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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Wind Tunnel Wall Interference Investigation of an Aspect Ratio13.5 Common Research Model using FUN3D

This paper presents a computation study of an aspect ratio 13.5 wind tunnel scale CommonResearch Model (CRM) inside a transonic wind tunnel using FUN3D CFD solver. The transonicwind tunnel is a slotted-test-section with 16 ft×16 ft test section wind tunnel. The simulationsare conducted under free-air, closed slots, and open slots conditions. The wind tunnel wallinterference effects are analyzed based on the simulations. The wall interference effects will beused to calibrate the aerodynamic database generated by VSPAERO solver for the real-timedrag optimization and maneuver load alleviation study of the wind tunnel scale CRM model.

CFD↗
NASA NTRSDate not supplied

Examination of L-Band Brightness Temperature Forecasts in the SMAP Level-4 Soil Moisture Analysis

The NASA Soil Moisture Active Passive (SMAP) mission [4] has been providing L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations since April 2015. By assimilating the Tb observations into the NASA Catchment land surface model [5] using a spatially distributed ensemble Kalman filter, the NASA Global Modeling and Assimilation Office generates the SMAP Level-4 Soil Moisture (L4_SM) product, which provides global, 3-hourly, 9-km resolution estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture with ~2.5-day latency for use in research and applications [6]. The L4_SM product also includes estimates of soil temperature, land surface fluxes, and assimilation diagnostics such as the model forecast and observed Tb values [7]. The output from the L4_SM system is routinely monitored by the L4_SM team. Such monitoring provides valuable information; instances of unusually large Tb observation-minus-forecast (O-F) residuals can indicate events during which soil moisture conditions are poorly described in the land modeling system [7,8]. For example, repeated occurrences of very large Tb O-F values in central Australia were traced back to deficiencies in the gauge-based precipitation product used in the land modeling system through L4_SM Version 5; this discovery prompted the use, beginning in Version 6, of satellite- and gauge-based precipitation observations outside of North America [9]. Recently, a statistical analysis of the Tb O-F residuals revealed systematic errors in the “tau-omega” L-band radiative transfer model that converts the land-model simulated soil moisture and temperature into the forecast Tb prior to the L4_SM analysis. The L4_SM analysis is built on the ensemble Kalman filter and assumes unbiased forecast errors. The presence of systematic Tb forecast errors could thus adversely impact the quality of the analyzed soil moisture. In this paper, we examine the Tb O-F residuals of the latest Version 7 L4_SM data (Science Version ID Vv7030 and Vv7032) [10,11,12]. In the L4_SM Version 7 algorithm, key parameters of the L-band radiative transfer model, including soil roughness, scattering albedo, and a (seasonally varying) climatology of vegetation opacity, are obtained from the SMAP Level-2 Radiometer retrieval product (Version 5) [1]. Additionally, we also use ground measurements of surface soil moisture and soil temperature from the SMAP core validation sites as in situ reference of soil conditions [2,3]. As part of the L4_SM system calibration, the seasonally-varying bias between the model forecast Tb and the observed values is removed prior to the assimilation of the SMAP Tb observations [7]. That is, the L4_SM system is designed to only correct errors in synoptic-scale and interannual variations from the long-term mean seasonal cycle while maintaining the model’s (potentially erroneous) climatology. The Tb O-F residuals examined here are thus computed using SMAP Tb observations after they are rescaled to the mean seasonal cycle of Tb from the modeling system without SMAP data assimilation. Consequently, the long-term average of the Tb O-F residuals has a global mean of only 0.13 K and locally small values, ranging from -1 to 3 K. Despite the small time-average values of the Tb O-F residuals, the model forecast Tb was nevertheless found to exhibit undesirable systematic errors. At some locations, the time-average Tb O-F values strongly depend on surface soil moisture (SM). At the Yanco SMAP core validation site, for example, the Tb O-F residuals typically range from 5 to 15 K under dry soil moisture conditions (SM < 0.15 m3 m-3) yet are predominantly negative under wet soil moisture conditions (SM > 0.25 m3 m-3), with values ranging from 0 to -40 K. This results in soil moisture analysis increments that persistently make the soil drier under dry SM conditions and persistently make the soil wetter under wet SM conditions, suggesting an error in the dynamic range of the simulated Tb, soil moisture or soil temperature. In this paper, we describe the higher-order systematic Tb forecast errors in more detail, examine their impact on the L4_SM product quality, and explore potential avenues to further improve the L4_SM algorithm.

