SEARCH · Engineering Papers

Results for “functional regression”

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 1,207 records · Page 67

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Machine learning-assisted ultrafast flash sintering of high-performance and flexible silver–selenide thermoelectric devices

Flexible thermoelectric generators (TEGs) have shown immense potential for serving as a power source for wearable electronics and the Internet of Things. A key challenge preventing large-scale application of TEGs lies in the lack of a high-throughput processing method, which can sinter thermoelectric (TE) materials rapidly while maintaining their high thermoelectric properties. Herein, we integrate high-throughput experimentation and Bayesian optimization (BO) to accelerate the discovery of the optimum sintering conditions of silver–selenide TE films using an ultrafast intense pulsed light (flash) sintering technique. Due to the nature of the high-dimensional optimization problem of flash sintering processes, a Gaussian process regression (GPR) machine learning model is established to rapidly recommend the optimum flash sintering variables based on Bayesian expected improvement. For the first time, an ultrahigh-power factor flexible TE film (a power factor of 2205 μW m -1 K -2 with a zT of 1.1 at 300 K) is demonstrated with a sintering time less than 1.0 second, which is several orders of magnitude shorter than that of conventional thermal sintering techniques. Further, the films also show excellent flexibility with 92% retention of the power factor (PF) after 10 3 bending cycles with a 5 mm bending radius. In addition, a wearable thermoelectric generator based on the flash-sintered films generates a very competitive power density of 0.5 mW cm -2 at a temperature difference of 10 K. This work not only shows the tremendous potential of high-performance and flexible silver–selenide TEGs but also demonstrates a machine learning-assisted flash sintering strategy that could be used for ultrafast, high-throughput and scalable processing of functional materials for a broad range of energy and electronic applications.

25 ENERGY STORAGE↗

DOE OSTI2015

Regional radiation dose susceptibility within the parotid gland: Effects on salivary loss and recovery

Purpose: Xerostomia is one of the most likely late toxic effects of radiotherapy treatment in patients with head-and-neck cancers. Modern treatment techniques can incorporate knowledge of complication risk into treatment plans. To this end, the authors attempt to quantify the regional radiotherapy dose-dependence of salivary output loss and recovery in a prospective study. Methods: Salivary output was collected from patients undergoing radiotherapy treatment for head-and-neck cancers at the BC Cancer Agency between February 2008 and May 2013. Regional dose-dependence (i.e., dose susceptibility) of loss and recovery is quantified using nonparametric (Spearman’s rank correlation coefficients, local linear regression) and parametric (least-sum of squares, least-median of squares) techniques. Results: Salivary flow recovery was seen in 79 of 102 patients considered (p < 0.0001, Wilcoxon sign rank test). Output loss was strongly correlated with left- and right parotid combined dose φ = min (D{sub L}, 45 Gy) + min (D{sub R}, 45 Gy), and can be accurately predicted. Median early loss (three months) was 72% of baseline, while median overall loss (1 yr) was 56% of baseline. Fitting an exponential model to whole parotid yields dose sensitivities A{sub 3m} = 0.0604 Gy{sup −1} and A{sub 1y} = 0.0379 Gy{sup −1}. Recovery was not significantly associated with dose. Hints of lateral organ sub-segment dose–response dimorphism were observed. Conclusions: Sub-segmentation appears to predict neither loss nor recovery with any greater precision than whole parotid mean dose, though it is not any worse. Sparing the parotid to a combined dose φ of <50 Gy is recommended for a patient to keep ≈40% of baseline function and thus avoid severe xerostomia at 12 months post-treatment. It seems unlikely that a population’s mean recovery will exceed 20%–30% of baseline output at 1 yr after radiotherapy treatment using current (whole-organ based) clinical guidelines.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Integrated characterization of CO 2 storage reservoirs on the Rock Springs Uplift combining geomechanics, geochemistry, and flow modeling

