Search NASA⌕ Search

SEARCH · Search NASA

Results for “Pilot Performance”

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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Risks to the DOW Mission From Global Advanced Energy Adoption

Advanced energy technologies have the potential to enhance energy security and resilience; however, they can introduce new vulnerabilities even as they mitigate existing ones. This dichotomy highlights that both action and inaction carry strategic risks in a contested and logistically complex operating environment. Regardless of U.S. civilian or military adoption of such technologies, key allies and adversaries are on adoption paths that will impact the U.S. military at the tactical, operational, and strategic level. The global adoption of advanced energy technologies presents the potential for both positive and negative consequences for U.S. Department of Defense (DOD) missions in the next 10-20 years. Three categories of risk drivers are presented in this analysis: infrastructure-related, adoption-related, and response-related. Each risk type can generate consequences for DOD, including power disruptions, suboptimal DOD mission performance and operational effectiveness, higher costs and shortages for both legacy and advanced energy technologies, and loss of U.S. influence and strategic deterrent value. Specific impacts could include tactical impacts, operational impacts, and strategic impacts. Mitigation strategies could include energy resource and technology planning, cybersecurity, supply chain resilience, workforce development, exercises and simulations, investments in commercialized technology, operational testing and pilot programs, research into emerging technologies, partnerships and working groups, common standards, budget planning, and repurposing infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

sCO2 Primary Power Large-Scale Pilot Plant FEED Summary

Echogen Power Systems and its project partners the Electric Power Research Institute, CDM Smith, Riley Power Inc. (a Babcock Power Inc. company), and the University of Missouri completed the Front-End Engineering Design (FEED) study of a nominal 10 MWe supercritical carbon dioxide (sCO2) large-scale pilot plant based on Recompression Brayton Cycle (RCBC) architecture. The FEED study was completed under Phase II of the U.S. Department of Energy Fossil Fuel Large-Scale Pilots program. The FEED study scope included the preliminary design of the combustion and environmental control systems, the primary heater, the sCO2 power cycle and its associated turbomachinery, and the balance of plant equipment to determine accurate cost and schedule requirements for fabrication and installation of the system at the University of Missouri’s Combined Cooling, Heat, and Power Plant, located in Columbia, Missouri. The RCBC sCO2 cycle presents superior thermodynamic efficiency compared to steam-Rankine cycles and is adaptable to a broad range of heat sources. The risk aversion of the power generation industry requires a successful large-scale pilot plant demonstration to enable financing and deployment of commercial-scale plants. Therefore, successful construction and demonstration of a 10 MWe RCBC sCO2 power plant would empower a transformational step forward for the efficiency of the next generation of utility power plants. This paper provides details on the primary heater, environmental control systems, and sCO2 power cycle design and performance.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Stirring and peristaltic pumping alter flow electrode particle size and morphology

Flowable carbon-based electrodes are widely studied in electrochemical systems for energy storage and water treatment. Most laboratory- and pilot-scale studies employ active mixing for slurry preparation and handling and use peristaltic pumps for slurry circulation. Magnetic stirring is the most prevalent mixing method at the laboratory scale, and the combination of magnetic mixing and peristaltic pumping is favored for convenience and compatibility with particulate suspensions. However, these methods can strongly affect particle size and morphology (and therefore cell performance). In this study, we evaluate how mixing and pumping influence particle degradation in two commonly used carbon materials: spherical carbon black (CB) beads and powdered activated carbon (AC). We performed 24 h stirring-only experiments using magnetic, orbital, and overhead stirring. Separately, we conducted 32 h circulation experiments using a commercial peristaltic pump. Particle morphology and size were analyzed using optical microscopy and laser diffraction. The initially spherical CB beads (~580 μm, 1.5 wt%) exhibit rapid and extensive breakdown under both peristaltic pumping and magnetic stirring. By comparison, AC particles (initially ~31 μm) tested at 1.5 and 15 wt% showed measurable size reductions of up to ~25 % under either stirring or pumping. In conclusion, these findings highlight the need to account for and quantify morphological changes caused by both stirring and pumping in the design and associated evaluation of flowable electrode systems.

