock conversion modelling methods that allow for consistent operation, higher carbon conversion, and reduced fouling and erosion rates. The purpose of this study is to demonstrate methods for model creation that take LIBS data as predictor features and estimate higher order material properties as a function of feedstock material properties. Six samples were chosen to represent a mixture of abundant and carbon rich waste materials. LIBS measurements were performed on these samples for elemental wavelengths and intensity values. Laboratory analytical results were obtained for each sample’s heating value, proximate and ultimate analysis, mineral ash composition, ash fusion temperatures, and viscosity temperatures. Thermal conductivity was measured using a HotDisk TPS 2500S. LIBS measurements were processed and used as predictor features for machine learning (ML) models to predict the sample’s material properties. Predictor feature selection algorithms, particularly minimum redundancy maximum relevance (mRMR), reduced the dimensionality of ML models. Many modelling methods such as Gaussian process regression (GPR), regression tree, neural networks (NN), and support vector machines (SVM) were demonstrated to be effective at predicting higher order properties; however, mRMR with GPR stood out as a clear winning combination.

01 COAL, LIGNITE, AND PEAT↗

Multi-purpose quantum laboratories from superconducting circuits

Superconducting circuits (SCs) are the cornerstone of modern quantum technology, enabling scalable computing through coherent control of macroscopic quantum states. Through a legacy that predates modern quantum computing, SCs have emerged as high-precision instruments for discovery. In this review, we highlight the role of SCs as general-purpose quantum laboratories, outlining the emerging landscape of correlated matter-circuit science. We review and unify the capabilities of superconducting quantum hardware across condensed matter, high energy and quantum information sciences. We trace the technical evolution of these architectures, illustrating how their foundational development has culminated in a toolkit for resolving the complexities of macroscopic quantum states.

Arora, Arpit [UCLA, Los Angeles (main); UCLA; Haim↗

HamLib: A Library of Hamiltonians for Benchmarking Quantum Algorithms and Hardware

For a considerable time, large datasets containing problem instances have proven valuable for analyzing computer hardware, software, and algorithms. One notable example of the value of large datasets is ImageNet [1], a vast repository of images that has been instrumental in testing numerous deep learning packages. Similarly, in the domain of computational chemistry and materials science, the availability of extensive datasets such as the Protein Data Bank [2], the Materials Project [3], and QM9 [4] has greatly facilitated the evaluation of new algorithms and software approaches, while also promoting standardization within the field. These well-defined datasets and problem instances, in turn, serve as the foundation for creating benchmarking suites like MLPerf [5] and LINPACK [6], [7]. These suites enable fair and rigorous comparisons of different methodologies and solutions, fostering continuous advancements in various areas of computer science and beyond.

Sawaya, Nicolas PD↗

YBCO-on-Kapton: Material for High-Density Quan-tum Computer Interconnects with Ultra-Low Thermal Conductance

Development of practical quantum computers would require 1000's of qubits and equally large number of read-outs, bias and drive lines. New materials that combine low passive loss and good RF properties are needed. In this study we report performance of signal interconnects comprised of high-temperature YBCO films that are exfoliated from a metal substrate and transferred to a E-Kapton tape. The technology offers low-loss transition be-tween 60 K to sub-mK environments and interfacing with off-shelf flexible silicon electronics. We demonstrate < 1 dB/m at-tenuation at 6 GHz at 77 K. The microstrip assemblies demon-strated no degradation upon multiple cycles to liquid Nitrogen and conduction cooling down to 18 K. We discuss designs of a practical high-density signal cable with passive heat load well below 1 μW per line. The YBCO-on-Kapton technology offers a practical pathway to large error-corrected quantum computing systems.

47 OTHER INSTRUMENTATION↗

Comparison of horizontal winds from the LIMS satellite instrument with rocket measurements

Statistical results are given for a comparison between horizontal geostrophic winds computed from satellite height data and all available in situ rocket wind soundings during a 7-month period. The satellite data are the daily mapped fields from the Nimbus 7 Limb Infrared Monitor of the Stratosphere (LIMS) instrument, which extend from 100 to 0.1 mbar. Results indicate that in both the tropics and the extratropical Northern Hemisphere, the average zonal and meridional wind speeds agree to within 2-4 m/s throughout the stratosphere. The rms differences are much larger, with values of 5-10 m/s in the lower stratosphere, increasing to 20-40 m/s in the lower mesosphere. Time series show that LIMS and rocketsonde zonal wind speeds show coherent variations with temporal periods of 1-2 weeks and more, and both exhibit irregular variations on time scales of less than one week.