This project improves understanding of the effects CO 2 injection and storage on geomechanical, petrophysical, and other properties on two potential reservoirs, the Madison Limestone and Weber Sandstone, at the Rock Springs Uplift, southwest Wyoming. Rock physics studies, laboratory experiments, and computer simulations were employed. Two groups of rock samples were prepared, one aged with formation brine and the second aged with CO 2 -saturated formation brine and subsequently used for geomechanical, geochemical, and petrophysical testing. Rock physics studies provided a facies classification to discriminate among different lithologies and to investigate the feasibility of predicting petrophysical properties using seismic inversion. By applying a zone-wise regression relation established in this facies analysis, a 3D reservoir model was developed. It was determined that time-lapse seismic data are not feasible for monitoring CO 2 displacement at the Rock Springs Uplift or to mitigate leakage-risks situations because pressure and fluid saturation have small effects on elastic velocities and synthetic seismograms. The small variations that do occur are due to the low porosity of the sandstone and the high stiffness of the dolomite. In the Madison Limestone, mineral dissolution due to reaction with CO 2 -saturated brine was observed in highly permeable pathways while mineral precipitation happened in intergranular or micropores. Changes in pore size were quantified via Time-Domain Nuclear Magnetic Resonance transverse relaxation time (T 2 ) and diffusion coefficient distributions. As opposed to continuous injection of CO 2 -saturated brine, a subtle increase in porosity after static ‘soaking’ with CO 2 -saturated brine increases permeability due to the heterogeneity of changes in pore and pore throat sizes. Porosity-permeability relationships are fitted using a power-law function that can be adopted to model time-evolved porosity and permeability in a transport-limited CO 2 injection process. The effect of CO 2 -saturated brine on the linear elastic properties of Weber Sandstone and Madison Limestone cannot be generally concluded. Considering horizontal and vertical orientations and different confining pressures, no consistent relationship between stress (or strain) data and reaction with CO 2 -saturated brine was observed in the nonlinear plastic and post-failure regime. The change of elastic constants due to reaction with CO 2 -saturated brine is more significant in Madison Limestone than in Weber Sandstone; however, no consistent trend was observed. Reservoir simulations suggest that injectivity for Weber Sandstone is adversely impacted by low permeability. Changes in confining pressure do not impact injection rate and can be ignored while the effect of changes to pore pressure cannot. The effect of confining pressure and pore pressure on the size of the CO 2 plume in the Madison Limestone is negligible because of higher permeability. Geomechanical and petrophysical properties induced by CO 2 -brine-rock reactions were time-dependent. Simulations considered these time-dependent properties and indicate that injectivity, porosity distribution, pressure distribution and CO 2 saturation were affected.

58 GEOSCIENCES↗

DOE OSTI2023

Protecting and Defending against Autonomous Control Systems and Digital Twin Cyber Attacks: Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Models in Nuclear Power Plants (Final)

Navigating through the complex tapestry of technological advancements, "Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Model in Nuclear Power Plants" stands at the intersection of cybersecurity and nuclear power plant operations, embarking on a journey through the intricacies of securing digital twins against malicious cyber activities. As nuclear power plants progressively integrate digital twin technology and machine learning models to optimize operations and ensure system reliability, they inadvertently expose themselves to a new spectrum of vulnerabilities, notably in the realm of hyperparameter attacks. Hyperparameters, integral in machine learning model tuning and optimal performance of digital twins, have emerged as a target for adversaries aiming to destabilize the predictive capabilities and therefore, the operational accuracy of these digital entities within critical infrastructures like nuclear plants. This paper, therefore, meticulously threads the needle through the development of a robust response strategy, poised to shield these digital reflections against calculated hyperparameter manipulations, ensuring that the digital twin can effectively and securely function as a reliable proxy for its physical counterpart. The ensuing sections delve into the orchestrated maelstrom of multi-rate time-changing intelligent coordinated hyperparameter attacks and the implementation of event-triggered predictive control, laying down a structured, predictive, and responsive framework that safeguards the nexus where the digital and physical realms of nuclear power plants coalesce. The operational integrity of digital twins in nuclear power plants depends critically on the security of machine learning hyperparameters. This study makes two different contributions. First, a decision-based idea known as a multi-rate time changing intelligent coordinated hyperparameter attack is put forth. In this attack, many hyperparameters are repeatedly changed using both random and intelligent optimal techniques by the attacker. These assaults introduce varied rates at different attack steps, compromise various amounts of hyperparameters, and improve stealth and flexibility. Second, a technique is developed for event triggered predictive control to rapidly respond to potential hyperparameter attacks. This control integrates a sliding window framework, retaining a history of previous data points and employing linear regression to predict the next data point from the current dataset. The control gain K is determined using the Lyapunov-Krasovskii method, and subsequently, an action is developed. Finally, the outcome of the simulation demonstrates the viability of the proposed method for defending nuclear power plant digital twins from hyperparameter attacks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2015

