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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

TBASS: A Robust Adaptation of Bayesian Adaptive Spline Surfaces

The R package TBASS is an extension of the BASS package created by Francom and Sansó (2019). The package is used to fit a Bayesian multivariate adaptive spline to a dataset that either follows a Student’s t-distribution or has outliers. Much of the framework for TBASS is adapted from the concepts of Bayesian Multivariate Adaptive Regression Splines (BMARS), specifically the work done by Denison, Mallick, and Smith (1998). The spline function is fit using a Reversible-Jump Markov Chain Monte Carlo algorithm,. By including this more robust generalization, a dataset with outliers can be accurately fit using the BMARS model, without the possibility of overfitting or variance inflation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection (Final Report)

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic, recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability”. More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture”. Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multi scale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator. The promise of advances in DL is apparent in the realm of human health and medicine. DL models have been validated for evaluating a variety of clinical threats to human health in a range of contexts, including infection and cancer, and they demonstrated improved performance in predicting stroke relative to human neurologists in some categories of data. Continuously evolving advances in AI/ML are expected to support more efficient evaluation of raw sequence, spectroscopy, and spectrometry data. For instance, recent advances and deployment of large language models (LLM) such as Generative Pre training Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) have already motivated application of these models for biological function prediction. As frameworks such as LLMs become larger and more complex in their representations, their capacity to serve as pre-trained models that can be fine-tuned for biological/biodetection purposes will similarly be amplified. While existing and emerging AI/ML have found broad applicability and use cases in the clinical sciences, development for environmental evaluation and biodetection has been limited. Functionalizing such capabilities for this purpose requires an understanding of the existing technical landscape and how the respective tools and algorithms are currently being employed. This landscape awareness then allows an assessment of the current practical capabilities of existing models and the anticipated requirements and development efforts that will be needed to adapt available algorithms for biodetection applications relevant to DHS. Leveraging expertise in biodetection, ML, and operational biodetection, the effort described in this report is comprised of a systematic landscape assessment (Subtask 2.1), comparative evaluation (Subtask 2.2), and formulation of a value proposition (Subtask 2.3) for the prospect of ML-enabled, agnostic biodetection from raw, or minimally-processed, datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

PRIMED for the Future: Purposing Raw Intake for Machine Learning-Enabled Detection

The COVID-19 pandemic demonstrated how a novel, elusive, and diffuse biological threat can engender uncertainty and misinformation, and it underscored the need for flexible analytical modalities agnostic to the identity of biological material. Yet even before the pandemic recognition of the limitations of the current, list-based approach, which focuses on known pathogens and biotoxins, and of the importance of agent-agnostic biodetection, was growing within the biosecurity community. In a 2018 report on “Biodefense in the Age of Synthetic Biology,” for example, the National Academy of Sciences stated that “an overreliance on the Select Agent List is a systematic weakness affecting many aspects of the United States’ current biodefense mitigation capability." More recently, a group of biodefense researchers proposed the identification and adoption of “bioagent-agnostic signatures (BASs)” as a way of detecting and characterizing not only existing agents but also novel ones, an approach they believe will “enable a more flexible and resilient biodefense posture." Indeed, the future of biodetection requires us to begin developing novel analytics that can identify anomalies and/or characteristics that indicate a potential threat, whether known or unknown, without looking for a specific signature that has been identified previously. To assess potential threats more rapidly, it is critical to develop agnostic artificial intelligence (AI)/machine learning (ML) systems that can be employed for real-time assessment of the nature and source of a perturbation. Such systems should be multiscale and multi-dimensional, integrating sensor data from a range of biological, chemical, and physical application spaces. Emerging deep learning (DL) models demonstrate exceptional promise for identification of discriminatory features within multi-dimensional datasets. DL models have the capacity to recognize and encode highly complex patterns in a wide range of input data modalities, including images, text, and biological/chemical/physical spectra. As such, they can execute a wide range of assessments and determinations that have traditionally required a human operator.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗
DOE OSTI2025

Optimizing Facility Operations by Applying Machine Learning to the Army Reserve Enterprise Building Control System (Final Report)

