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Results for “predictive state estimation”

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
DOE OSTI2025

Real-time elemental analysis of liquids for process monitoring using laser-induced breakdown spectroscopy with a liquid wheel sampling approach

This article presents an engineered sampling system that used a rotating wheel to form a thin liquid layer, permitting the use of laser-induced breakdown spectroscopy (LIBS) for in situ, real-time elemental impurity quantification during liquid processing. The sampling approach was demonstrated on eight elements from across the periodic table (Na, Al, K, Ca, Ti, Sr, Mo, and Yb). Univariate and multivariate calibrations were presented for each element. The average value for percent root mean square errors of cross-validation for the multivariate models was 3.64%, highlighting the method's strong prediction accuracy. Additionally, the limits of detection for each analyte were estimated from their univariate models: Na = 0.0532, Al = 18.5, K = 0.105, Ca = 0.273, Ti = 67.7, Sr = 0.640, Mo = 22.4, and Yb = 22.9 μg mL –1 . Finally, a test in which multivariate models were used to monitor a liquid system for 80 min was performed to investigate the real-time monitoring capabilities of this liquid LIBS sampling approach. Rigorous measurements were performed to effectively predict the absence and concentrations of multiple analytes as they were spiked and diluted. This demonstration showed the feasibility of using LIBS for real-time liquid quantification models with estimated precision ≤ 8.1%. Finally, the limitations of this approach and potential future improvements are discussed.

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

Leveraging Fine-Grained Occupancy Estimation Patterns for Effective HVAC Control

As occupancy sensing technologies become mature, various occupancy sensors are increasingly deployed in commercial buildings for pervasive occupancy monitoring. These sensors provide occupant-count data, which contains rich spatiotemporal information about occupancy patterns. With long-term occupant-count data collected from a commercial building, we design three different predictive models that capture the occupancy dynamics and examine how a model predictive control of the HVAC system benefits from actual occupancy count prediction. Our analysis reveals that mispredictions of occupancy states, especially false positives and false negatives, may introduce inefficient control that leads to energy waste or user discomfort. To address this issue, we take a step further to design an adaptive model predictive controller that minimizes inefficient control actions according to misprediction types and distributions. A comprehensive evaluation is performed in OpenBuild and EnergyPlus simulators to study the effectiveness of the proposed end-to-end control strategy. The evaluation shows that the proposed solution reduces energy consumption by 29.5% while improving the average weighted occupants comfort by 86.7% in Predicted Mean Vote (PMV) over the fixed schedule strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Collins function for pion-in-jet production in polarized pp collisions: a test of universality and factorization

We present an updated study of the Collins azimuthal asymmetries for pion-in-jet production in polarized pp collisions. To this aim, we employ a recent extraction of the transversity and Collins fragmentation functions from semi-inclusive deep inelastic scattering and e + e - annihilation into hadron pairs processes, obtained within a simplified transverse momentum dependent (TMD) approach at leading order in the strong coupling constant α s . In the present case we adopt a collinear configuration for the initial state, keeping transverse momentum effects only in the fragmentation mechanism. Our theoretical estimates, when compared against 200 GeV and 510 GeV data from the STAR Collaboration, show a generally good agreement for the distributions in the transverse momentum of the jet, the pion longitudinal momentum fraction and its transverse momentum with respect to the jet direction. While not being a proof, due the assumptions and limitations behind the entire approach, these findings corroborate the hypothesis of TMD factorization for such processes as well as of the universality of the Collins function and, once again, of a reduced impact of the proper TMD evolution on azimuthal asymmetries. We will also present predictions based on an extraction of the Collins and transversity distributions where information from data on single spin asymmetry for inclusive pion production in p ↑ p collisions is included through a Bayesian reweighting procedure.

Azimuthal asymmetries↗
DOE OSTI2024

Further adoption of conservation tillage can increase maize yields in the western US Corn Belt

Conservation tillage can reduce soil erosion, increase soil health, and decrease labor and fuel input costs. Despite these benefits, potential yield impacts remain an important concern for farmers considering adoption. Previous research suggests that conservation tillage is likely to have the largest yield benefits in more arid conditions, but a lack of field-level analyses across climatic, management and soil conditions limits confidence in such predictions. Satellite imagery provides the opportunity to monitor agricultural lands at sub-field resolution across large spatial scales and wide environmental gradients. Here we investigate the maize yield impacts of conservation tillage in the semi-arid western US Corn Belt, using sub-field resolution datasets on tillage practices and crop yields derived from satellite data spanning four states (Nebraska, Kansas, South Dakota, and North Dakota) between 2008 and 2020. On these datasets, we estimate heterogenous yield outcomes for several thousand maize fields across gradients in climate, soil quality and irrigation status by using a causal forests analysis, an adaptation of the random forests machine-learning algorithm for causal inference on observational data. We find that long-term adoption of conservation tillage increased rainfed maize yields by an average of 9.9% in the region. Impacts on irrigated yields were small and not statistically significant. These results, along with an analysis of variables related to greater than average yield benefits, indicate that improved water infiltration and retention are the primary reasons for conservation tillage benefits. Despite yield benefits, many fields estimated to see increased yields under long term low till have not adopted the practice. Therefore, we identify specific counties likely to benefit most from increased levels of adoption. Our results strengthen the understanding of the impacts of conservation agriculture on crop yields and help define environments and counties most likely to benefit from conservation tillage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Latent bone modelling for estimation of plutonium concentration in skeleton of former nuclear workers