Chemistry↗
DOE OSTI2022

Current Status of the DOE/NNSA Nuclear Criticality Safety Program Hands-On Criticality Safety Training

The U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA) Nuclear Criticality Safety Program (NCSP) has conducted two-week Nuclear Criticality Safety (NCS) Practitioner courses since 2011 to support the training and qualification of new NCS staff. The course was developed in accordance with the American National Standard Institute/American Nuclear Society (ANSI/ANS) standard for NCS training and qualifications (ANSI/ANS-8.26-2007). In 2013, an NCS Manager’s course was developed for process supervisors, managers, regulators, and other professionals with NCS-related responsibilities. This course was revised in 2019 for Criticality Safety Officers (CSOs) based on an NCSP Criticality Safety Support Group tasking (2018-01). This course was piloted at the Nevada Field Office and the National Criticality Experiments Research Center (NCERC) in June 2021. These courses consist of the following training components: classroom education, facility training, and hands-on subcritical and critical experiments training. The two-week Practitioner course offers a week of classroom training, with practical workshops and exercises focused on teaching students how to perform an NCS evaluation. The second week of training involves hands-on critical and subcritical experiments and measurements. The first week is offered in Las Vegas, Nevada, at the DOE Nevada Field Office or the National Atomic Testing Museum. Depending on the student’s clearance level, the second week is offered at Sandia National Laboratory (SNL) (uncleared and L-cleared students) or at the National Criticality Experiments Research Center (Q-cleared students). The one-week Manager’s course is offered at SNL or NCERC, depending on clearance or interest, and includes classroom and hands- on critical and subcritical experiments and measurements. This paper provides an overview and status report for the DOE/NNSA NCSP training courses in NCS and to provide information about future course offerings. This paper discusses the challenges associated with executing the training courses during the COVID-19 pandemic. The 2-week Practitioner and 1-week manager courses are currently offered twice per year and adjustments are made based upon demand.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Current Status of the DOE/NNSA Nuclear Criticality Safety Program Hands-On Criticality Safety Training [Abstract]

The U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA) Nuclear Criticality Safety Program (NCSP) has conducted two-week Nuclear Criticality Safety (NCS) Practitioner courses since 2011 to support the training and qualification of new NCS staff. The course was developed in accordance with the American National Standard Institute/American Nuclear Society (ANSI/ANS) standard for NCS training and qualifications (ANSI/ANS-8.26-2007). In 2013, an NCS Manager’s course was developed for process supervisors, managers, regulators, and other professionals with NCS-related responsibilities. This course was revised in 2019 for Criticality Safety Officers (CSOs) based on an NCSP Criticality Safety Support Group tasking (2018-01). This course was piloted at the Nevada Field Office and the National Criticality Experiments Research Center (NCERC) in June 2021. These courses consist of the following training components: classroom education, facility training, and hands-on subcritical and critical experiments training. The two-week Practitioner course offers a week of classroom training, with practical workshops and exercises focused on teaching students how to perform an NCS evaluation. The second week of training involves hands-on critical and subcritical experiments and measurements. The first week is offered in Las Vegas, Nevada, at the DOE Nevada Field Office or the National Atomic Testing Museum. Depending on the student’s clearance level, the second week is offered at Sandia National Laboratory (SNL) (uncleared and L-cleared students) or at the National Criticality Experiments Research Center (Q-cleared students). The one-week Manager’s course is offered at SNL or NCERC, depending on clearance or interest, and includes classroom and hands-on critical and subcritical experiments and measurements. This paper provides an overview and status report for the DOE/NNSA NCSP training courses in NCS and to provide information about future course offerings. This paper will also discuss the challenges associated with executing the training courses during the COVID-19 pandemic. The 2-week Practitioner and 1-week manager courses are currently offered twice per year and adjustments are made based upon demand.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Performance Evaluation of Intelligent Solar Control Software Through Hardware-in-the-Loop (CRADA Final Report)

