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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Integrating diagnostics and model-based optimization

Energy Management and Information Systems are a family of analytics technologies that include energy information systems, fault detection and diagnostics (FDD), and automated system optimization tools. Such systems have the potential to enable buildings to meet energy management goals of reducing total energy consumption and cost. Most current market offerings use data-driven and rule-based analytics. However, the use of physics-based models in the analytics offers potential improvements by providing an accurate estimation of outputs based on representation of the physical principles governing the building system behaviors. This also permits the use of design stage models to inform commissioning and operation. This paper describes the development and testing of a hybrid data-driven and physics model-based operational tool for energy efficiency in central cooling plants. The tool offers FDD functionality, setpoint optimization, and visualization of key performance parameters. It was demonstrated at a university campus in the mixed-humid ASHRAE Climate Zone 4A. Key performance metrics that were analyzed include plant electricity use reduction, plant model calibration, and system economics. Annual simulations indicate the tool can provide electricity savings of greater than 10% for approximately six months of the year, mainly during the winter season when wet bulb temperatures are low, though only 1.38% savings for the entire year. Additionally, over a 4-day period in April, recommended optimal setpoints were implemented, resulting in 17% savings versus metered baseline consumption. With respect to model calibration, the difference between model-predicted and measured parameters was less than 10% for 90% of data points acquired for three of six chillers, and for each ten cooling towers. Finally, the tool users reported that satisfaction with the capabilities was equal to or better than that with the preexisting BAS system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1973

Analysis of SRM model nozzle calibration test data in support of IA12B, IA12C and IA36 space shuttle launch vehicle aerodynamics tests

Variations of nozzle performance characteristics of the model nozzles used in the Space Shuttle IA12B, IA12C, IA36 power-on launch vehicle test series are shown by comparison between experimental and analytical data. The experimental data are nozzle wall pressure distributions and schlieren photographs of the exhaust plume shapes. The exhaust plume shapes were simulated experimentally with cold flow while the analytical data were generated using a method-of-characteristics solution. Exhaust plume boundaries, boundary shockwave locations and nozzle wall pressure measurements calculated analytically agree favorably with the experimental data from the IA12C and IA36 test series. For the IA12B test series condensation was suspected in the exhaust plumes at the higher pressure ratios required to simulate the prototype plume shapes. Nozzle calibration tests for the series were conducted at pressure ratios where condensation either did not occur or if present did not produce a noticeable effect on the plume shapes. However, at the pressure ratios required in the power-on launch vehicle tests condensation probably occurs and could significantly affect the exhaust plume shapes.

Baker, L. R., Jr.↗
NASA NTRS2014

Experimental and Computational Sonic Boom Assessment of Boeing N+2 Low Boom Models

Near-field pressure signatures were measured and computational predictions made for several sonic boom models representing Boeing’s Quiet Experimental Validation Concept (QEVC) supersonic transport, as well as for three axisymmetric calibration models. The concept was designed under a NASA Research Announcement (NRA) contract to address environmental and performance goals, specifically for low sonic boom loudness levels and high cruise efficiency, for an aircraft anticipated to enter service in the 2020-timeframe. Wind tunnel tests were conducted on the aircraft and calibration models during Phases I and II of the NRA contract from 2011 to 2013 in the NASA Ames 9- by 7-Foot and NASA Glenn 8- by 6-Foot Supersonic Wind Tunnels. Sonic boom pressure signatures were acquired primarily at Mach 1.6 and 1.8, and force and moment data were acquired from Mach 0.8 to 1.8. The sonic boom test data were obtained using a 2-in. flat-top pressure rail and a 14-in. tapered “reflection factor 1” (RF1) pressure rail. Both rails capture an entire pressure signature in one data point, and successive signatures at varying positions along or above the rail were used to improve data quality through spatial averaging. The sonic boom data obtained by the rails were validated with high-fidelity numerical simulations of offbody pressures. The test results showed good agreement between the computational and experimental data when a variety of testing techniques including spatial averaging of a series of pressure signatures were employed. The two wind tunnels generally produced comparable data.

