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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

A fast-initializing digital equalizer with on-line tracking for data communications

A theory is developed for a digital equalizer for use in reducing intersymbol interference (ISI) on high speed data communications channels. The equalizer is initialized with a single isolated transmitter pulse, provided the signal-to-noise ratio (SNR) is not unusually low, then switches to a decision directed, on-line mode of operation that allows tracking of channel variations. Conditions for optimal tap-gain settings are obtained first for a transversal equalizer structure by using a mean squared error (MSE) criterion, a first order gradient algorithm to determine the adjustable equalizer tap-gains, and a sequence of isolated initializing pulses. Since the rate of tap-gain convergence depends on the eigenvalues of a channel output correlation matrix, convergence can be improved by making a linear transformation on to obtain a new correlation matrix.

Houts, R. C.↗
NASA NTRS2006

Decision Making Methods in Space Economics and Systems Engineering

This viewgraph presentation reviews various methods of decision making and the impact that they have on space economics and systems engineering. Some of the methods discussed are: Present Value and Internal Rate of Return (IRR); Cost-Benefit Analysis; Real Options; Cost-Effectiveness Analysis; Cost-Utility Analysis; Multi-Attribute Utility Theory (MAUT); and Analytic Hierarchy Process (AHP).

trade studies↗
NASA NTRS2023

Identifying and Documenting Expert Knowledge, A Practical Study of Design Patterns

Globalization, powered by digitization, is increasing technology growth and knowledge transfer rates to levels not seen in the previous 3500 years, obscuring absolute truth and accelerating rates of innovation and production. Competing,or remaining competitive, in this global marketplace requires a learning organization adopt a method of capture, retention, and reuse for demonstrated tacit knowledge to accelerate the development of increasingly complex systems of high quality at a reasonable cost. The architectural theory of Patterns and Pattern Language is a validated methodology for mining tacit domain knowledge from a proven system. This work applies architectural theory to a multi-year development experiment and captures exposed knowledgeas a design patternin a model based systems engineering tool, demonstrating applicability ofdigital engineering initiativesin thedescription and reuse of expert design knowledge.By creating and archiving model based expressions of expert knowledge,a learning organization canimprovepractical decision making and avoid uninformed concept phase decision making

systems engineering↗
NASA NTRS1987

Radar scene generation for tactical decision aids

The Mueller matrix and polarization covariance matrix for polarimetric radar systems was studied. The clutter is modeled by a layer of random permittivity, described by a three-dimensional correlation function, with variance, and horizontal and vertical correlation lengths. A general mixing formula was derived for discrete scatters immersed in a host medium. The results are applicable to general multiphase mixtures. The strong fluctuation theory was used to derive the backscattering cross sections, and was further extended to include higher order co-polarized and cross-polarized moments. A two-layer anisotropic random medium model was developed for the active and passive remote sensing of ice fields. A three-layer random medium model was adopted to study the volume scattering effects for the active and passive microwave remote sensing of snow-covered ice fields. The snow layer was simulated by an isotropic random medium and the ice layer by an anisotropic random medium. The vegetation canopy and snow-covered ice field were studied with a three-layer model, an isotropic random medium layer overlying an anisotropic random medium. The dyadic Green's function of the three-layer medium and the scattered electromagnetic intensities with Born approximation were calculated.

Kong, J. A.↗
DOE OSTI2022

An ensemble data assimilation modeling system for operational outdoor microalgae growth forecasting

