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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 OSTI2022

Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

Transient stability assessment (TSA) is a cornerstone for resilient operations of todays interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. Here, we devise a quantum TSA (QTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth (HELD) quantum circuit is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD circuit training; 3) A systematical analysis on QTSAs performance under various quantum factors is per-formed. QTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1997

Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

Kopasakis, George↗
DOE OSTI2020

Accelerating Large-Format Metal Additive Manufacturing: How Controls R&D is Driving Speed, Scale, and Efficiency

This article highlights work at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility to develop closed-loop, feedback control for laser-wire based Directed Energy Deposition, a form of metal Big Area Additive Manufacturing (m-BAAM), a process being developed in partnership with GKN Aerospace specifically for the production of Ti-6Al-4V pre-forms for aerospace components. A large-scale structural demonstrator component is presented as a case-study in which not just control, but the entire 3D printing workflow for m-BAAM is discussed in detail, including design principles for large-format metal AM, toolpath generation, parameter development, process control, and system operation, as well as post-print net-shape geometric analysis and finish machining. In terms of control, a multi-sensor approach has been utilized to measure both layer height and melt pool size, and multiple modes of closed-loop control have been developed to manipulate process parameters (laser power, print speed, deposition rate) to control these variables. Layer height control and melt pool size control have yielded excellent local (intralayer) and global (component-level) geometry control, and the impact of melt pool size control in particular on thermal gradients and material properties is the subject of continuing research. Further, these modes of control have allowed the process to advance to higher deposition rates (exceeding 7.5 lb/hr), larger parts (1-meter scale), shorter build times, and higher overall efficiency. The control modes are examined individually, highlighting their development, demonstration, and lessons learned, and it is shown how they operate concurrently to enable the printing of a large-scale, near net shape Ti-6Al-4V component.

Gibson, Brian↗
NASA NTRS2008

Lunar and Mars Exploration: The Autonomy Factor

Long duration space flight crews have relied heavily on almost constant communication with ground control mission support. Ground control teams provide vehicle status and system monitoring, while offering near real time support for specific tasks, emergencies, and ensuring crew health and well being. With extended exploration goals to lunar and Mars outposts, real time communication with ground control teams and the ground s ability to conduct mission monitoring will be very limited compared to the resources provided to current International Space Station (ISS) crews. An operational shift toward more autonomy and a heavier reliance on the crew to monitor their vehicle and operations will be required for these future missions. NASA s future exploration endeavors and the subsequent increased autonomy will require a shift in crew skill composition, i.e. engineer, doctor, mission specialist etc. and lead to new training challenges and mission scenarios. Specifically, operational and design changes will be necessary in many areas including: Habitat Infrastructure and Support Systems, Crew Composition, Training, Procedures and Mission Planning. This paper will specifically address how to apply ISS lessons learned to further use ISS as a test bed to address decreased amounts of ground support to achieve full autonomous operations for lunar and Mars missions. Understanding these lessons learned and applying them to current operations will help to address the future impacts of increased crew autonomy for the lunar and Mars outposts and pave the way for success in increasingly longer mission durations.

Rando, Cynthia M.↗
DOE OSTI2023

Demonstration of acoustic monitoring for structural health of microreactors: Through use of neural networks and resonant ultrasound spectroscopy

Nuclear microreactors prioritize modularity and portability and are intended to be a cost-effective technology for non-conventional nuclear markets. As such, the development of microreactors into a safe and feasible solution for energy security applications will necessitate the development of non-destructive technologies to monitor the integrity of inaccessible reactors components during operation. This demonstration applies linear and nonlinear acoustic techniques, in combination with machine learning, to detect and classify mechanical changes (stress and damage) in a test article which are broadly representative of potential operating challenges within a functioning microreactor. All necessary data has been collected for this demonstration, with minor experimental issues identified that can be addressed in follow-on work. Motivated by the expected conditions within a functioning microreactor, we have demonstrated our monitoring techniques on a core-block-like test article using an unstructured excitation source that approximates the noisy acoustic environment expected during reactor operation. At all stress states mechanically applied to the test article, a machine learning model using an artificial neural network was able to classify with 100% accuracy whether a 3D laser vibrometry point measurement was made on an intact or artificially defective test article. Further, model predictions about whether the defect interface was rough or smooth were 95% accurate, indicating the ability of acoustic techniques to recover defect characteristics. Resonant ultrasound spectroscopy (RUS) was also applied to the dataset to provide further quantitative insights about material properties. RUS analysis was ultimately hampered by several minor experimental and data issues, limiting results to certain cases for this demonstration. Last, analysis using nonlinear RUS exhibited sensitivity to changing levels of applied stresses for each intact and defective state. As presented in this demonstration, acoustic monitoring exhibits sensitivity to stress changes, which are of concern due to high thermal gradients expected during startup and operation. Further, our techniques distinguish between measurements made on intact and damaged test articles.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Conceptual Design of Integrated Energy Systems with Market Interaction Surrogate Models

Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with the market and can result in overestimated economic values. In this work, we propose a machine learning surrogate-assisted optimization framework to quantify IES/market interactions and thus go beyond price-taker. We use time series clustering to generate representative IES operation profiles for the optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with a polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.

Chen, Xinhe↗
DOE OSTI2025

Deep-learning-based domain adaptation for cavity fault prediction at Jefferson Laboratory

Superconducting radio-frequency (SRF) cavities are the core components of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab, providing high-power electron beams for nuclear physics experiments. The facility comprises 418 SRF cavities, and any fault in these cavities can lead to interruptions in the electron beam supply. Cavity faults are the leading cause of beam trips in CEBAF. Predicting and mitigating those faults before onset can help maintain normal operation. Existing models face challenges in distinguishing between normal and fault signals when changes occur in the underlying time-series data, from changes in control software, operational parameters, or the environment. This work proposes a deep learning domain adaptation model that leverages transfer learning to address fault prediction challenges by improving accuracy. The model is trained and fine-tuned using a dataset collected for faulty and normal operation using a data acquisition system in CEBAF. Our deep learning-based domain adaptation model achieves a prediction accuracy of 89.61% of the fault and normal signals. The developed model effectively predicts normal running signals compared to the baseline approach without domain adaptation. This capacity is essential for the fault prediction task in the CEBAF because of heavily imbalanced data containing vast amounts of normal signals. The model performs well for predicting faults several hundred milliseconds before the fault onset compared to other models where no adaptation is applied. Incorporating deep learning-based domain adaptation techniques will significantly improve the fault prediction performance.

Rahman, Md Monibor [Old Dominion Univ., Norfolk, V↗
NASA NTRS1999

TRMM On Orbit Attitude Control System Performance

This paper presents an overview of the Tropical Rainfall Measuring Mission (TRMM) Attitude Control System (ACS) along with detailed in-flight performance results for each operational mode. The TRMM spacecraft is an Earth-pointed, zero momentum bias satellite launched on November 27, 1997 from Tanegashima Space Center, Japan. TRMM is a joint mission between NASA and the National Space Development Agency (NASDA) of Japan designed to monitor and study tropical rainfall and the associated release of energy. Launched to provide a validation for poorly known rainfall data sets generated by global climate models, TRMM has demonstrated its utility by reducing uncertainties in global rainfall measurements by a factor of two. The ACS is comprised of Attitude Control Electronics (ACE), an Earth Sensor Assembly (ESA), Digital Sun Sensors (DSS), Inertial Reference Units (IRU), Three Axis Magnetometers (TAM), Coarse Sun Sensors (CSS), Magnetic Torquer Bars (MTB), Reaction Wheel Assemblies (RWA), Engine Valve Drivers (EVD) and thrusters. While in Mission Mode, the ESA provides roll and pitch axis attitude error measurements and the DSS provide yaw updates twice per orbit. In addition, the TAM in combination with the IRU and DSS can be used to provide pointing in a contingency attitude determination mode which does not rely on the ESA. Although the ACS performance to date has been highly successful, lessons were learned during checkout and initial on-orbit operation. This paper describes the design, on-orbit checkout, performance and lessons learned for the TRMM ACS.

