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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modelling detector-specific reconstruction uncertainties in LAr-TPC

The Short-Baseline Neutrino (SBN) program features three Liquid Argon Time Projection Chamber (LAr-TPC) detectors positioned along the Booster Neutrino Beam (BNB) axis: the Short Baseline Neutrino Near Detector, MicroBooNE, and the ICARUS T600. As the largest operational LAr-TPC, ICARUS T600 serves as the far detector, located 600 m from the BNB target. While its primary goal is to record neutrino events, it also detects other ionizing events, including cosmic rays. This work focuses on analyzing and modeling detector-specific reconstruction uncertainties in LAr-TPC. These inefficiencies, identified during the Pattern Recognition phase handled by the PANDORA algorithm, impact subsequent Particle Fits and Offline Analysis. Specifically, inaccuracies in track reconstruction can lead to significant physical consequences, such as erroneous particle energy estimates and poor Particle Identification (PID), reducing the efficiency of neutrino event characterization. A key issue addressed is split tracks, caused by missing hits or incomplete track stitching by PANDORA. The aim of this internship is to characterize, model, and quantify the impact of split tracks on track reconstruction.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Evaluating the Effects of Detector Modeling Uncertainties on Sterile Neutrino Oscillation Analysis with the ICARUS Detector

he ICARUS T600 LArTPC detector was refurbished after an initial run at the underground LNGS labs and is currently taking data within its experimental hall at Fermilab after full commissioning. Regular data taking began in May 2021 with neutrinos from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector (NuMI) off-axis beam. As the far detector of the Short-Baseline Neutrino (SBN) Program, the ICARUS detector's capability in searching for both muon neutrino disappearance and electron neutrino appearance will allow for unprecedented sensitivity to light sterile neutrinos with eV-scale mass. The ultimate sensitivity of the detector to sterile neutrino oscillations depends on the understanding of the detector response and the uncertainties remaining after calibrating the detector model. This poster will review how detector-model-related uncertainties are quantified, evaluated and their impact on expected sterile neutrino oscillation sensitivities using the existing ICARUS data. The work pursued here is broadly applicable to other ICARUS analysis pathways such as neutrino-argon cross-sections and Beyond the Standard Model physics searches. It will finally discuss improvement pathways to reduce the systematic uncertainties to the level needed for the joint sterile neutrino oscillation analysis with the Short Baseline Near Detector (SBND) within the SBN Program.

43 PARTICLE ACCELERATORS↗
NASA NTRS1992

The NASA High-Speed Research Program

Since its inception, one of NASA's commitments has been to develop the technology to advance aeronautics. As such, a new High-Speed Research Program was activated to develop the technology for industry to build a High-Speed Civil Transport - a second generation Supersonic Transport (SST). The baseline for this program is the British Concorde, a major technological achievement for its time, but an aircraft which is now both technologically and economically outdated. Therefore, a second generation SST must satisfy environmental concerns and still be economically viable. In order to do this, it must have no significant effect on the ozone layer, meet Federal Air Regulation 36, Stage 3 for community noise, and have no perceptible sonic boom over populated areas. These three concerns are the focus of the research efforts in Phase 1 of the program and are the specific areas covered in the technical video report.

Beam, Sherilee F.↗
DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

First results from the search of $ν_μ$ disappearance with ICARUS

his work presents a search for muon neutrino disappearance signal using the ICARUS detector. ICARUS is currently working as the far detector of the Short-Baseline Neutrino (SBN) program. Its main goal is to investigate the possible oscillation of a sterile neutrino state ($\Delta m^2 \sim \text{eV}^2$), that would drive short baseline oscillations at the scale of L/E $\sim$ km/GeV. Neutrino interactions from the Booster Neutrino Beam (BNB) are investigated, looking for simple final state topology with a single muon and at least one proton (1$\mu$Np). This analysis tests the 3+1 model using the two flavor neutrino approximation, where careful consideration has been given to the systematic uncertainties arising from flux, neutrino interaction, and detector models. No statistically significant $\nu_\mu$ disappearance is observed, hence 90\% C.L. exclusion contours are reported in the $\Delta m^2_{41} - \sin^22\theta_{\mu\mu}$ parameter space.

