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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.

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At least 487 records · Page 27

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NASA NTRS2017

Mass Spectra Deconvolution of Gaseous Mixtures Containing Volatile Organic Compounds

The Spacecraft Atmosphere Monitor (S.A.M.) analyzes all gaseous pollutants in the 1-150Th mass-to-charge range based on their positive-ion mass spectra. Of particular interest is the Trace Gas Analysis (TGA) mode of operation in which S.A.M detects minute amounts of volatile organic compounds (VOC). Pollutant component of the ambient air is sampled on demand in twenty full mass spectra per second. All spectra is accumulated in two-second intervals by an on-board Field Programmable Gate Arrays (FPGA) unit. Mass spectra represents the number of detected fragment ions in a given mass-to-charge ratio channel. Electron impact ionization of neutral molecules results in creation of numerous fragment ions and most of organic compounds will contribute several identical fragments. Despite the presence of these molecular isobars, we developed the deconvolution algorithm capable of identifying target species based on their characteristic fragmentation patterns. We investigate the efficiency of deconvolution algorithm as a function of mass resolution with which mass spectrum is acquired. Higher the mass resolution, better the chances are to distinguish between stereoisomers using their fragmentation propensities, but also results in higher data volumes to be processed by a set of small footprint software stacks hosted by an on-board computer. Finding the balance between deconvolution accuracy and generated data volume under time constrains and limited computing resources is the main topic of this study.

Darrach, M.↗

NASA NTRS2022

Review of Solar Energetic Particle Models

Solar Energetic Particle (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars. Thus, it is critical to improve the scientific understanding of SEP events and use this understanding to develop and improve SEP forecasting capabilities to support operations. Many SEP models exist or are in development using a wide variety of approaches and with differing goals. These include computationally intensive physics-based models, fast and light empirical models, machine learning-based models, and mixed-model approaches. The aim of this paper is to summarize all of the SEP models currently developed in the scientific community, including a description of model approach, inputs and outputs, free parameters, and any published validations or comparisons with data.

Kathryn Whitman↗

NASA NTRS2022

Metrics for Flight Operations: Application to Europa Clipper Tour Selection

Objective measures are ubiquitous in the formulation, design and implementation of deep space missions. Tour durations, flyby altitudes, propellant budgets, power consumption, and other metrics are essential to developing and managing NASA missions. But beyond the simple metrics of cost and workforce, it has been difficult to identify objective, quantitative measures that assist in evaluating choices made during formulation or implementation phases in terms of their impact on flight operations. As part of the development of the Europa Clipper Mission system, a set of operations metrics have been defined along with the necessary design information and software tooling to calculate them. We have applied these methods and metrics to help assess the impact to the flight team on the six options for the Clipper Tour that are currently being vetted for selection in the fall of 2021. To generate these metrics, the Clipper MOS team first designed the set of essential processes by which flight operations will be conducted, using a standard approach and template to identify (among other aspects) timelines for each process, along with their time constraints (e.g., uplinks for sequence execution). Each of the resulting 50 processes is documented in a common format and concurred by stakeholders. Process timelines were converted into generic schedules and workforce-loaded using COTS scheduling software, based on the inputs of the process authors and domain experts. Custom code was generated to create an operations schedule for a specific portion of Clipper's prime mission, with instances of a given process scheduled based on specific timing rules (e.g., process X starts once per week on Thursdays) or relative to mission events (e.g., sequence generation process begins on a Monday, at least three weeks before each Europa closest approach). Over a 5-month period, and for each of six Clipper candidate tours, the result was a 20,000+ line, workforce-loaded schedule that documents all of the process-driven work effort at the level of individual roles, along with a significant portion of the level-of-effort work. Post-processing code calculated the absolute and relative number of work hours during a nominal 5 day / 40 hour work week, the work effort during 2nd and 3rd shift, as well as 1st shift on weekends. The resultant schedules and shift tables were used to generate objective measures that can be related to both human factors and to operational risk and showed that Clipper tours which utilize 6:1 resonant (21.25 day) orbits instead of 4:1 resonant (14.17 day) orbits during the first dozen or so Europa flybys are advantageous to flight operations. A similar approach can be extended to assist missions in more objective assessments of a number of mission issues and trades, including tour selection and spacecraft design for operability.

