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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

Improving noisy free-energy measurements by adding more noise

Estimating free-energy differences using nonequilibrium work relations, such as the Jarzynski equality, is hindered by poor convergence when work fluctuations are large. For systems governed by overdamped Langevin dynamics, we propose the counterintuitive approach of adding noise in order to increase the precision of such calculations. Here, by introducing additional stochastic fluctuations to the system and rescaling its potential energy accordingly, we leave the thermodynamics of the system unchanged while increasing its relaxation rate. For a given time-dependent protocol this modification reduces the dissipated reduced work, leading to more accurate free-energy estimates. The method is designed to be used in experiment, and we illustrate its operation using computer simulations applied to two model systems. However, the regime of applicability of this strategy is likely limited, because it requires control of the system's potential energy in a way that is feasible in only a few experimental settings.

Whitelam, Stephen [Lawrence Berkeley National Labo↗

Imaging systematics induced by galaxy subsample fluctuation: New systematics at second order

Imaging systematics refers to the inhomogeneous distribution of a galaxy sample caused by varying observing conditions and astrophysical foregrounds. Current mitigation methods correct the galaxy density fluctuations $n$ gal /$\bar{n}$ gal caused by imaging systematics assuming that all galaxies in a sample have the same $n$ gal /$\bar{n}$ gal . Under this assumption, the corrected sample cannot perfectly recover the true correlation function. Here, we name this effect subsample systematics. For a galaxy sample, even if its overall sample statistics [redshift distribution 𝑛⁡(𝑧), galaxy bias 𝑏⁡(𝑧)], are accurately measured, 𝑛⁡(𝑧), 𝑏⁡(𝑧) can still vary across the observed footprint. It makes the correlation function amplitude of galaxy clustering higher, while correlation functions for galaxy-galaxy lensing and cosmic shear do not have noticeable change. Such a combination could potentially degenerate with physical signals on small angular scales, such as the amplitude of galaxy clustering, the impact of neutrino mass on the matter power spectrum, etc. subsample systematics cannot be corrected using imaging systematics mitigation approaches that rely on the cross-correlation signal between imaging systematics maps and the observed galaxy density field. In this paper, we derive formulated expressions of subsample systematics, demonstrating its fundamental difference with other imaging systematics. We also provide several toy models to visualize this effect. Finally, we discuss a potential method to estimate and mitigate subsample systematics by forward modeling its behavior using synthetic source injection.

Kong, Hui [The Barcelona Institute of Science and ↗

Exponentially Reduced Circuit Depths Using Trotter Error Mitigation

Product formulas are a popular class of digital quantum simulation algorithms due to their conceptual simplicity, low overhead, and performance, which often exceeds theoretical expectations. Recently, Richardson extrapolation and polynomial interpolation have been proposed to mitigate the Trotter error incurred by the use of these formulas. This work provides a rigorous, general analysis of these techniques for computing time-evolved observables, simplifying the interpolation algorithm in the process, and shows that extrapolation generically improves the performance of product formulas for this task. We demonstrate that, to achieve error 𝜖 in a simulation of time 𝑇 using a 𝑝 ⁢th-order product formula with extrapolation, circuit depths of 𝑂⁡(𝑇 1+1/𝑝 ⁢polylog (1/𝜖)) are sufficient—an exponential improvement in the precision over product formulas alone. Furthermore, we prove that these algorithms achieve commutator scaling, and improve the 𝑇 complexity for the interpolation algorithm. By relaxing the requirement of performing exact Chebyshev interpolation, our simplified algorithm eliminates the need for fractional implementations of Trotter steps, reducing computational overhead. Finally, we show these techniques can be combined with the classical shadows method to estimate many time-evolved local observables. Taken together, our findings provide the strongest evidence yet for the utility of Trotter error-mitigation techniques in algorithmic applications.

quantum algorithms & computation↗

Optimizing stochastic algorithms for hadron correlation function computations in lattice QCD using a localized distillation basis

