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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Effect of vibration duration on human discomfort

The duration effects of random vertical vibration on passenger discomfort were studied in a simulated section of an aircraft cabin configured to seat six persons in tourist-class style. Variables of the study included time of exposure (0.25 min to 60 min) and the rms amplitude of vibration (0.025g to 0.100g). The vibrations had a white noise spectrum with a bandwidth of 10 Hz centered at 5 Hz. Data indicate that the discomfort threshold occurred at an rms vertical acceleration level of 0.027g for all durations of vibration. However, for acceleration levels that exceeded the discomfort threshold, a systematic decrease in discomfort occurred as a function of increasing duration of vibration. For the range of accelerations used, the magnitude of the discomfort decrement was shown to be independent of acceleration level. The results suggest that discomfort from vertical vibration applied in the frequency range at which humans are most sensitive decreases with longer exposure, which is the opposite of the recommendation of the International Standard ISO 2631-1974 (E) Guide for the Evaluation of Human Exposure to Whole-Body Vibration.

Clevenson, S. A.↗
NASA NTRS1984

Cockpit Window Edge Proximity Effects on Judgements of Horizon Vertical Displacement

To quantify the influence of a spatially fixed edge on vertical displacement threshold, twenty-four males (12 pilots, 12 non-pilots) were presented a series of forced choice, paired comparison trials in which a 32 deg arc wide, thin, luminous horizontal stimulus line moved smoothly downward through five angles from a common starting position within a three second-long period. The five angles were 1.4, 1.7, 2, 2.3, and 2.6 deg. Each angle was presented paired with itself and the other four angles in all combinations in random order. For each pair of trials the observer had to choose which trial possessed the largest displacement. A confidence response also was made. The independent variable was the angular separation between the lower edge of a stable 'window' aperture through which the stimulus was seen to move and the lowest position attained by the stimulus. It was found that vertical displacement accuracy is inversely related to the angle separating the stimulus and the fixed window edge (p = .05). In addition, there is a strong tendency for pilot confidence to be lower than that of non-pilots for each of the three angular separations. These results are discussed in erms of selected cockpit features and as they relate to how pilots judge changes in aircraft pitch attitude.

Haines, R. F.↗
DOE OSTI2022

Understanding the Seismic Ground Motion Spatial Variability Using Network Analysis Community Detection

This project is to explore ground motion spatial distribution using a new approach graph-based network analysis. In this study, we combine a large-N seismic array and graph analytics to explore spatial variability and correlation at a local scale using small local and regional earthquakes. In this method, each seismic station is modeled as a node and the similarities of the waveforms that represent ground motions between two stations are modeled as edges. By analyzing this graph network using the similarity matrices and community detection algorithm, we can group the stations spatially with similar patterns. A random forest algorithm is used to reveal the important features that affect the spatial grouping. The result suggests site conditions, and how they interact with the incident seismic wavefield, strongly condition the spatial correlation of ground motion. Future progress in characterizing ground motion spatial variability will require dense wavefield measurements, either through nodal deployments, or perhaps distributed acoustic sensing measurements of seismic wavefields.

58 GEOSCIENCES↗
NASA NTRS1996

Effects of Shocks on Emission from Central Engines of Active Galactic Nuclei. I

In this paper we show that perturbations of the accretion flow within the central engines of some active galactic nuclei (AGNS) are likely to form shock waves in the accreting plasma. Such shocks, which may be either collisional or collisionless, can contribute to the observed high-energy temporal and spectral variability. Our rationale is the following: Observations show that the continuum emission probably originates in an optically thin, hot plasma in the AGN central engine. The flux and spectrum from this hot plasma varies significantly over light crossing timescales. Several authors have suggested that macroscopic perturbations contained within this plasma are the sources of this variability. In order to produce the observed emission the perturbations must be radiatively coupled with the optically thin hot matter and must also move with high velocities. We suggest that shocks, which can be very effective in randomizing the bulk motion of the perturbations, are responsible for this coupling. Shocks should form in the central engine, because the temperatures and magnetic fields are probably reduced below their virial values by radiative dissipation. Perturbations moving at Keplerian speeds, or strong non-linear excitations, result in supersonic and super-Alfvenic velocities leading to shock waves within the hot plasma. We show that even a perturbation smaller than the emitting region can form a shock that significantly modifies the continuum emission in an AGN, and that the spectral and temporal variability from such a shock generally resembles those of radio-quiet AGNS. As an example, the shock inducing perturbation in our model is a small main-sequence star, the capturing and eventual accretion of which are known to be a plausible process. We argue that shocks in the central engine may also provide a natural triggering mechanism for the "cold" component of Guilbert & Rees two-phase medium and an efficient mecha- nism for angular momentum transfer. Current and future missions, such as ASCA, XTE, XMM, AXAF, and ASTRO-E may determine the importance of shock-related emission from the central engines of AGNS.

