SEARCH · Engineering Papers

Results for “data- limited”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Mathematics: The Tao of Data Science

The two pieces, "Ten Research Challenge Areas in Data Science" by Jeannette M. Wing and “Challenges and Opportunities in Statistics and Data Science: Ten Research Areas” by Xuming He and Xihong Lin, provide an impressively complete list of data science challenges from luminaries in the field of data science. They have done an extraordinary job, so this response offers a complementary viewpoint from a mathematical perspective and evangelizes advanced mathematics as a key tool for meeting the challenges they have laid out. Notably, we pick up the themes of scientific understanding of machine learning and deep learning, computational considerations such as cloud computing and scalability, balancing computational and statistical considerations, and inference with limited data. We propose that mathematics is an important key to establishing rigor in the field of data science and as such has an essential role to play in its future.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Controls From Above and Below: Snow, Soil, and Steepness Drive Diverging Trends of Subsurface Water and Streamflow Dynamics

ABSTRACT The importance of subsurface water dynamics, such as water storage and flow partitioning, is well recognised. Yet, our understanding of their drivers and links to streamflow generation has remained elusive, especially in small headwater streams that are often data‐limited but crucial for downstream water quantity and quality. Large‐scale analyses have focused on streamflow characteristics across rivers with varying drainage areas, often overlooking the subsurface water dynamics that shape streamflow behaviour. Here we ask the question: What are the climate and landscape characteristics that regulate subsurface dynamic storage, flow path partitioning, and dynamics of streamflow generation in headwater streams? To answer this question, we used streamflow data and a widely‐used hydrological model (HBV) for 15 headwater catchments across the contiguous United States. Results show that climate characteristics such as aridity and precipitation phase (snow or rain) and land attributes such as topography and soil texture are key drivers of streamflow generation dynamics. In particular, steeper slopes generally promoted more streamflow, regardless of aridity. Streams in flat, rainy sites (< 30% precipitation as snow) with finer soils exhibited flashier regimes than those in snowy sites (> 30% precipitation as snow) or sites with coarse soils and deeper flow paths. In snowy sites, less weathered, thinner soils promoted shallower flow paths such that discharge was more sensitive to changes in storage, but snow dampened streamflow flashiness overall. Results here indicate that land characteristics such as steepness and soil texture modify subsurface water storage and shallow and deep flow partitioning, ultimately regulating streamflow response to climate forcing. As climate change increases uncertainty in water availability, understanding the interacting climate and landscape features that regulate streamflow will be essential to predict hydrological shifts in headwater catchments and improve water resources management.

Kerins, Devon [Department of Civil and Environment↗

DOE OSTI2024

The Dynamic Assimilation Technique measures photosynthetic CO2 response curves with similar fidelity as steady-state approaches in half the time

A/Ci response↗

DOE OSTI2023

Using observational mean-flow data to drive large-eddy simulations of a diurnal cycle at the SWiFT site

Reproducing realistic date- and site-specific unsteady wind conditions in large-eddy simulations is becoming increasingly useful in wind energy. How to run a large-eddy simulation to match observed conditions, however, remains an open research question. One approach that has received considerable attention is mesoscale-to-microscale coupling, in which information about the mesoscale weather, most commonly acquired from a mesoscale numerical weather model, is passed on to a microscale model. In this paper, we demonstrate how the recently developed profile-assimilation technique, a form of mesoscale-to-microscale coupling, can be used to drive large-eddy simulations solely based on observed mean-flow profiles at a single location, bypassing the need for auxiliary mesoscale simulations. The new approach is evaluated for a diurnal cycle at the Scaled Wind Farm Technology site. Observed mean-flow profiles from the ground up to a height of 2 km are reconstructed by aggregating measurements from multiple instruments, and gaps in the data are infilled with natural neighbor interpolation. We perform nine simulations using various forcing approaches to deal with data limitations. The results show that it is indeed possible to drive microscale large-eddy simulation with observations using the profile-assimilation technique, notwithstanding large gaps in virtual potential temperature measurements. However, profile assimilation with vertical smoothing of the error between the desired and actual profiles is required. Without that smoothing, the microscale simulations develop unrealistically high turbulence levels under many situations. Finally, we show that simulated mesoscale data can account for missing observations, although care is needed as both data sources are not necessarily compatible.

