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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Landslide Likelihood Prediction using Machine Learning Algorithms

The supply of electricity via power plants is criticalto the operation of many critical infrastructure systems in mod-ern society. Natural hazards can disrupt the power supply, causepower outages that can halt economic growth, and impede emer-gency response until power is restored. The proposed work aimsto predict the landslides likelihood in these critical infrastructurelocations in the Northeastern USA using integrated databases ofexplanatory variables and machine learning algorithms. First,data related to landslides are obtained and merged, includingtopographic, soil moisture, and precipitation-related data. Fiveregression algorithms, namely: Random Forest, Extreme Gradi-ent Boosting (XGBoost), K-Nearest Neighbor regression (KNN),Linear Support Vector Regressor (SVR), and Linear regression,are utilized to predict the landslide probability and evaluatedon the dataset. The accuracy of the models is assessed by usingstatistical metrics such as mean absolute error (MAE), meansquared error (MSE), and root mean squared error (RMSE).The study results show that Random Forest outperformed othermodels with the mutual information feature selection method.It achieved an MSE of 0.0011 with mutual information-basedfeature selection and an MSE of 0.00157 without feature selection.KNN regressor outperformed the other models with an MSEof 0.00139 with correlation-based information selection. Theproposed landslide identification model with Random Forestalgorithm shows outstanding robustness and great potential intackling the landslide likelihood prediction by employing MLalgorithms.

Vasundhara Acharya↗
DOE OSTI2019

A Parallelised ROOT for Future HEP Data Processing

In the coming years, HEP data processing will need to exploit parallelism on present and future hardware resources to sustain the bandwidth requirements. As one of the cornerstones of the HEP software ecosystem, ROOT embraced an ambitious parallelisation plan which delivered compelling results. In this contribution the strategy is characterised as well as its evolution in the medium term. The units of the ROOT framework are discussed where task and data parallelism have been introduced, with runtime and scaling measurements. We will give an overview of concurrent operations in ROOT, for instance in the areas of I/O (reading and writing of data), fitting / minimization, and data analysis. This paper introduces the programming model and use cases for explicit and implicit parallelism, where the former is explicit in user code and the latter is implicitly managed by ROOT internally.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles

Extreme temperature events can have considerable negative impacts on sectors such as health, agriculture, and transportation. Observational evidence indicates the severity and frequency of warm extremes are increasing over much of the United States, but there are sizeable challenges both in estimating extreme temperature changes and in quantifying the relevant associated uncertainties. This study provides a simple statistical framework using a block maxima approach to analyze the representation of warm temperature extremes in several recent global climate model ensembles. Uncertainties due to structural model differences, grid resolution, and internal variability are characterized and discussed. Results show that models and ensembles differ greatly in the representation of extreme temperature over the United States, and variability in tail events is dependent on time and anthropogenic warming, which can influence estimates of return periods and distribution parameter estimates using generalized extreme value (GEV) distributions. These effects can considerably influence the uncertainty of model hindcasts and projections of extremes. Several idealized regional applications are highlighted for evaluating ensemble skill and trends, based on quantile analysis and root-mean-square errors in the overall sample and the upper tail. The results are relevant to regional climate assessments that use global model outputs and that are sensitive to extreme warm temperature. Accompanying this manuscript is a simple toolkit using the R statistical programming language for characterizing extreme events in gridded datasets.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Antisense expression of an Arabidopsis ran binding protein renders transgenic roots hypersensitive to auxin and alters auxin-induced root growth and development by arresting mitotic progress

We cloned a cDNA encoding an Arabidopsis Ran binding protein, AtRanBP1c, and generated transgenic Arabidopsis expressing the antisense strand of the AtRanBP1c gene to understand the in vivo functions of the Ran/RanBP signal pathway. The transgenic plants showed enhanced primary root growth but suppressed growth of lateral roots. Auxin significantly increased lateral root initiation and inhibited primary root growth in the transformants at 10 pM, several orders of magnitude lower than required to induce these responses in wild-type roots. This induction was followed by a blockage of mitosis in both newly emerged lateral roots and in the primary root, ultimately resulting in the selective death of cells in the tips of both lateral and primary roots. Given the established role of Ran binding proteins in the transport of proteins into the nucleus, these findings are consistent with a model in which AtRanBP1c plays a key role in the nuclear delivery of proteins that suppress auxin action and that regulate mitotic progress in root tips.

