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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Calibrated particle identification for Belle II

We present several efforts aimed at improving charged particle identification at the Belle II experiment. We define an ablation test to quantify and evaluate the impact of each sub-detector on the global particle identification performance. We demonstrate that the performance of the identification scheme can be improved via a simple calibration of the sub-detector likelihoods through a set of per hypothesis, per sub-detector weights. Finally, we present preliminary results on an improved definition of the likelihoods contributed by the electromagnetic calorimeter. A set of multiclass boosted decision trees is trained to exploit the shape of energy depositions of different particle species. In simulated $B\bar{B}$ events, the pion-to-electron and muon fake rates are reduced by 55% and 31% respectively at low-medium momentum.

Hohmann, Marcel [Univ. of Melbourne, Parkville, VI↗
DOE OSTI2017

The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Moment tensor event identification for collapses

SUMMARY We introduce a seismic identification method for collapse events using moment tensors (MTs). We start by computing full (six-element) MT solutions for 43 identified collapse events from around the world, and statistically characterizing the population on the MT hypersphere. We then test a large data set of over 1000 full MTs for the western U.S. against the distribution of collapses using a MT-based identification method similarly as used for testing explosions. Known collapses and explosions are readily identified, along with other anomalous events in the Geysers and central California coast. Misidentification rates are determined for various screening angles with optimal misidentification rates between earthquakes and collapses on the order of 3 per cent. The method is demonstrated to be very effective at identifying non-earthquake sources with a 97–98 per cent accuracy. It is likely to be transportable to other regions, and can be used for event identification anywhere full MT solutions are routinely calculated.

58 GEOSCIENCES↗
DOE OSTI2025

Online Dynamic Mode Decomposition Based System Identification of Multi-Zone Building HVAC Systems

Many works have recently been conducted to reduce the electricity consumption of smart buildings and allow them to support various grid services. Most of these works require accurate system models for the various appliances in the building including heating, ventilation, and air conditioning (HVAC) units. In this paper, we investigate a recursive data-driven system identification strategy to construct the thermal model for a time-varying building with a multi-zone HVAC unit. The online dynamic mode decomposition (DMD)-based strategy is employed to identify the multi-zone thermal building dynamics, where a simple information update (rank-1) is selected to avoid computational complexity. The DMD-based identification strategy is validated using a real gymnasium building equipped with a 4-zone HVAC unit, and its performance is compared with that of the traditional nuclear-norm subspace identification (N2SID) strategy.

Wu, Tumin [University of Tennessee, Knoxville (UTK↗
DOE OSTI2021

Identification of a Delay Attack in the Secondary Control of Grid-Tied Inverter Systems

This work is developed for the identification of a denial-of-service cyberattack on the secondary controller of inverter systems which is connected to the power grid. The identification is made through the dynamic response of the reactive power (Q) of the system under attack. By observing the dynamic characteristic of Q, it is possible to correlate the attack with the nominal response of the hierarchical controller. The article shows that the occurrence of the attack can be identified through a supervisory control that runs a model in parallel. The article argues that after an early identification of the attack, a local controller can take action to mitigate its effects on the system’s response.

Roig Greidanus, Mateo D.↗
DOE OSTI2025

Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection

This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.

Fayad, Samuel S. [University of Illinois at Urbana↗
DOE OSTI2024

Designing Remote Monitoring for Smart Manufacturing Facilities: Hazard Identification and Classification

This study investigates the process of hazard identification in complex manufacturing environments during the design phase, emphasizing the significance of the design process in developing designs that effectively mitigate hazards in contexts with numerous variables, such as a variety of machines, sensors, actuators, and agents. Through a mixed-methods approach, the objective of this work is to understand how the evolution of design outcomes across various stages might influence a designer’s ability to recognize both standard and novel hazards. To achieve this understanding, an experimental design task was conducted with six designers from a national lab specializing in manufacturing technologies. This approach combined qualitative and quantitative data analysis from a one-hour virtual session with participants. Findings suggest that the complexity of identifying hazards in a high-dimensional design space is challenging within a limited time frame and that the identification of hazards is significantly influenced by the stage of the design task and the initial design decisions, indicating the need for extended time and strategic initial planning in the design process to enhance hazard identification.