Rolf Reichle↗
DOE OSTI2025

LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields

Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of per-sample errors obtained during training and employing a distance-based similarity search in the model latent space. Our method, which we call LTAU (Loss Trajectory Analysis for Uncertainty), efficiently estimates the full probability distribution function of errors for any test point using the logged training errors, achieving speeds that are 2–3 orders of magnitudes faster than typical ensemble methods and allowing it to be used for tasks where training or evaluating multiple models would be infeasible. We apply LTAU towards estimating parametric uncertainty in atomistic force fields (LTAU-FF), demonstrating that it produces well-calibrated confidence intervals and predicts errors that correlate strongly with the true errors for data near the training domain. Furthermore, we show that the errors predicted by LTAU-FF can be used in practical applications for detecting out-of-domain data, tuning model performance, and predicting failure during simulations. We believe that LTAU will be a valuable tool for uncertainty quantification in atomistic force fields and is a promising method that should be further explored in other domains of machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Constructing a Simulation Surrogate with Partially Observed Output

Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. In conclusion, the proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output is calibrated.

42 ENGINEERING↗
NASA NTRS1976

A pseudo root-locus method for the design of a class of two-input-two-output systems

This paper presents the design of digital rebalance loops for a tuned-rotor gyro which is intended to be an attitude sensor in a strapdown inertial measurement unit. A tuned-rotor gyro is a two-degree-of-freedom gyro which has two input axes, with cross-coupling between them. The rebalance loop of the gyro serves for two purposes. First, it restores the position of the rotor to its null position after experiencing an attitude input. Second, the rebalancing signal, which is proportional to the attitude change, is calibrated to give the desired attitude information. Since the gyro has more than one input and one output, a multivariable control technique is needed for designing the rebalance loop. The analytic model for the gyro is described and the available design methods are examined. A new root-locus design technique specially developed for the present problem is outlined. The design of the rebalance loop using the new technique is given. The computer simulation result of the designed system is presented and discussed.

Kao, M. K.↗
NASA NTRS1988

Accuracy requirements and benchmark experiments for CFD validation

The role of experiment in the development of Computation Fluid Dynamics (CFD) for aerodynamic flow prediction is discussed. The CFD verification is a concept that depends on closely coordinated planning between computational and experimental disciplines. Because code applications are becoming more complex and their potential for design more feasible, it no longer suffices to use experimental data from surface or integral measurements alone to provide the required verification. Flow physics and modeling, flow field, and boundary condition measurements are emerging as critical data. Four types of experiments are introduced and examples given that meet the challenge of validation: flow physics experiments; flow modeling experiments; calibration experiments; and verification experiments. Measurement and accuracy requirements for each of these differ and are discussed. A comprehensive program of validation is described, some examples given, and it is concluded that the future prospects are encouraging.

Marvin, Joseph G.↗
NASA NTRS1989

Filtering information from human experts

The authors propose a model, or filter, for debiasing opinions from multiple experts and combining them into a single consistent estimate of some variable of interest. A distinguishing feature of the approach consists of making the calibration of experts an integral part of filtering. This enables the filter to learn from previous experience with the experts. The theoretical development takes a Bayesian perspective, using de Finetti's (1964) notion of exchangeability. Experimental results with a preliminary computer implementation of the filter show that its estimates are better than those from comparable filters that do not involve calibration.

Mendel, Max B.↗
NASA NTRSDate not supplied

High-Fidelity Simulations of HyMETS Arc-Jet Flows for PICA-N Modeling

Arc-jet testing is a fundamental tool in the screening of thermal protection systems under atmospheric entry conditions. In this work, high-fidelity computational fluid dynamics simulations coupled with machine learning methods for the Hypersonic Materials Environmental Test System arc-jet facility have been carried out. This effort improves the MEDLI-2 data reconstruction fidelity by understanding the impact of the NuSil coating over the PICA thermocouple plugs. Thermochemical non-equilibrium models are employed to simulate the flows inside the arc-jet nozzle and chamber and machine learning techniques are used to calibrate the arc-jet inflow conditions. The material response is simulated with the recession and in-depth numerical results compared to experimental measurements.