Sarrel, Marc↗

NASA NTRSDate not supplied

Preliminary Design of Robotic Control Software for Mars Sample Return - Capture, Containment, and Return System

The Mars Sample Return (MSR) campaign aims to acquire and return to Earth a set of Mars samples for investigation in terrestrial laboratories. Mars 2020 has collected an adequate number of samples and deposited them in sealed sample tubes at a designated Martian depot. Sample tubes will be placed in cylindrical containers called Orbiting Samples (OS) by the Perseverance Rover, and later brought to Earth by the Earth Return Orbiter (ERO) and the MSR - Capture, Containment, and Return System (CCRS). Robot Software (RSW) is a set of software processes for commanding and monitoring the avionics that controls robotic mechanisms designed to sterilize and install OS into Earth Entry System (EES) for return to Earth. This paper describes a preliminary design of RSW including motion modes, architecture, and finite state machine. Additionally, software engineering procedures and testing of RSW is provided. A preliminary performance analysis is presented and the paper concludes with future work and a discussion of design decisions.

MSR↗

DOE OSTI2020

Spatiotemporal Pattern Recognition in the PMU Signals in the WECC system

Phasor measurement unit (PMU) data has been used by multiple power system applications, including state estimation, post event analysis, oscillation detection, model validation, and many others. Still, due to its big data nature and availability to general research institutions, comprehensive understanding of the spatiotemporal patterns and underlying mechanisms are incomplete. This study applies a set of signal processing and machine learning approaches aiming at deciphering the characteristic behaviors of multiple phasor measurement units (PMUs) attributes (e.g., voltage, frequency, rate of change of frequency, phase angle), including their auto-correlation, cross-dependence, similarities and discrepancies across units and temporal scales, and distributions of anomalies and their linkages to potential external factors such as weather events. Data analytics are applied to PMUs from the U.S. Western Electricity Coordinating Council (WECC) system. The PMU measurements, recorded events, outages, and weather extremes are all from real world datasets. The findings from the study and mechanistic understanding of the PMU dynamics help provide guidance on system control or preventing blackouts. The derived metrics can be directly used for adjusting or filtering simulated PMU data used for advanced algorithm development.

Hou, Zhangshuan↗

DOE OSTI2025

Pursuing small-scale measures of penetration resistance in Ti-5553 alloy

This work was originally proposed to illustrate the performance of a particular set of differently processed Ti-5553 plates under dynamic threats. It became clear that the larger question, at present, is whether the small-scale shaped charge design of an RP-4 detonator can be employed to efficiently explore the dynamic penetration resistance of representative test articles. The initial penetration testing that employed three variants of thermomechanical processing of Ti-5553 plates appeared to demonstrate that there was a noticeable difference in their performance. The remaining pertinent question was regarding the variation in the shaped charge performance and how it compares to the variation from the three titanium plate tests. Based on the limited testing presented here the RP-4 shaped charge detonator appears to have an average depth of penetration in 6061 aluminum of 90mm with a 1.5CD stand off.

shaped charge penetration↗

NASA NTRS1991

Content addressable memory project

The progress on the Rutgers CAM (Content Addressable Memory) Project is described. The overall design of the system is completed at the architectural level and described. The machine is composed of two kinds of cells: (1) the CAM cells which include both memory and processor, and support local processing within each cell; and (2) the tree cells, which have smaller instruction set, and provide global processing over the CAM cells. A parameterized design of the basic CAM cell is completed. Progress was made on the final specification of the CPS. The machine architecture was driven by the design of algorithms whose requirements are reflected in the resulted instruction set(s). A few of these algorithms are described.

Hall, Josh↗

NASA NTRS1991

Geodynamics branch data base for main magnetic field analysis

The data sets used in geomagnetic field modeling at GSFC are described. Data are measured and obtained from a variety of information and sources. For clarity, data sets from different sources are categorized and processed separately. The data base is composed of magnetic observatory data, surface data, high quality aeromagnetic, high quality total intensity marine data, satellite data, and repeat data. These individual data categories are described in detail in a series of notebooks in the Geodynamics Branch, GSFC. This catalog reviews the original data sets, the processing history, and the final data sets available for each individual category of the data base and is to be used as a reference manual for the notebooks. Each data type used in geomagnetic field modeling has varying levels of complexity requiring specialized processing routines for satellite and observatory data and two general routines for processing aeromagnetic, marine, land survey, and repeat data.