Distillation is a quark-smearing method for the construction of a broad class of hadron operators useful in lattice QCD computations and defined via a projection operator into a vector space of smooth gauge-covariant fields. A new orthonormal basis for this space is constructed which builds in locality. This basis is useful for the construction of stochastic methods to estimate the correlation functions computed in Monte Carlo calculations relevant for hadronic physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Ocelot: An Interactive, Efficient Distributed Compression-As-a-Service Platform With Optimized Data Compression Techniques

Large volumes of data generated by scientific simulations, genome sequencing, and other applications need to be moved among clusters for data collection/analysis. Data compression techniques have effectively reduced data storage and transfer costs. However, users' requirements on interactively controlling both data quality and compression ratios are non-trivial to fulfill. Here, we propose a novel Compression-as-a-Service (CaaS) platform called Ocelot with four important contributions: (1) It offers real-time visualization, interactive compression, and transfer of scientific datasets. (2) It incorporates new strategies for compressing diverse types of datasets more effectively than traditional methods. (3) It provides an effective method for estimating the compression ratio and execution time of compression tasks. (4) Experiments on multiple real-world datasets on geographically distributed computers show that Ocelot can significantly improve data transfer efficiency with a performance gain of more than 10x in computing clusters with relatively slow networks.

compression as a service (CaaS)↗

Machine learning–based extreme event attribution

The observed increase in extreme weather has prompted recent methodological advances in extreme event attribution. We propose a machine learning–based approach that uses convolutional neural networks to create dynamically consistent counterfactual versions of historical extreme events under different levels of global mean temperature (GMT). We apply this technique to one recent extreme heat event (southcentral North America 2023) and several historical events that have been previously analyzed using established attribution methods. We estimate that temperatures during the southcentral North America event were 1.18° to 1.42°C warmer because of global warming and that similar events will occur 0.14 to 0.60 times per year at 2.0°C above preindustrial levels of GMT. Additionally, we find that the learned relationships between daily temperature and GMT are influenced by the seasonality of the forced temperature response and the daily meteorological conditions. Our results broadly agree with other attribution techniques, suggesting that machine learning can be used to perform rapid, low-cost attribution of extreme events.

54 ENVIRONMENTAL SCIENCES↗

Measurement of the electric potential and the magnetic field in the shifted analysing plane of the KATRIN experiment

The projected sensitivity of the effective electron neutrino-mass measurement with the KATRIN experiment is below 0.3 eV (90 % CL) after 5 years of data acquisition. The sensitivity is affected by the increased rate of the background electrons from KATRIN’s main spectrometer. A special shifted-analysing-plane (SAP) configuration was developed to reduce this background by a factor of two. The complex layout of electromagnetic fields in the SAP configuration requires a robust method of estimating these fields. We present in this paper a dedicated calibration measurement of the fields using conversion electrons of gaseous 83m Kr, which enables the neutrino-mass measurements in the SAP configuration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

ENnUI : Exemplar Navigator Using Inertial Sensors

SAND2025-04794O ENnUI: Exemplar Navigator Using Inertial Sensors helps users understand and compare different navigation algorithms that rely on inertial sensors. This reference library offers a collection of reference mechanization equations and methods for estimating the position and movement of vehicles over time. Rather than aiming for the highest precision or performance, ENnUI focuses on creating a user-friendly framework that allows users to benchmark their own algorithms against a standard set of tools. It is useful for post-processing data and real-time navigation solutions. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Walker II, Michael [Sandia National Lab. (SNL-CA),↗

Optimization of a Mixed Fleet of Aerial Drones for Medical Supplies: A Case Study of Blood Delivery Logistics

Aerial drones have emerged as an innovative solution for faster transportation of time-sensitive items (e.g., emergency medical supplies), potentially reducing the transmission of contagious diseases and enhancing healthcare availability through contactless autonomous delivery. We study fleet sizing and efficient scheduling of a mixed fleet of drones for delivering time-sensitive medical items having distinct release and due times to minimize the required fleet size and fleet composition, the required number of additional batteries, and the total energy consumption. We continuously track the remaining battery energy of drones to determine the optimal timing for battery replacement, rather than replacing the battery at each node. Using actual drone flight test data, we employed a machine learning (ML) method to estimate the energy consumption of different drone types during flight segments for different operating parameters. We present a novel mixed-integer programming model to efficiently formulate the problem that integrates the estimated energy consumption functions from ML. We propose a new greedy heuristic (GH) algorithm and a customized genetic algorithm (GA) for solving large-scale instances of this problem faster. Results demonstrate that the GH algorithm is substantially faster than the accelerated CPLEX and the GA, while sacrificing the solution quality by a small amount. Results based on an actual blood sample delivery case study from Pendleton, Oregon, United States, show that using a mixed fleet of drones reduces the total cost and total energy consumption up to 18.18% and 28.7%, respectively, compared to using a homogeneous fleet.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