Robertson, Brent↗
DOE OSTI2024

SUBTASK 1.6 – BASIN ELECTRIC CARBON STORAGE RESEARCH PROJECT: NOVEL MONITORING TECHNIQUES

The Energy & Environmental Research Center (EERC) conducted baseline activities associated with an applied research project at Basin Electric Power Cooperative’s (Basin’s) carbon capture and storage (CCS) site in Beulah, North Dakota, to establish novel carbon storage-monitoring techniques as commercial methods under Cooperative Agreement No. DE-FE0024233, Subtask 1.6. The following report summarizes the baseline activities performed and briefly describes the subsequent (operational monitoring) activities that have been proposed to the U.S. Department of Energy (DOE) as part of the overall project to develop and demonstrate novel monitoring techniques at North America’s largest permitted CCS operation. Dakota Gasification Company (DGC), a wholly owned subsidiary of Basin, owns and operates the Great Plains Synfuels Plant (GPSP) approximately 5 miles northwest of the town of Beulah, North Dakota (Figure 1). In 2023, DGC received approval from the North Dakota Industrial Commission (NDIC) to develop a storage facility on-site for injecting a stream of carbon dioxide (CO2) captured from GPSP. DGC will transport the captured CO2 stream with approximately 6.8 miles of transmission lines that extend north of GPSP and inject >1 million tonnes (MMt) of CO2 annually (>1 MMt/yr) over a 12-year period with up to six underground injection control (UIC) Class VI-compliant injection wells completed in the Broom Creek Formation, a predominantly sandstone reservoir and saline aquifer underlying GPSP. The Broom Creek Formation lies approximately 5900 feet (ft) below ground surface (bgs) at GPSP. The commercial scale (i.e., >1 MMt/yr) of DGC’s permitted carbon storage project is ideal for developing and testing the novel monitoring techniques included within Subtask 1.6. The goals of this project are to demonstrate 1) the cost-effectiveness of novel monitoring technologies included as part of this research, 2) technology capability for tracking the CO2 plume and/or associated pressure response in the subsurface and monitoring out-of-zone migration, and 3) compliance with UIC Class VI program requirements. The research activities proposed for the overall project include 1) design of an automated, integrated, modular (AIM) monitoring station; 2) time-lapse electromagnetic (EM) field surveys; 3) drone-based surveillance studies; 4) time-lapse monitoring with seismic methods; 5) advanced wellbore-monitoring methods; 6) deployment of an AIM monitoring network; 7) EM monitoring of CO2 with real-time data processing; 8) continued seasonal drone-based surveillance studies; 9) seismic monitoring with passive and active surveys; and 10) wellbore monitoring with nuclear magnetic resonance (NMR) for near-surface characterization. Completion of Activities 1.0–5.0 (baseline activities) are described in this report. Upon authorization of funding by DOE, the EERC will initiate Activities 6.0– 10.0 (operational monitoring activities). Current state-of-the-art (SOA) carbon storage-monitoring techniques require countless labor hours dedicated to the acquisition of data. Once data are gathered, these SOA techniques often rely on commercial facilities to process raw data from the field. However, it is anticipated that next-generation monitoring techniques, such as those being demonstrated, will lower acquisition footprints, be less operationally intensive, and improve data acquisition efficiencies. These new techniques are more conducive to the application of machine learning, artificial intelligence, and automation, thus providing a pathway for integration into active control systems, informing site operability, and improving the integration of data for future CCS projects across the United States. Additionally, reclaimed and active mining lands are present within the project site, creating a unique opportunity to demonstrate the effectiveness of remote sensing and surface-based geophysics monitoring techniques at similar project sites that may include disturbed, unconsolidated, or actively excavated near-surface environments. The efforts included in the overall project will produce necessary designs, learnings, and data acquired during the baseline and operational monitoring periods that are necessary for time-lapse demonstration and validation of the described monitoring techniques. In addition, it is anticipated that the monitoring technologies included in this study will be compliant with UIC Class VI requirements to enable the potential for implementation at other CCS sites across the United States.

42 ENGINEERING↗
DOE OSTI2022

An Efficient Distributed Reinforcement Learning for Enhanced Multi-Microgrid Management

Economic dispatch in multi-microgrid (MMG) systems requires coordinating distributed energy resources (DERs) of different microgrids, which leads to a significant increase in the number of states for energy management. In these cases, traditional reinforcement learning (RL) approaches become computationally expensive or output a solution that causes extra-operating costs for the system. This paper proposes an RL approach that employs local learning agents to interact with microgrid environments in a distributed manner and aggregates the outcomes to train the global agent to learn the policy for the MMG system. This distributed exploration and aggregation process provides an effective solution and guides the global agent to learn the dispatch policy efficiently. Case studies are performed on a system with three microgrids with different types of DERs. Results obtained using the proposed RL and comparisons with conventional methods substantiate the effectiveness of the proposed approach in terms of operation costs, computation time, and peak-to-average ratio.