Artero Pons, Maria [INFN, Padua; U. Padua (main)]↗
DOE OSTI2026

SBND Shower Reconstruction with SPINE

The Short-Baseline Near Detector (SBND) is a liquid argon time projection chamber (LArTPC) neutrino detector in the Short-Baseline Neutrino (SBN) program at Fermilab. SBND is designed to investigate the Low-Energy Excess (LEE), an unexplained excess of electron-like events observed by previous short-baseline neutrino experiments that may point to physics beyond the Standard Model. In LArTPC detectors, precise shower reconstruction is essential for distinguishing electrons from photons, a key requirement for testing possible explanations of the LEE and improving $\nu_e$ event selection. In this poster, the reconstruction studies using the Scalable Particle Imaging with Neural Embeddings (SPINE), a machine learning based reconstruction framework for particle imaging detectors will be presented. SPINE combines sparse convolutional neural networks (CNN) and graph neural networks (GNN) to enable detailed reconstruction and characterization of neutrino interactions in LArTPC detectors. Shower calorimetry and kinematic reconstruction are performed in dedicated post-processing stages. Strong agreement between data and Monte Carlo simulation will be demonstrated, indicating high-precision detector calibration and reconstruction performance. The agreement between reconstructed and true electron shower energy will also be discussed, emphasizing the robustness of the shower reconstruction performance. These results demonstrate the unprecedented precision achievable with SPINE in SBND, highlighting their potential for future high-resolution neutrino measurements.

Fan, Castaly [Florida U.; Fermilab] (ORCID:0000000↗
NASA NTRS1973

Space shuttle solid rocket booster recovery system definition. Volume 3: SRB water impact loads computer program, user's manual

This user's manual describes the FORTRAN IV computer program developed to compute the total vertical load, normal concentrated pressure loads, and the center of pressure of typical SRB water impact slapdown pressure distributions specified in the baseline configuration. The program prepares the concentrated pressure load information in punched card format suitable for input to the STAGS computer program. In addition, the program prepares for STAGS input the inertia reacting loads to the slapdown pressure distributions.

Source record↗
DOE OSTI2024

Charged Current Single Pion Production on SBND

The Short Baseline Neutrino (SBN) program at Fermilab is designed to provide precise measurements of neutrino oscillations using 3 Liquid Argon Time Projection Chambers (LArTPC) built along Fermilab's Booster Neutrino Beam (BNB). The Short Baseline Near Detector (SBND), located at only 110 m from the BNB target, will precisely characterize the neutrino flux before oscillations take place, thanks to its unprecedented neutrino interaction statistics. Due to its proximity to the neutrino production target, the detector expects over a million neutrino interactions annually, which will open the possibility of exploring exclusive channels of neutrino interactions. This poster will show SBND’s capabilities of exploring Muon Neutrino Charged Current Single Pion production, for which previous measurements in Argon were limited by statistics. The poster will introduce the relevance of this process for neutrino Physics, the unique possibilities of SBND and a preliminary event selection using SBND's current reconstruction tools.

43 PARTICLE ACCELERATORS↗
NASA NTRS2005

MSL Project Update

Mars Science Lab (MSL) Project activities over the last 3-4 months focused on: a) Payload accommodation; b) Flight system architecture definitization; c) Program architecture support. Baseline project concept is to develop and fly two identical vehicles with launches in 2009 and 2011: a) Significant programmatic uncertainty remains due to NASA strategic issues (Shuttle Return to flight, Hubble servicing, CEV, etc.); b) Final "decisions" on future program not likely for 3-4 months; c) To maintain viability for a 2009 launch, the project must rapidly move into implementation mode despite this uncertainty. Near term activities: a) Converge baseline flight system architecture & instrument accommodation - June; b) Instrument accomodation reviews (IARs) are completed for DAN, MSSS instruments; c) Develop project-wide implementation plan & integrated schedule - June; d) Conduct grassroots cost exercise - July/August; e) Hold Preliminary Mission System Review - September.