Thousands of U.S. Department of Defense (DoD) buildings have building automation systems (BASs) and/or advanced meters. Although these systems have a wealth of data, performance optimization requires time and expertise to review and act on that information. Machine learning (ML) can provide automated and actionable insights to controls operators. This demonstration implemented proven ML methods on the Army Reserve Enterprise Building Control System. ML refers to algorithms that “learn” from data and improve their performance on a given task over time. In the buildings domain these tasks range from predicting future energy consumption, to identifying operational issues before faults occur, to optimizing control decisions. To learn, ML requires input data, which – for buildings – typically consists of instrument data such as energy consumption data and subsystem controls information such as set-point temperatures, and context data consisting of information such as the physical location of the building, the area of the building, and the weather. ML models use the relationships learned from the input data to make predictions with new, previously unseen, data. The team was able to investigate and successfully implement the following ML use cases: labeling consumption data as anomalous or non-anomalous; baseline whole-building load prediction (unknown fault status); fault detection (validation not possible); and site prioritization for energy-related projects. Due to the constraints of the project, interventions were not able to be implemented during the demonstration; therefore, assessments of operational cost savings and maintenance avoided could not be performed. The project has been presented at two leading national building conferences and two additional publications to peer-reviewed journals are currently in preparation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Fish scale-loss caused by simulated hydraulic shear stress

Riverine fishes found within streams impacted by hydropower also often pass through hydropower turbines during annual downstream migrations. During turbine passage events, fish may experience a suite of stressors including rapid pressure changes, impacts from turbine blades, and exposure to hydraulic shear. Scale-loss is often observed in fish collected below dams and hydraulic shear is the most likely cause of this trauma. While exposure to shear may cause other injuries, scale-loss appears to be the most common non-lethal injury observed to date. We sought to quantify how hydraulic shear may cause scale-loss in rainbow trout, gizzard shad, and hybrid striped bass by precisely controlling the exposure level (velocity) and duration (seconds) of a water jet. We targeted the same area for all three species found between the operculum and dorsal fin on the left, lateral surface. Exposure velocities ranged from 0 (controls) to 11.0 meters/second but fish were only exposed to the water jet for 1.5 seconds. A 0.2 milligram/milliliter solution of fluorescein was used to stain the areas of scale-loss by submerging each fish in this solution for at least 6 minutes. Fluorescein was used because it interacts with epithelial tissue that has been damaged and it fluoresces under ultraviolet lighting. Fish were photographed under short- and longwave ultraviolet lighting in a dark chamber to capture fluorescence. Fish photographs were analyzed using ImageJ and the proportion of descaled area was estimated for each fish within a 35 by 35-millimeter square. In general, these data highlight how exposure to hydraulic shear may lead to significant levels of descaling in riverine fishes.

13 HYDRO ENERGY↗
DOE OSTI2021

Resource Occurrence and Productivity in Existing and Proposed Wind Energy Lease Areas on the Northeast US Shelf

States in the Northeast United States have the ambitious goal of producing more than 22 GW of offshore wind energy in the coming decades. The infrastructure associated with offshore wind energy development is expected to modify marine habitats and potentially alter the ecosystem services. Species distribution models were constructed for a group of fish and macroinvertebrate taxa resident in the Northeast US Continental Shelf marine ecosystem. These models were analyzed to provide baseline context for impact assessment of lease areas in the Middle Atlantic Bight designated for renewable wind energy installations. Using random forest machine learning, models based on occurrence and biomass were constructed for 93 species providing seasonal depictions of their habitat distributions. We developed a scoring index to characterize lease area habitat use for each species. Subsequently, groups of species were identified that reflect varying levels of lease area habitat use ranging across high, moderate, low, and no reliance on the lease area habitats. Among the species with high to moderate reliance were black sea bass ( Centropristis striata ), summer flounder ( Paralichthys dentatus ), and Atlantic menhaden ( Brevoortia tyrannus ), which are important fisheries species in the region. Potential for impact was characterized by the number of species with habitat dependencies associated with lease areas and these varied with a number of continuous gradients. Habitats that support high biomass were distributed more to the northeast, while high occupancy habitats appeared to be further from the coast. There was no obvious effect of the size of the lease area on the importance of associated habitats. Model results indicated that physical drivers and lower trophic level indicators might strongly control the habitat distribution of ecologically and commercially important species in the wind lease areas. Therefore, physical and biological oceanography on the continental shelf proximate to wind energy infrastructure development should be monitored for changes in water column structure and the productivity of phytoplankton and zooplankton and the effects of these changes on the trophic system.