Non-NASA Center↗
NASA NTRS1957

Interference Effects of Fuselage-Stored Missiles on Inlet Duct Model of an Interceptor-Type Aircraft at Mach Numbers 1.5 to 1.9

The effect of missile armament on the performance of an interceptor-type aircraft model has been determined at Mach numbers 1.5, 1.7, and 1.9 and at angles of attack to 19 deg. With this configuration missiles were carried in a bay located on the bottom of the aircraft fuselage and mounted to a rotatable missile door. Rotation of the door then brought the missiles into the external or firing position. The aircraft model was characterized by triangular-shaped normal-shock inlets located at the wing roots. Relatively short and curved subsonic diffusers fed simulated twin side-by-side turbojet engines. Inasmuch as the missile bay extended considerably ahead of the inlet station, rotation of the missile door created considerable disturbance of the flow entering the inlets. In comparison with the internal missile arrangement, the external missile configurations increased the model lift, drag, and pitching moment. While the diffuser-exit flow distortion and static-pressure fluctuations were not greatly affected, diffuser total-pressure recovery was reduced as much as 0.058 at Mach number 1.9 for one missile configuration. The most detrimental effect of missile-door rotation occurred at the transient door positions, or with the door halfwzy between the missiles-in and -out conditions. At this door position the flow into the inlets was highly asymmetrical. Although the performance of both left and right ducts was generally reduced, the inlet duct on the cavity side of the missile door was most severely penalized, becoming unstable recovery losses and increases in flow distortion. The installation of fuselage fences along the missile bay was only partially effective in reducing these losses.

Piercy, T. G.↗
NASA NTRS1986

Nitrogen uptake and utilization by intact plants

The results of experiments support the proposed conceptual model that relates nitrogen uptake activity by plants as a balanced interdependence between the carbon-supplying function of the shoot and the nitrogen-supplying function of the roots. The data are being used to modify a dynamic simulation of plant growth, which presently describes carbon flows through the plant, to describe nitrogen uptake and assimilation within the plant system. Although several models have been proposed to predict nitrogen uptake and partitioning, they emphasize root characteristics affecting nutrient uptake and relay on empirical methods to describe the relationship between nitrogen and carbon flows within the plant. Researchers, on the other hand, propose to continue to attempt a mechanistic solution in which the effects of environment on nitrogen (as well as carbon) assimilation are incorporated through their direct effects on photosynthesis, respiration, and aging processes.

Raper, C. D., Jr.↗
NASA NTRS1962

Jet Interference Effects on a Model of a Single-Engine Four Jet V/STOL Airplane at Mach Numbers from 0.60 to 1.00

An investigation was conducted in the Langley 16-foot transonic tunnel to determine the interference from four exhaust jets on the aerodynamic characteristics of a model of a V/STOL airplane. The single- engine four-jet turbofan power plant of the airplane was simulated by inducing tunnel airflow through two large side inlets and injecting the decomposition products of hydrogen peroxide into the internal flow. The heated gas mixture was exhausted through four nozzles located on the sides of the fuselage under the wing, two near the wing leading edge and two forward of the trailing edge; the nozzles were deflected downward 1.5 deg and outward 5.0 deg to simulate cruise conditions. The wing of the model was a clipped delta with leading-edge sweep of 40 deg, aspect ratio of 3.06, taper ratio of 0.218, thickness-chord ratio of 0.09 at the root and 0.07 at the tip, and 10 deg negative dihedral. Aerodynamic and longitudinal stability coefficients were obtained for the model with the tail removed, and for horizontal-tail incidences of 0 deg and -5 deg. Data were obtained at Mach numbers from 0.60 to 1.00, angles of attack from 0 deg to 12 deg, and with jet total-pressure ratios up to 3.1. Jet operation generally caused a decrease in lift, an increase in pitching-moment coefficient, and a decrease in longitudinal stability at subsonic speeds. The jet interference effects on drag were detrimental at a Mach number of 0.60 and favorable at higher speeds for cruising-flight attitudes.