Microalgae have received increasing attention as a potential feedstock for biofuel or biobased products. Forecasting the microalgae growth is beneficial for managers in planning pond operations and harvesting decisions. This study proposed a biomass forecasting system comprised of the Huesemann Algae Biomass Growth Model (BGM), the Modular Aquatic Simulation System in Two Dimensions (MASS2), ensemble data assimilation (DA), and numerical weather prediction Global Ensemble Forecast System (GEFS) ensemble meteorological forecasts. The novelty of this study is to seek the use of ensemble DA to improve both BGM and MASS2 model initial conditions with the assimilation of biomass and water temperature measurements and consequently improve short-term biomass forecasting skills. This study introduces the theory behind the proposed integrated biomass forecasting system, with an application undertaken in pseudo-real-time in three outdoor ponds cultured with Chlorella sorokiniana in Delhi, California, United States. Results from all three case studies demonstrate that the biomass forecasting system improved the short-term (i.e., 7-day) biomass forecasting skills by about 60% on average, comparing to forecasts without using the ensemble DA method. Given the satisfactory performances achieved in this study, it is probable that the integrated BGM-MASS2-DA forecasting system can be used operationally to inform managers in making pond operation and harvesting planning decisions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

TeV-scale lepton number violation: Connecting leptogenesis, neutrinoless double beta decay, and colliders

In the context of TeV-scale lepton number violating (LNV) interactions, we illustrate the interplay between leptogenesis, neutrinoless double beta ( 0 ν β β ) decay, and LNV searches at proton-proton colliders. Using a concrete model for illustration, we overcome the limitations of previous effective field theory analyses and are able to identify the parameter space where standard thermal leptogenesis is rendered unviable due to washout processes. Moreover, we show how 0 ν β β decay and p p collisions provide complementary probes. We find that the new particle spectrum can have a decisive impact on the relative sensitivity of these two probes. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
NASA NTRS1971

Some sequential, distribution-free pattern classification procedures with applications

Some sequential, distribution-free pattern classification techniques are presented. The decision problem to which the proposed classification methods are applied is that of discriminating between two kinds of electroencephalogram responses recorded from a human subject: spontaneous EEG and EEG driven by a stroboscopic light stimulus at the alpha frequency. The classification procedures proposed make use of the theory of order statistics. Estimates of the probabilities of misclassification are given. The procedures were tested on Gaussian samples and the EEG responses.

Poage, J. L.↗
DOE OSTI2025

HydroForecast Long-term: Improving hydropower’s resilience to climate change through accurate climate-scale

With hydrologic patterns and water availability across the globe shifting due to climate change, advancements in hydrologic prediction systems can help significantly reduce the uncertainties that utilities and water supply entities have in their decision making. Understanding and estimating hydrology at the climate scale is critical for managing water resources under changing climate scenarios. This project focuses on integrating state-of-the-art neural network modeling with downscaled climate projections to deliver the reliable water supply projections decades into the future to meet an urgent need from hydropower operators and water utilities. In this Phase 1 DOE SBIR proposal, we developed and validated a theory-guided neural network model, HydroForecast Long-term, for climate-scale hydrology and implemented the model within existing HydroForecast infrastructure. HydroForecast Long-term combines the most accurate streamflow modeling system with a flexible and scalable data architecture to generate water supply projections out to the year 2100. This report illustrates that we have achieved our four objectives: 1) create a prototype of HydroForecast Long-term, building the neural network prediction model, 2) build an automated data input pipeline that processes large amounts of data from the latest global temperature and precipitation climate models; 3) benchmark the accuracy of the hydrologic model over the recent two decades over a large set of diverse basins, and 4) create a set of output visuals and summary metrics informed by customer feedback that connect the data to critical decision points. This work empowers water users to make data-informed decisions supporting a resilient, renewable-powered grid and water system. The results advance the Department of Energy’s mission by addressing critical gaps in water supply planning under climate change.