Durston, Donald A.↗
DOE OSTI2023

Inference of strength and phase transition kinetics in dynamically-compressed tin

Dynamic compression experiments in condensed matter are of interest in part because they provide opportunities to examine material response under extreme conditions; however, the inference of material behavior from dynamic experiments is challenging in the presence of phase transitions exhibiting kinetic processes. Demonstrating an approach to quantitative interpretation of such dynamic experiments, here we present a Bayesian model calibration of strength and phase transformation parameters to data drawn from pulsed power and gas gun shot experiments. The posterior predictions of the Bayesian model capture the experimental measurements and account for the various uncertainties in the experimental configurations. This holistic approach to model calibration utilizing multiple types of experimental data identifies important cross correlations among kinetics, strength, and the phase boundary. Improved insight into potential sources of current model form error is provided by comparing the differences between calibrations against different subsets of the experimental data.

42 ENGINEERING↗
NASA NTRS2022

Are Soybean Models Ready for Climate Change Food Impact Assessments?

An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO 2 concentration [CO 2 ] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO 2 ]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO 2 ] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble, ensemble mean yield responses to temperature and [CO 2 ] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO 2 ] and temperature is needed to reduce the uncertainty from the individual models.

Agricultural Model Inter-comparison and Improvemen↗
NASA NTRS2019

A Multi-Physics Study on High-Specific Power Li-O2 Batteries for Electric Aircraft

Commercialization of lithium-air batteries faces many challenges, such as electrolyte decomposition, short cycle life, low energy and power density, etc. However, commercialization of Li-O2 batteries for aeronautics is much more challenging due to additional safety constraints on cyclability and performance (high specific power and specific energy). For this presentation, we will discuss inter-related aspects of physics-based modeling of a pack: cell and battery model calibration. In addition, we will evaluate and present optimal battery designs for high discharge current density, high discharge time, and low battery mass using simulation-based optimization.The Finite Element Model (FEM) used to simulate a Li-O2 cell is based on the work of Bevara [1]. The different aspects of the model are based on: porous electrode theory and concentrated electrolyte theory; quantum tunneling model for the resistance of conformal layer of discharge product (Li2O2) [1]; Butler-Volmer kinetics for electrochemical reaction; Fick's diffusion for oxygen transport; and an oxygen dissolution model is applied at the air/electrolyte interface [2]. The electrolyte properties such as ion conductivity, ion diffusion, oxygen diffusion, and mass density of the electrolyte were taken from Molecular Dynamics (MD) simulations [3]; while the other model parameters, which includes mass of cell components, were calibrated to match experiments at high discharge current densities. The cell mass includes the anode, cathode, separator, electrolyte, and other components (such as current collector). This calibrated model is used to perform parametric studies on cathode thickness, porosity, tortuosity, carbon particle size, electrolyte transport and material properties, partial pressure of oxygen, discharge time, and discharge current density to study optimal designs for high specific power and energy. References:1. Bevara, V. & Andrei, P. (2014), J. Electrochem. Soc. 161 (14), A2068-A2079.2.Mehta, M. & Andrei, P. (2015), J. Power Sources. 286, 299-308.3.Liyana-Arachchi, T.; Haskins, J.; Burke, C.; Diederichsen, K.; McCloskey, B.; & Lawson, J. (2018), J. Phys. Chem. B. 122 (36), 8548 - 8559.4.Choi, W.; Kikumoto, H.; Choudhary, R. & Ooka, R. (2018), Applied Energy, 209, 306-321.

Mehta, Mohit↗
NASA NTRS2007

Detectors for the James Webb Space Telescope Near-Infrared Spectrograph I: Readout Mode, Noise Model, and Calibration Considerations

We describe how the James Webb Space Telescope (JWST) Near-Infrared Spectrograph's (NIRSpec's) detectors will be read out, and present a model of how noise scales with the number of multiple non-destructive reads sampling-up-the-ramp. We believe that this noise model, which is validated using real and simulated test data, is applicable to most astronomical near-infrared instruments. We describe some non-ideal behaviors that have been observed in engineering grade NIRSpec detectors, and demonstrate that they are unlikely to affect NIRSpec sensitivity, operations, or calibration. These include a HAWAII-2RG reset anomaly and random telegraph noise (RTN). Using real test data, we show that the reset anomaly is: (1) very nearly noiseless and (2) can be easily calibrated out. Likewise, we show that RTN affects only a small and fixed population of pixels. It can therefore be tracked using standard pixel operability maps.

