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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ACTT: Automotive CAN Tokenization and Translation

Modern vehicles contain scores of Electrical Control Units (ECUs) that broadcast messages over a Controller Area Network (CAN). Vehicle manufacturers rely on security through obscurity by concealing their unique mapping of CAN messages to vehicle functions which differs for each make, model, year, and even trim. This poses a major obstacle for after-market modifications notably performance tuning and in-vehicle network security measures. We present ACTT: Automotive CAN Tokenization and Translation, a novel, vehicle-agnostic, algorithm that leverages available diagnostic information to parse CAN data into meaningful messages, simultaneously cutting binary messages into tokens, and learning the translation to map these contiguous bits to the value of the vehicle function communicated.

Verma, Miki↗
NASA NTRS2001

Relative effectiveness of kinetic analysis vs single point readings for classifying environmental samples based on community-level physiological profiles (CLPP)

The relative effectiveness of average-well-color-development-normalized single-point absorbance readings (AWCD) vs the kinetic parameters mu(m), lambda, A, and integral (AREA) of the modified Gompertz equation fit to the color development curve resulting from reduction of a redox sensitive dye from microbial respiration of 95 separate sole carbon sources in microplate wells was compared for a dilution series of rhizosphere samples from hydroponically grown wheat and potato ranging in inoculum densities of 1 x 10(4)-4 x 10(6) cells ml-1. Patterns generated with each parameter were analyzed using principal component analysis (PCA) and discriminant function analysis (DFA) to test relative resolving power. Samples of equivalent cell density (undiluted samples) were correctly classified by rhizosphere type for all parameters based on DFA analysis of the first five PC scores. Analysis of undiluted and 1:4 diluted samples resulted in misclassification of at least two of the wheat samples for all parameters except the AWCD normalized (0.50 abs. units) data, and analysis of undiluted, 1:4, and 1:16 diluted samples resulted in misclassification for all parameter types. Ordination of samples along the first principal component (PC) was correlated to inoculum density in analyses performed on all of the kinetic parameters, but no such influence was seen for AWCD-derived results. The carbon sources responsible for classification differed among the variable types with the exception of AREA and A, which were strongly correlated. These results indicate that the use of kinetic parameters for pattern analysis in CLPP may provide some additional information, but only if the influence of inoculum density is carefully considered. c2001 Elsevier Science Ltd. All rights reserved.

NASA Center KSC↗
DOE OSTI2022

Prioritizing ICS Beachhead Systems for Cyber Vulnerability Testing

Cyber Testing for Resilient Industrial Control Systems™ (CyTRICS™) is the Department of Energy’s (DOE’s) program for cybersecurity vulnerability testing, digital subcomponent enumeration, and forensic assessment. CyTRICS leverages best-in-class test facilities and analytic capabilities at six DOE National Laboratories and strategic partnerships with key stakeholders including technology developers, manufacturers, asset owners and operators, and interagency partners. During the program’s development, CyTRICS established a unique methodology for prioritizing digital components within operational technology (OT) and industrial control systems (ICS) in the Energy Sector Industrial Base (ESIB) for cyber vulnerability testing. The CyTRICS Prioritization Process leverages multiple characteristics of systems, components, and their contextual deployment to calculate a quantification of individual digital components for CyTRICS testing. The initial version of the CyTRICS Prioritization Process was premised largely upon the impact which could result to an industrial control system if the digital component under testing was compromised, either through malicious means, faulty engineering, or other modes. The worldwide compromise of the SolarWinds Orion platform, first reported in December 2020, through malicious interference with the digital patching cycle was a watershed event in cyber supply chain security. The SolarWinds compromised demonstrated the strategic importance of certain types of ubiquitous software, and the ability to generate widespread cybersecurity effects. To address this challenge and as a part of the Department of Energy’s response to the SolarWinds compromise, DOE’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER) directed the National Laboratories to evolve the CyTRICS Prioritization Process methodology to encompass additional factors related to the strategic importance of digital components. CESER directed CyTRICS researchers to identify, characterize, and append strategic factors to the CyTRICS Prioritization Process to provide additional weight to these characteristics. National Laboratory expert researchers identified functionality, distribution, and platform characteristics for digital components in ICS and OT that they assessed would be likely targeted in strategic initial-access cyber attack. CyTRICS has termed these factors “ICS Beachhead Systems,” leveraging a definition first advanced by Schneider Electric, which is intended as a blanket term to encompass digital components, products, and systems in OT. This paper describes the ICS Beachhead Systems identified and the rationale for inclusion. As a next step in the research and refinement process, the National Laboratories will validate this initial set of characteristics against digital components evaluated by the CyTRICS program and current implementation of the CyTRICS Prioritization Process. After validation, CyTRICS researchers will then develop a scoring methodology to generate a quantitative score to assess the degree to which a digital component is characterized as an ICS Beachhead System. Finally, the National Laboratories will append this scoring to the existing CyTRICS Prioritization Process algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The quantitative genetics of gene expression in Mimulus guttatus