Langel, Robert A.↗

NASA NTRS2017

Take-off Engine Particle Emission Indices for In-Service Aircraft at Los Angeles International Airport

We present ground‐based, advected aircraft engine emissions from flights taking off at Los Angeles International Airport. 275 discrete engine take‐off plumes were observed on 18 and 25 May 2014 at a distance of 400 m downwind of the runway. CO2 measurements are used to convert the aerosol data into plume‐average emissions indices that are suitable for modelling aircraft emissions. Total and non‐volatile particle number EIs are of order 1016‐1017 kg‐1 and 1014‐1016 kg‐1, respectively. Black‐carbon‐equivalent particle mass EIs vary between 175‐941 mg kg‐1 (except for the GE GEnx engines at 46 mg kg‐1). Aircraft tail numbers recorded for each take‐off event are used to incorporate aircraft‐ and engine‐specific parameters into the data set. Data acquisition and processing follow standard methods for quality assurance. A unique aspect of the data set is the mapping of aerosol concentration time series to integrated plume EIs, aircraft and engine specifications, and manufacturer‐reported engine emissions certifications. The integrated data enable future studies seeking to understand and model aircraft emissions and their impact on air quality.

Richard H Moore↗

DOE OSTI2022

Inclusive production cross sections at N 3 LO

We present for the first time the inclusive cross section for associated Higgs boson production with a massive gauge boson at next-to-next-to-next-to-leading order in QCD. Furthermore, we introduce n3loxs, a public, numerical program for the evaluation of inclusive cross sections at the third order in the strong coupling constant. Our tool allows to derive predictions for charged- and neutral-current Drell-Yan production, gluon- and bottom-quark-fusion Higgs boson production and Higgs boson associated production with a heavy gauge boson. We discuss perturbative and parton distribution function (PDF) uncertainties of the aforementioned processes. We perform a comparison of global PDF sets for a variety of process including associated Higgs boson production and observe 1σ deviations among predictions for several processes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

The Cell Utilized Partitioning Model as a Predictive Tool for Optimizing Counter-Current Chromatography Processes

Counter-current chromatography (CCC) is capable of unique elution modes that isolate analytes using the movement of the stationary phase in addition to moving the mobile phase. These modes include elution-extrusion CCC (EECCC) and dual-mode CCC (DM CCC) that are not possible in traditional solid-liquid chromatography systems. Although EECCC and DM CCC are widely used to recover highly retained components, to our knowledge, optimizing the elution process in these modes with predictive models has not been reported. To address this gap, we developed a predictive model for CCC dubbed the Cell Utilized Partitioning (CUP) model. The CUP model accurately predicts the effluents of multicomponent separations in EECCC and DM CCC modes when compared to experimental data. Furthermore, CUP model simulations were extended to investigate the influence of operating and intrinsic parameters on the yield and productivity, and to compare the separation performances of EECCC and DM CCC in various conditions. The results demonstrate that low distribution constants, usually a KD less than 1, and a selectivity > 1.3, under specific flowrate ranges, increase both productivity and yield. From these results, generalized optimization and scaleup guidelines are proposed that can apply to research settings and to industrial processes to maximize preparative CCC performance.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗

DOE OSTI2021

Resilience Framework for Electric Energy Delivery Systems (R.1)

The intent of this document is to provide a Resilience Framework for electrical energy delivery systems which can be applied to Distributed Wind. However, the framework is not limited by application to any resource or system. This framework represents the defined steps to a cyclical process similar in mechanism to both cybersecurity and risk frameworks, while providing a common set of language and process for all stakeholders involved. The need for this Resilience Framework was established in a previous document, “Distributed Wind Resilience Metrics for Electric Energy Delivery Systems.” One important characteristic we see in resilience is the unique needs and perspectives of different systems, geographies, resources, stakeholders, perceived risks, and consequences, which we term the distinctiveness property. This distinctiveness property drives the requirement to have a resilience framework or methodology that can be implemented by different types of organizations and systems. The process or methodology should be cyclic. Recognizing that a system’s resilience is based on finite resources and time, it must continually evolve through this framework’s risk management and capital investment steps at an appropriate pace for its distinctiveness property.