RECK is not an independent prognostic marker for breast cancer

Background The REversion-inducing Cysteine-rich protein with Kazal motif (RECK) is a well-known inhibitor of matrix metalloproteinases (MMPs) and cellular invasion. Although high expression levels of RECK have already been correlated with a better clinical outcome for several tumor types, its main function, as well as its potential prognostic value for breast cancer patients, remain unclear. Methods The RECK expression profile was investigated in a panel of human breast cell lines with distinct aggressiveness potential. RECK functional analysis was undertaken using RNA interference methodology. RECK protein levels were also analyzed in 1040 cases of breast cancer using immunohistochemistry and tissue microarrays (TMAs). The association between RECK expression and different clinico-pathological parameters, as well as the overall (OS) and disease-free (DFS) survival rates, were evaluated. Results Higher RECK protein expression levels were detected in more aggressive breast cancer cell lines (T4-2, MDA-MB-231 and Hs578T) than in non-invasive (MCF-7 and T47D) and non-tumorigenic (S1) cell lines. Indeed, silencing RECK in MDA-MB-231 cells resulted in elevated levels of pro-MMP-9 and increased invasion compared with scrambled (control) cells, without any effect on cell proliferation. Surprisingly, by RECK immunoreactivity analysis on TMAs, we found no association between RECK positivity and survival (OS and DFS) in breast cancer patients. Even considering the different tumor subtypes (luminal A, luminal B, Her2 type and basal-like) or lymph node status, RECK remained ineffective for predicting the disease outcome. Moreover, by multivariate Cox regression analysis, we found that RECK has no prognostic impact for OS and DFS, relative to standard clinical variables. Conclusions Although it continues to serve as an invasion and MMP inhibitor in breast cancer, RECK expression analysis is not useful for prognosis of these patients.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2010

Calibration Designs for Non-Monolithic Wind Tunnel Force Balances

This research paper investigates current experimental designs and regression models for calibrating internal wind tunnel force balances of non-monolithic design. Such calibration methods are necessary for this class of balance because it has an electrical response that is dependent upon the sign of the applied forces and moments. This dependency gives rise to discontinuities in the response surfaces that are not easily modeled using traditional response surface methodologies. An analysis of current recommended calibration models is shown to lead to correlated response model terms. Alternative modeling methods are explored which feature orthogonal or near-orthogonal terms.

Wind tunnel models↗

DOE OSTI2023

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

ppdx : Automated modeling of protein–protein interaction descriptors for use with machine learning

This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein–protein interactions. The descriptors can be used to predict various properties of interest, such as protein–protein binding affinities, or inhibitory concentrations (IC 50 ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC 50 of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems

Parallel I/O hardware and software infrastructure is a key contributor to performance variability for applications running on large-scale HPC systems. This variability confounds efforts to predict application performance for characterization, modeling, optimization, and job scheduling. We propose a modeling approach that improves predictive ability by explicitly treating the variability and by leveraging the sensitivity of application parameters on performance to group applications with similar characteristics. We develop a Gaussian process-based machine learning algorithm to model I/O performance and its variability as a function of application and file system characteristics. We demonstrate the effectiveness of the proposed approach using data collected from the Edison system at the National Energy Research Scientific Computing Center. The results show that the proposed sensitivity-based models are better at prediction when compared with application-partitioned or unpartitioned models. We highlight modeling techniques that are robust to the outliers that can occur in production parallel file systems. Using the developed metrics and modeling approach, we provide insights into the file system metrics that have a significant impact on I/O performance.

I/O performance variability↗

DOE OSTI2016

Mechanical vulnerability and resistance to snapping and uprooting for Central Amazon tree species