Gene expression can be influenced by genetic variants that are closely linked to the expressed gene (cis eQTLs) and variants in other parts of the genome (trans eQTLs). We created a multiparental mapping population by sampling genotypes from a single natural population of Mimulus guttatus and scored gene expression in the leaves of 1,588 plants. We find that nearly every measured gene exhibits cis regulatory variation (91% have FDR < 0.05). cis eQTLs are usually allelic series with three or more functionally distinct alleles. The cis locus explains about two thirds of the standing genetic variance (on average) but varies among genes and tends to be greatest when there is high indel variation in the upstream regulatory region and high nucleotide diversity in the coding sequence. Despite mapping over 10,000 trans eQTL / affected gene pairs, most of the genetic variance generated by trans acting loci remains unexplained. This implies a large reservoir of trans acting genes with subtle or diffuse effects. Mapped trans eQTLs show lower allelic diversity but much higher genetic dominance than cis eQTLs. Several analyses also indicate that trans eQTLs make a substantial contribution to the genetic correlations in expression among different genes. They may thus be essential determinants of “gene expression modules,” which has important implications for the evolution of gene expression and how it is studied by geneticists.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Preliminary Results Using Galvanic Vestibular Reduction as a Non-Pharmaceutical Tool for Motion Sickness Mitigation

Introduction: Alterations in vestibular sensory processing following G-transitions lead to motion sickness and spatial disorientation upon return to Earth’s gravity. The use of non-pharmaceutical mitigation for motion sickness has several potential advantages over drug treatment options. The purpose of this study was to validate a non-pharmaceutical tool using galvanic vestibular reduction (GVR) to mitigate G-transitional induced motion sickness and spatial disorientation. Methods: Using a repeated measures counter-balanced design, motion sickness and perception are obtained during Coriolis cross-coupling stimuli on a rotating chair across three GVR treatment interventions: throughout stimulus testing (prevention), following symptom onset (rescue), and placebo control. Subjects perform up to 10 sets of pitch head movements during constant rotation. For each set, head movement is cued every 10 seconds, alternating between pitch forward (chin resting to chest) and pitch backward (head upright) for a total of 7 forward and backward movements. During each head movement, subjects are asked to use a joystick to record the magnitude of their perceived rotation along all three axes. During the 2-minute pause between sets, motion sickness symptom scoring was obtained using the Pensacola Diagnostic Index and subject discomfort (0-20) ratings. Performance on a sensorimotor and cognitive test battery is measured during a fourth session to map changes in GVR level with functional performance. Results: Fifteen of 30 subjects have completed testing to date. Preliminary findings suggest GVR may be more effective in reducing symptoms in subjects who self-report less susceptibility on a pre-test motion sickness susceptibility questionnaire. Based on the joystick measures, GVR significantly reduces both the magnitude (mean 22% - 34%) and duration (mean 42% - 49%) of perceived roll and pitch sensation with head movements during constant rotation. It is important to note that comparable levels of GVR (up to 2.5mA) does not impair performance on a functional test battery including mobility and balance tasks. Discussion: Our preliminary findings suggest GVR may be useful in reducing disorienting roll and pitch illusions associated with Coriolis cross-coupling stimuli. While transfer to post-flight treatment will need to be validated, the potential advantages of our non-pharmaceutical countermeasure approach would be to provide rapid therapeutic effect while allowing continuous titration of GVR amplitude during recovery to maintain operational performance.