Das, Avijit↗
NASA NTRS2011

Mission Possible: BioMedical Experiments on the Space Shuttle

Biomedical research, both applied and basic, was conducted on every Shuttle mission from 1981 to 2011. The Space Shuttle Program enabled NASA investigators and researchers from around the world to address fundamental issues concerning living and working effectively in space. Operationally focused occupational health investigations and tests were given priority by the Shuttle crew and Shuttle Program management for the resolution of acute health issues caused by the rigors of spaceflight. The challenges of research on the Shuttle included: limited up and return mass, limited power, limited crew time, and requirements for containment of hazards. The sheer capacity of the Shuttle for crew and equipment was unsurpassed by any other launch and entry vehicle and the Shuttle Program provided more opportunity for human research than any program before or since. To take advantage of this opportunity, life sciences research programs learned how to: streamline the complicated process of integrating experiments aboard the Shuttle, design experiments and hardware within operational constraints, and integrate requirements between different experiments and with operational countermeasures. We learned how to take advantage of commercial-off-the-shelf hardware and developed a hardware certification process with the flexibility to allow for design changes between flights. We learned the importance of end-to-end testing for experiment hardware with humans-in-the-loop. Most importantly, we learned that the Shuttle Program provided an excellent platform for conducting human research and for developing the systems that are now used to optimize research on the International Space Station. This presentation will include a review of the types of experiments and medical tests flown on the Shuttle and the processes that were used to manifest and conduct the experiments. Learning Objective: This paper provides a description of the challenges related to launching and implementing biomedical experiments aboard the Space Shuttle.

Bopp, E.↗
DOE OSTI2019

Investigation and Recovery Following the Drum Event at INL's ARP-V Facility - 19517

Contractors working under the Department of Energy's (DoE's) Idaho Field Office have been exhuming and processing buried legacy waste from early weapons development and productions operations since 1984. Since operations began, nearly 400,000 drums have passed through this process. On April 11, 2018, repackaged drums were staged in an airlock (radiological buffer area) awaiting transfer to another facility for characterization. Late in the evening after operations had ended, four of the drums over pressurized causing an energetic displacement of their lids. A significant fraction of drum contents was ejected and dispersed throughout the airlock. The event activated the fire detection system in the facility initiating an emergency response from the site fire department, radiological protection, and operations. Initial intervention by fire department personnel was unsuccessful at quenching the reaction, so the responders egressed to a safe distance and monitored the situation. After determining the event had terminated and it was safe to enter, the facility was assessed revealing significant contamination in the airlock from the ejected waste. The area surrounding the facility was surveyed and it was determined that no contamination was released outside the facility. The Fluor Idaho, LLC (Fluor Idaho) Program Manager commissioned an investigation and recovery effort, which focused on three aspects: (1) technical investigation, (2) event and causal analysis, and (3) facility recovery. A team of highly qualified individuals from around the DOE Complex was assembled to support the technical investigation. The team included representatives from several Fluor Idaho, Fluor Corporate, Battelle Energy Alliance, LLC (INL), LANL, SRNL, South West Research Institute, DOE Carlsbad Field Office, and Defense Nuclear Facility Safety Board, and DOE-Headquarters. This team addressed the reaction mechanism of the event by guiding the sampling activities and coordinating analysis and testing by the analytical laboratories. Independent and experienced investigators were brought in to conduct a rigorous event and cause analysis. The Causal Analysis Team pursued direct, root, and contributing causes. Interviews were conducted, and documents were reviewed to develop an event and causal analysis. The Accelerated Retrieval Project V Operations team mobilized to decontaminate and recover the facility. Significant progress has been made in this effort, area and airborne contamination levels have been significantly reduced allowing limited activities to resume. In addition, this group supported sample collection efforts as directed by the Technical Investigation Team. This paper reviews key findings and milestones from the technical investigation, the event and causal analysis, and facility recovery operations. From these elements of the investigation, lessons learned are identified that would benefit other organizations involved with legacy waste management and disposition. Waste management is common to all sites in the DOE Complex and the events and recovery efforts described in this paper may provide beneficial insights to these operations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