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been developed by Latimer Controls, Inc. to estimate the headroom of large PV plants for grid operation and control; however, these technologies lack comprehensive validation under real-world application scenarios. Latimer Controls, Inc. received two voucher awards for research at a national laboratory from the Department of Energy American Made Solar Prize Round 6. The National Renewable Energy Laboratory (NREL) was selected to collaborate with Latimer staff to conduct a performance evaluation of Latimer PV control software. The NREL team will develop a hardware-in-the-loop (HIL) testbed to perform testing and validation of the Latimer PV control technology in a de-risked yet realistic testbed environment. Latimer and NREL worked together to analyze the test data, draw conclusions from the results, and disseminate the resulting scientific findings. In this CRADA work, we propose to test and validate the real-world application of the Latimer Control solution in an HIL environment. We evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. In particular, a data-driven potential high limit (PHL) estimation is developed for large solar plants to accurately estimate their headroom so that they have fast and short-time regulation and control capability to participate in grid services and respond to grid signals in real time (e.g., AGC). This PHL estimation algorithm is embedded in a hardware power plant controller (PPC) and tested with an IEEE-39 bus system model developed in RTDS. To account for the varying cloud conditions and diverse inverter dispatches, we developed a 135-MW PV plant with detailed modeling of 27 individual PV modules and inverters using RTDS. The real-world communications used in such big plants, such as ModBus TCP/IP for inverter level and DNP3 for plant level, were developed to emulate the real-world applications in big PV plants. The ML-based PHL estimation method is tested under nine separate weather scenarios against the ‘reference-control’ solution, hereafter referred to as the baseline solution. The baseline method reserves a subset of inverters (reference group) to operate at their PHL at all times and dispatches only the remaining inverters (control group) at curtailed levels to fulfill the flexibility need. Despite being successfully piloted by NREL in California in 2017 and Chile in 2020, there exist two gaps in the state of the art to fully unlock the flexibility of PV plants: a. There is a trade-off between the PHL estimation accuracy and the flexibility range. b. There lacks granularity in the PHL estimation to capture the variation across inverters. The Latimer solution seeks to address these gaps by applying machine learning methods to improve PHL estimation accuracy while accounting for variability at every inverter. Performance metrics were taken from the 2023 Georgia Power CARES utility-scale RFP. The results demonstrate that the ML-based approach outperforms the traditional baseline method in PHL estimation accuracy for 7 of 9 scenarios. The average PHL error across the nine scenarios was 7.40% for the ML-based method, 2.06% less than the 9.46% PHL error average across scenarios that was exhibited by the baseline method. Additionally, the PHL error was below 5% for at least 95% of the testing interval for 3 of 9 tested intervals with the ML approach, whereas it did not achieve this metric for any of the baseline tests. Overall, simulation results indicate the superior performance of an ML-based approach compared to the conventional baseline reference-control approach, showcasing its potential to support grid stability and operational efficiency. This laboratory HIL testing using real PPC, representative power system simulation models in real-time with detailed PV plant and inverter models, and real-world communication protocols gives us confidence that this machine learning based PHL estimation algorithm works well in the hardware PPC and therefore de-risks future field commissioning. The end goal of this project is to advance grid technology to address the grid operation challenges brought by solar plant’s variability and uncertainties in power generation.

14 SOLAR ENERGY↗
DOE OSTI2025

Advanced Transmission Technologies –GETs and HPCs Session 3: HPCs and Building Actions Plans to Digital Assurance Risks

The third session of the Idaho National Laboratory’s (INL) Technical Assistance for Digital Assurance (TADA) program, held on November 11, 2025, centered on High Performance Conductors (HPCs) and the formulation of action plans to address digital assurance risks associated with Grid-Enhancing Technologies (GETs). This session convened experts from utilities, vendors, and government agencies to examine the technical, operational, and cybersecurity aspects of HPC deployment. Discussions highlighted the benefits of HPCs, such as their ability to rapidly increase transmission capacity using existing corridors, improve grid resilience, reduce system losses, and align with FERC Orders 2023 and 1920. Participants evaluated supply chain and digital assurance risks, including reliance on imported materials, limited domestic manufacturing capacity, workforce shortages, and traceability issues. The session also emphasized the importance of digital trust, integration-layer cybersecurity, and unified risk frameworks, introducing tools like intrusion detection systems, encryption, zero trust networking, and firmware integrity. Recaps of earlier workshops on Dynamic Line Ratings (DLRs), Advanced Power Flow Control (APFC), and Transmission Topology Optimization (TTO) underscored institutional barriers and integration challenges. Action plans were proposed to mitigate issues such as inconsistent cybersecurity practices, SBOM usage, supply chain visibility, operator trust, and misaligned incentives. Additionally, INL presented its supply chain risk management tools and Cyber-Informed Engineering (CIE) principles to support secure procurement and system design. The session concluded with a commitment to share key takeaways, incorporate cohort feedback into future policy development, and continue collaborative engagement through upcoming pilot activities. Session 3 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Towards the scale-up production of cellulose nanocomposites using melt processing: A critical review on structure-processing-property relationships