Mars↗
DOE OSTI2026

Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗
DOE OSTI2025

Selection Algorithm for electron neutrino charged current interactions in SBND

The Short Baseline Neutrino (SBN) program at Fermilab is a joint proposal by three experimental collaborations primarily for the investigation of the cause behind the low-energy electron-like event excess observed by the MiniBoone experiment. This dissertation focuses on the near detector of the project, named the Short Baseline Near Detector (SBND), a Liquid Argon Time Projection Chamber apparatus which will conduct searches for sterile neutrinos in the mass range of 1 ${eV}^2/{c}^4$, as well as provide cross-section measurements for neutrino interactions in argon and perform other beyond the standard model studies.\\ \indent As is the case for all detectors in the program, the SBND will use the Booster Neutrino Beam as its source, which will provide it with both muon and electron neutrinos. Given that the ability to discern between the neutrino flavors will be crucial to the fulfillment of the detector's physics goals, the objective of this work is to provide the collaboration with a tool capable of doing so. As such, we here present the development process for an inclusive selection algorithm for the identification of electron neutrino charged current (CC) events regardless of their interaction channel. This is done through a combination of traditional techniques, such as the implementation of cuts on the reconstructed interaction properties, with the use of the Convolutional Visual Network, a machine learning algorithm capable of classifying particle interactions through the analysis of the topology of their final states. With this approach, we have developed a selection process that is capable of identifying $\nu_e$ CC interactions across a wide range of topologies with 34.4\% efficiency, as well as a purity of 91.2\%, making it especially promising for use in cross section studies.

Freire, Hector Moya [ABC Federal U.] (ORCID:000900↗
DOE OSTI2024

Neutrino Physics at the Intensity Frontier

This report covers the research efforts on Neutrino Physics conducted by the HEP group at the University of South Carolina (USC) within the Intensity Frontier (IF) program. The research activity summarized is related to the NOνA and DUNE experiments at Fermilab. The USC group has been one of the founding members of the NOvA collaboration, as well as a member of the third generation long-baseline neutrino oscillation program – originally LBNE and now DUNE/LBNF – since its inception.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2013

Pack Density Limitations of Hybrid Parachutes

The development and testing of the Orion crew capsule parachute system has provided a unique opportunity to study dense parachute packing techniques and limits, in order to establish a new baseline for future programs. The density of parachute packs has a significant influence on vibration loads, retention system stresses, and parachute mortar performance. Material compositions and pack densities of existing designs for space capsule recovery were compared, using the pack density of the Apollo main parachutes as the current baseline. The composition of parachutes has changed since Apollo, incorporating new materials such as Kevlar , Vectran , Teflon and Spectra . These materials have different specific densities than Nylon, so the densities of hybrid parachute packs cannot be directly compared to Nylon parachutes for determination of feasibility or volume allocation. Six parachute packs were evaluated in terms of weighted average solid density in order to achieve a non-dimensional comparison of packing density. Means of mitigating damage due to packing pressure and mortar firing were examined in light of the Capsule Parachute Assembly System (CPAS) and Apollo experience. Parachute design improvements including incorporation of modern materials and manufacturing processes serves to make CPAS the new knowledge base on which future spacecraft parachute systems will be built.

Zwicker, Matthew L.↗
NASA NTRS1987

Structural analysis demonstration of constitutive and life models

The overall objective of this program is to demonstrate the applicability of NASA-developed advanced constitutive and life damage models for calculating cyclic structural response and crack initiation in selected components of reusable space propulsion systems. The computer model resulting from this program will enable the user to produce an accurate life prediction of hot gas path, life limiting components of propulsion systems such as the space shuttle main engine (SSME). Previously developed computer models addressing constitutive modeling and life damage will be combined in an advanced finite element analysis to generate a sophisticated baseline life prediction program. A material data base will be established for the constitutive and life models parametrically involving temperature, strain range, strain rate, mean strain/stress, and dwell time. The verified computer program will be used to accomplish the life predictions of three SSME critical components as evidence of the model functionality.