Rauscher, Bernard J.↗
DOE OSTI2015

On the Statistical Calibration of Physical Models

ABSTRACT We introduce a novel statistical calibration framework for physical models, relying on probabilistic embedding of model discrepancy error within the model. For clarity of illustration, we take the measurement errors out of consideration, calibrating a chemical model of interest with respect to a more detailed model, considered as “truth” for the present purpose. We employ Bayesian statistical methods for such model‐to‐model calibration and demonstrate their capabilities on simple synthetic models, leading to a well‐defined parameter estimation problem that employs approximate Bayesian computation. The method is then demonstrated on two case studies for calibration of kinetic rate parameters for methane air chemistry, where ignition time information from a detailed elementary‐step kinetic model is used to estimate rate coefficients of a simple chemical mechanism. We show that the calibrated model predictions fit the data and that uncertainty in these predictions is consistent in a mean‐square sense with the discrepancy from the detailed model data.

Sargsyan, K.↗
DOE OSTI2018

Calibrated simulation analysis for integration of evaporative cooling and radiant cooling system for different Indian climatic zones

Radiant cooling system (RCS) has proven to be an energy efficient system for meeting a building's cooling requirements. RCS is energy efficient and provides better thermal comfort compared to conventional all-air heating ventilation air conditioning (HVAC) system. To further improve the efficiency of RCS, a parallel evaporative cooling system (cooling tower) is coupled with RCS and analyzed for different climatic condition using calibrated model. A statistical analysis of weather files (based on wet bulb temperature), was used to identify the availability of useful water for cooling tower integration with RCS. A comprehensive simulation feasibility study of the application of cooling tower in RCS was performed for different cities to cover every climatic zones of India. It was found that in summer, the wet bulb temperature (WBT) of different climatic zones, except warm-humid, is suitable for the integration of cooling tower with RCS. An experimental setup was designed and developed for integration of parallel chiller and cooling tower with panel-based RCS for cooling and a dedicated outdoor air system (DOAS) for dehumidification and ventilation. Experiments were conducted for chiller and cooling tower operated RCS in Composite climate of Jaipur, Rajasthan, India. Building Energy Models (BEM) were developed for both the chiller-operated RCS and cooling tower-operated RCS in EnergyPlus and calibrated with the measured data. Using the calibrated models, performance of the system was analyzed for different climatic zones of India. A chiller-operated RCS was considered as a baseline to compare the annual energy saving potential and monthly performance of the cooling tower integrated RCS. Here, in the cooling tower–operated RCS, a total annual savings of 7% in hot and dry climates, 11% in composite climates and 20% in temperate climates was achieved compared to the chiller-operated RCS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2010

THE LARGE-SCALE BIAS OF DARK MATTER HALOS: NUMERICAL CALIBRATION AND MODEL TESTS

We measure the clustering of dark matter halos in a large set of collisionless cosmological simulations of the flat {Lambda}CDM cosmology. Halos are identified using the spherical overdensity algorithm, which finds the mass around isolated peaks in the density field such that the mean density is {Delta} times the background. We calibrate fitting functions for the large-scale bias that are adaptable to any value of {Delta} we examine. We find a {approx}6% scatter about our best-fit bias relation. Our fitting functions couple to the halo mass functions of Tinker et al. such that the bias of all dark matter is normalized to unity. We demonstrate that the bias of massive, rare halos is higher than that predicted in the modified ellipsoidal collapse model of Sheth et al. and approaches the predictions of the spherical collapse model for the rarest halos. Halo bias results based on friends-of-friends halos identified with linking length 0.2 are systematically lower than for halos with the canonical {Delta} = 200 overdensity by {approx}10%. In contrast to our previous results on the mass function, we find that the universal bias function evolves very weakly with redshift, if at all. We use our numerical results, both for the mass function and the bias relation, to test the peak-background split model for halo bias. We find that the peak-background split achieves a reasonable agreement with the numerical results, but {approx}20% residuals remain, both at high and low masses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Cross-correlation of Dark Energy Survey Year 3 lensing data with ACT and Planck thermal Sunyaev-Zel’dovich effect observations. I. Measurements, systematics tests, and feedback model constraints