Ballestas, Caseysimone↗
DOE OSTI2023

Comparison of Machine Learning Algorithms for Natural Gas Identification with Mixed Potential Electrochemical Sensor Arrays

Mixed-potential electrochemical sensor arrays consisting of indium tin oxide (ITO), La 0.87 Sr 0.13 CrO 3 , Au, and Pt electrodes can detect the leaks from natural gas infrastructure. Algorithms are needed to correctly identify natural gas sources from background natural and anthropogenic sources such as wetlands or agriculture. We report for the first time a comparison of several machine learning methods for mixture identification in the context of natural gas emissions monitoring by mixed potential sensor arrays. Random Forest, Artificial Neural Network, and Nearest Neighbor methods successfully classified air mixtures containing only CH 4 , two types of natural gas simulants, and CH 4 +NH 3 with >98% identification accuracy. The model complexity of these methods were optimized and the degree of robustness against overfitting was determined. Finally, these methods are benchmarked on both desktop PC and single-board computer hardware to simulate their application in a portable internet-of-things sensor package. The combined results show that the random forest method is the preferred method for mixture identification with its high accuracy (>98%), robustness against overfitting with increasing model complexity, and had less than 10 ms training time and less than 0.1 ms inference time on single-board computer hardware.

03 NATURAL GAS↗
DOE OSTI2017

Route Sanitizer: Connected Vehicle Trajectory De-Identification Tool

Route Sanitizer is ORNL's connected vehicle moving object database de-identification tool and a graphical user interface to ORNL's connected vehicle de-identification algorithm. It uses the Google Chrome (soon to be Electron) platform so it will run on different computing platforms. The basic de-identification strategy is record redaction: portions of a vehicle trajectory (e.g. sequences of precise temporal spatial records) are removed. It does not alter retained records. The algorithm uses custom techniques to find areas within trajectories that may be considered private, then it suppresses those in addition to enough of the trajectory surrounding those locations to protect against "inference attacks" in a mathematically sound way. Map data is integrated into the process to make this possible.

Carter, JasonM↗
DOE OSTI2017

Cooperation on Improved Isotopic Identification and Analysis Software for Portable, Electrically Cooled High-Resolution Gamma Spectrometry Systems Final Report

Under a 2006 agreement between the Department of Energy (DOE) of the United States of America and the Institut de Radioprotection et de Sûreté Nucléaire (IRSN) of France, the National Nuclear Security Administration (NNSA) within DOE and IRSN initiated a collaboration to improve isotopic identification and analysis of nuclear material [i.e., plutonium (Pu) and uranium (U)]. The specific aim of the collaborative project was to develop new versions of two types of isotopic identification and analysis software: (1) the fixed-energy response-function analysis for multiple energies (FRAM) codes and (2) multi-group analysis (MGA) codes. The project is entitled Action Sheet 4 – Cooperation on Improved Isotopic Identification and Analysis Software for Portable, Electrically Cooled, High-Resolution Gamma Spectrometry Systems (Action Sheet 4). FRAM and MGA/U235HI are software codes used to analyze isotopic ratios of U and Pu. FRAM is an application that uses parameter sets for the analysis of U or Pu. MGA and U235HI are two separate applications that analyze Pu or U, respectively. They have traditionally been used by safeguards practitioners to analyze gamma spectra acquired with high-resolution gamma spectrometry (HRGS) systems that are cooled by liquid nitrogen. However, it was discovered that these analysis programs were not as accurate when used on spectra acquired with a newer generation of more portable, electrically cooled HRGS (ECHRGS) systems. In response to this need, DOE/NNSA and IRSN collaborated to update the FRAM and U235HI codes to improve their performance with newer ECHRGS systems. Lawrence Livermore National Laboratory (LLNL) and Los Alamos National Laboratory (LANL) performed this work for DOE/NNSA.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Multi-static Serial LiDAR for Surveillance and Identification of Marine Life at MHK Installations