We report high descending winds generated by convective storms are a frequent and a major source of tree mortality disturbance events in the Amazon, affecting forest structure and diversity across a variety of scales, and more frequently observed in western and central portions of the basin. Soil texture in the Central Amazon also varies significantly with elevation along a topographic gradient, with decreasing clay content on plateaus, slopes and valleys respectively. In this study we investigated the critical turning moments ( M crit - rotational force at the moment of tree failure, an indicator of tree stability or wind resistance) of 60 trees, ranging from 19.0 to 41.1 cm in diameter at breast height ( DBH ) and located in different topographic positions, and for different species, using a cable-winch load-cell system. Our approach used torque as a measure of tree failure to the point of snapping or uprooting. This approach provides a better understanding of the mechanical forces required to topple trees in tropical forests, and will inform models of wind throw disturbance. Across the topographic positions, size controlled variation in M crit was quantified for cardeiro ( Scleronema mincranthum (Ducke) Ducke), mata-matá ( Eschweilera spp.), and a random selection of trees from 19 other species. Our analysis of M crit revealed that tree resistance to failure increased with size (DBH and ABG) and differed among species. No effects of topography or failure mode were found for the species either separately or pooled. For the random species, total variance in M crit explained by tree size metrics increased from an R 2 of 0.49 for DBH alone, to 0.68 when both DBH and stem fresh wood density (SWD) were included in a multiple regression model. This mechanistic approach allows the comparison of tree vulnerability induced by wind damage across ecosystems, and facilitates the use of forest structural information in ecosystem models that include variable resistance of trees to mortality inducing factors. Furthermore, our results indicate that observed topographic differences in wind throw vulnerability are likely due to elevational differences in wind velocities, rather than by differences in soil-related factors that might effect M crit .

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1995

Image analysis used to count and measure etched tracks from ionizing radiation

We have developed techniques to use digitized scanning electron micrographs and computer image analysis programs to measure track densities in lunar soil grains and plastic dosimeters. Tracks in lunar samples are formed by highly ionizing solar energetic particles and cosmic rays during near surface exposure on the Moon. The track densities are related to the exposure conditions (depth and time). Distributions of the number of grains as a function of their track densities can reveal the modality of soil maturation. We worked on two samples identified for a consortium study of lunar weathering effects, 61221 and 67701. They were prepared by the lunar curator's staff as polished grain mounts that were etched in boiling 1 N NaOH for 6 h to reveal tracks. We determined that backscattered electron images taken at 10 percent contrast and approximately 50 percent brightness produced suitable high contrast images for analysis. We used the NIH Image program to cut out areas that were unsuitable for measurement such as edges, cracks, etc. We ascertained a gray-scale threshold of 25 to separate tracks from background. We used the computer to count everything that was two pixels or greater in size and to measure the area to obtain track densities. We found an excellent correlation with manual measurements for track densities below 1 x 10(exp 8) cm(exp -2). For track densities between 1 x 10(exp 8) cm(exp -2) to 1 x 10(exp 9) cm(exp -2) we found that a regression formula using the percentage area covered by tracks gave good agreement with manual measurements. We determined the track density distributions for 61221 and 67701. Sample 61221 is an immature sample, but not pristine. Sample 67701 is a submature sample that is very close to being fully mature. Because only 10 percent of the grains have track densities less than 10(exp 9) cm(exp -2), it is difficulty to determine whether the sample matured in situ or is a mixture of a mature and a submature soil. Although our analysis of plastic dosimeters is at an early stage of development, results are encouraging. The dosimeter was etched in 6.25 N NaOH at 70 deg C for 16 h. We took 200x secondary electron images of the sample and used the NIH Image software to count and measure major and minor diameters of the etched tracks. We calculated the relative track etch rate from a formula that relates it to the major and minor diameters. We made a histogram of the number of tracks versus their relative etch rate. The relative track etching rate is proportional to the linear energy transfer of the particle. With appropriate calibration experiments, the histogram could be used to calculate the radiation dose.