Here we present a tomographic measurement of the cross-correlation between thermal Sunyaev-Zel’dovich (TSZ) maps from Planck and the Atacama Cosmology Telescope and weak galaxy lensing shears measured during the first three years of observations of the Dark Energy Survey. This correlation is sensitive to the thermal energy in baryons over a wide redshift range and is therefore a powerful probe of astrophysical feedback. We detect the correlation at a statistical significance of 21σ, the highest significance to date. We examine the TSZ maps for potential contaminants, including cosmic infrared background and radio sources, finding that cosmic infrared background has a substantial impact on our measurements and must be taken into account in our analysis. We use the cross-correlation measurements to test different feedback models. In particular, we model the TSZ using several different pressure profile models calibrated against hydrodynamical simulations. Our analysis marginalizes over redshift uncertainties, shear calibration biases, and intrinsic alignment effects. We also marginalize over Ω m and σ 8 using Planck or DES priors. We find that the data prefer the model with a low amplitude of the pressure profile at small scales, compatible with a scenario with strong active galactic nuclei feedback and ejection of gas from the inner part of the halos. When using a more flexible model for the shear profile, constraints are weaker, and the data cannot discriminate between different baryonic prescriptions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Quantification of Lanthanides in a Molten Salt Reactor Surrogate Off-Gas Stream Using Laser-Induced Breakdown Spectroscopy

To enable the deployment of molten salt reactor technology, the development of off-gas treatment systems and advanced monitoring tools capable of operating with high temperatures and radiation fields while delivering near real-time information is necessary. Here, we aim to fulfill this requirement and proposes laser-induced breakdown spectroscopy (LIBS) for monitoring molten salt aerosol streams. A sheath gas measuring method was developed to protect optical elements from aerosol particles and to ensure a relatively constant aerosol stream for measurement. An aqueous system was studied to demonstrate the utility of LIBS for monitoring possible fission products in an aerosol stream: Gd, Nd, and Sm up to 2000 parts per million (ppm). A calibration model was built using partial least squares (PLS) regression with five, six, and nine latent variables for Gd, Nd, and Sm, respectively. This calibration model successfully estimated the concentrations of three test samples, which were validated with inductively charged plasma optical emission spectroscopy measurements at a 99.9% confidence interval. To enhance these models, a genetic algorithm was used to filter the spectra before entering the PLS model, thereby limiting the spectral features being regressed to those with greater correlations to concentration. This allowed for the number of latent variables used in the PLS models to be reduced to four, three, and three for Gd, Nd, and Sm, respectively. Finally, the genetic algorithm-filtered PLS models were used to predict the concentrations of the aerosol stream on a real-time dataset and resulted in a 73%, 18%, and 25% improvement in root mean squared error of prediction compared to the original PLS models developed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2003

Investigation of electrolyte measurement in diluted whole blood using spectroscopic and chemometric methods

The feasibility of using near-infrared (NIR) spectroscopy in combination with partial least-squares (PLS) regression was explored to measure electrolyte concentration in whole blood samples. Spectra were collected from diluted blood samples containing randomized, clinically relevant concentrations of Na+, K+, and Ca2+. Sodium was also studied in lysed blood. Reference measurements were made from the same samples using a standard clinical chemistry instrument. Partial least squares (PLS) was used to develop calibration models for each ion with acceptable results (Na+, R2 = 0.86, CVSEP = 9.5 mmol/L; K+, R2 = 0.54, CVSEP = 1.4 mmol/L; Ca2+, R2 = 0.56, CVSEP = 0.18 mmol/L). Slightly improved results were obtained using a narrower wavelength region (470-925 nm) where hemoglobin, but not water, absorbed indicating that ionic interaction with hemoglobin is as effective as water in causing measurable spectral variation. Good models were also achieved for sodium in lysed blood, illustrating that cell swelling, which is correlated with sodium concentration, is not required for calibration model development.

Non-NASA Center↗
DOE OSTI2012

Pre-Shot Simulations of Near-Field and Far-Field Ground Motion for the Source Physics Experiment (SPE) Explosions at the Climax Stock, Nevada National Security Site: SPE3