Cellulose nanomaterials (CNMs) naturally exist in plant biomass. The success of extraction of CNMs opened up a new era of using plant biomass for innovative industrial applications. Because CNMs are abundant, renewable, biodegradable, transparent, light weight and low in cost, they are ideal materials for large volume applications such as packaging, automotive, building and infrastructure. In many potential application areas, CNM-enable products appear in a composite form, mostly polymer composites. Additionally, the industrial-scale manufacturing of CNM/thermoplastic composites remains as a set of unsolved problems for academia and industry. A prime challenge in applications is the nanoscale dispersion of CNMs in thermoplastic matrices during melt processing. Both bench-scale and pilot-scale studies have been conducted to solve the dispersion issue of CNMs. In this article, research related to the dispersion of CNMs in thermoplastic matrices during melt processing were critically reviewed. All research papers were classified into three groups: chemically-aided dispersion, physically-aided dispersion and mechanically-aided dispersion. Numerous factors affect the CNM dispersion and the mechanical performance of its nanocomposites. There are material-related factors, including CNM types and forms, polymer matrices, surface modification, coupling agents, etc. Furthermore, extrusion processing parameters also play a significant role, covering screw rotation speed, extrusion barrel temperature settings and screw design. In addition, the material-related factors interact with the processing-related factors. Understanding all factors and their interactions are important for moving CNM nanocomposites research a step further towards industrial-relevant production, which is the final ambitious goal of this manuscript.

42 ENGINEERING↗
DOE OSTI2023

Novel Transformational Membranes and Process for CO 2 Capture from Flue Gas (FE0031731)

The objectives of this project are to develop a cost-effective design and fabrication process for a novel transformational membrane and its membrane modules that capture CO 2 from flue gas. Optimization of the novel transformational membrane, scale-up of the membrane to a prototype size of about 20" wide in continuous roll-to-roll fabrication, and construction and testing of a skid for the integrated membrane process will be performed. For the design of this membrane, we use a cost-effective polymer support and coat a thin top layer of the membrane. The simplicity of this membrane design offers a low cost for the membrane element in commercial spiral-wound (SW) configuration (<$2.00/ft 2 or $21.5/m 2 ). The prototype membrane will be used to fabricate 6 pilot-size membrane modules (each about 20" length, 3 modules each at the commercial-size diameter of 8 inches with 35 m 2 membrane area for Stage 1 and 3 modules each at 5-inch diameter with 12 m 2 membrane area for Stage 2) for testing with simulated flue gas at OSU and with actual flue gas at the National Carbon Capture Center, Wilsonville, AL using the skid to capture the CO 2 (at 60 – 90%) with at least 95% CO 2 purity. The prototype membrane modules will be in commercial SW configuration with a minimal pressure drop (<0.103 bar/meter (1.5 psi/meter)). These objectives have been achieved successfully.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Measurement report: Properties of aerosol and gases in the vertical profile during the LAPSE-RATE campaign

Abstract. Unmanned aerial systems (UASs) are increasingly being used as observation platforms for atmospheric applications. The Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) in Alamosa, Colorado, USA, on 14–20 July 2018 investigated and validated different UASs, measurement sensors and setup configurations. Flight teams from the Finnish Meteorological Institute (FMI) and Kansas State University (KSU) participated in LAPSE-RATE to measure and investigate properties of aerosol particles and gases in the lower atmosphere. During the experiment, the performance of different UAS configurations were investigated and confirmed to operate reliably, resulting in a scientifically sound observational dataset. As an example, concentration of aerosols – including two new particle formation events, CO2 and water vapor, and meteorological parameters in the atmospheric vertical profile were measured during the short experiment. Such observations characterizing atmospheric phenomena of this specific environment would have not been possible in any other way and, thus, demonstrate the power of UASs as new, promising tools in atmospheric and environmental research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Department of Energy and IDEA team up to document industry best practices