17 WIND ENERGY↗

DOE OSTI2021

Using Change Models to Envision Better Applications of Animal Behavior Research in Conservation Management and Beyond

While most animal behavior researchers have mastered the process of knowledge creation, generating knowledge that can readily be applied requires a different set of skills. The process and timeframe of fundamental scientific knowledge production is often not relevant to those who might apply it, such as conservation or wildlife managers. Additionally, the complex challenges that policy makers, managers and practitioners face are often not adequately communicated to and among scientists. This mutual disconnect in discourse, relationships, common terms, and practices is especially apparent when animal behavior researchers seek to have applied impact. We argue that bridging the complex implementation gap in animal behavior requires a formalized vision for change. We turn to change model theory, a tool commonly used in other fields for identifying the links between actions and outcomes necessary for enacting large-scale change. We focus on the subfield of conservation behavior with a change model that outlines specific ways to improve collaboration and coordination between animal behavior science and conservation practice. We present this targeted change model, review each strategy the model outlines, and highlight pressing actions that people from various career stages and backgrounds can take. We encourage researchers to further the alignment of science with management needs by developing the proper communication mechanisms for improved cultural exchange and plan future change model efforts directly targeting managers. Beyond the conservation behavior change model we present, we also discuss the broad applicability of change models to enhance the application of academic research to other fields. Fundamental science researchers are increasingly required to show impact of their work on society; the change model process we describe here can enable further impact.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Verification and validation of developed short-term forecasting models

Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Defining Golden Batches in Biomanufacturing Processes From Internal Metabolic Activity to Detect Process Changes That May Affect Product Quality

ABSTRACT Cellular metabolism plays a role in the observed variability of a drug substance's Critical Quality Attributes (CQAs) made by biomanufacturing processes. Therefore, here we describe a new approach for monitoring biomanufacturing processes that measures a set of metabolic reaction rates (named Critical Metabolic Parameters (CMP) in addition to the macroscopic process conditions currently being used as Critical Process Parameters (CPP) for biomanufacturing. Constraint‐based systems biology models like Flux Balance Analysis (FBA) are used to estimate metabolic reaction rates, and metabolic rates are used as inputs for multivariate Batch Evolution Models (BEM). Metabolic activity was reproducible among batches and could be monitored to detect a deliberately induced macroscopic process shift (i.e., temperature change). The CMP approach has the potential to enable “golden batches” in biomanufacturing processes to be defined from the internal metabolic activity and to aid in detecting process changes that may impact the quality of the product. Overall, the data suggested that monitoring of metabolic activity has promise for biomanufacturing process control.

Biotechnology & Applied Microbiology↗

NASA NTRS1975

Preliminary data processing system for telemetry data from Interkosmos-8 type satellites

The preliminary data processing system (PDPS), a set of algorithms, programs, equipment, data storage and memory devices, and all devices controlling the processing, is described using the Intercosmos-8 satellite as an example. Each stage of the system is considered along with its content and sequence. The essential elements of the data processing structure are described and the concepts underlying the selection of these elements are discussed.

Vilchinskaya, A. S.↗

DOE OSTI2025

A machine-learning-driven data labeling pipeline for scientific analysis in MLExchange

This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)-guided tagging techniques. This pipeline includes a set of interconnected web-based graphical user interfaces (GUIs), where Data Clinic and MLCoach enable the preparation of machine learning (ML) models for data reduction and classification, respectively, while Label Maker is used for label assignment. Throughout this pipeline, data can be accessed through a direct connection to a file system or through Tiled for access through Hypertext Transfer Protocol (HTTP). Our experimental results present three use cases where this labeling pipeline has been instrumental for the study of large X-ray scattering data sets in the area of pattern recognition, the remote analysis of resonant soft X-ray scattering data and the fine-tuning process of foundation models. These use cases highlight the labeling capabilities of this pipeline, including the ability to label large data sets in a short period of time, to perform remote data analysis while minimizing data movement and to enhance the fine-tuning process of complex ML models with human involvement.

Chavez, Tanny (ORCID:0000000193172896)↗

NASA NTRS2015

Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

Machine learning↗