17 WIND ENERGY↗

DOE OSTI2024

Nuclear Structure and Decay Data for A=165 Isobars

Experimental nuclear spectroscopic data are compiled and evaluated for 18 known nuclides of mass 165 (Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, W, Re, Os, Ir, Pt). Detailed information is presented for each reaction and decay experiment. Combining all the available data, recommended values are provided for energies, spins and parities, and half-lives of levels, with energies, branching ratios and multipolarities of γ radiations, and characteristics of β and α radiations in radioactive decays. 165 Dy, 165 Ho, 165 Er, 165 Tm, 165 Yb, 165 Lu and 165 Hf are among the most extensively studied nuclides via decay and high-spin gamma-ray spectroscopy measurements, followed by limited data for 165 Tb, 165 Ta, 165 W, 165 Re, and 165 Os. No excited states have yet been identified in 165 Sm, 165 Gd, and 165 Pt, while for 165 Eu and 165 Ir, information is available for only the g.s. and an isomer, but with no γ rays. As a result, this work supersedes earlier evaluation of A=165 nuclides by 2006Ja09.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

The Dynamic Assimilation Technique measures photosynthetic CO 2 response curves with similar fidelity to steady-state approaches in half the time

The net CO 2 assimilation (A) response to intercellular CO 2 concentration (C i ) is a fundamental measurement in photosynthesis and plant physiology research. The conventional A/C i protocols rely on steady-state measurements and take 15–40 min per measurement, limiting data resolution or biological replication. Additionally, there are several CO 2 protocols employed across the literature, without clear consensus as to the optimal protocol or systematic biases in their estimations. We compared the non-steady-state Dynamic Assimilation Technique (DAT) protocol and the three most used CO 2 protocols in steady-state measurements, and tested whether different CO 2 protocols lead to systematic differences in estimations of the biochemical limitations to photosynthesis. The DAT protocol reduced the measurement time by almost half without compromising estimation accuracy or precision. The monotonic protocol was the fastest steady-state method. Estimations of biochemical limitations to photosynthesis were very consistent across all CO 2 protocols, with slight differences in Rubisco carboxylation limitation. The A/C i curves were not affected by the direction of the change of CO 2 concentration but rather the time spent under triose phosphate utilization (TPU)-limited conditions. Our results suggest that the maximum rate of Rubisco carboxylation (V cmax ), linear electron flow for NADPH supply (J), and TPU measured using different protocols within the literature are comparable, or at least not systematically different based on the measurement protocol used.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2023

Machine Learning Framework for Hazard Extraction and Analysis of Trends (HEAT) in Wildfire Response

This research proposes a natural language processing enabled risk analysis framework, named Hazard Extraction andAnalysis of Trends (HEAT), and applies the framework to the ICS-209-PLUS data set of wildfire incident responseforms. The HEAT framework produces safety- and risk- relevant analyses, consisting of: (1) a set of hazards extractedfrom text data, (2) a primary analysis using hazard-relevant metrics, such as rate and severity, to form an FMEA-styletable and risk matrix, (3) a time series analysis of metric trends, and (4) a secondary analysis examining potentialpredictors for hazards. Results from HEAT provide quantitative risk-relevant information for high-level hazards doc-umented in existing-state operations. Because of the generalizability of the steps and limited data requirements, HEATcan be applied to any dataset containing narrative text, thus providing a framework for data-driven machine learning-enabled quantitative risk analysis across a variety of domains. To demonstrate HEAT in a case study, we apply theframework to the ICS-209-PLUS dataset of wildland fire incident response forms. Hazards identified in wildfire re-sponse arise from environmental conditions, the mission, and the wildland urban interface. The resulting risk matrixidentifies evacuations as high-risk hazards, while all other identified hazards are medium or serious risk.

