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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Tuning and Robustness Analysis for the Orion Absolute Navigation System

The Orion Multi-Purpose Crew Vehicle (MPCV) is currently under development as NASA's next-generation spacecraft for exploration missions beyond Low Earth Orbit. The MPCV is set to perform an orbital test flight, termed Exploration Flight Test 1 (EFT-1), some time in late 2014. The navigation system for the Orion spacecraft is being designed in a Multi-Organizational Design Environment (MODE) team including contractor and NASA personnel. The system uses an Extended Kalman Filter to process measurements and determine the state. The design of the navigation system has undergone several iterations and modifications since its inception, and continues as a work-in-progress. This paper seeks to show the efforts made to-date in tuning the filter for the EFT-1 mission and instilling appropriate robustness into the system to meet the requirements of manned space ight. Filter performance is affected by many factors: data rates, sensor measurement errors, tuning, and others. This paper focuses mainly on the error characterization and tuning portion. Traditional efforts at tuning a navigation filter have centered around the observation/measurement noise and Gaussian process noise of the Extended Kalman Filter. While the Orion MODE team must certainly address those factors, the team is also looking at residual edit thresholds and measurement underweighting as tuning tools. Tuning analysis is presented with open loop Monte-Carlo simulation results showing statistical errors bounded by the 3-sigma filter uncertainty covariance. The Orion filter design uses 24 Exponentially Correlated Random Variable (ECRV) parameters to estimate the accel/gyro misalignment and nonorthogonality. By design, the time constant and noise terms of these ECRV parameters were set to manufacturer specifications and not used as tuning parameters. They are included in the filter as a more analytically correct method of modeling uncertainties than ad-hoc tuning of the process noise. Tuning is explored for the powered-flight ascent phase, where measurements are scarce and unmodelled vehicle accelerations dominate. On orbit, there are important trade-off cases between process and measurement noise. On entry, there are considerations about trading performance accuracy for robustness. Process Noise is divided into powered flight and coasting ight and can be adjusted for each phase and mode of the Orion EFT-1 mission. Measurement noise is used for the integrated velocity measurements during pad alignment. It is also used for Global Positioning System (GPS) pseudorange and delta- range measurements during the rest of the flight. The robustness effort has been focused on maintaining filter convergence and performance in the presence of unmodeled error sources. These include unmodeled forces on the vehicle and uncorrected errors on the sensor measurements. Orion uses a single-frequency, non-keyed GPS receiver, so the effects due to signal distortion in Earth's ionosphere and troposphere are present in the raw measurements. Results are presented showing the efforts to compensate for these errors as well as characterize the residual effect for measurement noise tuning. Another robustness tool in use is tuning the residual edit thresholds. The trade-off between noise tuning and edit thresholds is explored in the context of robustness to errors in dynamics models and sensor measurements. Measurement underweighting is also presented as a method of additional robustness when processing highly accurate measurements in the presence of large filter uncertainties.

Holt, Greg N.↗
DOE OSTI2025

Neural correspondence to spectrum of environmental uncertainty in multiple-cue probability judgment system with time delay

Despite state-of-the-art technologies like artificial intelligence, human judgment is critically essential in cooperative systems, such as the multi-agent system (MAS), which collect information among agents based on multiple-cue judgment. Human agents can prevent impaired situational awareness of automated agents by confirming situations under environmental uncertainty. System error caused by uncertainty can result in an unreliable system environment, and this environment affects the human agent, resulting in non-optimal decision-making in MAS. Thus, it is necessary to know how human behavior is changed to capture system reliability under uncertainty. Another issue affecting MAS is time delay, which can delay agent information transfer, resulting in low performance and instability. However, it is difficult to find studies on the influence of time delay on human agents. This study is about understanding the human decision-making process under a specific system reliability environment by uncertainty with time delay. We used concepts of expected and unexpected uncertainty to implement reliability of the system usage environment with three types of time delay conditions: no time delay, regular time delay, and irregular time delay conditions. We used electroencephalogram (EEG) for human cognitive neural mechanisms in multiple-cue judgment systems to understand human decision-making. In the reliability of system usage environment, the unreliable system environment significantly creates less memory load by less utilization of system rules for decision-making. In terms of time delay, delayed information delivery does not significantly affect memory load for decision-making.