The Source Physics Experiment (SPE) is designed to improve ground motion prediction capabilities for underground nuclear testing through the development of new physics-based models together with validation via new chemical explosion data. The SPE shots consist of different size and depth chemical explosions in a granite borehole at the Climax Stock on the Nevada National Security Site (NNSS). The third shot in the series (SPE3) will be the same size (1000 kg TNT equivalent) and depth (45 m canister centroid) as the previous shot (SPE2). The main purpose of the SPE3 is to investigate the effects of the damage zone caused by the SPE2 chemical explosion on the SPE3 explosion, keeping all other parameters (e.g. size, depth, explosive type) the same. This SPE3 pre-shot report addresses progress made in simulation of the expected wavefield in the both the near- and the far-field. In this report we consider the far-field to be where seismic wave motion can be characterized by elastic or visco-elastic approximations, in contrast to the near-field where other, often non-linear effects, play a major role. The current simulations build on previous work from the SPE1 and SPE2 shots. The simulations are intended to highlight differences between SPE2 and SPE3 using a variety to approximations intended to mimic the expected effect of the damage zone. The simplest physics of damage zone effects suggest changes in the source region material properties (density, velocity), and in the yield strength (coupling) of the rock, should both impact the far-field signal. We can illustrate this in a simplistic way for P-waves in Figure A using the empirical Mueller-Muphy (1971) far-field explosion model (MM). First if the SPE2 explosion-induced damage reduces velocities and densities without affecting the yield strength or coupling, then by energy conservation the amplitudes for SPE 3 would increase as shown in the top of Figure A. Similarly if the explosion induced damage increased density (say by compacting pore space) the amplitude would decrease. This impedance effect, where the product of the density, wave-velocity, and ground-velocity squared is conserved, is well known. The second and harder effect to quantify, are changes in the energy coupling or yield strength. In the simple MM model this is represented by the coupling factor between cavity radius and yield. If we assume the granite was slightly weakened by SPE2 such that the cavity radius increases slightly then the overall amplitudes of SPE3 decrease relative to SPE2 as shown in the bottom of Figure A. In reality both effects, may exist due to the damage zone, and furthermore we know they are interrelated in complex ways. For S-waves we expect even more complex effects due to motion on joints and faults. For this reason we are developing physics-based near field simulation models coupled to far-field propagation codes to predict the overall effects of changes in the source emplacement region on observed signals. Near-field simulations have been performed using the Eulerian code (GEODYN). An ensemble model, calibrated to match records from past large-scale nuclear events is used. Both 2D axisymmetric (no faults) and fully three-dimensional GEODYN calculations (with /without faults) were conducted in which the damage zones sustained by SPE2 are retained as the initial condition for the SPE3 calculations. The results indicate that the waveform peak amplitudes are slightly decayed since the ensemble model includes no softening mechanism. The pulse widths are reduced, indicating the energy is dissipated more by the damage zone as expected. The dominant motions calculated in all the scenarios are spherically symmetric and non-spherical components are small as seen for the SPE2 calculations. The arrival time of the SPE3 waveform at the boreholes is shorter than SPE2 due to compaction during the previous explosion. As shown in the SPE 1 and 2 observations shear waves are generated, indicating the potential contributions from the induced motion on distributed joints. The effects of joints are evaluated using the Lagrangian hydrocode (GEODYN-L), which was benchmarked against the GEODYN calculations for the ensemble model. A new granite model calibrated for samples from the SPE site is used. The new model includes effects of poroelasticity and water saturation and may be scaled allowing application to both small and large scales. Parametric tests were performed to evaluate the effect of joints within the granite and the results demonstrate that in situ joints introduce anisotropy and significant tangential motions.

58 GEOSCIENCES↗
NASA NTRS2001

Reusable Solid Rocket Motor Nozzle Joint-4 Thermal Analysis

This study provides for development and test verification of a thermal model used for prediction of joint heating environments, structural temperatures and seal erosions in the Space Shuttle Reusable Solid Rocket Motor (RSRM) Nozzle Joint-4. The heating environments are a result of rapid pressurization of the joint free volume assuming a leak path has occurred in the filler material used for assembly gap close out. Combustion gases flow along the leak path from nozzle environment to joint O-ring gland resulting in local heating to the metal housing and erosion of seal materials. Analysis of this condition was based on usage of the NASA Joint Pressurization Routine (JPR) for environment determination and the Systems Improved Numerical Differencing Analyzer (SINDA) for structural temperature prediction. Model generated temperatures, pressures and seal erosions are compared to hot fire test data for several different leak path situations. Investigated in the hot fire test program were nozzle joint-4 O-ring erosion sensitivities to leak path width in both open and confined joint geometries. Model predictions were in generally good agreement with the test data for the confined leak path cases. Worst case flight predictions are provided using the test-calibrated model. Analysis issues are discussed based on model calibration procedures.

