Search NASA⌕ Search

SEARCH · Search NASA

Results for “Pattern recognition”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Stabilization of the 81-channel coherent beam combination using machine learning

We develop a rapidly converging algorithm for stabilizing a large channel-count diffractive optical coherent beam combination. An 81-beam combiner is controlled by a novel, machine-learning based, iterative method to correct the optical phases, operating on an experimentally calibrated numerical model. A neural-network is trained to detect phase errors based on interference pattern recognition of uncombined beams adjacent to the combined one. Due to the non-uniqueness of solutions in the full space of possible phases, the network is trained within a limited phase perturbation/error range. This also reduces the number of samples needed for training. Simulations have proven that the network can converge in one step for small phase perturbations. When the trained neural-network is applied to a realistic case of 360 degree full range, an iterative scheme exploits random walking at the beginning, with the accuracy of prediction on phase feedback direction, to allow the neural-network to step into the training range for fast convergence. This neural-network-based iterative method of phase detection works tens of times faster than the commonly used stochastic parallel gradient descent approach (SPGD) using a single-detector and random dither when both are tested with random phase perturbations.

Wang, Dan↗
DOE OSTI2019

Identifying Muon Neutrino Charged-Current Interactions in the MicroBooNE Detector

The MicroBooNE experiment is a short-baseline neutrino experiment at the Fermi National Accelerator Laboratory (FNAL) in the US, receiving a highly pure muon neutrino beam from the Booster Neutrino Beam (BNB) and taking data since October 2015. The main physics goal of MicroBooNE is to clarify the nature of the low-energy excess of electron-like events observed by the MiniBooNE Cherenkov detector, which due to its detector technology is unable to resolve whether the observed excess shower-like events are due to electrons or photons. Instead, MicrooBooNE employs cutting-edge liquid argon time projection chamber (LArTPC) technology, which offers excellent spatial and calorimetric resolution, which makes it possible to reconstruct complex neutrino interactions and to efficiently distinguish different final state particle types. This thesis presents a fully-automated event selection of interactions of the types ν μ +Ar → μ - + X and ν μ + Ar → μ - + p + X developed using the Pandora multi-algorithm approach to pattern recognition for LArTPC experiments. The analysis in this work is performed on a subset of the MicroBooNE Run 1 dataset corresponding to 4.52 × 10 19 protons on target acquired by the MicroBooNE detector between February and October 2016. The analysis quantifies the main signal and background interaction channels that produce a reconstructed neutrino interaction with one and two particles in the final state, and quantifies the extent to which different aspects of the Pandora reconstruction affect the selection performance. MicroBooNE is a surface detector, and therefore cosmic-ray tracks are the main observed background. A direction fitting procedure has been implemented in this analysis to formulate directional probabilities, using the Bethe equation and the high-resolution calorimetric information of MicroBooNE, to reduce this cosmic-ray background. This functionality is used to enhance the efficiency of the Pandora cosmic ray tagging logic, and the impact of this procedure on the reconstruction and event selection performance is analysed. The result of this study is two fully-automated selections of ν μ +Ar → μ - + X (57.91% pure) and ν μ + Ar → μ - + p + X (78.30% pure) with efficiencies of 64.35% and 62.65%, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2010

Fast Track Reservoir Modeling of Shale Formations in the Appalachian Basin. Application to Lower Huron Shale in Eastern Kentucky

In this paper a fast track reservoir modeling and analysis of the Lower Huron Shale in Eastern Kentucky is presented. Unlike conventional reservoir simulation and modeling which is a bottom up approach (geo-cellular model to history matching) this new approach starts by attempting to build a reservoir realization from well production history (Top to Bottom), augmented by core, well-log, well-test and seismic data in order to increase accuracy. This approach requires creation of a large spatial-temporal database that is efficiently handled with state of the art Artificial Intelligence and Data Mining techniques (AI & DM), and therefore it represents an elegant integration of reservoir engineering techniques with Artificial Intelligence and Data Mining. Advantages of this new technique are a) ease of development, b) limited data requirement (as compared to reservoir simulation), and c) speed of analysis. All of the 77 wells used in this study are completed in the Lower Huron Shale and are a part of the Big Sandy Gas field in Eastern Kentucky. Most of the wells have production profiles for more than twenty years. Porosity and thickness data was acquired from the available well logs, while permeability, natural fracture network properties, and fracture aperture data was acquired through a single well history matching process that uses the FRACGEN/NFFLOW simulator package. This technology, known as Top-Down Intelligent Reservoir Modeling, starts with performing conventional reservoir engineering analysis on individual wells such as decline curve analysis and volumetric reserves estimation. Statistical techniques along with information generated from the reservoir engineering analysis contribute to an extensive spatio-temporal database of reservoir behavior. The database is used to develop a cohesive model of the field using fuzzy pattern recognition or similar techniques. The reservoir model is calibrated (history matched) with production history from the most recently drilled wells. The calibrated model is then further used for field development strategies to improve and enhance gas recovery.