Smith, A. K.↗

Computer sciences

The Computer Science Program provides advanced concepts, techniques, system architectures, algorithms, and software for both space and aeronautics information sciences and computer systems. The overall goal is to provide the technical foundation within NASA for the advancement of computing technology in aerospace applications. The research program is improving the state of knowledge of fundamental aerospace computing principles and advancing computing technology in space applications such as software engineering and information extraction from data collected by scientific instruments in space. The program includes the development of special algorithms and techniques to exploit the computing power provided by high performance parallel processors and special purpose architectures. Research is being conducted in the fundamentals of data base logic and improvement techniques for producing reliable computing systems.

Smith, Paul H.↗

BEAM: A Computational Workflow System for Managing and Modeling Material Characterization Data in HPC Environments

Improvements in scientific instrumentation allow imaging at mesoscopic to atomic length scales, many spectroscopic modes, and now—with the rise of multimodal acquisition systems and the associated processing capability—the era of multidimensional, informationally dense data sets has arrived. Technical issues in these combinatorial scientific fields are exacerbated by computational challenges best summarized as a necessity for drastic improvement in the capability to transfer, store, and analyze large volumes of data. The Bellerophon Environment for Analysis of Materials (BEAM) platform provides material scientists the capability to directly leverage the integrated computational and analytical power of High Performance Computing (HPC) to perform scalable data analysis and simulation via an intuitive, cross-platform client user interface. This framework delivers authenticated, “push-button” execution of complex user workflows that deploy data analysis algorithms and computational simulations utilizing the converged compute-and-data infrastructure at Oak Ridge National Laboratory's (ORNL) Compute and Data Environment for Science (CADES) and HPC environments like Titan at the Oak Ridge Leadership Computing Facility (OLCF). In this work we address the underlying HPC needs for characterization in the material science community, elaborate how BEAM's design and infrastructure tackle those needs, and present a small sub-set of user cases where scientists utilized BEAM across a broad range of analytical techniques and analysis modes.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Application of Reconfigurable Computing Technology to Multi-KiloHertz Micro-Laser Altimeter (MMLA) Data Processing

The Multi-KiloHertz Micro-Laser Altimeter (MMLA) is an aircraft based instrument developed by NASA Goddard Space Flight Center with several potential spaceflight applications. This presentation describes how reconfigurable computing technology was employed to perform MMLA signal extraction in real-time under realistic operating constraints. The MMLA is a "single-photon-counting" airborne laser altimeter that is used to measure land surface features such as topography and vegetation canopy height. This instrument has to date flown a number of times aboard the NASA P3 aircraft acquiring data at a number of sites in the Mid-Atlantic region. This instrument pulses a relatively low-powered laser at a very high rate (10 kHz) and then measures the time-of-flight of discrete returns from the target surface. The instrument then bins these measurements into a two-dimensional array (vertical height vs. horizontal ground track) and selects the most likely signal path through the array. Return data that does not correspond to the selected signal path are classified as noise returns and are then discarded. The MMLA signal extraction algorithm is very compute intensive in that a score must be computed for every possible path through the two dimensional array in order to select the most likely signal path. Given a typical array size with 50 x 6, up to 33 arrays must be processed per second. And for each of these arrays, roughly 12,000 individual paths must be scored. Furthermore, the number of paths increases exponentially with the horizontal size of the array, and linearly with the vertical size. Yet, increasing the horizontal and vertical sizes of the array offer science advantages such as improved range, resolution, and noise rejection. Due to the volume of return data and the compute intensive signal extraction algorithm, the existing PC-based MMLA data system has been unable to perform signal extraction in real-time unless the array is limited in size to one column, This limits the ability of the MMLA to operate in environments with sparse signal returns and a high number of noise return. However, under an IR&D project, an FPGA-based, reconfigurable computing data system has been developed that has been demonstrated to perform real-time signal extraction under realistic operating constraints. This reconfigurable data system is based on the commercially available Firebird Board from Annapolis Microsystems. This PCI board consists of a Xilinx Virtex 2000E FPGA along with 36 MB of SRAM arranged in five separately addressable banks. This board is housed in a rackmount PC with dual 850MHz Pentium processors running the Windows 2000 operating system. This data system performs all signal extraction in hardware on the Firebird, but also runs the existing "software based" signal extraction in tandem for comparison purposes. Using a relatively small amount of the Virtex XCV2000E resources, the reconfigurable data system has demonstrated to improve performance improvement over the existing software based data system by an order of magnitude. Performance could be further improved by employing parallelism. Ground testing and a preliminary engineering test flight aboard the NASA P3 has been performed, during which the reconfigurable data system has been demonstrated to match the results of the existing data system.

Powell, Wesley↗

A return mapping algorithm for isotropic and anisotropic plasticity models using a line search method

The numerical integration of constitutive models in computational solid mechanics codes allows for the solution of boundary value problems involving complex material behavior. Metal plasticity models, in particular, have been instrumental in the development of these codes. Here, most plasticity models implemented in computational codes use an isotropic von Mises yield surface. The von Mises, of J 2 , yield surface has a simple predictor-corrector algorithm - the radial return algorithm - to integrate the model.