Abstract The skeleton is a major plutonium retention site in the human body. Estimation of the total plutonium activity in the skeleton is a challenging problem. For most tissue donors at the United States Transuranium and Uranium Registries, a limited number of bone samples is available. The skeleton activity is calculated using plutonium activity concentration (Cskel) and skeleton weight. In this study, latent bone modelling was used to estimate Cskel from the limited number of analysed bone samples. Data from 13 non-osteoporotic whole-body donors were used to develop latent bone model (LBM) to estimate Cskel for seven cases with four to eight analysed bone samples. LBM predictions were compared to Cskel estimated using an arithmetic mean in terms of accuracy and precision. For the studied cases, LBM offered a significant reduction of uncertainty of Cskel estimate.

Environmental Sciences & Ecology↗
DOE OSTI2025

Electron-ion recombination in composite interactions in liquid xenon

The response of liquid xenon to various types of ionizing radiation has been extensively studied theoretically and experimentally. Recent progress in direct detection dark matter experiments highlights the significance of composite events, where multiple particles interact with xenon simultaneously and generate overlapping ionization signatures. In these events, recombination of electrons and ions associated with different primary particles leads to additional suppression of the ionization signal, introducing a new source of uncertainty in dark matter searches and Migdal effect studies. We developed a model to estimate the recombination enhancement for overlapping low-energy particle interactions. This method, which has minimal dependence on xenon microphysics and is primarily driven by existing experimental data, yields predictions that are consistent with available measurements of composite interactions. Furthermore, we demonstrate that the model predictions are robust against xenon microphysics assumptions.

charge↗
DOE OSTI2022

MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.

Tabassum, Anika↗
NASA NTRS2012

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

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

Santanello, Joseph A.↗
NASA NTRS2012

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

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

Santanello, Joseph A., Jr.↗
NASA NTRS2011

Model-Based Wavefront Control for CCAT

The 25-m aperture CCAT submillimeter-wave telescope will have a primary mirror that is divided into 162 individual segments, each of which is provided with 3 positioning actuators. CCAT will be equipped with innovative Imaging Displacement Sensors (IDS) inexpensive optical edge sensors capable of accurately measuring all segment relative motions. These measurements are used in a Kalman-filter-based Optical State Estimator to estimate wavefront errors, permitting use of a minimum-wavefront controller without direct wavefront measurement. This controller corrects the optical impact of errors in 6 degrees of freedom per segment, including lateral translations of the segments, using only the 3 actuated degrees of freedom per segment. The global motions of the Primary and Secondary Mirrors are not measured by the edge sensors. These are controlled using a gravity-sag look-up table. Predicted performance is illustrated by simulated response to errors such as gravity sag.

edge sensor↗
NASA NTRSDate not supplied

Improved Assessment of Recent Trends in NOx and VOC Emissions and Ozone Production Sensitivity Regimes Using Satellite Data

This presentation highlights results from a NASA Aura Science Team and Atmospheric Composition Modeling and Analysis Program (ACMAP) project which study the capability to observe and model trends in ozone (O3) production regimes using spaceborne sensors. Ultraviolet– visible (UV–Vis) tropospheric column satellite retrievals of formaldehyde (HCHO) (a proxy for volatile organic compound [VOC] reactivity) and nitrogen dioxide (NO2) (a proxy for nitrogen oxides [NOx]) are frequently used to investigate the sensitivity of O3 production to emissions of NOx and VOCs. There are challenges that come from using satellite-derived ratios of HCHO and NO2 (FNR) to study O3 production sensitivity with the largest uncertainties associated with specific spaceborne sensor’s retrieval biases and errors. This study quantifies the differences and improvements in satellite retrievals of O3 production sensitivity regimes using FNRs when moving from legacy polar orbiting satellites such as the Ozone Monitoring Instrument (OMI) onboard NASA’s Aura satellite and Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) onboard the NASA/NOAA Suomi-NPP platform to newer, higher spatiotemporal resolution satellite sensors TROPOspheric Monitoring Instrument (TROPOMI) and eventually the recently launched NASA geostationary sensor Tropospheric Emissions: Monitoring of Pollution (TEMPO). Furthermore, we investigate how using retrievals of NO2 and HCHO from these different satellites to constrain model predictions impacts the ability to accurately simulate O3 chemistry including chemical production regimes. To this end, we have conducted inverse model simulations, using the WRF-CMAQ-DDM data assimilation system at 12 km × 12 km, to constrain emissions of NOx and VOCs over the contiguous United States (CONUS) when assimilating OMI and TROPOMI retrievals of NO2 and HCHO. Two advantages of this are that we a) account for each satellite’s errors/biases in the emission estimation and b) update the prior profile to ensure that only radiance information is used for optimizing the emissions. This presentation will demonstrate: a) the varying accuracy of different satellite retrieved FNRs and ability to capture known sub-annual emission trends (e.g., seasonal, weekend/weekday) and emission anomalies during the COVID-19 lockdown of 2020, b) the differences and improvements in top-down emission estimates of NOx and VOCs when constrained by newer satellite sensors compared to legacy systems, and c) multi-sensor optimized emission estimates of summer-time NOx and VOCs between 2019-2021.