cognitive process↗
NASA NTRS1989

Visual scanning with or without spatial uncertainty and time-sharing performance

An experiment is reported that examines the pattern of task interference between visual scanning as a sequential and selective attention process and other concurrent spatial or verbal processing tasks. A distinction is proposed between visual scanning with or without spatial uncertainty regarding the possible differential effects of these two types of scanning on interference with other concurrent processes. The experiment required the subject to perform a simulated primary tracking task, which was time-shared with a secondary spatial or verbal decision task. The relevant information that was needed to perform the decision tasks were displayed with or without spatial uncertainty. The experiment employed a 2 x 2 x 2 design with types of scanning (with or without spatial uncertainty), expected scanning distance (low/high), and codes of concurrent processing (spatial/verbal) as the three experimental factors. The results provide strong evidence that visual scanning as a spatial exploratory activity produces greater task interference with concurrent spatial tasks than with concurrent verbal tasks. Furthermore, spatial uncertainty in visual scanning is identified to be the crucial factor in producing this differential effect.

Liu, Yili↗
DOE OSTI2020

Imprecise global sensitivity analysis using bayesian multimodel inference and importance sampling

Global Sensitivity Analysis (GSA) aims to understand the relative importance of uncertain input variables to model response. Conventional GSA involves calculating sensitivity (Sobol’) indices for a model with known model parameter distributions. However, model parameters are affected by aleatory and epistemic uncertainty, with the latter often caused by lack of data. In this paper, we propose a new framework to quantify uncertainty in probability model-form and model parameters resulting from small datasets and integrate these uncertainties into Sobol’ index estimates. First, the process establishes, through Bayesian multimodel inference, a set of candidate probability models and their associated probabilities. Imprecise Sobol’ indices are calculated from these probability models using an importance sampling reweighting approach. This results in probabilistic Sobol’ indices, whose distribution characterizes uncertainty in the sensitivity resulting from small dataset size. The imprecise Sobol’ indices thus provide a measure of confidence in the sensitivity estimate and, moreover, can be used to inform data collection efforts targeted to minimize the impact of uncertainties. Through an example studying the parameters of a Timoshenko beam, we show that these probabilistic Sobol’ indices converge to the true/deterministic Sobol’ indices as the dataset size increases and hence, distribution-form uncertainty reduces. The approach is then applied to assess the sensitivity of the out-of-plane properties of an E-glass fiber composite material to its constituent properties. This second example illustrates the approach for an important class of materials with wide-ranging applications when data may be lacking for some input parameters.

42 ENGINEERING↗
DOE OSTI2018

Measurement of Sauter Mean Diameter (SMD) in Diesel Sprays Using a Scattering-Absorption Measurement Ratio (SAMR) Technique

A new diagnostic for the quantification of Sauter Mean Diameter (SMD) in high-pressure fuel sprays has been recently developed using combined optical and x-ray measurements at the Georgia Institute of Technology and Argonne National Laboratory, respectively. This diagnostic utilizes liquid scattering extinction measurements from diffuse back-illumination imaging, conducted at Georgia Tech, and liquid absorption measurements from x-ray radiography, conducted at Argonne’s Advanced Photon Source. The new diagnostic, entitled the Scattering Absorption Measurement Ratio (SAMR), quantifies two-dimensional distributions of path integrated SMD, enabling construction of the spatial history of drop size development within practical fuel sprays. This technique offers unique benefits over conventional drop-sizing methods in that it can be more robust in optically dense regions of the spray, while also providing high spatial and temporal resolution of the corresponding droplet field. The methodology for quantification of SMD distributions using the SAMR technique has been previously introduced and demonstrated in diesel sprays using the Engine Combustion Network Spray D injector, however a more detailed treatment of measurement uncertainties has been needed. In the current work, we present a summary of the various sources of measurement uncertainty in the SAMR diagnostic, like those due to the experimental setup, data processing methods, and theoretical assumptions, and assess how these sources of uncertainty affect the quantified SMD. The spatially-resolved SMD measurements that result from the SAMR diagnostic will be especially valuable to the engine modeling community for the quantitative validation of spray submodels in engine CFD codes. Careful evaluation and quantification of measurement uncertainties is important to support accurate model validation and to ensure the development of more predictive spray models.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Uncertainty Quantification of Extratropical Forest Biomass in CMIP5 Models over the Northern Hemisphere