Schmeer, James W.↗
DOE OSTI2023

Classical integrability of root-$T\overline{T}$ flows

The root-$T\overline{T}$ flow was recently introduced as a universal and classically marginal deformation of any two-dimensional translation-invariant field theory. The flow commutes with the (irrelevant) $T\overline{T}$ flow, and it can be integrated explicitly for a large class of actions, leading to nonanalytic Lagrangians reminiscent of the four-dimensional modified-Maxwell theory (ModMax). It is not a priori obvious whether the root-$T\overline{T}$ flow preserves integrability, as is the case for the $T\overline{T}$ flow. In this paper we demonstrate that this is the case for a large class of classical models by explicitly constructing a deformed Lax connection. We discuss the principal chiral model and the nonlinear sigma models on symmetric and semisymmetric spaces, without or with the Wess-Zumino term. We also construct Lax connections for the two-parameter families of theories deformed by both root-$T\overline{T}$ and $T\overline{T}$ for all of these models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Temporal change in chromatin accessibility predicts regulators of nodulation in Medicago truncatula

Symbiotic associations between bacteria and leguminous plants lead to the formation of root nodules that fix nitrogen needed for sustainable agricultural systems. Symbiosis triggers extensive genome and transcriptome remodeling in the plant, yet an integrated understanding of the extent of chromatin changes and transcriptional networks that functionally regulate gene expression associated with symbiosis remains poorly understood. In particular, analyses of early temporal events driving this symbiosis have only captured correlative relationships between regulators and targets at mRNA level. Here, we characterize changes in transcriptome and chromatin accessibility in the model legume Medicago truncatula, in response to rhizobial signals that trigger the formation of root nodules. We profiled the temporal chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) dynamics of M. truncatula roots treated with bacterial small molecules called lipo-chitooligosaccharides that trigger host symbiotic pathways of nodule development. Using a novel approach, dynamic regulatory module networks, we integrated ATAC-seq and RNA-seq time courses to predict cis-regulatory elements and transcription factors that most significantly contribute to transcriptomic changes associated with symbiosis. Regulators involved in auxin (IAA4-5, SHY2), ethylene (EIN3, ERF1), and abscisic acid (ABI5) hormone response, as well as histone and DNA methylation (IBM1), emerged among those most predictive of transcriptome dynamics. RNAi-based knockdown of EIN3 and ERF1 reduced nodule number in M. truncatula validating the role of these predicted regulators in symbiosis between legumes and rhizobia. Our transcriptomic and chromatin accessibility datasets provide a valuable resource to understand the gene regulatory programs controlling the early stages of the dynamic process of symbiosis. The regulators identified provide potential targets for future experimental validation, and the engineering of nodulation in species is unable to establish that symbiosis naturally.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Train, Inform, Borrow, or Combine? Approaches to Process–Guided Deep Learning for Groundwater–Influenced Stream Temperature Prediction

Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater-relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater-dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Experimental fire increases soil carbon dioxide efflux in a grassland long‐term multifactor global change experiment