Bechtel, G.↗
NASA NTRS1990

Artificial intelligence (AI) based tactical guidance for fighter aircraft

A research program investigating the use of artificial intelligence (AI) techniques to aid in the development of a Tactical Decision Generator (TDG) for Within Visual Range air combat engagements is discussed. The application of AI programming and problem solving methods in the development and implementation of the Computerized Logic For Air-to-Air Warfare Simulations (CLAWS), a second generation TDG, is presented. The knowledge-based systems used by CLAWS to aid in the tactical decision-making process are outlined in detail, and the results of tests to evaluate the performance of CLAWS versus a baseline TDG developed in FORTRAN to run in real time in the Langley Differential Maneuvering Simulator, are presented. To date, these test results have shown significant performance gains with respect to the TDG baseline in one-versus-one air combat engagements, and the AI-based TDG software has proven to be much easier to modify and maintain than the baseline FORTRAN TDG programs.

Mcmanus, John W.↗
NASA NTRS1990

Artificial Intelligence (AI) Based Tactical Guidance for Fighter Aircraft

A research program investigating the use of Artificial Intelligence (AI) techniques to aid in the development of a Tactical Decision Generator (TDG) for Within Visual Range (WVR) air combat engagements is discussed. The application of AI programming and problem solving methods in the development and implementation of the Computerized Logic For Air-to-Air Warfare Simulations (CLAWS), a second generation TDG, is presented. The Knowledge-Based Systems used by CLAWS to aid in the tactical decision-making process are outlined in detail, and the results of tests to evaluate the performance of CLAWS versus a baseline TDG developed in FORTRAN to run in real-time in the Langley Differential Maneuvering Simulator (DMS), are presented. To date, these test results have shown significant performance gains with respect to the TDG baseline in one-versus-one air combat engagements, and the AI-based TDG software has proven to be much easier to modify and maintain than the baseline FORTRAN TDG programs. Alternate computing environments and programming approaches, including the use of parallel algorithms and heterogeneous computer networks are discussed, and the design and performance of a prototype concurrent TDG system are presented.

McManus, John W.↗
NASA NTRS1985

Evolution of systems concepts for a 100 kWe class Space Nuclear Power System

Conceptual designs for the SP-100 Space Nuclear Power System have been prepared that meet baseline, backup and growth program scenarios. Near-term advancement in technology was considered in the design of the Baseline Concept. An improved silicon-germanium thermoelectric technique is used to convert the heat from a fast-spectrum, liquid lithium cooled reactor. This system produces a net power of 100 kWe with a 10-year end of life, under the specific constraints of area and volume. Output of the Backup Concept is estimated to be 60 kWe for a 10-year end of life. This system differs from the Baseline Concept because currently available thermoelectric conversion is used from energy supplied by a liquid sodium cooled reactor. The Growth Concept uses Stirling engine conversion to produce 100 kWe within the constraints of mass and volume. The Growth Concept can be scaled up to produce a 1 MWe output that uses the same type reactor developed for the Baseline Concept. Assessments made for each of the program scenarios indicate the key development efforts needed to initiate detailed design and hardware program phases. Development plans were prepared for each scenario that detail the work elements and show the program activities leading to a state of flight readiness.

Katucki, R.↗
NASA NTRS1990

Aeropropulsion facilities configuration control: Procedures manual

Lewis Research Center senior management directed that the aeropropulsion facilities be put under configuration control. A Configuration Management (CM) program was established by the Facilities Management Branch of the Aeropropulsion Facilities and Experiments Division. Under the CM program, a support service contractor was engaged to staff and implement the program. The Aeronautics Directorate has over 30 facilities at Lewis of various sizes and complexities. Under the program, a Facility Baseline List (FBL) was established for each facility, listing which systems and their documents were to be placed under configuration control. A Change Control System (CCS) was established requiring that any proposed changes to FBL systems or their documents were to be processed as per the CCS. Limited access control of the FBL master drawings was implemented and an audit system established to ensure all facility changes are properly processed. This procedures manual sets forth the policy and responsibilities to ensure all key documents constituting a facilities configuration are kept current, modified as needed, and verified to reflect any proposed change. This is the essence of the CM program.

Lavelle, James J.↗