Uncertainty Propagation from Experiment Measurements to Modeling Approaches: A Case for SMR Steam Entrainment Testing

To license new and advanced reactor designs, regulators must be convinced that their unique safety cases—relative to existing large scale reactors—have been adequately addressed by the designed reactor protection systems. In water cooled small modular reactors (SMRs), droplet entrainment in steam flow has significant implications on the progression of accident scenarios due to its compact design features, which requires representative test data applicable to SMR designs. Computer code, modeling and simulation (M&S) tools and models require adequate verification, assessment, and qualification. This includes M&S results validation against scaled empirical data within allowable uncertainty bands to gain regulatory approvals during the various stages of reactor system design, demonstration, and commercialization. However, measurement uncertainty within the empirical datasets and test data applicability ranges requires careful consideration of M&S inputs (i.e., boundary conditions, and initial conditions), and verification and validation efforts. This study focuses on uncertainty quantification in designing scaled test facilities for SMR applications with appropriate measurements and a standard data-reduction method to estimate thermal hydraulics characteristics parameters that incorporate physics phenomena of interest. In addition, this study supports the evaluation model development and assessment process using M&S that interfaces with advanced computing tools and digital twin capabilities. This will allow synchronization between experiment and modeling approaches for droplet entrainment testing and analysis, improving diagnostics, prognostics, and decision-making to accelerate regulatory approval.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

High-Burnup BWR LOCA Burst Analysis Using High-Fidelity Multiphysics Simulations

The US nuclear industry is looking to improve on the operating economics of the current fleet of light-water reactors (LWRs). One way of achieving this is by operating fuel to higher burnup. In pressurized water reactors (PWRs), relaxing the current burnup limit will allow for cycle length extensions and power uprates; in boiling water reactors (BWRs) it may allow for improved fuel utilization and reduced feed assemblies, as well as more efficient power uprates and increased capacity factors that will support the Administration’s Executive Order to facilitate 5 GW of power uprates at existing nuclear facilities. However, one of the key limitations to operating fuel to higher burnup is the risk of fuel fragmentation, relocation, and dispersal (FFRD). Recognizing the high interest in extending burnup limits, the US Nuclear Regulatory Commission (NRC) has issued Draft Regulatory Guide DG-1434, which defines an approach that would be acceptable to the NRC for addressing FFRD risk. The approach defined will require better understanding of the phenomena leading to FFRD as well as best-estimate simulation methods to understand FFRD risk in high-burnup cores. The Nuclear Energy Advanced Modeling and Simulation program is supporting the FFRD industry challenge problem through development of state-of-the-art, high-fidelity modeling and simulation LWR analysis capabilities; namely, the BISON fuel performance code and the VERA core simulator software. These tools, along with the US NRC TRACE system analysis code, have been utilized for analysis of FFRD risk in both PWR and BWR cores in recent years. The work documented in this report addresses the lack of high-fidelity research for BWRs and builds on a previous activity where the framework has been applied to Cycles 16 through 18 of Limerick Unit 1, a BWR/4, with introduction of 8 high-burnup lead use assemblies (HBLUAs) that were representative of the 8 HBLUAs loaded into Limerick Unit 2 in 2021. VERA was used in this previous activity to model rod-by-rod depletion in these cycles, and its solution was used to initialize a TRACE simulation of a large-break loss-of-coolant accident (LBLOCA) at the end of Cycle 18. In the work documented in this report, the TRACE model was improved by refining the core mesh and utilizing a new feature that allows for capturing the full 3D VERA power distribution in the model. This allows for a more detailed solution for setting BISON boundary conditions. Furthermore, the solutions from VERA and TRACE were used to set up and perform BISON simulations of about 1,000 rods sampled from the core, including all burnup levels. Utilizing two cladding burst models, it was shown that no fuel rods were predicted to burst during the postulated LBLOCA transient. Additionally, a sensitivity study was performed by artificially increasing linear heat rate during the postulated LBLOCA to identify parameters that correlate with rod burst susceptibility. Burnup, fission gas release, and hoop strain were all found to be positively correlated with rod burst susceptibility. Small-break loss-of-coolant accident (SBLOCA) analyses were also performed; these analyses predicted cladding temperature increases that were bounded by the LBLOCA cladding temperatures for all small break sizes studied for this plant. However, future refinements to the plant response assumptions during the SBLOCA could impact the predicted cladding response. Finally, a benchmark study was performed between CTF and TRACE for LOCA conditions to better qualify CTF for BWR LOCA modeling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Design Report on the MiniFuel Instrumented Test Apparatus for Understanding Radiation Effects