Abstract Numerous studies have demonstrated that soil respiration rates increase under experimental warming, although the long‐term, multiyear dynamics of this feedback are not well constrained. Less is known about the effects of single, punctuated events in combination with other longer‐duration anthropogenic influences on the dynamics of soil carbon (C) loss. In 2012 and 2013, we assessed the effects of decadal‐scale anthropogenic global change – warming, increased nitrogen (N) deposition, elevated carbon dioxide ( CO 2 ), and increased precipitation – on soil respiration rates in an annual‐dominated Mediterranean grassland. We also investigated how controlled fire and an artificial wet‐up event, in combination with exposure to the longer‐duration anthropogenic global change factors, influenced the dynamics of C cycling in this system. Decade‐duration surface soil warming (1–2 °C) had no effect on soil respiration rates, while +N addition and elevated CO 2 concentrations increased growing‐season soil CO 2 efflux rates by increasing annual aboveground net primary production ( NPP ) and belowground fine root production, respectively. Low‐intensity experimental fire significantly elevated soil CO 2 efflux rates in the next growing season. Based on mixed‐effects modeling and structural equation modeling, low‐intensity fire increased growing‐season soil respiration rates through a combination of three mechanisms: large increases in soil temperature (3–5 °C), significant increases in fine root production, and elevated aboveground NPP . Our study shows that in ecosystems where soil respiration has acclimated to moderate warming, further increases in soil temperature can stimulate greater soil CO 2 efflux. We also demonstrate that punctuated short‐duration events such as fire can influence soil C dynamics with implications for both the parameterization of earth system models ( ESM s) and the implementation of climate change mitigation policies that involve land‐sector C accounting.

Strong, Aaron L.↗
DOE OSTI2019

Towards Improved Real-Time Observability of Behind-Meter PhotoVoltaic Systems: A Data-Driven Approach

The stochasticity in energy supply introduced by increased PV penetration in grid distribution systems can lead to frequent and sudden supply-demand imbalances, thereby causing voltage and frequency fluctuations. Enhanced observability, in real-time, of grid attributes such as real and reactive power flows, power injections/consumption and voltages is becoming increasingly essential to ensure smooth grid operation under these new large-scale PV integration scenarios. Two significant challenges prevent grid operators from achieving this objective: 1) Computational complexity of quasi-static timeseries simulations, and 2) Partial observability of behind-meter grid assets. In this work, we take a step towards this objective by enhancing the observability of smart inverter equipped PV systems located behind AMI meters. Smart inverters convert the DC output of PV systems into real and reactive power, the ratio of which is governed by a power factor setting. This in turn is governed by the real-time voltage at its terminal, as per a pre-set volt/var curve. While it is possible to infer the power factor setting - thereby, real and reactive power injection of the PV system - at each time interval by quasi-static time series simulations, such methods are not scalable to large grid sizes. We propose to reduce the reliance on simulations by developing a deep neural network based data-driven model which predicts the time dependent power factors of the PV systems in the grid. The model uses a small amount of simulated data to learn the dependence of power factor on 1) load and apparent PV power injection (root mean square of real and reactive power) data, and 2) AMI net-load data (for BTM PV assets). Our results show that our model can predict power factors with mean absolute error of less than 6.2% under full observability scenario and under 8.6% under the partial observability scenario. Thus, our data-driven model can reduce the amount of simulations required to compute real and reactive injections from PV systems in the distribution system with or without complete information.

14 SOLAR ENERGY↗
DOE OSTI2015

Directional Point-Contact Josephson Junctions on Ba0.4K0.6(FeAs)2 Single Crystals

We investigated the Josephson effect between the conventional superconductor Pb0.7In0.3 and Ba0.4K0.6 (FeAs)2 single crystals by performing directional point-contact experiments. The junctions were irradiated with microwaves and the behavior of the Shapiro steps as a function of the square root of the microwave power was studied by means of the RSJ model extended to the nonautonomous case with an rf current-source term. The analysis reveals that, for current injection along the ab plane, the current-phase relation deviates from being simply described by the standard sin(Φ) (as we indeed observed along the c axis) and a dominant sin(2Φ) component is observed. The results are compared with a theory of the dc Josephson effect, formulated by using a method based on the calculation of temperature Green’s function in the junction within the tight-binding model (Burmistrova et al., Phys. Rev. B 91, 214501, 2015). The comparison shows that the experimental results here presented strongly favor an s ±-wave symmetry of the order parameter in this compound.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Leveraging Calibration Transfer Techniques for Remote Monitoring of Samarium and Europium in LiCl Using Laser-Induced Florescence Spectroscopy for Radioisotope Production Applications