STMD↗
NASA NTRS2000

Physics for the Correction of a Calibrated Airborne Scanner, Visible to Thermal Bands

To use remote sensing modalities in a reproducible manner it is essential that extraneous phenomena be removed from the signal. For those interested in the surface of the Earth, airborne and satellite systems, which are sensitive in wavelengths ranging from the visible to the infrared are significantly degraded by the atmosphere. The authors have developed a series of mathematical models to describe and correct the degradation. The models are based directly on the physics of the systems and are computationally tractable. Modeling of the atmosphere is done using public domain code, loaded with data and configured using information form systems developed by Schiller and Luvall. The results of this are then integrated with a physical model of the sensor to permit reduction of data to geophysically meaningful units. The components of the overall modeling, the logic of the components, and the limitations of the approach are discussed. The authors are employing there technology on applications ranging from measurements of urban heat islands to precision agriculture.

Rickman, Doug L.↗
DOE OSTI2024

Archetype-based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey

We present a computationally efficient galaxy archetype-based redshift estimation and spectral classification method for the Dark Energy Survey Instrument (DESI) survey. The DESI survey currently relies on a redshift fitter and spectral classifier using a linear combination of principal component analysis–derived templates, which is very efficient in processing large volumes of DESI spectra within a short time frame. However, this method occasionally yields unphysical model fits for galaxies and fails to adequately absorb calibration errors that may still be occasionally visible in the reduced spectra. Our proposed approach improves upon this existing method by refitting the spectra with carefully generated physical galaxy archetypes combined with additional terms designed to absorb data reduction defects and provide more physical models to the DESI spectra. We test our method on an extensive data set derived from the survey validation (SV) and Year 1 (Y1) data of DESI. Our findings indicate that the new method delivers marginally better redshift success for SV tiles while reducing catastrophic redshift failure by 10%–30%. At the same time, results from millions of targets from the main survey show that our model has relatively higher redshift success and purity rates (0.5%–0.8% higher) for galaxy targets while having similar success for QSOs. These improvements also demonstrate that the main DESI redshift pipeline is generally robust. Additionally, it reduces the false-positive redshift estimation by 5%–40% for sky fibers. We also discuss the generic nature of our method and how it can be extended to other large spectroscopic surveys, along with possible future improvements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1985

Heat Flux Sensor Calibrator

The heat flux to space shuttle main engine (SSME) turbopump turbine blades may be as high as 10 to the 7th power. The heat flux causes thermal transients that are of the order of 1 sec as temperature varies from perhaps 1500 K to 100 K. It is suspected that these transients cause durability problems in the turbine blades. To quantitatively evaluate the effect of these transients, heat flux sensors or gauges were developed to obtain data to verify analytical models. The objective is to design and fabricate a system for steady state and transient calibration and durability testing of heat flux sensors for use in SSME turbine blades. The calibrator consists of: (1) the arc lamp, (2) a high speed positioning table for placing standard and special heat flux sensors in the incident beam of radiant heat flux, (3) provision for cooling the blade and special sensors inserted in the blade, (4) a computer for controlling the positioning table, storing electrical output values from sensors, and calculating heat flux from these values, and (5) a pyrometer for measuring sensor surface temperatures.

Liebert, C. H.↗
NASA NTRS1988

Accuracy requirements and benchmark experiments for CFD validation

The role of experiment in the development of Computational Fluid Dynamics (CFD) for aerodynamic flow prediction is discussed. The CFD verification is a concept that depends on closely coordinated planning between computational and experimental disciplines. Because code applications are becoming more complex and their potential for design more feasible, it no longer suffices to use experimental data from surface or integral measurements alone to provide the required verification. Flow physics and modeling, flow field, and boundary condition measurements are emerging as critical data. Four types of experiments are introduced and examples are given that meet the challenge of validation: flow physics experiments; flow modeling experiments; calibration experiments; and verification experiments. Measurement and accuracy requirements for each of these differ and are discussed. A comprehensive program of validation is described, some examples given, and it is concluded that the future prospects are encouraging.