Abstract Simplified representations of processes influencing forest biomass in Earth system models (ESMs) contribute to large uncertainty in projections. We evaluate forest biomass from eight ESMs outputs archived in the Coupled Model Intercomparison Project Phase 5 (CMIP5) using the biomass data synthesized from radar remote sensing and ground-based observations across northern extratropical latitudes. ESMs exhibit large biases in the forest distribution, forest fraction, and mass of carbon pools that contribute to uncertainty in forest total biomass (biases range from −20 Pg C to 135 Pg C). Forest total biomass is primarily positively correlated with precipitation variations, with surface temperature becoming equally important at higher latitudes, in both simulations and observations. Relatively small differences in forest biomass between the pre-industrial period and the contemporary period indicate uncertainties in forest biomass were introduced in the pre-industrial model equilibration (spin-up), suggesting parametric or structural model differences are a larger source of uncertainty than differences in transient responses. Our findings emphasize the importance of improved (1) models of carbon allocation to biomass compartments, (2) distribution of vegetation types in models, and (3) reproduction of pre-industrial vegetation conditions, in order to reduce the uncertainty in forest biomass simulated by ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Archie's Saturation Exponent for Natural Gas Hydrate in Coarse-Grained Reservoirs

Accurately quantifying the amount of naturally occurring gas hydrate in marine and permafrost environments is important for assessing its resource potential and understanding the role of gas hydrate in the global carbon cycle. Electrical resistivity well logs are often used to calculate gas hydrate saturations, S h , using Archie's equation. Archie's equation, in turn, relies on an empirical saturation parameter, n . Though n = 1.9 has been measured for ice–bearing sands and is widely used within the hydrate community, it is highly questionable if this n value is appropriate for hydrate–bearing sands. In this work, we calibrate n for hydrate–bearing sands from the Canadian permafrost gas hydrate research well, Mallik 5L–38, by establishing an independent downhole S h profile based on compressional–wave velocity log data. Using the independently determined S h profile and colocated electrical resistivity and bulk density logs, Archie's saturation equation is solved for n , and uncertainty is tracked throughout the iterative process. In addition to the Mallik 5L–38 well, we also apply this method to two marine, coarse–grained reservoirs from the northern Gulf of Mexico Gas Hydrate Joint Industry Project: Walker Ridge 313–H and Green Canyon 955–H. All locations yield similar results, each suggesting n ≈ 2.5 ± 0.5. Furthermore, for the coarse–grained hydrate bearing ( S h > 0.4) of greatest interest as potential energy resources, we suggest that n = 2.5 ± 0.5 should be applied in Archie's equation for either marine or permafrost gas hydrate settings if independent estimates of n are not available.

58 GEOSCIENCES↗
DOE OSTI2023

Explicit model predictive control through robust optimization

A strategy that calculates an explicit state feedback policy to regulate constrained uncertain discrete‐time uncertain linear systems is presented. We consider uncertain processes, affected by box‐bounded multiplicative uncertainty as well as bounded additive uncertainty with linear state and inputs constraints. The proposed method includes (i) the calculation of a terminal set constraint and (ii) the robust reformulation of state constraints in the prediction horizon. These features allow the derivation of the desired policy by solving a single multiparametric quadratic programming problem that guarantees feasible operation in the presence of uncertainty. Additionally, we employ variable and constraint elimination approaches to enhance the computational performance of the strategy. We demonstrate the steps and benefits of these developments with a numerical example and a chemical engineering case study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Adaptive anomaly detection for identifying attacks in cyber-physical systems: A systematic literature review

Modern cyberattacks in cyber-physical systems (CPS) rapidly evolve and cannot be deterred effectively with most current methods, which focus on characterizing past threats. Adaptive anomaly detection (AAD) is among the most promising techniques to detect evolving cyberattacks, with an emphasis on fast data processing and model adaptation. AAD has been researched extensively; however, to the best of our knowledge, our work is the first systematic literature review (SLR) on current research in this field. We present a comprehensive SLR, gathering 397 relevant papers and systematically analyzing 65 of them (47 research and 18 survey papers) on AAD in CPS from 2013 to November 2023. We introduce a novel taxonomy considering attack types, CPS application, learning paradigm, data management, and algorithms. Our findings show that most studies addressed either model adaptation or data processing, but rarely both simultaneously. This indicates a research gap in fully adaptive solutions. We also categorize algorithms, datasets, and attack characteristics, and summarize strengths and weaknesses across the literature. Our review provides a structured and accessible reference for researchers and practitioners, offering insights into key trends and highlighting limitations in current approaches. Finally, we outline several future research directions, including the need for integrated real-time processing and adaptive learning, explainability, and uncertainty quantification in AAD for CPS.