Radioisotope production relies on complex chemical processes that must be performed in radiological hot cells or glove boxes because of the radioactive and otherwise hazardous materials being used. In these situations, optical sensors can provide real-time monitoring to users, which is unobtainable by more traditional methods. This study explores the use of calibration transfer methods to train a model on one instrument and date and then transfer it to another instrument of the same or different configuration on a different date. By performing laser-induced fluorescence measurements of Eu(III) and Sm(III) in 10 M LiCl over the course of 6 months using two disparate spectrometers and two different training sets, a strategy for calibrating and deploying models for online monitoring was established. Three transfer techniques were compared: direct standardization (DS), piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). DS and PDS outperformed EPO for day-to-day transfers, and EPO was not effective for instrument-to-instrument transfers. Transferring the initial date’s full factorial model provided better prediction performance compared with retraining models the day of measurements using a D-optimal designed calibration set. For both day-to-day and instrument-to-instrument transfers, five Kennard–Stone selected samples were sufficient. Based on this choice, the initial-date, high-resolution spectrometer model was transferred to a lower-resolution, compact spectrometer 6 months later to monitor a simulated, real-time demonstration. Here, the combined predictions of the DS and PDS transferred models were able to accurately track the anticipated concentration profiles, maintaining root-mean-square error of prediction values below 10 ppm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Building fault detection data to aid diagnostic algorithm creation and performance testing

It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Remote sensing of Pu in uranyl nitrate crystals using reflectance spectroscopy and chemometrics

Remote quantification of Pu(VI) (0–5 mol%) co-crystallized with U in uranyl nitrate hexahydrate (UNH) crystals was achieved in a glove box using reflectance spectroscopy coupled with chemometric modeling. Reflectance spectra were also acquired for Pu(IV) and Np(VI) (0–5 mol%) crystallized with UNH; revealing spectral features consistent with their solution-phase analogs. Principal component analysis revealed Pu(IV/VI) and Np(VI) concentrations as the primary source of variation in the data, informing the development of a supervised partial least squares regression model for Pu(VI). The resulting calibration demonstrated robust performance, with replicate root mean square errors near 10% and quantifiable limits near 0.2 mol% Pu(VI) relative to U. The Pu(VI) remained stable in the crystalline UNH matrix for at least one week with minimal reduction to Pu(IV). Notably, Pu(VI) and Np(VI) incorporation in UNH quenched U(VI) fluorescence while Pu(IV) did not. This study presents a noninvasive, spectroscopic approach for solid-state Pu quantification, with direct implications for material accountability and nuclear nonproliferation monitoring.

Sadergaski, Luke R. [Oak Ridge National Laboratory↗
DOE OSTI2026

A Machine Learning based Approach of Estimating Equivalent Circuit Model Parameters at Different SoCs of Li-ion Batteries from Voltage Relaxation

Abstract: In this study, an approach of estimating the equivalent circuit model (ECM) parameters for Li-ion batteries (LIBs) is proposed based on the voltage value at different intervals while relaxing the LIB after discharge. The typical approach for estimating ECM parameters of a LIB is to conduct electrochemical impedance spectroscopy (EIS) measurements at different frequencies and fit them to a predefined circuit model, which requires additional measuring arrangements and specialized devices. The proposed methodology utilizes four different voltages at 0s, 60s, 360s, and 1800s alongside the specific state of charge (SoC) value for a specific constant discharge current value of ~1C until the relaxation stage to train and evaluate three regression-based machine learning models— Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Gaussian Process Regression (GPR)—for estimating the ECM parameters of the selected model. Bayesian optimization is employed for hyperparameter tuning to achieve optimal performance for all the regressor models, among which, the GPR provided the best performance with the root-mean-squared error (RMSE) of less than 4x10-4 on average for the resistive components and less than 0.27 for capacitive components with excellent R2 scores. The simplicity of the approach enables it to eliminate the need for sophisticated measuring equipment and computation power.

Sagar, Md. Samiul [The University of Alabama (UA)]↗