Gaurav Pradhan↗
NASA NTRS2023

Galvanic Vestibular Reduction Modifies Perception of Coriolis Cross-Coupling and Delays Motion Sickness Onset

INTRODUCTION: Alterations in vestibular sensory processing following G-transitions lead to head movement sensitivity and motion sickness upon return to Earth’s gravity. The purpose of this study was to evaluate whether a non-pharmaceutical tool using galvanic vestibular reduction (GVR) could suppress disorienting illusions and mitigate motion sickness. A similar approach using anodal (inhibitory) currents delivered to both ears has been shown to result in a selective reversible ablation of irregular vestibular afferents [1]. METHODS: Using a repeated measures counter-balanced design, motion sickness and perception were obtained in 26 subjects during Coriolis cross-coupling stimuli on a rotating chair across three GVR treatment interventions: throughout stimulus testing (prevention), following symptom onset (rescue), and placebo control. The GVR peak current was maintained at 2.5 mA across subjects and across prevention / rescue sessions. Subjects performed up to 10 sets of pitch head movements during constant rotation. For each set, head movement was cued every 10 seconds, alternating between pitch forward (chin resting to chest) and pitch backward (head upright) for a total of 7 forward and backward movements. During each head movement, subjects were asked to use a joystick to record the magnitude of their perceived rotation along three axes. During the 2-minute pause between sets, motion sickness symptom scoring was obtained using the Pensacola Diagnostic Index and subject discomfort (0-20) ratings. Performance on a sensorimotor and cognitive test battery was measured during a fourth session to map changes in GVR level with functional performance. RESULTS: Fourteen of the 26 subjects were not susceptible to the motion stressor (i.e., did not reach an endpoint in the control condition). While the time to endpoint, or number of head movements, did not significantly vary across the three GVR conditions in the remaining subjects, the symptom levels were significantly lower through the third set of head movements when GVR was on throughout the testing. Initiating GVR following symptom onset did not appear to alter the symptom progression nor time to motion sickness endpoint. Based on the joystick measures, GVR significantly modified the perceived roll and pitch sensation during head movements, reducing the amplitude of tilt in most subjects. It is important to note that comparable levels of GVR did not impair performance on a functional test battery including mobility, balance and cognitive tasks. DISCUSSION: Our findings suggest GVR may be useful in reducing disorienting roll and pitch illusions and delaying the onset of motion sickness. Further enhancements will be required to individualize the stimulation amplitude and optimize the waveform delivery. Adapting this non-pharmaceutical countermeasure approach to allow self-administered titration of current amplitude during recovery would enable transfer to post-flight treatment of motion sickness. [1] Minor L. B. and Goldberg J. M. (1991) J Neurosci 11, 1636-48. 109, 889-894.

G N Pradhan↗
NASA NTRS2009

Return to Flying Duties Following Centrifuge or Vibration Exposures

Introduction: In an effort to determine the human performance limits for vibration in spacecraft being developed by NASA, astronauts were evaluated during a simulated launch profile in a centrifuge/vibration environment and separate vibration-only simulation. Current USAF and Army standards for return to flight following centrifuge exposures require 12-24 hours to pass before a crewmember may return to flying duties. There are no standards on vibration exposures and return to flying duties. Based on direct observation and provocative neurological testing of the astronauts, a new standard for return to flying duties following centrifuge and/or vibration exposures was established. Methods: 13 astronaut participants were exposed to simulated launch profiles in a + 3.5 Gx bias centrifuge/vibration environment and separately on a vibration table at the NASA-Ames Research Center. Each subject had complete neurological evaluations pre- and post-exposure for the centrifuge/vibration runs with the NASA neurological function rating scale (NFRS). Subjects who participated in the vibration-only exposures had video oculography performed with provocative maneuvers in addition to the NFRS. NFRS evaluations occurred immediately following each exposure and at 1 hour post-run. Astronauts who remained symptomatic at 1 hour had repeat NFRS performed at 1 hour intervals until the crewmember was asymptomatic. Results: Astronauts in the centrifuge/vibration study averaged a 3-5 point increase in NFRS scores immediately following exposure but returned to baseline 3 hours post-run. Subjects exposed to the vibration-only simulation had a 1-3 point increase following exposure and returned to baseline within 1-2 hours. Pre- and post- vibration exposure video oculography did not reveal any persistent ocular findings with provocative testing 1 hour post-exposure. Discussion: Based on direct observations and objective measurement of neurological function in astronauts following simulated launch profiles, asymptomatic individuals are allowed to return to flying duties within 3 hours following centrifuge/vibration and 2 hours after vibration-only exposures.