03 NATURAL GAS↗
DOE OSTI2012

Fuel Cycle Research & Development, Technical Monthly - February 2012

Topics discussed in this monthly report are: (1) MPACT Campaign management and integration; (2) Accounting and control technologies - microcalorimetry, electrochemical sensor, fast neutron multiplicity analysis, lead slowing down spectrometer; (3) MPACT analysis tools - modeling and simulation for analysis of safeguards performance (electrochemical), material control including process monitoring (pattern recognition, sensors), sensor for measuring density and depth of molten electrolyte, MPACT system integration and technical support.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

Fuel Cycle Research & Development: Technical Monthly – June 2012

Topics are: (1) MPACT Campaign - (a) Management and Integration • Coordination meetings between NE and NA-22, NA-24, and NA-82 were conducted the week of June 11th. Preparations are being made for the next MPACT working group meeting, scheduled for Aug 28-30 at Idaho Falls. In addition to covering accomplishments and discussing future plans, a site tour of INL facilities (MFC, EBR, ATR, INTEC) is being organized. (2) Accounting and Control Technologies - (a) Microcalorimetry • Now operating 256-pixel array at LANL. We are in the process of tuning detector parameters to improve and optimize performance. Preliminary measurements show approximate number of live pixels is similar to that observed previously at NIST. Continuing to study contribution to systematic error from uncertainties in tabulated gamma-ray energies. (b) Electrochemical Sensor • Testing of sensors fashioned from different precursor materials continued. SEM analysis of all used sensors has been or will be performed. (c) Lead Slowing Down Spectrometer • Ongoing perturbation calculations are providing information on the fundamental systematic error limits of LSDS. In order to achieve separating the contribution of Pu and 235U to the signal, there will need to be tight controls on systematic errors. Continuing to look into a He4 detector. Research into local construction of a He4 detector continued. We have started to apply the algorithm to test the LSDS using experimental data from previous RPI measurements. PNNL also developed a plan to address the lack of statistics in the MCNP modeling of the NGSI 64 assemblies. The ISU graduate student built and tested a fission chamber to gain experience with them. (d) Fast Neutron Imaging to Quantify Nuclear Materials • The imaging detector design was modified for each pixel to have an 8 x 8 pixel array. Quotations and purchasing process for components, including the new PSD scintillator are in progress. (e) Fast Neutron Multiplicity Analysis • The team submitted two papers to the upcoming INMM meeting that are related to the fast neutron multiplicity R&D effort. Progress was made on the project's main goal of designing a concept for a prototype fast-neutron multiplicity counter. We started laying out the outline for the final report. We have been working with our ORNL collaborators to develop a new digitizer system to support our experimental campaign planned for next year. (3) MPACT Analysis Tools - (a) Multi-isotope Process Monitor • Fuel characterization framework development continued during June. A report describing the methodologies is being completed. Kenneth Dayman, from University of Texas, spent a week at PNNL wrapping up his master's research and working on a journal submission covering that work. The target journal is the IEEE transactions on Nuclear Science; submission is planned for the end of July. A proposal to instrument H-Canyon is being prepared in conjunction with SRNL and the NNSA's NGSI program. The impact of gamma-ray spectrum counting statistics on the precision of relative radioisotope component intensities as reconstructed via Principal Component Regression (PCR) continued in June with Monte Carlo simulations of a two-component (i.e., two radioisotope) system. This work generalizes earlier studies in FY12 in which Poisson counting variations of only a single spectrum component were simulated. (b) Modeling and Simulation for Analysis of Safeguards Performance (Electrochemical) • Preliminary insights into safeguards challenges and the initial design for an electrochemical plant have been written up into an INMM paper and will be presented at the INMM Summer Meeting. Work is currently adding a new visualization capability for integrating materials accountancy with physical protection. (c) Material Control including Process Monitoring (Pattern Recognition, Sensors) • Fabrication of quartz chips continued at an external foundry. Awaiting delivery of the heat exchange manifold and chip holder. (d) MPACT System Integration and Technical Support • The initial report on cost-basis metrics for nuclear material security risk and proliferation risk was completed as scheduled. (4) Safeguards and Security by Design - (a) Used fuels storage security analysis, guidance and best practices • RFP was finalized and sent out. Awaiting response from World Institute for Nuclear Security. Coordinated with Scott Demuth on joint report • LANL milestone on best practices. Attended Nuclear Energy Institute National Nuclear Security Conference. Developed plan for soliciting input on prioritized issues for used fuel storage security. Worked on setting up a meeting with the Nuclear Regulatory Commission. Worked on FY2013 work package planning.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation and Completion of Episodic Information

Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach by which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.

Aimone, James Bradley↗
DOE OSTI2013

The Multi-Isotope Process (MIP) Monitor Project: FY13 Final Report

The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in reprocessing facilities in support of the goal of “… (minimization of) the risks of nuclear proliferation and terrorism.” The MIP Monitor measures the distribution of the radioactive isotopes in product and waste streams of a nuclear reprocessing facility. These isotopes are monitored online by gamma spectrometry and compared, in near-real-time, to spectral patterns representing “normal” process conditions using multivariate analysis and pattern recognition algorithms. The combination of multivariate analysis and gamma spectroscopy allows us to detect small changes in the gamma spectrum, which may indicate changes in process conditions. By targeting multiple gamma-emitting indicator isotopes, the MIP Monitor approach is compatible with the use of small, portable, relatively high-resolution gamma detectors that may be easily deployed throughout an existing facility. The automated multivariate analysis can provide a level of data obscurity, giving a built-in information barrier to protect sensitive or proprietary operational data. Proof-of-concept simulations and experiments have been performed in previous years to demonstrate the validity of this tool in a laboratory setting for systems representing aqueous reprocessing facilities. However, pyroprocessing is emerging as an alternative to aqueous reprocessing techniques.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2012

A Measurement of the Two Track Charged Current Quasi-Elastic Cross Section with the MINER$\nu$A Detector

The MINER$\nu$A detector collected 1.2 × 10 20 POT of $\nu$ μ data with the NuMI beam at Fermi National Laboratory from 03/2010 to 07/2010. I will describe an analysis to identify two track charged current muon neutrino quasi-elastic interactions using this data set. The final state muon was identified by curvature using the MINOS detector. The final state contained proton was identified by the energy deposited per unit length at the end of the track. Two additional cuts were applied to separate signal from background: a cut on the extra energy in the event and a cut on a proton energy dependent variable. The dσ/dQ2 using this selection is presented. An initial list of systematic errors is introduced and calculated. In addition to this analysis work, I also developed both pattern recognition schemes used by the experiment, wrote MINOS muon track matching algorithms, implemented a Kalman Filter track fitting algorithm and contributed to various other tasks to the MINER$\nu$A experiment; I will provide an explanation of these. I will also give a brief overview of neutrino physics, the NuMI beam, and the MINER$\nu$A detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

In-Situ MVA of CO 2 Sequestration Using Smart Field Technology

Capability of underground carbon dioxide storage to confine and sustain injected CO 2 for a long period of time is the main concern for geologic CO 2 sequestration. If a leakage from a geological CO 2 sequestration site occurs, it is crucial to find the approximate amount and the location of the leak, in a timely manner, in order to implement proper remediation activities. An overwhelming majority of research and development for storage site monitoring has been concentrated on atmospheric, surface or near surface monitoring of the sequestered CO 2 . This study aims to monitor the integrity of CO 2 storage at the reservoir level. This work proposes developing in-situ CO 2 Monitoring and Verification technology based on the implementation of Permanent Down-hole Gauges (PDG) or “Smart Wells” along with Artificial Intelligence and Data Mining (AI&DM). The technology attempts to identify the characteristics of the CO 2 leakage by de-convolving the pressure signals collected from Permanent Down-hole Gauges (PDG). Citronelle field, a saline aquifer reservoir, located in the U.S. was considered as the basis for this study. A reservoir simulation model for CO 2 sequestration in the Citronelle field was developed and history matched. PDGs were installed, and therefore were considered in the numerical model, at the injection well and an observation well. Upon completion of the history matching process, high frequency pressure data from PDGs were generated using the history matched numerical model using different CO 2 leakage scenarios. Since pressure signal behaviors were too complicated to de-convolute using any existing mathematical formulations, a Machine Learning-based technology was introduced for this purpose. An Intelligent Leakage Detection System (ILDS) was developed as the result of this effort using the machine learning and pattern recognition technologies. The ILDS is able to detect leakage characteristics in a short period of time (less than a day from its occurrence) demonstrating the capability of the system in quantifying leakage characteristics subject to complex rate behaviors. The performance of ILDS is examined under different conditions such as multiple well leakages, cap rock leakage, availability of an additional monitoring well, presence of pressure drift and noise in the pressure sensor and uncertainty in the reservoir model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Light Water Reactor Sustainability Program Potential to Extend the Range of Established Online Monitoring Technologies, Such as Guided Waves in Nuclear Power Plant Systems