13 HYDRO ENERGY↗
DOE OSTI2021

Learning to Unknot

We introduce natural language processing into the study of knot theory, as made natural by the braid word representation of knots. We study the UNKNOT problem of determining whether or not a given knot is the unknot. After describing an algorithm to randomly generate N-crossing braids and their knot closures and discussing the induced prior on the distribution of knots, we apply binary classification to the UNKNOT decision problem. We find that the Reformer and shared-QK Transformer network architectures outperform fully-connected networks, though all perform at 95% accuracy. Perhaps surprisingly, we find that accuracy increases with the length of the braid word, and that the networks learn a direct correlation between the confidence of their predictions and the degree of the Jones polynomial. Finally, we utilize reinforcement learning (RL) to find sequences of Markov moves and braid relations that simplify knots and can identify unknots by explicitly giving the sequence of unknotting actions. Trust region policy optimization (TRPO) performs consistently well, reducing 80% of the unknots with up to 96 crossings we tested to the empty braid word, and thoroughly outperformed other RL algorithms and random walkers. Studying these actions, we find that braid relations are more useful in simplifying to the unknot than one of the Markov moves.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Multilane Automated Driving With Optimal Control and Mixed-Integer Programming

Road vehicle lane changes often initiate traffic disturbances and can therefore impact road networks’ energy and time efficiency. Furthermore, unexpected changes in traffic conditions may also render lane changes counterproductive for the lane-changing vehicle. Vehicle-to-vehicle connectivity combined with anticipative control could address these challenges via improved lane change decisions by automated vehicles. In a move toward this objective, receding horizon control cast as a mixed-integer quadratic program is used to plan lane changing and acceleration in a coupled optimization. A long-term pacing module, based on Pontryagin’s minimum principle from optimal control theory, sets terminal and input references for receding horizon control to target a user’s expected travel time. To remove nonlinear vehicle dynamics from the receding horizon controller, lane change commands are passed to a pure pursuit steering module whose response is approximated by a second-order linear model. Here, comparison against a rule-based reactive algorithm in arterial and highway scenarios shows an 8.9%–13.7% reduction in energy consumption and a 5.2%–10.3% reduction in the travel time, along with navigational improvements.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2004

Stress, Cognition, and Human Performance: A Literature Review and Conceptual Framework

The following literature review addresses the effects of various stressors on cognition. While attempting to be as inclusive as possible, the review focuses its examination on the relationships between cognitive appraisal, attention, memory, and stress as they relate to information processing and human performance. The review begins with an overview of constructs and theoretical perspectives followed by an examination of effects across attention, memory, perceptual-motor functions, judgment and decision making, putative stressors such as workload, thermals, noise, and fatigue and closes with a discussion of moderating variables and related topics. In summation of the review, a conceptual framework for cognitive process under stress has been assembled. As one might imagine, the research literature that addresses stress, theories governing its effects on human performance, and experimental evidence that supports these notions is large and diverse. In attempting to organize and synthesize this body of work, I was guided by several earlier efforts (Bourne & Yaroush, 2003; Driskell, Mullen, Johnson, Hughes, & Batchelor, 1992; Driskell & Salas, 1996; Haridcock & Desmond, 2001; Stokes & Kite, 1994). These authors should be credited with accomplishing the monumental task of providing focused reviews in this area and their collective efforts laid the foundation for this present review. Similarly, the format of this review has been designed in accordance with these previous exemplars. However, each of these previous efforts either simply reported general findings, without sufficient experimental illustration, or narrowed their scope of investigation to the extent that the breadth of such findings remained hidden from the reader. Moreover, none of these examinations yielded an architecture that adequately describes or explains the inter-relations between information processing elements under stress conditions.

Stress↗
NASA NTRS2009

Numerical Evaluation of the "Dual-Kernel Counter-flow" Matric Convolution Integral that Arises in Discrete/Continuous (D/C) Control Theory

Discrete/Continuous (D/C) control theory is a new generalized theory of discrete-time control that expands the concept of conventional (exact) discrete-time control to create a framework for design and implementation of discretetime control systems that include a continuous-time command function generator so that actuator commands need not be constant between control decisions, but can be more generally defined and implemented as functions that vary with time across sample period. Because the plant/control system construct contains two linear subsystems arranged in tandem, a novel dual-kernel counter-flow convolution integral appears in the formulation. As part of the D/C system design and implementation process, numerical evaluation of that integral over the sample period is required. Three fundamentally different evaluation methods and associated algorithms are derived for the constant-coefficient case. Numerical results are matched against three available examples that have closed-form solutions.