Final Report for project DE-EE0006787: Multi-static Serial LiDAR for Surveillance and Identification of Marine Life at MHK Installations. This project developed and tested an optical monitoring system prototype that will be suitable for marine and hydrokinetic (MHK) full project lifecycle observation (baseline, commissioning, and decommissioning), with automated real-time classification of marine animals. This system can be deployed to collect pre-installation baseline species observations at a proposed deployment site with minimal post-processing overhead. To satisfy deployed MHK project species of concern (e.g. Endangered Species Act-listed) monitoring requirements, the system provides automated tracking and notification of the presence of managed animals within established perimeters of MHK equipment and provides high resolution imagery of their behavior through a wide range of conditions. During a project’s decommissioning stage, the system can remain installed to provide resource managers with post-installation data. Our technology, known as an Unobtrusive Multi-static Serial LiDAR Imager (UMSLI), is a technology transfer of underwater distributed LiDAR imaging technology that preserves the advantages of traditional optical and acoustic solutions while overcoming associated disadvantages for MHK environmental monitoring applications. This new approach is a purposefully-designed, reconfigurable adaptation of an existing technology that can be easily mounted on or around different classes of MHK equipment. The system uses low average power red (638nm) laser illumination to be invisible and eye-safe to marine animals and is compact and cost effective. The equipment is designed for long term, maintenance-free operations, to inherently generate a sparse primary dataset that only includes detected anomalies (animal presence information), and to allow robust real-time automated animal classification/identification with a low data bandwidth requirement. Advantages of the technology over others currently being used or being considered for MHK monitoring include: Unlike a conventional camera, the depth of field is near-infinite and limited by attenuation (approximately 5-8 m) rather than focal properties of a lens; Operation in an adaptive mode which can project a sparse grid of pulses with higher peak power for longer range detection (>10 meters) and track animals within a zone of interest with high resolution imagery for identification of marine life at closer range (<5m); System detection limit and Signal-to-Noise-Ratio is superior to a camera, due to rejection of both backscattering component and ambient solar background; Multiple wide-angle pulsed laser illuminators and bucket detectors can be flexibly configured to cover a 4pi steradian (i.e. omnidirectional) scene volume, while also retrieving 3D features of animal targets from timing information; Process and classification framework centered around a novel active learning and incremental classification classifier that enables accurate identification of a variety of marine animals automatically; A two-tiered monitoring architecture and invisible watermarking-based data archiving and retrieving approach ensures significant data reduction while preserving high fidelity monitoring. A methodology to train and optimize the classifier for target species of concern to optimize site monitoring effectiveness. This technological innovation addresses a high priority regulatory requirement to observe marine life interaction near MHK projects. Our solution improves resource manager confidence that any interactions between marine animals and equipment are observed in a cost-effective and automated manner. Without EERE funding, this novel application of multi-static LiDAR would not have been available to the MHK community for environmental monitoring.

16 TIDAL AND WAVE POWER↗
DOE OSTI2019

Benchmarking Algorithm for Radio Nuclide Identification (BARNI) Literature Review

This is a literature review of published radioisotope identification (ID) methods, and an attempt at systematic organization of the approaches for solving this problem. In addition, there is an overview of the related open and closed source software, and patents related to radioisotope identification. This document is also part of the development plan for BARNI (Benchmarking Algorithm for RadioNuclide Identification) software, since it is meant to be a comparison tool, a summary of radiation ID performance evaluations is also included.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Application of artificial neural network to prompt gamma neutron activation analysis for chemical warfare agents identification

The Portable Isotopic Neutron Spectroscopy (PINS) is a commercialized system developed by Idaho National Laboratory (INL) to examine chemical warfare agents (CWA) non-destructively, utilizing Prompt Gamma Neutron Activation Analysis (PGNAA) techniques. The PINS system takes advantage of a high-resolution gamma-ray spectrum from a mechanically-cooled high-purity germanium (HPGe) detector, and gamma-ray peak analysis provides input to its chemical identification logic with a probabilistic decision tree (PDT). The effectiveness of the chemical identification algorithm is determined by the availability of a wide range of data to train the algorithm to identify chemical-fills with accuracy. INL has a collection of gamma-ray spectra of various chemical-fills from the field-deployed PINS systems over the years, and it was envisaged to leverage such a database with the Artificial Neural Network (ANN) technique. Therefore, an ANN-based chemical identification algorithm was developed as an independent verification of the current algorithm. The ANN-based algorithm’s performance was evaluated against the U.S. Army blind test data, and results were presented and discussed in this study.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Optimizing Classifiers for Radionuclide Identification

Identifying threat nuclear materials is a critical for the prevention of acts of nuclear terrorism on the homeland. For this purpose, many radionuclide identification devices are deployed in the field. However, these will not necessarily be in the hands of non-experts, therefore these devices need to provide ready-made answers for the personnel in the field. This is where advanced algorithms are employed to both interpret the data and provide the identification of the nuclear material being interrogated. We took a machine learning approach to identification, by using training and validation data sets to create and optimize classifiers which determine which radionuclide is consistent with the data. The classifiers investigated were the Random Forest, Decision Tree, Support Vector Machine, and XG Boost and their performance was judged using the F1 Score for both hyperparameter tuning and comparison. In the end, we found out that the Random Forest Classifier worked the best based off the F1 Score they got which was 0.98.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Identification of Faults Susceptible to Induced Seismicity (Final Report)