Scheuring, Richard A.↗
NASA NTRS2007

Sensitive Quantitative Assessment of Balance Disorders

Computerized dynamic posturography (CDP) has become a standard technique for objectively quantifying balance control performance, diagnosing the nature of functional impairments underlying balance disorders, and monitoring clinical treatment outcomes. We have long used CDP protocols to assess recovery of sensory-motor function in astronauts following space flight. The most reliable indicators of post-flight crew performance are the sensory organization tests (SOTs), particularly SOTs 5 and 6, which are sensitive to changes in availability and/or utilization of vestibular cues. We have noted, however, that some astronauts exhibiting obvious signs of balance impairment after flight are able to score within clinical norms on these tests, perhaps as a result of adopting competitive strategies or by their natural skills at substituting alternate sensory information sources. This insensitivity of the CDP protocol could underestimate of the degree of impairment and, perhaps, lead to premature release of those crewmembers to normal duties. To improve the sensitivity of the CDP protocol we have introduced static and dynamic head tilt SOT trials into our protocol. The pattern of postflight recovery quantified by the enhanced CDP protocol appears to more aptly track the re-integration of sensory-motor function, with recovery time increasing as the complexity of sensory-motor/biomechanical task increases. The new CDP protocol therefore seems more suitable for monitoring post-flight sensory-motor recovery and for indicating to crewmembers and flight surgeons fitness for return to duty and/or activities of daily living. There may be classes of patients (e.g., athletes, pilots) having motivation and/or performance characteristics similar to astronauts whose sensory-motor treatment outcomes would also be more accurately monitored using the enhanced CDP protocol. Furthermore, the enhanced protocol may be useful in early detection of age-related balance disorders.

Paloski, Willilam H.↗
NASA NTRS1979

Evaluation of a pilot workload metric for simulated VTOL landing tasks

A methodological approach to measuring workload was investigated for evaluation of new concepts in VTOL aircraft displays. Multivariate discriminant functions were formed from conventional flight performance and/or visual response variables to maximize detection of experimental differences. The flight performance variable discriminant showed maximum differentiation between crosswind conditions. The visual response measure discriminant maximized differences between fixed vs. motion base conditions and experimental displays. Physiological variables were used to attempt to predict the discriminant function values for each subject/condition/trial. The weights of the physiological variables in these equations showed agreement with previous studies. High muscle tension, light but irregular breathing patterns, and higher heart rate with low amplitude all produced higher scores on this scale and thus, represented higher workload levels.

North, R. A.↗
DOE OSTI2023

Efficient generalized boundary detection

Fast, efficient, and robust compression-based methods for detecting boundaries in arbitrary datasets, including sequences (1D datasets), are desired. The methods, each employing three simple algorithms, approximate the information distance between two adjacent sliding windows within a dataset. One of the algorithms calculates an initial ordered list of subsequences; while a second algorithm updates the ordered list of subsequences by dropping a first entry and appending a last entry rather than calculating completely new ordered lists with each iteration. Large values in the distance metric are indicative of boundary locations. A smoothed z-score or a wavelet-based algorithm may then be used to locate peaks in the distance metric, thereby identifying boundary locations. An adaptive version of the method employs a collection of window sizes and corresponding weighting functions, making it more amenable to real datasets with unknown, complex, and changing structures.

Ting, Christina↗
DOE OSTI2023

Determining Catalytically Relevant Surfaces through Coverage-Dependent Lattice Gas Models: Carbon Adsorption on Fe(100)

Here, we have quantified the C–C lateral interactions on Fe(100) using a density functional theory (DFT)-parameterized lattice gas cluster expansion (LG CE) model trained using 265 unique configurations spanning a C coverage from 0 to 1 monolayer (ML). Our LG CE model shows high predictive accuracy with a leave-multiple-out cross-validation score of 10.2 and 16.6 meV/site for systems with and without the top two layers of Fe atoms fixed, respectively. Electronic ground-state structures identified from the lattice gas model (including the structures at 0 and 1 monolayers) were further used to generate ab initio phase diagrams under a range of temperatures and pressures. At low temperatures (<400 K), we found that the 1.0 monolayer structure is dominant, whereas at higher temperatures (>500 K), the 0.88 ML structure is most likely to form on the Fe surface. Interestingly, our model identified a c (2 × 2) ordered structure at 1/2 ML, which correlates well with previous DFT studies for carbon adsorption on iron surfaces and matches with the experimentally observed low-energy electron diffraction structure. Overall, the DFT-parameterized energies for the C/Fe system including effects of coverage and configurational space can further help in developing multiscale models for various heterogeneous reactions involving C–C and C–Fe interactions.

