SEARCH · Search NASA

Results for “Identification”

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 271 records · Page 15

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Multi-area parameter error identification for large power systems

Power grid model parameters may contain errors due to various reasons. Detecting and correcting parameter errors typically requires significant computational effort due to the size and complexity of the parameter database. While the normalized Lagrange multiplier (NLM) method can effectively detect, identify and correct parameter errors, its computational burden could rapidly grow with increasing system size. This paper addresses this issue by proposing a multi-area parameter error identification method. Each area has its own outlier detection tool for detecting the incorrect parameters and measurements within the area. On the other hand, due to the reduced redundancy at area boundaries, parameter errors on branches incident to boundary buses may not be detected. Such errors are subsequently detected by a coordination level estimator completing the system-wide parameter detection procedure. In conclusion, performance of the developed method is demonstrated using the IEEE 118-bus and 2000-bus Texas synthetic systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Identification and quantification of multinuclear Cu active sites derived from monomeric Cu moieties for dry NO oxidation over Cu-SSZ-13

The identification and quantification of multinuclear Cu-oxo cations in Cu-zeolites is a challenge amidst the dynamic nature of Cu ions in Cu-zeolites. Herein, we synthesized Cu-SSZ-13 zeolites containing varying densities of monomeric Cu cations (Z 2 Cu and ZCuOH, where Z represents an anionic site on the zeolite framework). The number of Cu-oxo cations, quantified using carbon monoxide temperature programmed reduction (CO-TPR), quadratically increased with increasing ZCuOH density, evidencing that proximal ZCuOH sites are precursors to Cu-oxo dimers. We also find that dehydrated proximal ZCuOH with overlapping diffusion radii of at most 0.5 nm can form Cu-oxo dimers. CO-TPR also revealed the presence of three distinct pools (i.e types) of Cu-oxo dimers; these pools catalyze dry NO oxidation at different turnover rates, with the fastest pool 2 to 30 times faster than the slowest pool. Finally, from reaction kinetics and rate law analysis, we also proposed a cohesive dry NO oxidation mechanism and demonstrated that nitrates detected by in situ FTIR are a spectator species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Latent space dynamics identification for interface tracking with application to shock-induced pore collapse

Capturing sharp, evolving interfaces remains a central challenge in reduced-order modeling, especially when data is limited and the system exhibits localized nonlinearities or discontinuities. Here, we propose LaSDI-IT (Latent Space Dynamics Identification for Interface Tracking), a data-driven framework that combines low-dimensional latent dynamics learning with explicit interface-aware encoding to enable accurate and efficient modeling of physical systems involving moving material boundaries. At the core of LaSDI-IT is a revised autoencoder architecture that jointly reconstructs the physical field and an indicator function representing material regions or phases, allowing the model to track complex interface evolution without requiring detailed physical models or mesh adaptation. The latent dynamics are learned through linear regression in the encoded space and generalized across parameter regimes using Gaussian process interpolation with greedy sampling. We demonstrate LaSDI-IT on the problem of shock-induced pore collapse in high explosives, a process characterized by sharp temperature gradients and dynamically deforming pore geometries. The method achieves relative prediction errors below 9% across the parameter space, accurately recovers key quantities of interest such as pore area and hot spot formation, and matches the performance of dense training with only half the data. This latent dynamics prediction was 10 6 times faster than the conventional high-fidelity simulation, proving its utility for multi-query applications. These results highlight LaSDI-IT as a general, data-efficient framework for modeling discontinuity-rich systems in computational physics, with potential applications in multiphase flows, fracture mechanics, and phase change problems.

Gaussian process↗

DOE OSTI2022

Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding

The paper considers testing and signal identification for covariance matrices from two populations of marginally sub-Gaussian distributed. A multi-level thresholding procedure is proposed for testing the equality of two high-dimensional covariance matrices, which is designed to detect sparse and faint differences between the covariances. A novel -statistic composition is developed to establish the asymptotic distribution of the thresholding statistics in conjunction with the matrix blocking and the coupling techniques. It is shown that the proposed test is more powerful than the existing tests in detecting sparse and weak signals in covariances. Multiple testing procedures are constructed to discover different covariances and the sub-groups of variables with different covariance structures between the two populations. The proposed procedures are based on the multi-level thresholding test, which are able to control the false discovery proportion () with high power. Simulation experiments and a case study on the returns of the S&P 500 stocks before and after the COVID-19 pandemic are conducted to demonstrate and compare the utilities of the proposed methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Method for identification of redox control parameters for corrosion mitigation in molten fluoride salts