Marvin, Joseph G.↗
NASA NTRS2011

Remote Infrared Imaging of the Space Shuttle During Hypersonic Flight: HYTHIRM Mission Operations and Coordination

The Hypersonic Thermodynamic Infrared Measurements (HYTHIRM) project has been responsible for obtaining spatially resolved, scientifically calibrated in-flight thermal imagery of the Space Shuttle Orbiter during reentry. Starting with STS-119 in March of 2009 and continuing through to the majority of final flights of the Space Shuttle, the HYTHIRM team has to date deployed during seven Shuttle missions with a mix of airborne and ground based imaging platforms. Each deployment of the HYTHIRM team has resulted in obtaining imagery suitable for processing and comparison with computational models and wind tunnel data at Mach numbers ranging from over 18 to under Mach 5. This paper will discuss the detailed mission planning and coordination with the NASA Johnson Space Center Mission Control Center that the HYTHIRM team undergoes to prepare for and execute each mission.

Schwartz, Richard J.↗
NASA NTRS2012

Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction

Land-atmosphere (L-A) Interactions playa critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (US-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF Simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Santanello, Joseph A.↗
NASA NTRS2012

Impact of Calibrated Land Surface Model Parameters on the Accuracy and Uncertainty of Land-Atmosphere Coupling in WRF Simulations

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (LIS-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Santanello, Joseph A., Jr.↗
DOE OSTI2023

AutoSourceID-Classifier: Star-galaxy classification using a convolutional neural network with spatial information

Aims.Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification’s reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images. Methods.The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results. Results.We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C’s direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.

Astronomy & Astrophysics↗
DOE OSTI2022

On the Fidelity of Computational Models for the Flow of Milled Loblolly Pine: A Benchmark Study on Continuum-Mechanics Models and Discrete-Particle Models

The upstream of bioenergy industry has suffered from unreliable operations of granular biomass feedstocks in handling equipment. Computational modeling, including continuum-mechanics models and discrete-particle models, offers insightful understandings and predictive capabilities on the flow of milled biomass and can assist equipment design and optimization. This paper presents a benchmark study on the fidelity of the continuum and discrete modeling approaches for predicting granular biomass flow. We first introduce the constitutive law of the continuum-mechanics model and the contact law of the coarse-grained discrete-particle model, with model parameters calibrated against laboratory characterization tests of the milled loblolly pine. Three classical granular material flow systems (i.e., a lab-scale rotating drum, a pilot-scale hopper, and a full-scale inclined plane) are then simulated using the two models with the same initial and boundary conditions as the physical experiments. The close agreement of the numerical predictions with the experimental measurements on the hopper mass flow rate, the hopper critical outlet width, the material stopping thickness on the inclined plane, and the dynamic angle of repose, clearly indicates that the two methods can capture the critical flow behavior of granular biomass. The qualitative comparison shows that the continuum-mechanics model outperforms in parameterization of materials and wall friction, and large-scale systems, while the discrete-particle model is more preferred for discontinuous flow systems at smaller scales. Industry stakeholders can use these findings as guidance for choosing appropriate numerical tools to model biomass material flow in part of the optimization of material handling equipment in biorefineries.

Jin, Wencheng↗
NASA NTRS1997

Comet Shoemaker-Levy 9 Fragment Size Estimates: How Big was the Parent Body?

The impact of Comet Shoemaker-Levy 9 on Jupiter in July, 1994 was the largest, most energetic impact event on a planet ever witnessed. Because it broke up during a close encounter with Jupiter in 1992, it was bright enough to be discovered more than a year prior to impact, allowing the scientific community an unprecedented opportunity to assess the effects such an event would have. Many excellent observations were made from Earth-based telescopes, the Hubble Space Telescope (HST), and the Galileo spacecraft en route to Jupiter. In this paper, these observations are used in conjunction with computational simulations performed with the CTH shock-physics hydrocode to determine the sizes of the fifteen fragments that made discernible impact features on the planet. To do this, CTH was equipped with a radiative ablation model and a postprocessing radiative ray-trace capability that enabled light-flux predictions (often called the impact flash) for the viewing geometries of Galileo and ground-based observers. The five events recorded by Galileo were calibrated to give fragment size estimates. Compared against ground-based and HST observations, these estimates were extended using a least-squares analysis to assess the impacts of the remaining ten fragments. Some of the largest impacts (L, G, and K) were greater that 1 km in diameter, but the density of the fragments was low, about 0.25 g/cm(exp 3). The volume of the combined fifteen fragments would make a sphere 1.8 km in diameter. Assuming a prebreakup density of 0.5 g/cm(exp 3), the parent body of Shoemaker-Levy 9 had a probable diameter of 1.4 km. The total kinetic energy of all the impacts was equivalent to the explosive yield of 300 Gigatons of TNT.

Crawford, David A.↗