natural language processing↗

NASA NTRS1999

Prediction and Assessment of Reynolds Number Sensitivities Associated with Wing Leading-Edge Radius Variations

The primary objectives of this study were to expand the data base showing the effects of LE radius distribution and corresponding . sensitivity to Rn at subsonic and transonic conditions, and to assess the predictive capability of CFD for these effects. Several key elements led to the initiation of this project: 1) the necessity of meeting multipoint design requirements to enable a viable HSCT, 2) the demonstration that blunt supersonic leading-edges can be associated with performance gain at supersonic speeds , and 3) limited data. A test of a modified Reference H model with the TCA planform and 2 LE radius distributions was performed in the NTF, in addition to Navier-Stokes analysis for an additional 3 LE radius distributions. Results indicate that there is a tremendous potential to improve high-lift performance through the use of a blunt LE across the span given an integrated, fully optimized design, and that low Rn data alone is probably not sufficient to demonstrate the benefit.

Wahls, Richard A.↗

NASA NTRS1999

Prediction and Assessment of Reynolds Number Sensitivities Associated with Wing Leading-Edge Radius Variations

The primary objectives of this study were to expand the data base showing the effects of LE radius distribution and corresponding sensitivity to Rn at subsonic and transonic conditions, and to assess the predictive capability of CFD for these effects. Several key elements led to the initiation of this project: 1) the necessity of meeting multipoint design requirements to enable a viable HSCT, 2) the demonstration that blunt supersonic leading-edges can be associated with performance gain at supersonic speeds , and 3) limited data. A test of a modified Reference H model with the TCA planform and 2 LE radius distributions was performed in the NTF, in addition to Navier-Stokes analysis for an additional 3 LE radius distributions. Results indicate that there is a tremendous potential to improve high-lift performance through the use of a blunt LE across the span given an integrated, fully optimized design, and that low Rn data alone is probably not sufficient to demonstrate the benefit.

Wahls, Richard A.↗

NASA NTRS2017

Evaluation of Fast Switching Diode 1N4448 Over a Wide Temperature Range

Electronic parts used in the design of power systems geared for space applications are often exposed to extreme temperatures and thermal cycling. Limited data exist on the performance and reliability of commercial-off-the-shelf (COTS) electronic parts at temperatures beyond the manufacturers specified operating temperature range. This report summarizes preliminary results obtained on the evaluation of automotive-grade, fast switching diodes over a wide temperature range and thermal cycling. The investigations were carried out to establish a baseline on functionality of these diodes and to determine suitability for use outside their recommended temperature limits.

Thermal Cycling↗

NASA NTRS1985

Statistical methods for astronomical data with upper limits. I - Univariate distributions

The statistical treatment of univariate censored data is discussed. A heuristic derivation of the Kaplan-Meier maximum-likelihood estimator from first principles is presented which results in an expression amenable to analytic error analysis. Methods for comparing two or more censored samples are given along with simple computational examples, stressing the fact that most astronomical problems involve upper limits while the standard mathematical methods require lower limits. The application of univariate survival analysis to six data sets in the recent astrophysical literature is described, and various aspects of the use of survival analysis in astronomy, such as the limitations of various two-sample tests and the role of parametric modelling, are discussed.