08 HYDROGEN↗
DOE OSTI2024

Phase Selection Rules of Multi‐Principal Element Alloys

Abstract Computational prediction of phase stability of multi‐principal element alloys (MPEAs) holds a lot of promise for rapid exploration of the enormous design space and autonomous discovery of superior structural and functional properties. Regardless of many plausible works that rely on phenomenological theory and machine learning, precise prediction is still limited by insufficient data and the lack of interpretability of some machine learning algorithms, e.g., convolutional neural network. In this work, a comprehensive approach is presented, encompassing the development of a complete dataset that contains 72 387 density functional theory calculations, as well as a predictive global phenomenological descriptor. The phase selection descriptor, based on atomic electronegativity and valence electron concentration, significantly outperforms the widely used valence electron concentration, excelling in both accuracy (with an f1 score of 63% compared to 47%) and its ability to predict the HCP phase (0.48 recall compared to 0). The comprehensive data mining on the global design space of 61 425 quaternary MPEAs made from 28 possible metals, together with the phenomenological theory and physical interpretation, will set up a solid computational science foundation for data‐driven exploration of MPEAs.

Chemistry↗
NASA NTRS1979

Performance, physiological, and oculometer evaluation of VTOL landing displays

A methodological approach to measuring workload was investigated for evaluation of new concepts in VTOL aircraft displays. Physiological, visual response, and conventional flight performance measures were recorded for landing approaches performed in the NASA Visual Motion Simulator (VMS). Three displays (two computer graphic and a conventional flight director), three crosswind amplitudes, and two motion base conditions (fixed vs. moving base) were tested in a factorial design. Multivariate discriminant functions were formed from flight performance and/or visual response variables. The flight performance variable discriminant showed maximum differentation between crosswind conditions. The visual response measure discriminant maximized differences between fixed vs. motion base conditions and experimental displays. Physiological variables were used to attempt to predict the discriminant function values for each subject/condition trial. The weights of the physiological variables in these equations showed agreement with previous studies. High muscle tension, light but irregular breathing patterns, and higher heart rate with low amplitude all produced higher scores on this scale and thus represent higher workload levels.

North, R. A.↗
DOE OSTI2026

Temporal Convolutional Network Using Empirical Mode Decomposition to Detect Faults in Grid Connected Systems

Grid-connected power electronic systems require timely and reliable fault detection to prevent equipment damage and reduce downtime. This paper presents a forecasting-based anomaly detection pipeline that decomposes voltage and current measurements into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD), then trains a causal temporal convolutional network (TCN) on normal-operation IMF data to predict short-horizon future dynamics. Deviations between forecasts and observations are summarized as reliability-weighted residual scores and thresholded per sensor using robust statistics with temporal persistence constraints to suppress false positives. To reduce runtime, EMD is performed on downsampled signals for detection, while raw-rate EMD is applied only within a short region of interest for high-frequency interpretability near detected events. Results on a simulated grid-connected converter system demonstrate that IMF-domain forecasting improves anomaly separability relative to raw-signal forecasting and provides interpretable evidence of faults across decomposition channels.

Sutton, Elizabeth [ORNL] (ORCID:0009000078885935)↗
DOE OSTI2025

Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction

Abstract The rapid rise of deep learning (DL) in numerical weather prediction (NWP) has led to a proliferation of models which forecast atmospheric variables with comparable or superior skill than traditional physics-based NWP. However, among these leading DL models, there is a wide variance in both the training settings and architecture used. Further, the lack of thorough ablation studies makes it hard to discern which components are most critical to success. In this work, we show that it is possible to attain high forecast skill even with relatively off-the-shelf architectures, simple training procedures, and moderate compute budgets. Specifically, we train a minimally modified Swin Transformer V2 (SwinV2) on ERA5 data and find that it attains superior skill in terms of mean-square errors of deterministic forecasts when compared against the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS). Almost all DL–NWP systems share a core set of hyperparameters and design decisions. To aid and expedite future DL–NWP research, we present an in-depth, systematic exploration of different loss functions, model sizes and depths, patch sizes, and multistep training objectives. We also examine the model performance with metrics beyond the typical accuracy (ACC) and RMSE and investigate how the performance scales with model size. Through our open-source code, scoring pipelines, and models, we share our findings on key aspects of the training pipeline. These ablations reduce the necessity for expensive hyperparameter tuning and lower the barrier to entry for future DL–NWP research. Significance Statement This study investigates the potential of using large-scale transformer-based models for weather prediction, showing that it is possible to achieve high forecast accuracy with simpler, off-the-shelf architectures. By training a minimally modified SwinV2 transformer on ERA5 data, we show that the model achieves competitive forecast skill in terms of mean-square error for key variables, outperforming the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS) at all lead times. Our findings suggest that effective training strategies, such as multistep fine-tuning and channel-weighted losses, significantly enhance the model’s performance. However, we also highlight that these improvements come with trade-offs in other areas, such as ensemble spread and high-frequency spatial detail. This work highlights the promise of deep learning in improving weather forecasts, which could lead to better preparedness and response to weather events, ultimately benefiting society by providing more reliable weather predictions.