36 MATERIALS SCIENCE↗

Design and Challenges of Edge Computing ASICs on Front-End Electronics

In situ or hardware-embedded data processing of raw signals, close to their source, in radiation detectors is expected to provide dramatic improvements in data quality and volumes. However, the implementation of artificial neural networks (ANNs) in the front-end electronics, and the design of custom integrated circuits (ASICs), comes with challenges. In addition, detectors have to operate with limited power budget and implement complex functionalities in a very dense space. They often are exposed to extreme conditions as they work in high-radiation environments and/or cryogenic temperatures. This paper presents examples of applications and design methodologies for in-situ ANNs, along with the challenges of retaining the fidelity of the trained networks. For illustration, we use the problem of estimating the energy deposited by the radiation from digitized waveforms. The proposed implementation starts with an ML algorithm trained in Qkeras and eventually leads to an equivalent ASIC implementation. The associated design challenges in realizing energy and area efficient implementations in CMOS processes are reviewed. Novel approaches that employ hybrid technologies (combination of CMOS with memristors), in-memory computing models, and bio-inspired spiking neural networks are also highlighted

47 OTHER INSTRUMENTATION↗

The Automated Instrumentation and Monitoring System (AIMS): Design and Architecture

Whether a researcher is designing the 'next parallel programming paradigm', another 'scalable multiprocessor' or investigating resource allocation algorithms for multiprocessors, a facility that enables parallel program execution to be captured and displayed is invaluable. Careful analysis of such information can help computer and software architects to capture, and therefore, exploit behavioral variations among/within various parallel programs to take advantage of specific hardware characteristics. A software tool-set that facilitates performance evaluation of parallel applications on multiprocessors has been put together at NASA Ames Research Center under the sponsorship of NASA's High Performance Computing and Communications Program over the past five years. The Automated Instrumentation and Monitoring Systematic has three major software components: a source code instrumentor which automatically inserts active event recorders into program source code before compilation; a run-time performance monitoring library which collects performance data; and a visualization tool-set which reconstructs program execution based on the data collected. Besides being used as a prototype for developing new techniques for instrumenting, monitoring and presenting parallel program execution, AIMS is also being incorporated into the run-time environments of various hardware testbeds to evaluate their impact on user productivity. Currently, the execution of FORTRAN and C programs on the Intel Paragon and PALM workstations can be automatically instrumented and monitored. Performance data thus collected can be displayed graphically on various workstations. The process of performance tuning with AIMS will be illustrated using various NAB Parallel Benchmarks. This report includes a description of the internal architecture of AIMS and a listing of the source code.

Yan, Jerry C.↗

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

The mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can easily cause problems if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flight MS. In this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases with highly saturated species and dynamic range increased by 1-2 orders of magnitude for peptides in a blood serum sample.

Environmental Molecular Sciences Laboratory↗

LiveCheckHSI: a Hardware/Software Co-verification Tool for Hyperspectral Imaging Systems with Embedded System-on-Chip Instrument Avionics

The emergent technology of system-on-chip (SoC) devices promises lighter, smaller, cheaper, and more capable and reliable space electronic systems that could help to unveil some of the most treasured secrets in our universe. This technology is an improvement over the technology that is currently used in space applications, which lags behind state-of-the-art commercial-off-the-shelf (COTS) equipment by several generations. SoC technology integrates all computational power required by next-generation space exploration science instruments onto a single chip. This paper describes hardware/software co-verification tools for the Xilinx Zynq-based control and data handling system that have been developed at the Jet Propulsion Laboratory (JPL) for visible-infrared imaging spectrometers. The system acquires and compresses images in real-time, in addition to programming the spectrometer (frame rate, exposure time), focus step motor, and heaters and reporting telemetry.

Dolman, David↗

The Process of Developing a Multi-Cell KEMS Instrument

Multi-cell KEMS offers many advantages over single cell instruments in regard to in-situ temperature calibration and studies on high temperature alloys and oxides of interest to NASA. The instrument at NASA Glenn is a 90 deg magnetic sector instrument originally designed for single cell operation. The conversion of this instrument to a multi-cell instrument with restricted collimation is discussed. For restricted collimation, the 'field aperture' is in the copper plate separating the Knudsen Cell region and the ionizer and the 'source aperture' is adjacent to the ionizer box. A computer controlled x-y table allows positioning of one of the three cells into the sampling region. Heating is accomplished via a Ta sheet element and temperature is measured via an automatic pyrometer from the bottom of the cells. The computer control and data system have been custom developed for this instrument and are discussed. Future improvements are also discussed.

Copland, E. H.↗