17 WIND ENERGY↗
DOE OSTI2023

A Probabilistic Methodology for Determining Collision Risk of Marine Animals with Tidal Energy Turbines

Commercial development of tidal stream energy is hampered by technical and financial challenges, and impeded by uncertainty about potential environmental effects that drive environmental risk assessments and permitting (consenting) processes. The effect of greatest concern for operational tidal stream energy devices is the potential for marine animals to collide with turbine blades, resulting in injury or death. Due to the turbulent and often turbid waters that frequently characterize tidal turbine sites, there is an absence of empirical evidence about collisions with marine animals. This paucity of observations often leads to risk-averse permitting decisions that further restrict the deployment of tidal energy devices that are needed to collect this evidence. This paper relies on the framework of stressors and receptors that is widely used in marine energy studies and outlines a stepwise probabilistic methodology that applies existing knowledge to further elucidate the risk to marine animals from operational tidal turbines. A case study using striped bass from the Bay of Fundy, Canada, accompanies the methodology, to partially demonstrate its application.

16 TIDAL AND WAVE POWER↗
NASA NTRS1972

Evaluation of ERTS data for certain oceanographic uses

The author has identified the following significant results. A thick algal mat in the Bass Islands area several hundred feet wide and 10-15 miles long can be seen along the western edge of the ERTS-1 imagery.

Strong, A. E.↗
NASA NTRS1976

Formation of ferrobasalt at east Pacific midocean spreading centers

A numerical modeling using a least squares petrologic mixing program demonstrates that ridge ferrobasalts from three east Pacific regions are generated from normal midocean ridge balsalt by crystal fractionation of plagioclase, clinopyroxene, and minor olivine at shallow mantle depths. Clinopyroxene is an important fractionating phase in this model in contrast to most previously proposed fractionation models. Calculations demonstrate that the ferrobasalts represent a much smaller percentage of residual liquid than has previously been proposed. Implications of extensive shallow crystal fractionation for the determination of depths of magma generation are discussed. The different fields on an anorthite-olivine-hypersthene ternary diagram for basalts from east Pacific fast and moderate spreading ridges can be explained by variable degrees of shallow crystal fractionation rather than by different depths of magma generation as suggested by Bass (1971) and Scheidegger (1973).

Clague, D. A.↗
NASA NTRS1976

A stabilization algorithm for linear discrete constant systems

A procedure is derived for stabilizing linear constant discrete systems which is a discrete analog to the extended Bass algorithm for stabilizing linear constant continuous systems. The procedure offers a method for constructing a stabilizing feedback without the computational difficulty of raising the unstable open-loop response matrix to powers thus making the method attractive for high order or poorly conditioned systems.

Armstrong, E. S.↗
NASA NTRS1983

GSFC optical ozonesonde results during the Gap, France, intercomparisons, June 1981

O3 soundings made with balloon-borne optical sensors during the French Intercomparison Ozone Campaign in June, 1981, are reported. The GSFC four-channel UV-filter-photometer ozonesonde designed for the Super Loki rocket was attached to the gondola roof for redundant O3-density measurements at altitudes of 22-32 km, using the 300 and 303-nm filters. Sensor calibration, data processing, input constants, and vertical O3-distribution calculations are discussed. The density profiles for three legs of the flight, calculated using Bass or Vigroux cross sections, agreed within a standard error of 1.4 percent for each channel, but showed a consistent 4-5 percent (significant) difference between channels. Total average O3-column amounts determined by combining the GSFC data with ECC measurements for altitudes below 21 km were in good agreement with bias-corrected TOMS data from Nimbus-VII and with ground-based data from the Observatoire de Haute-Provence at Mt. Chiran, when the latter were computed using the pre-campaign calibration. A 9-percent discrepancy arose when the Chiran data were corrected for a post-campaign recalibration of the Dobson spectrophotometer.

Krueger, A. J.↗
NASA NTRS1989

On sampling band-pass signals

Four techniques for uniform sampling of band-bass signals are examined. The in-phase and quadrature components of the band-pass signal are computed in terms of the samples of the original band-pass signal. The relative implementation merits of these techniques are discussed with reference to the Deep Space Network (DSN).

Sadr, R.↗
NASA NTRS1987

Real-time analysis of telemetry data

This paper descibes a knowledge-based system for performing real-time monitoring and analysis of telemetry data from the NASA Hubble Space Telescope (HST). In order to handle asynchronous inputs and perform in real time the system consists of three or more separate processes, which run concurrently and communicate via a message passing scheme. The data management process gathers, compresses, and scales the incoming telemetry data befoe sending it to the other tasks. The inferencing process uses the incoming data to perform a real-time analysis of the state and health of the Space Telescope. The I/O process receives telemetry monitors from the data management process, updates its graphical displays in real time, and acts as the interface to the console operator. The three processes may run on the same or different computers. This system is currently under development and is being used to monitor testcases produced by the Bass Telemetry System in the Hardware/Software Integration Facility at Lockheed Missile and Space Co. in Sunnyvale, California.