The Light Water Reactor Sustainability Program began to evaluate technologies that could be used to perform online monitoring of piping and other secondary system structural components in commercial NPPs. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real-time measurements, rather than at a pre-determined inspection interval. This transition to condition-based, risk informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for most nuclear power generation costs. This report describes the current state of research related to ultrasonic guided wave testing and its application to defect detection in commercial nuclear power plants. The report analyses the applicability of the guided wave technology to secondary piping systems and also study the potential for expanding the range of guided wave technology to include bent piping and other piping components. The ultrasonic guided waves can inspect long stretches of straight piping, however more complex geometries such as elbows, welds, and tees are causing spurious reflections and coherent noise which significantly decrease the sensitivity of the technique. The report analyses two approaches to suppression of coherent noise in guided wave systems: advanced signal processing and pattern recognition based on independent component analysis and blind source separation and hardware-based technique of piezoelectric paint which can be used to facilitate the propagation of mechanical stress waves through piping. It is demonstrated on simulated guided wave data that the independent component analysis is capable of separating different coherent noise components and segregating them from useful signal. The piezoelectric paint approach seem to be a viable alternative to the signal processing methods, however it requires significantly more complicated logistics and initial financial outlay. The future efforts will be concentrated on applying the independent component analysis to real-world guided wave data recorded by the guided wave monitoring system installed on the shell of the low-pressure feedwater heater 13A at Exelon Corporation’s Braidwood Nuclear Generating Station. Also, the joint EPRI-INL research efforts will focus on experimental evaluation of piezoelectric paint as a technique to extend the range of guided wave technology.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level

To ensure the usefulness of simulation technologies in practice, their credibility needs to be established with Uncertainty Quantification (UQ) methods. In this project, smart proxy is introduced to significantly reduce the computational cost of conducting large number of multiphase CFD simulations, which is typically required for non-intrusive UQ analysis. Smart proxy for CFD models are developed using pattern recognition capabilities of Artificial Intelligence (AI) and Data Mining (DM) technologies. Several CFD simulation runs with different inlet air velocities for a rectangular fluidized bed are used to create a smart CFD proxy that is capable of replicating the CFD results for the entire geometry and inlet velocity range. The smart CFD proxy is validated with blind CFD runs (CFD runs that have not played any role during the development of the smart CFD proxy). The developed and validated smart CFD proxy generates its results in seconds with reasonable error (less than 10%). Upon completion of this project, UQ studies that rely on hundreds or thousands of smart CFD proxy runs can be accomplished in minutes. Following figure demonstrates a validation example (blind CFD run) showing the results from the MFiX simulation and the smart CFD proxy for pressure distribution across a fluidized bed at a given time-step (the layer number corresponds to the vertical location in the bed).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Part Three: Model Building at the Layer Level