Most nuclear fuels irradiations at Oak Ridge National Laboratory (ORNL) over the past decade have been conducted using MiniFuel—a static capsule design employing subscale fuel specimens to collect separate-effects irradiated fuel performance data. Irradiation conditions for MiniFuel experiments are predicted pre-test using reactor physics, and thermal models are verified post-irradiation via SiC dilatometry and various spectrometry methods. Relevant fuel performance parameters are also observed post-irradiation in a hot cell, thereby providing a single data point for each parameter representing the cumulative effects of the irradiation conditions. Substantially more data can be harvested from a single test and within a shorter duration by instrumenting irradiation vehicles and measuring desired quantities in situ. This report presents the design and analysis of the MiniFuel INstrumented Irradiation Test Apparatus for Understanding Radiation Effects (MINITAURE)—an instrumented test rig based on the separate-effects MiniFuel concept that aims to capture fission gas release (FGR) and thermal conductivity degradation of fuel specimens during irradiation in the High Flux Isotope Reactor (HFIR). MINITAURE will be integrated with the Materials Irradiation Facility (MIF) located in the HFIR building outside the reactor containment. The MIF will act as the instrumentation and control center for the experiment, enabling real-time feedback from in situ sensors and control of irradiation temperatures via a gas delivery system. Two unique capsule designs were developed to capture each phenomenon: the thermal conductivity capsule, which uses a thermopile method to estimate fuel specimen thermal conductivity, and the fission gas release capsule, which will have continuous flowing gas communication to high-purity germanium detectors that are housed in the MIF for monitoring FGR. This report details the reactor physics and heat transfer modeling activities that were used to inform the experiment design and predict capsule performance. It also describes out-of-pile activities conducted to stand up this new capability and verify the measurement techniques. Modeling efforts to date have demonstrated the feasibility of the in situ measurement techniques and supported the development of the MINITAURE assembly configuration. Out-of-pile testing of the thermal conductivity measurement shows promise in capturing relative changes in thermal conductivity. However, significant errors exist in the measured absolute value, posing a need for further refinement.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Sitka Energy Inventory

The City and Borough of Sitka was chosen for an Energy Technology Innovation Partner Project award starting in Fall 2023, the third cohort of the program. The scope of the following work was driven by the city, as well as the city-appointed Sustainability Commission. The community asked for a method to track their reliance on outside fuel sources, as well as the emissions associated with those fuels and other processes in Sitka. The Pacific Northwest National Laboratory (PNNL) provided research and methodology in line with this community driven ask. This project is a community wide inventory, requiring methods to estimate the fuel usage and emissions from various industries. For each estimate, PNNL presents methodologies that can be repeated by the community in future years to understand how the city is changing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Integrated Life Cycle and Techno-Economic Assessments of Central Appalachian Legacy Mine Sites for Biomass Development and Waste Coal Utilization