Sivron, R.↗
NASA NTRS1975

Demographic and psychological variables affecting test subject evaluations of ride quality

Ride-quality experiments similar in objectives, design, and procedure were conducted, one using the U.S. Air Force Total In-Flight Simulator and the other using the Langley Passenger Ride Quality Apparatus to provide the motion environments. Large samples (80 or more per experiment) of test subjects were recruited from the Tidewater Virginia area and asked to rate the comfort (on a 7-point scale) of random aircraft motion typical of that encountered during STOL flights. Test subject characteristics of age, sex, and previous flying history (number of previous airplane flights) were studied in a two by three by three factorial design. Correlations were computed between one dependent measure, the subject's mean comfort rating, and various demographic characteristics, attitudinal variables, and the scores on Spielberger's State-Trait Anxiety Inventory. An effect of sex was found in one of the studies. Males made higher (more uncomfortable) ratings of the ride than females. Age and number of previous flights were not significantly related to comfort ratings. No significant interactions between the variables of age, sex, or previous number of flights were observed.

Duncan, N. C.↗
DOE OSTI2025

Data-Driven Closures and Assimilation for Stiff Multiscale Random Dynamics

Here, we introduce a data-driven and physics-informed framework for propagating uncertainty in stiff, multiscale random ordinary differential equations (RODEs) driven by correlated (colored) noise. Unlike systems subjected to Gaussian white noise, a deterministic equation for the joint probability density function (PDF) of RODE state variables does not exist in closed form. Moreover, such an equation would require as many phase-space variables as there are states in the RODE system. To alleviate this curse of dimensionality, we instead derive exact, albeit unclosed, reduced-order PDF (RoPDF) equations for low-dimensional observables/quantities of interest. The unclosed terms take the form of state-dependent conditional expectations, which are directly estimated from data at sparse observation times. However, for systems exhibiting stiff, multiscale dynamics, data sparsity introduces regression discrepancies that compound during RoPDF evolution. This is overcome by introducing a kinetic-like defect term to the RoPDF equation, which is learned by assimilating in sparse, low-fidelity RoPDF estimates. Two assimilation methods are considered, namely nudging and deep neural networks, which are successfully tested against Monte Carlo simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A review of imputation strategies for isobaric labeling-based shotgun proteomics

The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is uniquely different than unlabeled studies. In this review, we compare the efficacy of nine imputation methods on a CPTAC proteomics iTRAQ dataset. Imputation methods were evaluated with regard to accuracy, variability, statistical hypothesis test inference and run time over datasets consisting of varying number of iTRAQ plexes and percentages of missing data. In general, expectation maximization and random forest imputation methods yielded the best performances, and constant-based methods performed poorly consistently across all dataset sizes and percentages of missing values. For datasets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. Based on the findings in this review, there are core imputation methods that perform higher for isobaric-labeled proteomics data, but great care and consideration as to whether imputation should be used should be given for datasets comprised of a small number of samples, as well as to factors such as computational time and reproducibility of imputation values.

Bramer, Lisa M.↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2020

ROM-Based Surrogate Systems Modeling of EBR-II

We report the System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non-light water reactors, including sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), and molten salt reactor (MSR)/fluoride-salt-cooled high-temperature reactor (FHR) designs. SAM is primarily constructed as a systems-level analysis tool, with the potential to incorporate reduced order models from three-dimensional computational fluid dynamics (CFD) simulations to improve characterization of complex, multidimensional physics. It is recognized that the computational expense associated with CFD can be intractable for various engineering analyses, such as uncertainty quantification, inference, and design optimization. This paper explores the reducibility of a SAM model using recent advances in randomized linear algebra techniques, which attempt to find recurring patterns in the various realizations generated by a model after randomly perturbing all its input parameters. The reduction is described in terms of fewer degrees of freedom (DOFs), referred to as the active DOFs, for the model variables such as input model parameters and model responses. The results indicate that there is significant room for additional reduction that may be leveraged for additional computational gains when employing SAM for engineering-intensive analyses that require repeated model executions. Different from physics-based reduction approaches, the proposed approach allows one to estimate upper bounds on the reduction errors, which are rigorously developed in this work. Finally, different methods for surrogate model construction, such as regression and neural network-based training, are employed to correlate the input and output active DOFs, which are related back to the original variables using matrix-based linear transformations.