To ensure the usefulness of simulation technologies in practice, their credibility needs to be established with Uncertainty Quantification (UQ) methods. In this project smart proxy is introduced to significantly reduce the computational cost of conducting large number of multiphase CFD simulations. Smart proxy for CFD models are developed using pattern recognition capabilities of Artificial Intelligence (AI), Machine Learning (LM) and Data Mining (DM) technologies. Several CFD simulation runs with different inlet air velocities for a rectangular fluidized bed are used to create a smart CFD proxy that is capable of replicating the CFD results for a wide variety of inlet velocities. The smart CFD proxy is validated with blind CFD runs (CFD runs that have not played any role during the development [training, calibration and validation] of the smart CFD proxy). In our earlier work, CFD data was used to train ANN at the cell level. That is an ANN was trained for each computational cell used in the CFD simulations. In the present work, the ANN is constructed and trained based on cross sectional area average of each variable, such as pressure, velocities and volume fraction (Layer Level). This leads to improvements in the training time at the expense of less spatial resolution. The resulting trained ANN provides spatially average value of parameters of interest, along the length of the fluidized bed. Upon completion of this project, UQ studies that rely on hundreds or thousands of smart CFD proxy runs can be accomplished in minutes. Following figure demonstrates a validation example (blind CFD run) showing the results from the MFiX and the smart CFD proxy for pressure drop across a fluidized bed at time-step of 1400 (the layer number corresponds to the vertical location in the bed).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Automation of Data Collection Methods for Online Monitoring of Nuclear Power Plants

Maintenance activities in the United States nuclear power industry are mainly centered on preventive and corrective maintenance. Predictive maintenance, which is a variable time basis maintenance strategy that is dependent on the condition of the equipment, is less utilized but potentially more effective. In the current maintenance practices, the nuclear power plants rely on manual processes to supply information to a human decision making process. The plants have several processes that generate information that is not typically used beyond the intended target for collecting that information. This information is a source of data that is manually collected by labor intensive tasks. The data is therefore expensive to collect, yet limited in use. The industry has realized the opportunity present in reducing the labor intensive tasks by automating them and increasing the uses of the data collected by either developing advanced predictive maintenance methods using data-driven decision making that could span from trending to machine learning and artificial intelligence. This shift in maintenance strategy may result in high cost saving impact. In addition to reducing labor requirements and frequency of maintenance activities, this shift in strategy will reduce the materials portion of the operations and maintenance cost as well. The aim of this specific effort is to focus on the automation of data collection process. It is part of a series of planned efforts to target multiple elements of migrating the current preventive maintenance approach in nuclear power plants to a data-driven approach. These elements include data collection, management, analysis, visualization, in addition to value analysis and change enabling. The other five elements of migration were not considered because they are planned to be explicitly studied in multiple parallel efforts. Challenges including regulations requirements or limitations are planned in a future effort that focuses on change enabling. The risk and benefit/cost of the approach shift is planned in another effort targeted towards value. The storage, communication, computational power, data architecture, and protection are part of the information infrastructure and are planned in dedicated data management effort. The best means to present the data for assisted human decision making is planned in a data visualization effort. The data interpretation and patterns recognition for machine decision making is planned through multiple efforts in data analytics over the following years. This work identified seventeen data sources collection methods in nuclear power plants that could be automated for an integrated data platform that enables comprehensive and informed decision making. For each of the identified seventeen data sources, the base, modern, and state of the art states were described. The current state of data collection used by most utilities was identified as the "base state". The current achievable state was identified as the "modern state", a state that has either been achieved by at least some US utilities or in other industries. The future state that requires research and development to reach was identified as the "state of the art" state. The described states used an out-of-the-box thinking approach. This effort found that there is a hug potential for task automation and data fidelity improvements by evolving the current industry data collection methods. The majority of the processes analyzed were found to be manual and labor intensive and fell in the base state. As a result, the modern state often targeted reducing the manual logging efforts by using electronic and semi-intelligent means or complementing the work with sensors and mobile or fixed technologies that can reduce the demand of labor activities. The state of the art state involved applications advanced methods of data correlation, data mining, and machine learning along with increasing the spatial distribution and fidelity of data, coupling multiple data sources, and enabling the use of sophisticated mobile devices and machine comprehension methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Induced Current Detector Simulation

We describe simulations of a detector concept with small, low capacitance, pixels with pitch/thickness ratio on the order of 10. The simulation utilizes 2D and 3D TCAD simulations followed by a generic SPICE front-end model including noise and Landau fluctuations. We also investigate the properties of weighting fields of various geometries. We investigate whether the transient induced current signals can be used to improve pattern recognition and establish track angle.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Verification of Triso Fuel Burnup Using Machine Learning Algorithms

Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134 Cs, 137 Cs, 154 Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Developing Non-Food Grade Brassica Biofuel Feedstock Cultivars with High Yield, Oil Content, and Oil Quality that are Suitable for Low Input Production Dryland Systems (Final Report)