This article describes a collaboration between the U.S. Department of Energy and the International District Energy Association (IDEA) to document best practices in district energy systems. The initiative focuses on analyzing real-world performance data and highlighting effective strategies for improving efficiency, integrating renewable energy, and supporting decarbonization efforts. It highlights case studies from the University of California, Davis, and Princeton University to show how long-term master planning, phased project implementation, and strong financial analysis can drive successful transitions to more sustainable energy systems. U.C. Davis emphasizes incremental, results-driven planning and pilot projects, while Princeton focuses on aligning climate goals with financially viable infrastructure investments and life-cycle cost analysis. Overall, the article demonstrates that combining strategic planning, data-driven decision-making, and financial accountability is essential for advancing sustainable district energy systems.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Evaluation of sterol-o-acyl transferase 1 and cholesterol ester levels in plasma, peritoneal fluid and tumor tissue of patients with endometrial cancer: A pilot study

Endometrial cancer (EC) is the most prevalent gynecological malignancy. Abnormal accumulation of sterol-O-acyl transferase 1 (SOAT1) and SOAT1-mediated cholesterol ester (CE) contributes to cancer progression in various malignancies, including ovarian cancer. Therefore, it was hypothesized that similar molecular changes may occur in EC. The present study aimed to evaluate the diagnostic and/or prognostic potential of SOAT1 and CE in EC by: i) Determining SOAT1 and CE levels in plasma, peritoneal fluid and endometrial tissue from patients with EC and control subjects; ii) performing receiver operating characteristic curve analysis to determine diagnostic performance; iii) comparing SOAT1 and CE expression to that of the tumor proliferation marker Ki67; and iv) assessing the association between SOAT1 expression and survival. Enzyme-linked immunosorbent assay was used to determine the levels of SOAT1 protein in tissue, plasma and peritoneal fluid. The mRNA and protein expression levels of SOAT1 and Ki67 in tissues were detected by reverse transcription-quantitative polymerase chain reaction and immunohistochemistry, respectively. CE levels were determined colorimetrically in plasma and peritoneal fluid. SOAT1-associated survival data from the cBioPortal cancer genomics database were used to assess prognostic relevance. The results revealed that SOAT1 and CE levels were significantly elevated in tumor tissue and peritoneal fluid samples collected from the EC group. By contrast, the plasma levels of SOAT1 and CE in the EC and control groups were similar. Significant positive associations between CE and SOAT1, SOAT1/CE and Ki67, and SOAT1/CE and poor overall survival in patients with EC suggested that SOAT1/CE may be associated with malignancy, aggressiveness and poor prognosis. In conclusion, SOAT1 and CE may serve as potential biomarkers for prognosis and target-specific treatment of EC.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Pilot microbial electrolysis cell closes the hydrogen loop for hydrothermal wet waste conversion to jet fuel

The global shift toward net-zero emissions necessitates resource recovery from wet waste. In this study, we demonstrate the first feasibility of combining pilot-scale microbial electrolytic cells (MECs) with hydrothermal liquefaction (HTL) for simultaneous post-hydrothermal liquefaction wastewater (PHW) treatment and efficient hydrogen (H₂) production to meet biocrude upgrading requirements. Long-term single reactor operation revealed that fixed anode potential enabled rapid startup, and low catholyte pH and high salinity were effective in suppression of cathodic methanogenesis and acetogenesis – resulting in high current density of 16.6 A m –2 and 9.3 A m –2 when feeding synthetic wastewater and PHW respectively. Additionally, the anode biofilm exhibited spatial variations in response to local environmental conditions. In conclusion, onsite parallel or serial operations of multiple MECs showed good performance using actual PHW with a record-high H 2 production rate of 0.5 L L R day –1 for MEC over 10 liters scale, and the optimal chemical oxygen demand (COD)-to-H 2 yield reached 0.127 kg-H 2 per kg-COD, supporting a self-sufficient, closed-loop upgrade to jet fuel.