Nixon, Douglas D.↗
NASA NTRS1991

A consensual neural network

A neural network architecture called a consensual neural network (CNN) is proposed for the classification of data from multiple sources. Its relation to hierarchical and ensemble neural networks is discussed. CNN is based on the statistical consensus theory and uses nonlinearly transformed input data. The input data are transformed several times, and the different transformed data are applied as if they were independent inputs. The independent inputs are classified using stage neural networks and outputs from the stage networks are then weighted and combined to make a decision. Experimental results based on remote-sensing data and geographic data are given.

Benediktsson, J. A.↗
NASA NTRS1995

A k-Omega Turbulence Model for Quasi-Three-Dimensional Turbomachinery Flows

A two-equation k-omega turbulence model has been developed and applied to a quasi-three-dimensional viscous analysis code for blade-to-blade flows in turbomachinery. the code includes the effects of rotation, radius change, and variable stream sheet thickness. The flow equations are given and the explicit runge-Kutta solution scheme is described. the k-omega model equations are also given and the upwind implicit approximate-factorization solution scheme is described. Three cases were calculated: transitional flow over a flat plate, a transonic compressor rotor, and transonic turbine vane with heat transfer. Results were compared to theory, experimental data, and to results using the Baldwin-Lomax turbulence model. The two models compared reasonably well with the data and surprisingly well with each other. Although the k-omega model behaves well numerically and simulates effects of transition, freestream turbulence, and wall roughness, it was not decisively better than the Baldwin-Lomax model for the cases considered here.

Chima, Rodrick V.↗
NASA NTRS2013

Data Mining for Understanding and Impriving Decision-Making Affecting Ground Delay Programs

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. The work reported in this paper is focused on ground delay programs. Data mining algorithms have the potential to develop associations between weather patterns and the corresponding ground delay program responses. If successful, they can be used to improve and standardize TFM decision resulting in better predictability of traffic flows on days with reliable weather forecasts. The approach here seeks to develop a set of data mining and machine learning models and apply them to historical archives of weather observations and forecasts and TFM initiatives to determine the extent to which the theory can predict and explain the observed traffic flow behaviors.

data mining↗
NASA NTRS1989

A two-phase methodology for technology selection and system design

A two-phase methodology that can be used to guide R&D managers in the evaluation and selection of competing technologies is presented. Deterministic multiattribute utility theory is used in the first phase to rank the technological alternatives; the example presented involves the evaluation of electric and hybrid passenger vehicles. In all, 39 individuals from eight automotive firms were interviewed to assess their risk preferences and attitudes toward the vehicle design. In the second phase, the decision-maker must allocate a fixed amount of resources to different projects for the technology selected, some of which may be undertaken in parallel, to maximize a given measure of performance. When parallel funding is pursued the best outcome is chosen. The problem is formulated as a probabilistic network and solved heuristically using Monte Carlo simulation. Results are presented for two decision-makers and three budget options. In each case, the heuristic finds the optimal allocation of funds.

Bard, Jonathan F.↗
NASA NTRS1992

Spring polar ozone behavior

Understanding of the springtime behavior of polar stratospheric ozone as of mid 1990 is summarized. Heterogeneous reactions on polar stratospheric clouds as hypothesis for ozone loss are considered and a simplified description of the behavior of Antarctic ozone in winter and spring is given. Evidence that the situation is more complicated than described by the theory is produced. Many unresolved scientific issues remain and some of the most important problems are identified. Ozone changes each spring since 1979 have clearly established for the first time that man made chlorine compounds influence stratospheric ozone. Long before important advances in satellite and in situ investigations, it was Dobson's decision to place a total ozone measuring spectrometer at Halley Bay in Antarctica during the International Geophysical Year and subsequent continuous monitoring which led to the discovery that ozone was being destroyed each spring by chlorine processed by polar stratospheric clouds.

Aikin, Arthur C.↗
DOE OSTI2022

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

42 ENGINEERING↗