This project, funded by the U.S. Department of Energy – National Energy Technology Laboratory (DOE-NETL) under award DE-FE0032212, evaluated how legacy coal mine lands and coal refuse piles in Central Appalachia (West Virginia and Pennsylvania) can be reclaimed and repurposed to support biomass development and beneficial utilization of waste coal, with the long-term goal of supporting net-zero or net-negative greenhouse gas (GHG) pathways. The project had two primary objectives: 1. Characterize legacy mine sites (including site conditions, waste coal/refuse resources, and soil/ecosystem indicators) and develop reclamation and best management practices (BMPs) for biomass cultivation; and 2. Conduct integrated machine learning (ML)-assisted life cycle assessment (LCA) and techno-economic analysis (TEA) to quantify environmental and economic outcomes for multiple biomass and waste-coal utilization pathways. Across West Virginia, the team identified ~625 coal refuse sites covering ~19,705 acres, and developed methods to estimate refuse pile volume using digital elevation models (DEMs) and geospatial workflows. A large subset of sites received volume estimates totaling ~1.6 billion m³.

01 COAL, LIGNITE, AND PEAT↗

A Vibrational Energy Harvesting Sensor Based on Linear and Rotational Electromechanical Effects

In this investigation, a magnetically coupled double-spring design is presented for harvesting low-level non-stationary random vibrational energy. The sensor relies on multimodal coupling between the translation and rotation of a two-spring magnet and coil system to widen the harvesting bandwidth. Energy methods are used to develop a model to characterize the electromechanical response of the system, the solution of which is obtained using stochastic techniques based on a particle swarm algorithm. This approach provides an efficient method to estimate system parameters that otherwise are difficult or impossible to determine with independent measurements. The experimental results demonstrate agreement with the theoretical predictions over a limited bandwidth. The sensor can effectively harvest non-stationary vibration energy down to 10 -4 g within a limited bandwidth of 130–150 Hz. The sensor prototype has an operational volume of 2.6 cm 3 with a calculated power density of 0.2 W/cm 3 . The sensor’s small size results in a coupling efficiency of approximately 6% across the tested bandwidth.

42 ENGINEERING↗

Multiparameter Constraints on Empirical Infrasound Period-Yield Relations for Bolides and Implications for Planetary Defense

How effective are methods for estimating bolide energies from infrasound signal period-yield relationships? A single global period–energy relation can obscure significant variability introduced by parameters such as the atmospheric Doppler wind profile and the bolide’s energy deposition profile as a function of altitude. Bolide speed, entry angle, burst altitude, and multiepisode fragmentation may all play a role in defining the detected period of the shockwave. By leveraging bolide light-curve data from the Center for Near Earth Object Studies, we re-examined the period–energy relation as a function of these parameters. Through a bootstrap approach, we show that various event subsets can deviate from widely cited period–energy models and we identify which specific conditions most strongly reshape the period–energy scaling. The results define both the fidelity and reliability of period–energy relations when no additional data beyond the infrasound record is available and improve the outcome when supporting data from bolide trajectories and light curves are included. Ultimately, these findings expand the scope of earlier models, providing a nuanced and robust framework for infrasound-only yield estimation under a range of bolide scenarios.

Planetary science↗

Stochastic Averaging of Radiative Transfer Coefficients for Relativistic Electrons

Abstract Synchrotron emissivities, absorptivities, and Faraday rotation and conversion coefficients are needed in modeling a variety of astrophysical sources, including Event Horizon Telescope (EHT) sources. We develop a method for estimating transfer coefficients that exploits their linear dependence on the electron distribution function, decomposing the distribution function into a sum of parts each of whose emissivity can be calculated easily. We refer to this procedure as stochastic averaging and apply it in two contexts. First, we use it to estimate the emissivity of an isotropicκdistribution function with a high-energy cutoff. The resulting coefficients can be evaluated efficiently enough to be used directly in ray-tracing calculations, and we provide an example calculation. Second, we use stochastic averaging to assess the effect of subgrid turbulence on the volume-averaged emissivity and along the way provide a prescription for a turbulent emissivity. We find that for parameters appropriate to EHT sources turbulence reduces the emissivity slightly. In the infrared, turbulence can dramatically increase the emissivity.

Astronomy & Astrophysics↗