08 HYDROGEN↗
DOE OSTI2020

A long-term pilot-scale algal cultivation on power plant flue gas – Cultivation stability and biomass accumulation

A long-term culture of a selected freshwater microalgal assemblage on power plant flue gas was realized using a pilot-scale photobioreactor. Different culture conditions of light cycle, harvesting amount, pH stabilization, and sulfur addition were tested during a 12-month continuous operation at a natural gas fired power plant. The results concluded that the daily harvesting amount of 60% with no light-cycle and NH 4 NO 3 as the nitrogen source and pH stabilization agent, resulted in optimal cultivation performance. Biomass concentration and biomass productivity reached 1.07 ± 0.23 g L -1 and 0.43 ± 0.14 g L -1 d -1 , respectively. During the year-long cultivation period, the algal assemblage was relatively stable with average abundances of 88 ± 4% and 12 ± 4% for algae and bacteria, respectively, and no significant differences between individual case scenarios. The stable culture fostered a stable biomass composition. Protein, carbohydrate, and lipid contents of the algal biomass were 58.60 ± 2.41%, 19.5 ± 2.44%, and 9.70 ± 1.68%, respectively.

09 BIOMASS FUELS↗
DOE OSTI2023

Scale-up and techno-economic analysis of microbial electrolysis cells for hydrogen production from wastewater

Microbial electrolysis cells (MECs) have demonstrated high-rate H 2 production while concurrently treating wastewater, but the transition in scale from laboratory research to systems that can be practically applied has encountered challenges. It has been more than a decade since the first pilot-scale MEC was reported, and in recent years, many attempts have been made to overcome the barriers and move the technology to the market. This study provided a detailed analysis of MEC scale-up efforts and summarized the key factors that should be considered to further develop the technology. We compared the major scale-up configurations and systematically evaluated their performance from both technical and economic perspectives. We characterized how system scale-up impacts the key performance metrics such as volumetric current density and H 2 production rate, and we proposed methods to evaluate and optimize system design and fabrication. In addition, preliminary techno-economic analysis indicates that MECs can be profitable in many different market scenarios with or without subsidies. Here, we also provide perspectives on future development needed to transition MEC technology to the marketplace.

42 ENGINEERING↗
DOE OSTI2023

Deep learning for morphological identification of extended radio galaxies using weak labels

Abstract The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25–35 $\mu$ Jy beam $^{-1}$ . We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP $_{50}$ of 67.5% and 76.8% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link: https://github.com/Nikhel1/Gal-CAM

Astronomy & Astrophysics↗
DOE OSTI2026

A Short-Lived Isotope Counting System (SLICS) to measure reaction cross sections producing light, beta-decaying nuclei on OMEGA

A new activation detector optimized for short-lived isotopes was designed, installed, and tested on OMEGA. The Short-Lived Isotope Counting System (SLICS) can be positioned as close as 35 cm from the OMEGA target chamber center in a ten-inch-manipulator. Stable target samples can be mounted on the front end of the detector assembly to undergo triton-, deuteron-, or neutron-induced reactions that produce short-lived reaction products that beta decay. The emitted betas are detected in a phoswich configuration to eliminate the gamma background. The detector is optimized for isotopes with half-lives from 150 ms to 20 s, filling a gap in OMEGA’s current activation detectors for isotopes with half-lives >5 min. In a pilot study, SLICS achieved an efficiency of 24.5% ± 2.0% for counting 8Li, with a total of 505.8 ± 4.4 qualifying events. Using the existing triton laser-ion acceleration platform on OMEGA, this detector can be used to measure rarely studied tritium-induced reactions on light nuclei with an estimated uncertainty of 12%. As a result, the design of the detector and its performance during a first experiment employing deuterium as a surrogate will be discussed.

Beta spectrometry↗
DOE OSTI2023

Data-driven quantum approximate optimization algorithm for power systems

Abstract Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems. It is especially promising for Distributed Energy Resources (DERs) dominant systems that have been widely developed to promote energy sustainability. In those systems, knowing the maximum sections of power and data delivery is essential for monitoring, operation, and control. However, high computational effort is required. By leveraging quantum resources, Quantum Approximate Optimization Algorithm (QAOA) provides a means to search for these sections efficiently. However, QAOA performance relies heavily on critical parameters, especially for weighted graphs. Here we present a data-driven QAOA, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density. We verify the strategy with 39,774 expectation value calculations. Without parameter optimization, our data-driven QAOA is comparable with the Goemans-Williamson algorithm. This work advances QAOA and pilots its practical application to power systems in noisy intermediate-scale quantum devices.

Jing, Hang (ORCID:0000000256343489)↗