Central to the work documented in this report is the capability of geocellular models to represent the geologic conceptual model updated with fault identification from machine learning and joint inversion modeling of microseismic data measured and recorded as a consequence of CO 2 injection at a field demonstration site: the Illinois Basin - Decatur Project (IBDP). This work required seven unique geocellular models with 100s of simulated variations to gain a very high degree of confidence in the identification of geologic features present that contributed to induced microseismicity at IBDP. All forward modeling: pressure modeling, stress modeling, and seismic modeling used the same geologic conceptual model and representations of that model at different scales. The pressure modeling and poroelastic modeling created “snapshots” of pore pressure and stress field changes at different times during CO 2 injection, in which microseismic events were clustered (in time). These pressure and stress snapshots, within the framework and architecture of the geologic conceptual model via the geocellular model, informed the single fault and fault network models to ascertain the likelihood of fault movement (seismic or aseismic). The outcomes of the pressure, stress, and fault/fault network (seismic) modeling confirmed that the faults in the geologic conceptual model in Task 2 were likely the source of microseismic events measured at IBDP and acted as conduits for pressure to be transmitted from the injection interval into the Precambrian crystalline basement rock. This closely coordinated and integrated unique modeling approach was conducted to prove the viability of our proposed workflow 1) to better resolve crystalline basement faults, 2) detect subseismic faults that could be activated by injection, 3) increase the certainty in fault detection and their susceptibility to release seismic energy, and 4) understand transmission of pressure vertically from the well to the underlying fractured crystalline basement. The proposed methodology was effective in guiding an iterative process of calibrating forward modeling results based on similar geocellular models while honoring the geologic conceptual model (i.e., characterization data and knowledge of regional geology); this led to higher level of certainty in the identification of fault/faults zones to control seismicity and transmission of pressure to the regions of recorded and located injection induced seismicity.

58 GEOSCIENCES↗
DOE OSTI2017

Identification Approach to Alleviate Effects of Unmeasured Heat Gains for MIMO Building Thermal Systems

It is important to have practical methods for constructing a good mathematical model for a building's thermal system for energy audits, retrofit analysis and advanced building controls, e.g. model predictive control. Identification approaches based on semi-physical model structures are popular in building science for those purposes. However conventional gray box identification approaches applied to thermal networks would fail when significant unmeasured heat gains present in estimation data. Although this situation is very common and practical, there has been little research to tackle this issue in building science. This paper presents an overall identification approach to alleviate influences of unmeasured disturbances, and hence to obtain improved gray-box building models. The approach was applied to an existing open space building and the performance is demonstrated.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Taxonomy and Identification of Principal Foliar Nematode Species ( Aphelenchoides and Litylenchus )

Nematodes are Earth’s most numerous multicellular animals and include species that feed on bacteria, fungi, plants, insects, and animals. Foliar nematodes are mostly pathogens of ornamental crops in greenhouses, nurseries, forest trees, and field crops. Nematode identification has traditionally relied on morphological and anatomical characters using light microscopy and, in some cases, scanning electron microscopy (SEM). This review focuses on morphometrical and brief molecular details and key characteristics of some of the most widely distributed and economically important foliar nematodes that can aid in their identification. Aphelenchoides genus includes some of the most widely distributed nematodes that can cause crop damages and losses to agricultural, horticultural, and forestry crops. Morphological details of the most common species of Aphelenchoides (A. besseyi, A. bicaudatus, A. fragariae, A. ritzemabosi) are given with brief molecular details, including distribution, identification, conclusion, and future directions, as well as an updated list of the nominal species with its synonyms. Litylenchus is a relatively new genus described in 2011 and includes two species and one subspecies. Species included in the Litylenchus are important emerging foliar pathogens parasitizing trees and bushes, especially beech trees in the United States of America. Brief morphological details of all Litylenchus species are provided.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Identification of Differential Equations by Dynamics-Guided Weighted Weak Form with Voting

In the identification of differential equations from data, significant progresses have been made with the weak/integral formulation. In this paper, we explore the direction of finding more efficient and robust test functions adaptively given the observed data. While this is a difficult task, we propose weighting a collection of localized test functions for better identification of differential equations from a single trajectory of noisy observations on the differential equation. We find that using high dynamic regions is effective in finding the equation as well as the coefficients, and propose a dynamics indicator per differential term and weight the weak form accordingly. For stable identification against noise, we further introduce a voting strategy to identify the active features from an ensemble of recovered results by selecting the features that frequently occur in different weighting of test functions. Systematic numerical experiments are provided to demonstrate the robustness of our method.

97 MATHEMATICS AND COMPUTING↗