Blanford, George E.↗

DOE OSTI2018

Dichotomous development of the gut microbiome in preterm infants

Preterm infants are at risk of developing intestinal dysbiosis with an increased proportion of Gammaproteobacteria. In this study, we sought the clinical determinants of the relative abundance of feces-associated Gammaproteobacteria in very low birth weight (VLBW) infants. Fecal microbiome was characterized at ≤ 2 weeks and during the 3rd and 4th weeks after birth, by 16S rRNA amplicon sequencing. Maternal and infant clinical characteristics were extracted from electronic medical records. Data were analyzed by linear mixed modeling and linear regression. Clinical data and fecal microbiome profiles of 45 VLBW infants (gestational age 27.9 ± 2.2 weeks; birth weight 1126 ± 208 g) were studied. Three stool samples were analyzed for each infant at mean postnatal ages of 9.9 ± 3, 20.7 ± 4.1, and 29.4 ± 4.9 days. The average relative abundance of Gammaproteobacteria was 42.5% (0–90%) at ≤ 2 weeks, 69.7% (29.9–86.9%) in the 3rd, and 75.5% (54.5–86%) in the 4th week (p < 0.001). Hierarchical and K-means clustering identified two distinct subgroups: cluster 1 started with comparatively low abundance that increased with time, whereas cluster 2 began with a greater abundance at ≤ 2 weeks (p < 0.001) that decreased over time. Both groups resembled each other by the 3rd week. Single variants of Klebsiella and Staphylococcus described variance in community structure between clusters and were shared between all infants, suggesting a common, hospital-derived source. Fecal Gammaproteobacteria was positively associated with vaginal delivery and antenatal steroids. We detected a dichotomy in gut microbiome assembly in preterm infants: some preterm infants started with low relative gammaproteobacterial abundance in stool that increased as a function of postnatal age, whereas others began with and maintained high abundance. Vaginal birth and antenatal steroids were identified as predictors of Gammaproteobacteria abundance in the early (≤ 2 weeks) and later (3rd and 4th weeks) stool samples, respectively. These findings are important in understanding the development of the gut microbiome in premature infants.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2002

Doppler-derived myocardial systolic strain rate is a strong index of left ventricular contractility

BACKGROUND: Myocardial fiber strain is directly related to left ventricular (LV) contractility. Strain rate can be estimated as the spatial derivative of velocities (dV/ds) obtained by tissue Doppler echocardiography (TDE). The purposes of the study were (1) to determine whether TDE-derived strain rate may be used as a noninvasive, quantitative index of contractility and (2) to compare the relative accuracy of systolic strain rate against TDE velocities alone. METHODS AND RESULTS: TDE color M-mode images of the interventricular septum were recorded from the apical 4-chamber view in 7 closed-chest anesthetized mongrel dogs during 5 different inotropic stages. Simultaneous LV volume and pressure were obtained with a combined conductance-high-fidelity pressure catheter. Peak elastance (Emax) was determined as the slope of end-systolic pressure-volume relationships during caval occlusion and was used as the gold standard of LV contractility. Peak systolic TDE myocardial velocities (Sm) and peak (epsilon'(p)) and mean (epsilon'(m)) strain rates obtained at the basal septum were compared against Emax by linear regression. Emax as well as TDE systolic indices increased during inotropic stimulation with dobutamine and decreased with the infusion of esmolol. A stronger association was found between Emax and epsilon'(p) (r=0.94, P<0.01, y=0.29x+0.46) and epsilon'(m) (r=0.88, P<0.01) than for Sm (r=0.75, P<0.01). CONCLUSIONS: TDE-derived epsilon'(p) and epsilon'(m) are strong noninvasive indices of LV contractility. These indices appear to be more reliable than S(m), perhaps by eliminating translational artifact.

Non-NASA Center↗

NASA NTRS1997

Combined influences of gravitoinertial force level and visual field pitch on visually perceived eye level

Psychophysical measurements of the level at which observers set a small visual target so as to appear at eye level (VPEL) were made on 13 subjects in 1.0 g and 1.5 g environments in the Graybiel Laboratory rotating room while they viewed a pitched visual field or while in total darkness. The gravitoinertial force was parallel to the z-axis of the head and body during the measurements. The visual field consisted of two 58 degrees high, luminous, pitched-from-vertical, bilaterally symmetric, parallel lines, viewed in otherwise total darkness. The lines were horizontally separated by 53 degrees and presented at each of 7 angles of pitch ranging from 30 degrees with the top of the visual field turned away from the subject (top backward) to 30 degrees with the top turned toward the subject (top forward). At 1.5 g, VPEL changed linearly with the pitch of the 2-line stimulus and was depressed with top backward pitch and elevated with top forward pitch as had been reported previously at 1.0 g (1,2); however, the slopes of the VPEL-vs-pitch functions at 1.0 g and 1.5 g were indistinguishable. As reported previously also (3,4), the VPEL in darkness was considerably lower at 1.5 g than at 1.0 g; however, although the y-intercept of the VPEL-vs-pitch function in the presence of the 2-line visual field (visual field erect) was also lower at 1.5 g than at 1.0 g as it was in darkness, the G-related difference was significantly attenuated by the presence of the visual field. The quantitative characteristics of the results are consistent with a model in which VPEL is treated as a consequence of an algebraic weighted average or a vector sum of visual and nonvisual influences although the two combining rules lead to fits that are equally good.