Feigelson, E. D.↗

NASA NTRS2003

The Chandra X-Ray Observatory Radiation Environment Model

CRMFLX (Chandra Radiation Model of ion FluX) is an environmental risk mitigation tool for use as a decision aid in planning the operations times for Chandra's Advanced CCD Imaging Spectrometer (ACIS) detector. The accurate prediction of the proton flux environment with energies of 100 - 200 keV is needed in order to protect the ACIS detector against proton degradation. Unfortunately, protons of this energy are abundant in the region of space Chandra must operate, and the on-board Electron, Proton, and Helium Instrument (EPHIN) does not measure proton flux levels of the required energy range. In addition to the concerns arising from the radiation belts, substorm injections of plasma from the magnetotail may increase the protons flux by orders of magnitude in this energy range. The Earth's magnetosphere is a dynamic entity, with the size and location of the magnetopause driven by the highly variable solar wind parameters (number density, velocity, and magnetic field components). Operational times for the telescope must be made weeks in advance, decisions which are complicated by the variability of the environment. CRMFLX is an engineering model developed to address these problems and provides proton flux and fluence statistics for the terrestrial outer magnetosphere, magnetosheath, and solar wind for use in scheduling ACIS operations. CRMFLX implements a number of standard models to predict the bow shock, magnetopause, and plasma sheet boundaries based on the sampling of historical solar wind data sets. Measurements from the GEOTAIL and POLAR spacecraft are used to create the proton flux database. This paper describes the recently released CRMFLX v2 implementation that includes an algorithm that propagates flux from an observation location to other regions of the magnetosphere based on convective ExB and VB-curvature particle drift motions in electric and magnetic fields. This technique has the advantage of more completely filling out the database and makes maximum use of limited data obtained during high Kp periods or in areas of the magnetosphere with poor satellite coverage.

Blackwell, W. C.↗

DOE OSTI2022

An improved representation of the relationship between photosynthesis and stomatal conductance leads to more stable estimation of conductance parameters and improves the goodness-of-fit across diverse data sets

Stomata play a central role in surface-atmosphere exchange by controlling the flux of water and CO 2 between the leaf and the atmosphere. Representation of stomatal conductance (g sw ) is therefore an essential component of models that seek to simulate water and CO 2 exchange in plants and ecosystems. For given environmental conditions at the leaf surface (CO 2 concentration and vapor pressure deficit or relative humidity), models typically assume a linear relationship between g sw and photosynthetic CO 2 assimilation (A). However, measurement of leaf-level g sw response curves to changes in A are rare, particularly in the tropics, resulting in only limited data to evaluate this key assumption. Here, we measured the response of g sw and A to irradiance in six tropical species at different leaf phenological stages. We showed that the relationship between g sw and A was not linear, challenging the key assumption upon which optimality theory is based-that the marginal cost of water gain is constant. Our data showed that increasing A resulted in a small increase in g sw at low irradiance, but a much larger increase at high irradiance. We reformulated the popular Unified Stomatal Optimization (USO) model to account for this phenomenon and to enable consistent estimation of the key conductance parameters g 0 and g 1 . Our modification of the USO model improved the goodness-of-fit and reduced bias, enabling robust estimation of conductance parameters at any irradiance. In addition, our modification revealed previously undetectable relationships between the stomatal slope parameter g 1 and other leaf traits. We also observed nonlinear behavior between A and g sw in independent datasets that included data collected from attached and detached leaves, and from plants grown at elevated CO 2 concentration. We propose that this empirical modification of the USO model can improve the measurement of g sw parameters and the estimation of plant and ecosystem-scale water and CO 2 fluxes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Scientific machine learning for modeling and simulating complex fluids

The formulation of rheological constitutive equations—models that relate internal stresses and deformations in complex fluids—is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine-learning based constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these “digital fluid twins” to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation—a task that is not achievable using any previously developed data-driven rheological equation of state.