Structural material corrosion is a significant concern for the development of molten salt-cooled reactors (MSR) and redox control was proposed to mitigate the corrosion. This study presents an efficient approach to identify the partial pressure of F 2 gas for fluorine driven corrosion, the concentration ratios of UF 4 /UF 3 and ZrF 4 /ZrF 2 , and the partial pressure ratio of HF/H 2 needed to limit metal corrosion and its subsequent dissolution in molten fluoride salts. The identification procedure is made by simply using auxiliary axes in Ellingham diagrams. This approach greatly simplifies the optimization of redox control methods in studying the material corrosion in molten salts.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Identification and demonstration of roGFP2 as an environmental sensor for cryogenic correlative light and electron microscopy

Cryogenic correlative light and electron microscopy (cryo-CLEM) seeks to leverage orthogonal information present in two powerful imaging modalities. While recent advances in cryogenic electron microscopy (cryo-EM) allow for the visualization and identification of structures within cells at the nanometer scale, information regarding the cellular environment, such as pH, membrane potential, ionic strength, etc., which influences the observed structures remains absent. Fluorescence microscopy can potentially be used to reveal this information when specific labels, known as fluorescent biosensors, are used, but there has been minimal use of such biosensors in cryo-CLEM to date. Here we demonstrate the applicability of one such biosensor, the fluorescent protein roGFP2, for cryo-CLEM experiments. At room temperature, the ratio of roGFP2 emission brightness when excited at 425 nm or 488 nm is known to report on the local redox potential. When samples containing roGFP2 are rapidly cooled to 77 K in a manner compatible with cryo-EM, the ratio of excitation peaks remains a faithful indicator of the redox potential at the time of freezing. Using purified protein in different oxidizing/reducing environments, we generate a calibration curve which can be used to analyze in situ measurements. As a proof-of-principle demonstration, we investigate the oxidation/reduction state within vitrified Caulobacter crescentus cells. The polar organizing protein Z (PopZ) localizes to the polar regions of C. crescentus where it is known to form a distinct microdomain. Finally, by expressing an inducible roGFP2-PopZ fusion we visualize individual microdomains in the context of their redox environment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Assessment of the associated particle technique with high-resolution gamma-ray spectroscopy for in-field identification of chemical warfare agents and explosives

In this work, high-resolution prompt γ-ray neutron activation analysis (PGNAA) is performed using the associated particle (AP) technique with a deuterium-tritium neutron generator and high-purity germanium detector. Although the time resolution is inferior compared to similar systems employing fast scintillation γ-ray detectors, the strong background suppression combined with the high γ-ray energy resolution provides an important boost in sensitivity to certain key elements, especially relevant to field measurements of complex chemicals encased within thick layers of metal, such as in the case of recovered chemical warfare (CW) munitions. Results for various CW and explosive simulants contained within mock munitions are presented and compared with results obtained from traditional PGNAA measurements without the aid of the AP technique. Significant improvements in identification and discrimination between certain chemicals are achieved with the high energy resolution AP technique.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Noise signal identification in time projection chamber data using deep learning model

Deep learning has been employed in various scientific fields and has provided promising results. Here, in this study, a deep learning classifier was implemented to improve the quality of data obtained from a time projection chamber. Digital waveforms of the detected signals were classified into the following three categories: particles, noises, and particles piled up with noises. A simple 1-dimensional convolutional neural network was developed for the classification. The model demonstrated an excellent performance on the test dataset. Its practical performance was also examined using track images and particle identification plots by comparing the original and clean data without the noise signals. The comparison clearly showed that the deep learning model improved the quality of data. The current study presents an effective application of the deep learning model for the time projection chamber data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Characterization of a platform for the gas transport, collection, and identification of fission products in the high-intensity laser environment