42 ENGINEERING↗
DOE OSTI2023

Quantification and prediction of error in MIMO tests

Dynamic testing of systems is most realistic of real-world conditions when multiple input and multiple output (MIMO) techniques are used. To replicate measured environmental conditions, a series of desired outputs (responses) on the system must be realized by inverting the frequency response functions (FRF) matrix for input estimation. System identification is dependent on the number of inputs and outputs, which affect the type of solutions. In input estimation, the auto spectra of the determined inputs are affected by both the auto spectra and cross spectra of the desired outputs. This research evaluates errors and investigates two sources in an experimental setting. The first significant error source is test-to-test variability in FRFs from system identification tests, quantified by magnitude variability from long-duration tests. The second significant error source is the realization of time histories from frequency domain inputs. The spectral content of the realized random process deviates from the desired content and is also quantified. The structure is a linear three-story frame fixed at the base; two inputs are provided uniaxially with the use of suspended electrodynamic shakers. The proposed model for error sources was found to be effective in predicting errors in a SISO (single-input single-output) and a square (2-input 2-output) MIMO test.

Yousef, Odey↗
DOE OSTI2022

Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology

Modeling real-world phenomena to any degree of accuracy is a challenge that the scientific research community has navigated since its foundation. Lack of information and limited computational and observational resources necessitate modeling assumptions which, when invalid, lead to model-form error (MFE). The work reported herein explored a novel method to represent model-form uncertainty (MFU) that combines Bayesian statistics with the emerging field of universal differential equations (UDEs). The fundamental principle behind UDEs is simple: use known equational forms that govern a dynamical system when you have them; then incorporate data-driven approaches – in this case neural networks (NNs) – embedded within the governing equations to learn the interacting terms that were underrepresented. Utilizing epidemiology as our motivating exemplar, this report will highlight the challenges of modeling novel infectious diseases while introducing ways to incorporate NN approximations to MFE. Prior to embarking on a Bayesian calibration, we first explored methods to augment the standard (non-Bayesian) UDE training procedure to account for uncertainty and increase robustness of training. In addition, it is often the case that uncertainty in observations is significant; this may be due to randomness or lack of precision in the measurement process. This uncertainty typically manifests as “noisy” observations which deviate from a true underlying signal. To account for such variability, the NN approximation to MFE is endowed with a probabilistic representation and is updated using available observational data in a Bayesian framework. By representing the MFU explicitly and deploying an embedded, data-driven model, this approach enables an agile, expressive, and interpretable method for representing MFU. In this report we will provide evidence that Bayesian UDEs show promise as a novel framework for any science-based, data-driven MFU representation; while emphasizing that significant advances must be made in the calibration of Bayesian NNs to ensure a robust calibration procedure.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

Investigation of the performance characteristics of Doppler radar technique for aircraft collision hazard warning, phase 3

System studies, equipment simulation, hardware development and flight tests which were conducted during the development of aircraft collision hazard warning system are discussed. The system uses a cooperative, continuous wave Doppler radar principle with pseudo-random frequency modulation. The report presents a description of the system operation and deals at length with the use of pseudo-random coding techniques. In addition, the use of mathematical modeling and computer simulation to determine the alarm statistics and system saturation characteristics in terminal area traffic of variable density is discussed.

Source record↗
NASA NTRS1997

Foreshock Langmuir Waves for Unusually Constant Solar Wind Conditions: Data and Implications for Foreshock Structure

Plasma wave data are compared with ISEE 1's position in the electron foreshock for an interval with unusually constant (but otherwise typical) solar wind magnetic field and plasma characteristics. For this period, temporal variations in the wave characteristics can be confidently separated from sweeping of the spatially varying foreshock back and forth across the spacecraft. The spacecraft's location, particularly the coordinate D(sub f) downstream from the foreshock boundary (often termed DIFF), is calculated by using three shock models and the observed solar wind magnetometer and plasma data. Scatterplots of the wave field versus D(sub f) are used to constrain viable shock models, to investigate the observed scatter in the wave fields at constant D(sub f), and to test the theoretical predictions of linear instability theory. The scatterplots confirm the abrupt onset of the foreshock waves near the upstream boundary, the narrow width in D(sub f) of the region with high fields, and the relatively slow falloff of the fields at large D(sub f), as seen in earlier studies, but with much smaller statistical scatter. The plots also show an offset of the high-field region from the foreshock boundary. It is shown that an adaptive, time-varying shock model with no free parameters, determined by the observed solar wind data and published shock crossings, is viable but that two alternative models are not. Foreshock wave studies can therefore remotely constrain the bow shock's location. The observed scatter in wave field at constant D(sub f) is shown to be real and to correspond to real temporal variations, not to unresolved changes in D(sub f). By comparing the wave data with a linear instability theory based on a published model for the electron beam it is found that the theory can account qualitatively and semiquantitatively for the abrupt onset of the waves near D(sub f) = 0, for the narrow width and offset of the high-field region, and for the decrease in wave intensity with increasing D(sub f). Quantitative differences between observations and theory remain, including large overprediction of the wave fields and the slower than predicted falloff at large D(sub f) of the wave fields. These differences, as well as the unresolved issue of the electron beam speed in the high-field region of the foreshock, are discussed. The intrinsic temporal variability of the wave fields, as well as their overprediction based on homogeneous plasma theory, are indicative of stochastic growth physics, which causes wave growth to be random and varying in sign, rather than secular.