Adaptation↗
DOE OSTI2021

Optimization models for integrated biorefinery operations

Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US’s cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system’s reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. Furthermore, the sample average approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory’s biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution, increases utilization of the reactor and reduces operational costs.

09 BIOMASS FUELS↗
DOE OSTI2021

Sensitivity of cellular automata grain structure predictions for high solidification rates

Understanding as-solidified grain structure development during constrained (G 0) alloy solidification conditions is a necessary component of understanding the larger process-microstructure relationships during additive manufacturing (AM) processing. This study applies a cellular automata (CA) model, for static solidification velocities and thermal gradients on the order of those expected in AM melt pools, to investigate grain shape development across ranges of input parameters governing temperature field evolution, nucleation, and growth. Variation in nucleated grain shape from columnar to equiaxed as a function of thermal gradient and nucleation density is modeled with CA and verified against the established transition model of Hunt and Gaumann. Furthermore, while thermal conditions and nucleation density are shown to have the largest roles on microstructure development, the sensitivity of grain structure was very unevenly distributed across parameter space. In particular, it was found that microstructure is very sensitive to mean nucleation undercooling and the interfacial response function in process space regimes near grain structure transitions, but entirely controlled by a combination of thermal gradient, solidification velocity, and nucleation density away from such transitions. The understanding of grain shape sensitivity obtained through CA modeling, particularly at large thermal gradient and nucleation density, will be necessary for accurate microstructure prediction during the extreme processing conditions and large input parameter uncertainty common to AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Spectral line-shape in collinear laser spectroscopy after atomic charge exchange

Collinear laser spectroscopy experiments on fast, neutral beams have been extensively used for studies on short-lived radioactive nuclei, taking advantage of its high sensitivity. The resulting resonance line-shape is known to show significant distortion, due to the energy exchange during the charge-exchange neutralization process, which can cause large systematic uncertainty in the determined centroid. A model for the line shape was constructed and simulated to be compared to measured Al, Si, and Ni hyperfine spectra. It is shown that the distortion is caused mainly by the transfer of electron into many different energy levels in the projectile atom and subsequent decays, rather than secondary inelastic collisions, which were often assumed in the line shape analysis before. Furthermore, the model can also be applied to other projectile–alkali pairs, providing a reliable line-shape with less fitting parameters than conventional phenomenological models.

Atom↗
DOE OSTI2013

Mechanisms and Kinetics of Alkaline Hydrolysis of the Energetic Nitroaromatic Compounds 2,4,6-Trinitrotoluene (TNT) and 2,4-Dinitroanisole (DNAN)

The environmental impacts of energetic compounds can be minimized through the design and selection of new energetic materials with favorable fate properties. Building predictive models to inform this process, however, is difficult because of uncertainties and complexities in some major fate-determining transformation reactions such as the alkaline hydrolysis of energetic nitroaromatic compounds (NACs). Prior work on the mechanisms of the reaction between NACs and OH– has yielded inconsistent results. In this study, the alkaline hydrolysis of 2,4,6-trinitrotoluene (TNT) and 2,4-dinitroanisole (DNAN) was investigated with coordinated experimental kinetic measurements and molecular modeling calculations. For TNT, the results suggest reversible formation of an initial product, which is likely either a Meisenheimer complex or a TNT anion formed by abstraction of a methyl proton by OH–. For DNAN, the results suggest that a Meisenheimer complex is an intermediate in the formation of 2,4-dinitrophenolate. Despite these advances, the remaining uncertainties in the mechanisms of these reactions—and potential variability between the hydrolysis mechanisms for different NACs—mean that it is not yet possible to generalize the results into predictive models (e.g., quantitative structure–activity relationships, QSARs) for hydrolysis of other NACs.