Science & Technology - Other Topics↗

NASA NTRS2003

Chandra Radiation Environment Modeling

CRMFLX (Chandra Radiation Model of ion FluX) is a radiation environment risk mitigation tool for use as a decision aid in planning the operations times for Chandra's Advanced CCD Imaging Spectrometer (ACIS) detector. The accurate prediction of the proton flux environment with energies of 100 - 200 keV is needed in order to protect the ACIS detector against proton degradation. Unfortunately, protons of this energy are abundant in the region of space Chandra must operate, and on-board particle detectors do not measure proton flux levels of the required energy range. This presentation will describe the plasma environment data analysis and modeling basis of the CRMFLX engineering environment model developed to predict the proton flux in the solar wind, magnetosheath, and magnetosphere phenomenological regions of geospace. The recently released CRMFLX Version 2 implementation includes an algorithm that propagates flux from an observation location to other regions of the magnetosphere based on convective ExB and VB-curvature particle drift motions. This technique has the advantage of more completely filling out the database and makes maximum use of limited data obtained during high Kp periods or in areas of the magnetosphere with poor satellite flux measurement coverage.

Minow, Joseph I.↗

NASA NTRS1988

Design point variation of 3-D loss and deviation for axial compressor middle stages

The available data on middle-stage research compressors operating near design point are used to derive simple empirical models for the spanwise variation of three-dimensional viscous loss coefficients for middle-stage axial compressor blading. The models make it possible to quickly estimate the total loss and deviation across the blade span when the three-dimensional distribution is superimposed on the two-dimensional variation calculated for each blade element. It is noted that extrapolated estimates should be used with caution since the correlations have been derived from a limited data base.

Roberts, William B.↗

DOE OSTI2021

Sensitivity of the simulation of passive neutron emission from UF 6 cylinders to the uncertainties in both 19 F(α,n) energy spectrum and thick target yield of 234 U in UF 6

Interest in safeguards verification measurements using passive thermal neutron counting to assay 235 U content in large 30B UF 6 canisters has grown in recent years. Here, the prohibitively high cost and impracticality of using reference 30B calibration cylinders extensively will likely make accurate simulations of increasing interest. Accuracy of the simulated response will define the confidence in the predicted response and the extent to which simulations can reasonably be relied upon. With 234 U driven 19 F(α, n) reactions being the main neutron source in low enriched UF 6 the uncertainties of the 19 F(α, n) energy spectrum and the thick target yield of 234 U in UF 6 propagate into the uncertainty in the predicted response and represent a major influence of basic nuclear data. Here sensitivity of the simulated total (Singles) and coincidence (Doubles) count rates are assessed for the Passive Neutron Enrichment Meter using six potential 19 F(α, n) neutron energy spectra over a range of enrichments and material distributions. The results indicate that variations in the Singles and Doubles due to simulated (α, n) neutron spectrum are less than 1.5% for this set of simulated neutron spectra, with dependence varying inversely with enrichment. Singles uncertainty is only slightly less than that of the thick target 19 F(α, n) yield corresponding to the primary neutron source, whereas the 19 F(α, n) yield dependence of the Doubles is reduced by the non-negligible 238 U(SF) coincident neutron emissions. Based on available thick target 19 F(α, n) yield estimates the uncertainty is on the order of 5%, establishing this as the main nuclear data limitation when simulating thermal neutron detectors response for 30B UF 6 storage cylinders. Based on these findings, it appears that the measurement and evaluation of the thick target 19 F(α, n) yield for uranium hexafluoride is due.

19F(α,n) neutron spectrum↗

NASA NTRS1994

Recalibration of the Viyager PRA antenna for polarization sense measurement

The Voyager Planetary Radio Astronomy (PRA) antenna and receiver system provides an indication of the sense of elliptical or circular polarization of radiation that is not correct for all directions of incidence. The true sense could be determined for all directions if accurate calibration data were available. It was not feasible to make the calibration before the Voyagers were launched. Lecacheux & Ortega-Molina (1987), however, were able to derive such calibration data from planetary radio observations made in flight. They expressed their results in terms of the tilt of a plane (the E-plane) that divides the incident ray directions for which the indicated polarization sense is correct from those directions for which the indicated sense is reversed. We demonstrate that there are certain directions for which this calibration is itself in error, and that the surface dividing the two sets of incident rays is more complex than a tilted plane. We are able to make a crude approximation to the true surface from the limited data available.

Wang, L.↗