Non-NASA Center↗

DOE OSTI2021

A surrogate-model-based approach for estimating the first and second-order moments of offshore wind power

Power curve, the functional relationship that governs the process of converting a set of weather variables experienced by a wind turbine into electric power, is widely used in the wind industry to estimate power output for planning and operational purposes. Existing methods for power curve estimation have three main limitations: (i) they mostly rely on wind speed as the sole input, thus ignoring the secondary, yet possibly significant effects of other environmental factors, (ii) they largely overlook the complex marine environment in which offshore turbines operate, potentially compromising their value in offshore wind energy applications, and (ii) they solely focus on the first-order properties of wind power, with little (or null) information about the variation around the mean behavior, which is important for ensuring reliable grid integration, asset health monitoring, and energy storage, among others. In light of that, this study investigates the impact of several wind-and wave-related factors on offshore wind power variability, with the ultimate goal of accurately predicting its first two moments. Further, our approach couples OpenFAST—a multi-physics wind turbine simulator—with Gaussian Process (GP) regression to reveal the underlying relationships governing offshore weather-to-power conversion. We first find that a multi-input power curve which captures the combined impact of wind speed, direction, and air density, can provide double-digit improvements, in terms of prediction accuracy, relative to univariate methods which rely on wind speed as the sole explanatory variable (e.g. the standard method of bins). Wave-related variables are found not important for predicting the average power output, but interestingly, appear to be extremely relevant in describing the fluctuation of the offshore power around its mean. Tested on real-world data collected at the New York/New Jersey bight, our proposed multi-input models demonstrate a high explanatory power in predicting the first two moments of offshore wind generation, testifying their potential value to the offshore wind industry.

17 WIND ENERGY↗

DOE OSTI2020

Predicting boron coordination in multicomponent borate and borosilicate glasses using analytical models and machine learning

Accurate prediction of boron coordination in multicomponent glasses is critical in glass science and technology as it strongly affects the properties of borate and borosilicate glasses. We have collected a dataset containing 657 glasses from literature with boron coordination values and developed models using analytical functions based on the well accepted Dell, Xiao and Bray model. Good prediction of boron coordination with a R 2 value higher than 0.8 was obtained. The large variation of boron coordination from experiments, originated from sample preparations and characterizations, led to difficulties in obtaining models with better prediction performance. Various machine learning (ML) algorithms were evaluated and slightly better prediction performance was observed; however, interpretation of the ML models is less straight forward. In conclusion, this study developed various models capable of providing quantitative boron coordination predictions, providing insights into its structural roles in multi-component glasses, and suggesting fruitful areas for future research.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Quantifying Temperature Dependence of Pu(IV) Absorbance Spectra for Advanced Online Monitoring of Nuclear Processes

This article presents a systematic study of Pu(IV) absorbance spectral features as a function of temperature to develop an understanding of this parameter’s effect on chemometric models that can be used as online monitoring tools to support nuclear processing. The descriptive and predictive models that provide real-time feedback of these processes are usually constructed with data collected in conditions typical of a laboratory environment, which can differ drastically from a processing environment. To assess the impact of temperature on Pu(IV) absorbance spectra, 11 samples of Pu(IV) were synthesized with varying HNO 3 concentrations ranging from 0.6 to 9.5 M and heated between 15 and 45 °C. Ultraviolet (UV)–visible (vis)–near-infrared (NIR) absorption spectra collected at different HNO 3 concentrations and temperatures revealed that features associated with Pu(IV) are sensitive to temperature at all HNO 3 concentrations and that changes in features depend on HNO 3 concentration. The contributions of temperature and HNO 3 concentration to variation in Pu(IV) spectral features were evaluated using the principal component analysis of spectra that were baseline-corrected with an asymmetric least-squares method. Furthermore, predictive modeling for HNO 3 concentration with partial least-squares regression of UV–vis–NIR spectra highlighted the importance of accounting for temperature in the calibration set to optimize model performance. This methodology constitutes a new, systematic approach to account for the effect of temperature on the absorption spectra of metal ions and is useful for process monitoring applications in many industries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R 2 values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4% to 8.4% for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. In conclusion, these findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

Environmental drivers↗