Activation energy↗
DOE OSTI2022

Are Land‐Use Change Emissions in Southeast Asia Decreasing or Increasing?

Abstract Southeast Asia is a region known for active land‐use changes (LUC) over the past 60 years; yet, how trends in net CO 2 uptake and release resulting from LUC activities (net LUC flux) have changed through past decades remains uncertain. The level of uncertainty in net LUC flux from process‐based models is so high that it cannot be concluded that newer estimates are necessarily more reliable than older ones. Here, we examined net LUC flux estimates of Southeast Asia for the 1980s−2010s from older and newer sets of Dynamic Global Vegetation Model simulations (TRENDY v2 and v7, respectively), and forcing data used for running those simulations, along with two book‐keeping estimates (H&N and BLUE). These estimates yielded two contrasting historical LUC transitions, such that TRENDY v2 and H&N showed a transition from increased emissions from the 1980s to 1990s to declining emissions in the 2000s, while TRENDY v7 and BLUE showed the opposite transition. We found that these contrasting transitions originated in the update of LUC forcing data, which reduced the loss of forest area during the 1990s. Further evaluation of remote sensing studies, atmospheric inversions, and the history of forestry and environmental policies in Southeast Asia supported the occurrence of peak emissions in the 1990s and declining thereafter. However, whether LUC emissions continue to decline in Southeast Asia remains uncertain as key processes in recent years, such as conversion of peat forest to oil‐palm plantation, are yet to be represented in the forcing data, suggesting a need for further revision.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Link Between CO 2 –Induced Wettability and Pore Architecture Alteration

Changes in pore (throat) size, surface roughness and mineralogy induced by supercritical-CO 2 -water-rock reactions impact petrophysical properties such as porosity, permeability, and especially wettability. Herein, we show that these changes directly impact relative permeability and capillary pressure curves, a fact rarely studied in the literature. In this work, we show that CO 2 contact angle changes emerge after Madison Limestone samples were soaked for 400 hours in CO 2 -enriched brine. Coreflooding results show that the water production rate and cumulative water production increased after the rock was exposed to carbonic acid. Moreover, the mercury capillary pressure decreased in meso- and macro-pores, indicating the increase of size in these pores due to reactions. This compounded wettability and pore network alteration can directly affect CO 2 injectivity, migration and storage capacity. Furthermore, this fundamental insight into CO 2 geological storage processes should aid practitioners to reduce uncertainties in forecasting CO 2 distribution via injection simulation.

58 GEOSCIENCES↗
DOE OSTI2025

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2019

The effect of plant physiological responses to rising CO 2 on global streamflow

River flow statistics are expected to change as a result of increasing atmospheric CO 2 but uncertainty in Earth system model projections is high. While this is partly driven by changing precipitation, with well-known Earth system model uncertainties, in this work we show that the influence of plant stomatal conductance feedbacks can cause equally large changes in regional flood extremes and even act as the main control on future low latitude streamflow. Over most tropical land masses, modern climate predictions suggest that plant physiological effects will boost streamflow, overwhelming opposing effects of soil drying driven by the effects of CO 2 on atmospheric radiation, warming and rainfall redistribution. The relatively unknown uncertainties in representing eco-physiological processes must therefore be better constrained in land-surface models. To this end, we identify a distinct plant physiological fingerprint on annual peak, low and mean discharge throughout the tropics and identify river basins where physiological responses dominate radiative responses to rising CO 2 in modern climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Informed Investments in Clean Energy Technologies

Governments and companies face consequential decisions about allocating resources to the research, development, demonstration and deployment of energy technologies to meet environmental, economic and social goals. Here we discuss how research insights can inform and potentially improve these decisions to make effective use of limited resources and time in shaping the next-generation energy infrastructure. We outline three key research steps: forecasting technological change, relating investments to economic, social and environmental outcomes and informing decision-making processes. We recommend advances to address uncertainty as well as to make methods and results more practicable, emphasizing the importance of model validation, streamlining and interactivity. Progress has been made, yet further work is needed-for example, in the development of reduced-order, testable models and more comprehensive data collection. Overall, this research is beginning to inform decisions but could be adopted more widely by governments and the private sector to help support technological progress for energy affordability, equitable climate change mitigation, health benefits and other objectives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