International MNSR Training Center

Plans for the conversion of the Chinese origin miniature neutron source reactors (MNSRs) to low-enriched uranium (LEU), and the return of the high- enriched uranium (HEU) core to China, were initiated in February 2011 when an international MNSR Working Group (WG) was established to coordinate these activities. Based on a Ghana Atomic Energy Commission (GAEC) decision, the MNSR core at the GHARR-1 facility was selected for pilot shipment. Working on the preparation of the HEU MNSR materials’ removal from the GHARR-1 facility as a training tool, a mock-up MNSR vessel was built for operator training. Experiencing the usefulness of the mock-up vessel in operator training in the Ghana case has since been developed further into a full-scale training facility, named the International MNSR Training Center (IMTC), which is now available for training operators from MNSR operating countries. The main objective of this Book is to document the IMTC as a full-scale MNSR training facility in operator training for HEU MNSR core discharge and packaging. The goal is to describe the IMTC in its full complexity — that is: (1) to present the IMTC’s buildings with its internal and outer areas; (2) to provide a full listing of the installed equipment, as well as a functional description, available at the IMTC site; (3) to introduce the training materials, tutorials, and training program; and (4) to present the training sessions and Dry-run demonstration of the recently completed NIRR-1 operator training, with their experiences and lessons-learned included. This Book serves as a kind of user manual for the IMTC, which contains all of the information to launch training courses at the site at any time. The target audience for this Book is the MNSR community, especially MNSR operators and regulators who are facing HEU-LEU core conversion and HEU material transportation, who require operator training to carry out their jobs safely and professionally.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS2013

NASA Tech Briefs, January 2013

Topics include: Single-Photon-Sensitive HgCdTe Avalanche Photodiode Detector; Surface-Enhanced Raman Scattering Using Silica Whispering-Gallery Mode Resonators; 3D Hail Size Distribution Interpolation/Extrapolation Algorithm; Color-Changing Sensors for Detecting the Presence of Hypergolic Fuels; Artificial Intelligence Software for Assessing Postural Stability; Transformers: Shape-Changing Space Systems Built with Robotic Textiles; Fibrillar Adhesive for Climbing Robots; Using Pre-Melted Phase Change Material to Keep Payloads in Space Warm for Hours without Power; Development of a Centrifugal Technique for the Microbial Bioburden Analysis of Freon (CFC-11); Microwave Sinterator Freeform Additive Construction System (MS-FACS); DSP/FPGA Design for a High-Speed Programmable S-Band Space Transceiver; On-Chip Power-Combining for High-Power Schottky Diode-Based Frequency Multipliers; FPGA Vision Data Architecture; Memory Circuit Fault Simulator; Ultra-Compact Transputer-Based Controller for High-Level, Multi-Axis Coordination; Regolith Advanced Surface Systems Operations Robot Excavator; Magnetically Actuated Seal; Hybrid Electrostatic/Flextensional Mirror for Lightweight, Large-Aperture, and Cryogenic Space Telescopes; System for Contributing and Discovering Derived Mission and Science Data; Remote Viewer for Maritime Robotics Software; Stackfile Database; Reachability Maps for In Situ Operations; JPL Space Telecommunications Radio System Operating Environment; RFI-SIM: RFI Simulation Package; ION Configuration Editor; Dtest Testing Software; IMPaCT - Integration of Missions, Programs, and Core Technologies; Integrated Systems Health Management (ISHM) Toolkit; Wind-Driven Wireless Networked System of Mobile Sensors for Mars Exploration; In Situ Solid Particle Generator; Analysis of the Effects of Streamwise Lift Distribution on Sonic Boom Signature; Rad-Tolerant, Thermally Stable, High-Speed Fiber-Optic Network for Harsh Environments; Towed Subsurface Optical Communications Buoy; High-Collection-Efficiency Fluorescence Detection Cell; Ultra-Compact, Superconducting Spectrometer-on-a-Chip at Submillimeter Wavelengths; UV Resonant Raman Spectrometer with Multi-Line Laser Excitation; Medicine Delivery Device with Integrated Sterilization and Detection; Ionospheric Simulation System for Satellite Observations and Global Assimilative Model Experiments - ISOGAME; Airborne Tomographic Swath Ice Sounding Processing System; flexplan: Mission Planning System for the Lunar Reconnaissance Orbiter; Estimating Torque Imparted on Spacecraft Using Telemetry; PowderSim: Lagrangian Discrete and Mesh-Free Continuum Simulation Code for Cohesive Soils; Multiple-Frame Detection of Subpixel Targets in Thermal Image Sequences; Metric Learning to Enhance Hyperspectral Image Segmentation; Basic Operational Robotics Instructional System; Sheet Membrane Spacesuit Water Membrane Evaporator; Advanced Materials and Manufacturing for Low-Cost, High-Performance Liquid Rocket Combustion Chambers; Motor Qualification for Long-Duration Mars Missions.