Data↗
DOE OSTI2025

Dynamics modeling of molten salt reactor with reduced and expanded representations of delayed neutron precursors

Molten salt reactors (MSRs) present unique challenges in dynamic behavior due to the mobility of their fuel. In these reactors, delayed neutron precursors (DNPs) drift with the fuel circulation through the primary loop. As a result, a fraction of DNPs decays outside the core, effectively reducing the available delayed neutron population for reactivity control. Consequently, precise modeling of the distribution and behavior of DNPs is critical for accurate reactor dynamics simulations. In this study, the System Dynamics Analysis Tool (SDAT) was used to simulate a thermal-spectrum MSR under steady-state conditions and following transients. The effects of using reduced and expanded representations of DNPs with fewer or more groups than the conventional 6-group model were investigated. Their impact on the simulated distribution of precursors in the primary loop, reactivity loss value, and reactor response to transients was analyzed. Simulation results showed that reduced models lead to the loss of the actual DNPs distribution data, resulting in less accurate estimates of reactivity loss. Reactor power predictions using these reduced models showed significant deviations compared to those using the conventional 6-group model in transient simulations. Expanded models offered a more accurate representation of the distribution of DNPs and reactivity loss estimates. Reactor power predictions using expanded models showed minimal deviation from the conventional 6-group model during the simulated transients.

analysis↗
DOE OSTI2024

Simple self-consistent method for excited states in density functional theory to characterize defect-derived behavior in wide-band-gap-based microelectronic materials

This final report summarizes the results of the Laboratory Direct Research and Development (LDRD) Project Number 229740. Wide band gap semiconductors such as gallium nitride (GaN) have features highly desirable for multiple mission electronic applications. Realization of their potential requires atomic-scale understanding of electronic behavior. The principal experimental tools for electronically probing defects in GaN are chemically undifferentiating and lack a practical theoretical counterpart needed to identify and characterize specific defects. This project investigated whether a simple idea for modeling defect excited states and their associated photoluminescence (PL) energies is viable, as a path to accelerate the understanding of defect behavior and gain valuable insights into engineering new electronic materials and devices. The research implemented a non-self-consistent total-energy evaluation of a Koopmans-type estimation of an excited electronic state energy in density functional theory (DFT) calculations, and proceeded to design, implement, and assess a self-consistent method for computing excited states based upon an OCcupation-Constrained-DFT (occ-DFT). The occ-DFT was verified in test calculations of defect excited states and validated against well-characterized PL data for 3d transition metal defects in GaN. The method proved stable and robust in computing excited states and gave accurate predictions compared to experimental PL data. The combined ground state/excited-state capability proved capable of chemically differentiating defect species in GaN. In application to 3d dopants in GaN, we reinterpreted extensive experimental literature, proposed new defects as prospective candidates for use in quantum information applications, and outlined design strategies to create and exploit these potentially useful functional defects in GaN.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Transforming the $v$ World: A New Multivariate Transformer Energy Estimator for NOvA

The NOvA Transformer Energy Estimator (Transformer_EE) is a universal machine learning tool currently used to infer the incoming beam neutrino energy and the outgoing lepton energy in both near andfar detectors. It uses a unique, highly flexible framework for simultaneous multivariate prediction that supports many possible loss functions. A spectral reweighting and flattening scheme lessens training bias. A feature noising subroutine enables adversarial-like training, mitigating sensitivities to certain systematic effects at marginal resolution loss at inference time. The state of the Transformer_EE will be reviewed, and its robustness with respect to several NOvA Near and Far Detector systematics highlighted.