Recent progress in the production of laser accelerated high flux proton beams opens new possibilities for the study of fission in unique environments. To this end, we are currently pursuing the development of a platform for the swift gas transport, collection, and identification of fission products. Fission is induced in targets fixed inside a sealed chamber through which a carrier gas flows, transporting fission products to a carbon filter for collection and online spectroscopy. This has been recently demonstrated using target normal sheath accelerated protons at the PHELIX laser facility. There were large discrepancies between measured rates and those expected based on established fission yields and measured beam parameters. These discrepancies prompted a large number of tests at the Idaho Accelerator Center (IAC), where fission in uranium is induced by bremsstrahlung photons generated by 21 MeV electrons from a linear electron accelerator. Here, the results are compared to a series of models that account for the slowing of energetic fission fragments in the carrier gas and the fluid dynamics of the gas flow that transports fission products to the collection filter. Characterization of the apparatus reveals a few mechanisms that together account for a portion of the previously observed discrepancies at the PHELIX laser facility. However, additional research is necessary before high-accuracy experiments can be performed with the apparatus. Work performed under the auspices of the U.S. Department of Energy by LLNL under contract DE-AC52-07NA27344.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Simulation studies related to the particle identification by the forward and backward RICH detectors at Electron Ion Collider

The Electron-Ion collider (EIC) will be the ultimate facility to study the dynamics played by colored quarks and gluons in the phenomenology of nucleons and nuclei, described by Quantum Chromodynamics. The physics programs will greatly rely on efficient particle identification (PID) in both the forward and the backward regions. The forward and the backward RICHes of the EIC have to be able to cover wide acceptance and momentum ranges; in the forward region a dual radiator RICH (dRICH) is foreseen and in the backward region a proximity-focusing RICH can be foreseen to be employed. The geometry and the performance studies of the dRICH have been performed as prescribed in the EIC Yellow Report using the ATHENA software framework. Furthermore, this part of our work reports the effort following the call for EIC detector proposal and the studies related to the forward and the backward RICH performance. In the forward region, the dRICH performance showed a pion- kaon separation from around 1 GeV/c to 50 GeV/c at a three sigma level; the proximity focusing RICH (pfRICH) foreseen for the backward region can reach three sigma separation up to 3 GeV/c for e/$π$ and up to 10 GeV/c for $π$/K mass hypotheses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

A virtual Frisch-grid geometry-based CZT gamma detector for in-field radioisotope identification

Here, we present a Virtual Frisch-Grid geometry-based CZT gamma detector developed for identifying different radioisotopes over an energy range from a few keV up to 2 MeV, and useful for efficient characterization of CZT crystals. The detector is built with a 3 x 3 matrix of CZT crystals, each measuring approximately 6 mm x 6 mm x 15 mm. The charge generated within the sensor’s active volume is read out via an anode connected directly to the AVG3_Dev integrated circuit. A current signal induced by charge drift is collected on side pads of the crystals, enabling reconstruction of a 3D interaction position. This paper discusses the design, development, and performance of the standalone, mobile detector system, which integrates the AVG3_Dev readout IC developed at Brookhaven National Laboratory, a high-speed FPGA-based with per-channel digital signal processing, and embedded system capabilities. The device is compact, battery-powered, and supports wireless data streaming, making it suitable for field operations for radioisotope identification.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Simulation study for particle identification with the dRICH of the ePIC experiment at the EIC

The dual-radiator Imaging Cherenkov detector (dRICH), employing an aerogel and a gas radiator, is a key component of the forward particle identification system for the ePIC experiment at the Electron-Ion Collider (EIC). This study evaluates the dRICH performance using Geant4 simulations in the context of the global ePIC simulation stack, focusing on the optimization of the aerogel radiator and the impact of sensor noise. We compare two aerogel configurations: the initial design (n = 1.019) and the current default (n = 1.026). The latter, characterized by improved optical properties and a higher refractive index, demonstrates enhanced π/K separation at high momenta, effectively extending the operational overlap with the C 2 F 6 gas radiator (n = 1.00076 at 25 °C, as implemented in the simulation software). Additionally, the study investigates the impact of Silicon Photomultiplier (SiPM) dark noise, showing that a 300 kHz noise rate per 3mm x 3mm channel leads to a moderate reduction (approximately 1.5 GeV/c) in the 3σ separation threshold. These results validate the current dRICH design and quantify the purity levels achievable for both radiators under expected experimental conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this challenge, but the accuracy of models trained on simulated data can deteriorate substantially when deployed to an out-of-distribution operational environment. In this study, we demonstrate that unsupervised domain adaptation (UDA) can improve the ability of a model trained on synthetic data to generalize to a new testing domain, provided unlabeled data from the target domain are available. Conventional supervised techniques are unable to utilize this data because the absence of isotope labels precludes defining a supervised classification loss. Instead, we first pretrain a spectral classifier using labeled synthetic data and subsequently leverage unlabeled target data to align the learned feature representations between the source and target domains. We compare a range of different UDA techniques, finding that minimizing the maximum mean discrepancy (MMD) between source and target feature vectors yields the most consistent improvement to testing scores. For instance, using a custom transformer-based neural network, we achieved a testing accuracy of $0.904 \pm 0.022$ on an experimental LaBr test set after performing unsupervised feature alignment via MMD minimization, compared to $0.754 \pm 0.014$ before alignment. Overall, our results highlight the potential of using UDA to adapt a radioisotope classifier trained on synthetic data for real-world deployment.