The U.S. uses a substantial amount of fossil fuel as an energy source for a wide range for functions including home heating, agriculture and transportation. In the transportation sector, diesel and jet fuel are consumed at a rapid rate, and alternative liquid energy is being investigated globally and nationally to reduce our dependence on fossil fuel and reduce the impact of our carbon footprint on global climate change. Non-food Brassica crops have the potential of producing high oil yield (over 250 gal acre-1) and have oil quality highly desirable for use as biodiesel or bio jet fuel. Developing oilseed feedstock Brassica cultivars with higher seed and oil yield, with high oil quality and with resistance to pathogens, that can be grown with few chemical inputs will helping break our dependence on fossil fuels and reduce importation of fossil fuels. While some oilseed Brassicas have been grown on a small scale for many years in the Pacific Northwest (PNW), adoption has been limited, and the potential of the crops have not been realized or even fully investigated. This report summarizes the results of a study to develop superior non-food grade winter (B. napus) and spring (B. napus and B. juncea) oilseed cultivars suitable for a range of PNW, and other US environments with high resistance to the biotic and abiotic stresses suitable for high-quality biofuel feedstocks. In conducting this work, genome-wide association selection was used to dissect the genetic architecture of industrial Brassica oilseed germplasm for yield, quality, and resistance to biotic and abiotic stresses. A genome-wide bioinformatics approach was used to identify putative PRR (pattern recognition receptor) - type resistance genes that confer durable resistance to blackleg. A novel transgenic approach was developed to generate resistant non-food oilseed lines using PPR genes Br1033 and Br8486. These genes were inserted into regionally adapted oilseed cultivars.

09 BIOMASS FUELS↗
DOE OSTI2024

Track vs Shower Discrimination in the Event Reconstruction of the ICARUS Experiment

The ICARUS collaboration has employed the 760-ton T600 liquid argon TPC detector in a successful three-year physics run at the underground LNGS laboratory, performing a sensitive search for LSND-like anomalous appearance in the CERN Neutrino to Gran Sasso beam, which contributed to the constraints on the allowed neutrino oscillation parameters to a narrow region around 1 eV. After a significant overhaul at CERN, the T600 detector has been installed at Fermilab. The detector commissioning phase lasted until June 2022, then ICARUS moved to data taking for neutrino oscillation physics collecting events from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis. The initial experiment goals are to either confirm or refute the claim by Neutrino-4 short-baseline reactor experiment, perform measurements of neutrino cross sections with the NuMI beam and several Beyond Standard Model searches. Then, ICARUS will jointly search for evidence of sterile neutrinos with the Short-Baseline Near Detector (SBND). In this contribution, we discuss recent changes to the standard TPC event reconstruction that uses Pandora, a pattern recognition software common to liquid argon-based detectors. In particular, we performed a new training of the Boosted Decision Tree (BDT) employed to separate track-like and shower-like reconstructed particles using Monte Carlo simulations of neutrino events from BNB in ICARUS. We compare the discrimination capabilities of the old and new BDT training and discuss further improvements of this algorithm.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Search for Electron Neutrinos in Multiple Topologies with the MicroBooNE Experiment

This note presents the status of the measurement of electron neutrinos from the Fermilab Booster Neutrino Beamline (BNB) with the MicroBooNE experiment. The analysis is aimed at investigating the nature of the low energy excess of electromagnetic activity observed by the MiniBooNE experiment. The $ν_e$ event selection relies on topological and calorimetric information to characterise particles produced in these interactions, leveraging the Pandora multi-algorithm reconstruction framework as well as custom particle identification and pattern recognition tools. Results presented in this note use 5.88 × 10 20 protons on target of data collected between 2015 and 2018. These include the performance of particle identification tools for $\mu/p$ and $e/\gamma$ separation, along with electromagnetic shower calibration with the largest sample of $\pi^0$ events measured on argon. Progress towards the completion of this analysis is shown through the measurement of high-energy charged-current $ν_e$ interactions and their kinematic distributions using both inclusive (1eX) and exclusive (1$e$0$p$0$\pi$ and 1$e$N$p$0$\pi$) channels. Measurements of charged-current $ν_µ$ interactions aimed at constraining flux and cross-section systematic uncertainties are also shown. We present the analysis’ preliminary sensitivity of an electron-like signal hypothesis to the MiniBooNE excess which includes flux, cross-section, and detector systematic uncertainties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