Tong, Leon [Minnesota U.] (ORCID:0000000231625965)↗
NASA NTRS1982

Space Shuttle Orbiter descent navigation

The entry operational sequence (OPS 3) begins approximately 2 hours prior to the deorbit maneuver and continues through atmospheric entry, terminal area energy management (TAEM), approach and landing, and rollout. During this flight phase, the navigation state vector is estimated by the Space Shuttle Orbiter onboard navigation system. This estimate is computed using a six-element sequential Kalman filter, which blends inertial measurement unit (IMU) delta-velocity data with external navaid data. The external navaids available to the filter are tactical air navigation (TACAN), barometric altimeter, and microwave scan beam landing system (MSBLS). Attention is given to the functional design of the Orbiter navigation system, the descent navigation sensors and measurement processing, predicted Kalman gains, correlation coefficients, and current flights navigation performance.

Montez, M. N.↗
NASA NTRS2004

On the Measurement of Elemental Abundance Ratios in Inner Galaxy H II Regions

Although abundance gradients in the Milky Way Galaxy certainly exist, details remain uncertain, particularly in the inner Galaxy, where stars and H II regions in the Galactic plane are obscured optically. In this paper we revisit two previously studied, inner Galaxy H II regions: G333.6-0.2 and W43. We observed three new positions in G333.6-0.2 with the Kuiper Airborne Observatory and reobserved the central position with the Infrared Space Observatory's Long Wavelength Spectrometer in far-infrared lines of S++, N++, N+, and O++. We also added the N+ lines at 122 and 205 microns to the suite of lines measured in W43 by Simpson et al.. The measured electron densities range from approx. 40 to over 4000 per cu cm in a single HII region, indicating that abundance analyses must consider density variations, since the critical densities of the observed lines range from 40 to 9000 per cu cm. We propose a method to handle density variations and make new estimates of the S/H and N/H abundance ratios. We find that our sulfur abundance estimates for G333.6-0.2 and W43 agree with the S/H abundance ratios expected for the gradient previously reported by Simpson et al., with the S/H values revised to be smaller owing to changes in collisional excitation cross sections. The estimated N/H, S/H, and N/S ratios are the most reliable because of their small corrections for unseen ionization states (< or approx. 10%). The estimated N/S ratios for the two sources are smaller than what would be calculated from the N/H and S/H ratios in our previous paper. If all low excitation H II regions had similar changes to their N/S ratios as a result of adding measurements of N+ to previous measurements of N++, there would be no or only a very small gradient in N/S. This is interesting because nitrogen is considered to be a secondary element and sulfur is a primary element in galactic chemical evolution calculations. We compute models of the two H II regions to estimate corrections for the other unseen ionization states. We find, with large uncertainties, that oxygen does not, have a high abundance, with the result that the N/O ratio is as high (approx. 0.35) as previously reported. The reasons for the uncertainty in the ionization corrections for oxygen are both the non-uniqueness of the H II region models and the sensitivity of these models to different input atomic data and stellar atmosphere models. We discuss these predictions and conclude that only a few of the latest models adequately reproduce H II region observations, including the well-known, relatively-large observed Ne++/O++ ratios in low- and moderate-excitation H II regions.

Simpson, Janet P.↗
NASA NTRS2004

Channel Temperature Model for Microwave AlGaN/GaN HEMTs on SiC and Sapphire MMICs in High Power, High Efficiency SSPAs

A key parameter in the design trade-offs made during AlGaN/GaN HEMTs development for microwave power amplifiers is the channel temperature. An accurate determination can, in general, only be found using detailed software; however, a quick estimate is always helpful, as it speeds up the design cycle. This paper gives a simple technique to estimate the channel temperature of a generic microwave AlGaN/GaN HEMT on SiC or Sapphire, while incorporating the temperature dependence of the thermal conductivity. The procedure is validated by comparing its predictions with the experimentally measured temperatures in microwave devices presented in three recently published articles. The model predicts the temperature to within 5 to 10 percent of the true average channel temperature. The calculation strategy is extended to determine device temperature in power combining MMICs for solid-state power amplifiers (SSPAs).

Freeman, Jon C.↗
DOE OSTI2024

Low-Energy Neutrino-Nucleus Scattering and New Physics

The interactions of low-energy neutrinos with nuclei provide a unique window to explore various Standard Model (SM) and Beyond the Standard Model (BSM) processes. In particular, the recent observation of coherent elastic neutrino-nucleus scattering (CEvNS), predicted over five decades ago, has generated significant interest across disciplines. With its high cross section and suitability for compact detectors, particularly with stopped pion neutrinos, CEvNS offers a powerful probe for light, weakly coupled new physics. Ongoing global experimental efforts now aim to leverage CEvNS to test SM predictions and search for BSM signals, where deviations in event rates or spectra could reveal new physics. We present here an estimate of the number of recoil events obtained from CEvNS using the current and upcoming liquid argon based experiments. Furthermore, the event rate due to the inclusion of neutrino magnetic moment is also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