Kao, Simon A.↗
NASA NTRS1992

Analysis of sonic boom data to quantify distortions of shock profiles

Researchers at Penn State have been examining some sonic boom waveforms recorded during overflights by the Air Force which have become available to NASA and its contractors. The quality of the digitized data and the supporting meteorological data was such that one could test the applicability of molecular relaxation theories. In the late sixties, it had been supposed that the finite rise times in the absence of turbulence had neglected the vibrational relaxation of nitrogen molecules. Bass et al. have demonstrated that molecular relaxation definitely gives the correct order of magnitude of the observed rise times. However, the Air Force data in conjunction with the recent steady-state shock profile model theory of Kang and Pierce give the first opportunity to make a detailed quantitative assessment of the molecular relaxation hypothesis. Currently an investigation is ongoing to establish a method of quantifying the distortion of a sonic boom wave from a classic N-wave shape using the Air Force data taken at Edwards AFB in 1987. Using the premise that energy will be conserved approximately for a sonic boom wave both before and after the boom passes through the Earth's turbulent boundary layer, a classic undistorted waveform is constructed from the distorted signature received at the ground. A correlation between the mean-squared deviation of the distorted and undistorted waveforms and the distance the boom travels through the turbulence is sought.

Gionfriddo, Thomas A.↗
NASA NTRS1990

The effect of molecular relaxation processes in air on the rise time of sonic booms

A theory is developed to explain the effect of molecular relaxation processes on the rise time of sonic booms. To determine the rise time of sonic booms, both O2 and N2 relaxation processes must be included. The N2 relaxation process delays the shock pressure reaching the maximum pressure, and the O2 relaxation process causes a shock profile to have a gentle slope. The N2 relaxation controls the lower part of overpressure; the O2 relaxation controls the higher part. The constant rise time curves show that the rise times increase as the overpressures and humidity decrease. The present approach gives longer rise times than those acquired by Bass et al. for given shock overpressures.

Kang, Jongmin↗
NASA NTRS1993

The Bunny: A simulated commercial air transportation study

The Bunny is a single-engine, 100 passenger commercial transport designed to serve the high density short-to-medium range markets in AEROWORLD. The aircraft's design range is 10,000 feet at a cruise velocity of 30 ft/s. The aircraft features a low wing which incorporates polyhedral for roll control. Yaw and pitch control are accomplished by a rudder and elevator, respectively. Propulsion is provided by a nose-mounted Astro 15 electric motor powered by thirteen 1.2 V, 1000 mah batteries with a Zinger 12-6 propeller. The aircraft is structurally designed with a safety factor of 1.5 and is constructed primarily of balsa, bass, and birch wood. Passenger seating is arranged on two levels, with three-abreast on the lower level and two-abreast on the upper level. The factors which had the most significant influence on the final design were the direct operating cost and the take-off distance. The primary strength of The Bunny is its ability to compete economically with the HB-40. At full capacity and mid-range fuel costs, the cost per seat per thousand feet (CPSK) of this aircraft is 25% less than the HB-40. Another principal strength is its ability to operate in all airports in AEROWORLD. Also, The Bunny's two-piece removable wing is an advantage from a transportability standpoint.

Fulton, David↗
NASA NTRS1994

The latitudinal distribution of ozone to 35 km altitude from ECC ozonesonde observations, 1982-1990

Electrochemical concentration cell (ECC) ozone-sonde observations, made in recent years at ten stations whose locations range from the Arctic to Antarctica, have yielded a self-consistent ozone data base from which mean seasonal and annual latitudinal ozone vertical distributions to 35 km have been derived. Ozone measurement uncertainties are estimated, and results are presented in the Bass-Paur (1985) ozone absorption coefficient scale adopted for use with Dobson ozone spectrophotometers January 1, 1992. The data should be useful for comparison with model calculations of the global distribution of atmospheric ozone, for serving as apriori statistical information in deriving ozone vertical distributions from satellite and Umkehr observations, and for improving the satellite and Umkehr ozone inversion algorithms. Attention is drawn to similar results based on a less comprehensive data set published in Ozone in the Atmosphere, Proceedings of the 1988 Quadrennial Ozone Symposium where errors in data tabulations occurred for three of the stations due to inadvertent transposition of ozone partial pressure and air temperature values.

Komhyr, W. D.↗