Source record↗
NASA NTRS2017

CubeSat Infrared Atmospheric Sounder (CIRAS) NASA InVEST Technology Demonstration

Infrared sounders measure the upwelling radiation of the Earth in the Midwave Infrared (MWIR) and Longwave Infrared (LWIR) region of the spectrum with global daily coverage from space. The observed radiances are assimilated into weather forecast models and used to retrieve lower tropospheric temperature and water vapor for climate studies. There are several operational sounders today including the Atmospheric Infrared Sounder (AIRS) on Aqua, the Crosstrack Infrared Sounder (CrIS) on Suomi NPP and JPSS, and the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp spacecraft. The CubeSat Infrared Atmospheric Sounder (CIRAS) is a NASA In-flight Validation of Earth Science Technologies (InVEST) program to demonstrate three new instrument technologies in an imaging sounder configuration. The first is a 2D array of High Operating Temperature Barrier Infrared Detector (HOT-BIRD) material, selected for its high uniformity, low cost, low noise and higher operating temperatures than traditional materials. The detectors are hybridized to a commercial ROIC and commercial camera electronics. The second technology is a MWIR Grating Spectrometer (MGS) designed to provide imaging spectroscopy for atmospheric sounding in a CubeSat volume. The MGS employs an immersion grating or grism, has no moving parts, and is based on heritage spectrometers including the OCO-2. The third technology is a Black Silicon infrared blackbody calibration target. The Black Silicon offers very low reflectance over a broad spectral range on a flat surface and is more robust than carbon nanotubes. JPL will also develop the mechanical, electronic and thermal subsystems for the CIRAS payload. The spacecraft will be a commercially available CubeSat. The integrated system will be a complete 6U CubeSat capable of measuring temperature and water vapor profiles with good lower tropospheric sensitivity. The low cost of CIRAS enables multiple units to be flown to improve temporal coverage or measure 3D Atmospheric Motion Vector (AMV) winds. CIRAS will launch in 2019 and is only a technology demonstration. However, what we learn will benefit future instruments that support operational weather forecasting and climate studies.

Pagano, Thomas S.↗
DOE OSTI2021

Probing effective field theory operators in the associated production of top quarks with a Z boson in multilepton final states at $\sqrt{s}$ = 13 TeV

A search for new top quark interactions is performed within the framework of an effective field theory using the associated production of either one or two top quarks with a Z boson in multilepton final states. The data sample corresponds to an integrated luminosity of 138 fb -1 of proton-proton collisions at √s = 13 TeV collected by the CMS experiment at the LHC. Five dimension-six operators modifying the electroweak interactions of the top quark are considered. Novel machine-learning techniques are used to enhance the sensitivity to effects arising from these operators. Distributions used for the signal extraction are parameterized in terms of Wilson coefficients describing the interaction strengths of the operators. All five Wilson coefficients are simultaneously fit to data and 95% confidence level intervals are computed. All results are consistent with the SM expectations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Physics-informed machine learning of the correlation functions in bulk fluids

The Ornstein–Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids. In this work, machine learning models, notably physics-informed neural networks and physics-informed neural operator networks, are explored to solve the OZ equation. The physics-informed machine learning models demonstrate great accuracy and high efficiency in solving the forward and inverse OZ problems of various bulk fluids. The results highlight the significant potential of physics-informed machine learning for applications in thermodynamic state theory.

97 MATHEMATICS AND COMPUTING↗