Clayton, J. Louie↗
DOE OSTI2025

SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks

Modern neural networks (NNs) often do not generalize well in the presence of a ‘covariate shift’; that is, in situations where the training and test data distributions differ, but the conditional distribution of classification labels given the data remains unchanged. In such cases, NN generalization can be reduced to a problem of learning more robust, domain-invariant features. Domain adaptation (DA) methods include a broad range of techniques aimed at achieving this; however, these methods have struggled with the need for extensive hyperparameter tuning, which then incurs significant computational costs. In this work, we introduce SInkhorn Dynamic Domain Adaptation (SIDDA), an out-of-the-box DA training algorithm built upon the Sinkhorn divergence, that can achieve effective domain alignment with minimal hyperparameter tuning and computational overhead. We demonstrate the efficacy of our method on multiple simulated and real datasets of varying complexity, including simple shapes, handwritten digits, real astronomical observations, and remote sensing data. These datasets exhibit covariate shifts due to noise, blurring, differences between telescopes, and variations in imaging wavelengths. SIDDA is compatible with a variety of NN architectures, and it works particularly well in improving classification accuracy and model calibration when paired with symmetry-aware equivariant NNs (ENNs). We find that SIDDA consistently enhances the generalization capabilities of NNs, achieving up to a ${\approx}40\%$ improvement in classification accuracy on unlabeled target data, while also providing a more modest performance gain of $\lesssim 1\%$ on labeled source data. We also study the efficacy of DA on ENNs with respect to the varying group orders of the dihedral group DN, and find that the model performance improves as the degree of equivariance increases. Finally, if SIDDA achieves proper domain alignment, it also enhances model calibration on both source and target data, with the most significant gains in the unlabeled target domain—achieving over an order of magnitude improvement in the expected calibration error and Brier score. SIDDA’s versatility across various NN models and datasets, combined with its automated approach to domain alignment, has the potential to significantly advance multi-dataset studies by enabling the development of highly generalizable models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Predicting the ethanol potential of wheat straw using near-infrared spectroscopy and chemometrics: The challenge of inherently intercorrelated response functions

Here, the combination of NIR spectroscopy and chemometrics is a powerful correlation method for predicting the chemical constituents in biological matrices, such as the glucose and xylose content of straw. However, difficulties arise when it comes to predicting enzymatic glucose and xylose release potential, which is matrix dependent. Further complications are caused by xylose and glucose release potential being highly intercorrelated. This study emphasizes the importance of understanding the causal relationship between the model and the constituent of interest. It investigates the possibility of using near-infrared spectroscopy to evaluate the ethanol potential of wheat straw by analyzing more than 1000 samples from different wheat varieties and growth conditions. During the calibration model development, the prime emphasis was to investigate the correlation structure between the two major quality traits for saccharification of wheat straw: glucose and xylose release. The large sample set enabled a versatile and robust calibration model to be developed, showing that the prediction model for xylose release is based on a causal relationship with the NIR spectral data. In contrast, the prediction of glucose release was found to be highly dependent on the intercorrelation with xylose release. If this correlation is broken, the model performance breaks down. A simple method was devised for avoiding this breakdown and can be applied to any large dataset for investigating the causality or lack of causality of a prediction model.

09 BIOMASS FUELS↗
DOE OSTI2023

Grain size estimation in fluvial gravel bars using uncrewed aerial vehicles: A comparison between methods based on imagery and topography

Abstract Grain size assessments are necessary for understanding the various geomorphological, hydrological and ecological processes that occur within rivers. Recent research has shown that the application of Structure‐from‐Motion (SfM) photogrammetry to imagery from uncrewed aerial vehicles (UAVs) shows promise for rapidly characterising grain sizes along rivers in comparison to traditional field‐based methods. Here, we evaluated the applicability of different methods for estimating grain sizes in gravel bars along a study reach in the Olentangy River in Columbus, Ohio. We collected imagery of these gravel bars with a UAV and processed those images with SfM photogrammetry software to produce three‐dimensional point clouds and orthomosaics. Our evaluation compared statistical models calibrated on topographic roughness, which was computed from the point clouds, and to those based on image texture, which was computed from the orthomosaics. Our results showed that statistical models calibrated on image texture were more accurate than those based on topographic roughness. This might be because of site‐specific patterns of grain size, shape and imbrication. Such patterns would have complicated the detection of topographic signatures associated with individual grains. Our work illustrates that UAV‐SfM approaches show potential to be used as an accessible method for characterising surface grain sizes along rivers at higher spatial and temporal resolutions than those provided by traditional methods.

Wong, Tyler↗