Cairns, Iver H.↗
DOE OSTI2023

Process Variability Effects on Tensile Response in Injection Molded, Fluorinated Thermoplastics

The mechanical properties of fluorinated thermoplastics (i.e., tensile strength and elongation) can vary with changes in injection molding processing parameters. Four fluoropolymers are examined: poly(vinylidene fluoride) (PVDF) and random poly(vinylidene fluoride-co-chlorotrifluoroethylene) (PVDF-CTFE) with three CTFE concentrations. Dog bones were manufactured with various cylinder dwell times and mold cooling times to assess the manufacturing sensitivity to the tensile response. Dwell and cooling times increasingly impact mechanical performance as CTFE concentration increases. Specimens exhibit higher tensile strength as a function of injection order. The first injected specimen exhibits the lowest tensile strength and highest elongation in all copolymers. This trend becomes more pronounced among fluoropolymers with higher CTFE concentration and lower weight-averaged molecular weight. Parallel plate rheology was used to obtain the zero-shear viscosity as a function of material type, process, and injection order. We found that in the copolymers, the first injected sample exhibited a lower zero-shear viscosity than the next, which indicates a lower molecular weight in the first injected specimen. This phenomenon was not presented for the PVDF homopolymer. Copolymer mechanical uncertainties are hypothesized to result from the shorter molecular weight chains extruding out of the specimens' sides as a flash due to higher mobility with CTFE segments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Process Variability Effects on Tensile Response in Injection Molded, Fluorinated Thermoplastics

The mechanical properties of fluorinated thermoplastics (i.e., tensile strength and elongation) can vary with changes in injection molding processing parameters. Four fluoropolymers are examined: poly(vinylidene fluoride) (PVDF) and random poly(vinylidene fluoride-co-chlorotrifluoroethylene) (PVDF-CTFE) with three CTFE concentrations. Dog bones were manufactured with various cylinder dwell times and mold cooling times to assess the manufacturing sensitivity to the tensile response. Dwell and cooling times increasingly impact mechanical performance as CTFE concentration increases. Specimens exhibit higher tensile strength as a function of injection order. The first injected specimen exhibits the lowest tensile strength and highest elongation in all copolymers. This trend becomes more pronounced among fluoropolymers with higher CTFE concentration and lower weight-averaged molecular weight. Parallel plate rheology was used to obtain the zero-shear viscosity as a function of material type, process, and injection order. We found that in the copolymers, the first injected sample exhibited a lower zero-shear viscosity than the next, which indicates a lower molecular weight in the first injected specimen. This phenomenon was not present for the PVDF homopolymer. Copolymer mechanical uncertainties are hypothesized to result from the shorter molecular weight chains extruding out of the specimens’ sides as a flash due to higher mobility with CTFE segments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Structure-aware Initialization via Numerical Continuation and Informed Priors

Scientific machine learning (SciML) often operates in ill-conditioned, weakly identifiable regimes due to limited data or indirect observations. In such settings, optimization and inference are highly sensitive to the starting point, making initialization--often under-reported--a consequential degree of freedom. Random initialization is not a neutral default as it induces an implicit prior over candidate solutions and can systematically bias the result, producing large run-to-run variability. Here, we formalize this view by treating initialization as a hidden confounder in SciML and develop a unifying theory for structure-aware initialization via numerical continuation, constructing warm starts from related problem instances. Across representative tasks, including physics-informed neural networks, maximum likelihood estimation, and variational inference, warm starts have been shown to consistently reduce optimization effort and improve reliability.

Data integrity↗
DOE OSTI2026

2001 KIPDA Regional Household Travel Survey

The Kentuckiana Planning and Development Agency (KIPDA) conducted this four-month study from October 2000 through January 2001 to update regional travel demand models. The universe for the survey consisted of households in the five-county Louisville transportation planning study area, including Clark and Floyd counties in Indiana and Bullitt, Jefferson, and Oldham counties in Kentucky. Demographic variables and travel behavior characteristics were collected for 4,433 households in a 24-hour period. Of these, 4,113 were completed with households that were selected at random, and the remaining 320 were completed with representatives. The data from the survey only reflect travel by the selected residents of the five counties listed above and do not account for travel in the region by persons who were not residents of those five counties.

1Hz data↗