Lalor, Peter W.↗

DOE OSTI2026

Identification of Common Types of Plastics by Vibrational Spectroscopic Techniques

Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polyvinyl Chloride (PVC), Low-Density Polyethylene (LDPE), Polypropylene (PP), and Polystyrene (PS) account for most plastic use worldwide, with production nearing 380 million tons annually. A considerable portion enters municipal solid waste and landfills, creating long-term environmental concerns. Scaling recycling operations requires automated sorting technologies, with spectroscopy and machine learning offering promising solutions. In this study, a six-class convolutional neural network (CNN) was developed for plastic identification using vibrational spectroscopies. Raman Scattering (RS) spectra collected from recycling samples enabled accurate chemical differentiation while assessing the influence of visible features such as color. A CNN trained on RS data achieved 100% classification accuracy. To strengthen field applicability, Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy was incorporated, achieving 95% accuracy with a similar CNN model. These findings demonstrate the potential of integrating spectroscopy with deep learning for reliable plastic classification, advancing development of scalable, field-ready recycling technologies.

Garcia Tovar, Maria P.↗

DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗

DOE OSTI2022

Identification and properties of isolated field elliptical galaxies from CFHTLS-W1

We present a catalogue of isolated field elliptical (IfE) galaxies drawn from the W1 field of the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS). 228 IfEs were identified from a flux-limited (r < 21.8) galaxy catalogue which corresponds to a density of 3 IfE/sq.deg. For comparison we consider a sample of elliptical galaxies living in dense environments, based on identification of the brightest cluster galaxies (BGCs) in the same survey. Using the same dataset for the comparison sample ensures a uniform selection, including in the redshift range as IfEs (i.e. 0.1 < z <0.9). A comparison of elliptical galaxies in different environments reveals that IfEs and BCGs have similar behaviours in their colours, star formation activities, and scaling relations of mass–size and size–luminosity. IfEs and BCGs have similar slopes in the scaling relations with respect to cluster ellipticals within the $ -24 \leq M_{r} \leq -22 $ magnitude and $10.2 < \textrm{log}(M_{*}/ \textrm M_\odot)\leq12.0 $ mass ranges. Three IfEs identified in this study can be associated with fossil groups found in the same survey area which gives clues for future studies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Rapid Identification of Synthetic Routes to Functional Metastable Phases Using X-ray Probed Laser Anneal Mapping (XPLAM) Time-Temperature Quench Maps

Many material systems have known or predicted functional phases that are metastable at standard temperature and pressure. While substantial advances have been made in the high-throughput and combinatorial synthesis of materials with a range of stoichiometries, investigation of thermal processing remains largely the domain of iterative uniform anneals or static gradients. Here we develop X-ray probed laser anneal mapping (XPLAM), a high throughput technique coupling spatially resolved X-ray diffraction with microsecond to millisecond laser gradient anneals to produce temperature–dwell–transformation (TDT) diagrams of the phase as a function of quench time and temperature. In addition to showing regimes where specific metastable phases form preferentially, TDT diagrams provide insight into the submillisecond kinetics of solid–solid phase transitions. This is a unique tool for mapping reaction pathways for metastable phases. As a first demonstration of XPLAM, we study Bi2O3, which has a rich set of polytypes, including the d-phase with an exceptionally high oxygen ion conductivity. We demonstrate the first annealing-driven synthesis of room temperature d-Bi2O3. We expect XPLAM to prove a powerful technique for rapid identification of synthetic routes to metastable phases and to generate the exhaustive data sets required for machine learning-guided exploration of materials processing.

36 MATERIALS SCIENCE↗