Willard, Jared D. [Lawrence Berkeley National Labo↗
NASA NTRS2013

Decadal Prediction Skill in the GEOS-5 Forecast System

A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office's (GMAO's) GEOS-5 Atmosphere-Ocean general circulation model. The hind casts are initialized every December 1st from 1959 to 2010, following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multivariate ensemble optimal interpolation ocean and sea-ice reanalysis, and from GMAO's atmospheric reanalysis, the modern-era retrospective analysis for research and applications. The mean forecast skill of a three-member-ensemble is compared to that of an experiment without initialization but also forced with observed greenhouse gases. The results show that initialization increases the forecast skill of North Atlantic sea surface temperature compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. On the other hand, the initialization reduces the skill in predicting the warming trend over some regions outside the Atlantic. The annual-mean Atlantic meridional overturning circulation index, which is defined here as the maximum of the zonally-integrated overturning stream function at mid-latitude, is predictable up to a 4-year lead time, consistent with the predictable signal in upper ocean heat content over the North Atlantic. While the 6- to 9-year forecast skill measured by mean squared skill score shows 50 percent improvement in the upper ocean heat content over the subtropical and mid-latitude Atlantic, prediction skill is relatively low in the sub-polar gyre. This low skill is due in part to features in the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region that differ from observations. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.

Decadal Prediction↗
NASA NTRS1962

An Investigation of the Effects of the Time Lag Due to Long Transmission Distance upon Remote Control. Phase II - Vehicle Experiments. Phase III - Conclusions

An experimental program is undertaken to define the effects upon remote control of long transmission delays. Investigation centers around remote control of a ground vehicle, which is considered to be a representative remote control task. A series of pursuit tracking tests is performed with transport lags ranging from 0 to 6 seconds between the control and the controlled quantity. Various target speeds are tracked with both velocity and acceleration controls. Two types of tracking are performed in an attempt to bracket the actual vehicle situation. In the first the operator attempts to follow the target with his controlled quantity in real time, using the delayed position and rates as feedback. In the second he attempts to follow the target with the delayed controlled quantity. In addition, tests are performed substituting simple electronic models for the human in an attempt to gain an understanding of human response with time delays in the control loop. A series of tests with an actual vehicle are performed with the intent of relating the tracking tests to the actual situation of interest. Time delays of from 0 to 3 seconds are included in the control loop. Performance is scored at various speeds over both continuous and obstacle courses. Both two and four-wheel steering are investigated. In the experiments the effects of all variables except delay magnitude and target complexity are minimized. Curves are obtained showing performance as a function of time delay and target complexity. These curves are discussed. Correlation of the tracking and vehicle experiments is discussed. Comments are made concerning the design of a minimum control system for a remotely controlled lunar roving vehicle.

REMOTE CONTROL↗
NASA NTRS2015

Can Simulation Credibility Be Improved Using Sensitivity Analysis to Understand Input Data Effects on Model Outcome?

Model and simulation (MS) credibility is defined as, the quality to elicit belief or trust in MS results. NASA-STD-7009 [1] delineates eight components (Verification, Validation, Input Pedigree, Results Uncertainty, Results Robustness, Use History, MS Management, People Qualifications) that address quantifying model credibility, and provides guidance to the model developers, analysts, and end users for assessing the MS credibility. Of the eight characteristics, input pedigree, or the quality of the data used to develop model input parameters, governing functions, or initial conditions, can vary significantly. These data quality differences have varying consequences across the range of MS application. NASA-STD-7009 requires that the lowest input data quality be used to represent the entire set of input data when scoring the input pedigree credibility of the model. This requirement provides a conservative assessment of model inputs, and maximizes the communication of the potential level of risk of using model outputs. Unfortunately, in practice, this may result in overly pessimistic communication of the MS output, undermining the credibility of simulation predictions to decision makers. This presentation proposes an alternative assessment mechanism, utilizing results parameter robustness, also known as model input sensitivity, to improve the credibility scoring process for specific simulations.

Credibility↗