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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Optimizing entanglement and Bell inequality violation in top antitop events

A top quark and an antitop quark produced together at colliders have correlated spins. These spins constitute a quantum state that can exhibit entanglement and violate Bell’s inequality. In realistic collider experiments, most analyses allow the axes, as well the Lorentz frame, to vary event by event, thus introducing a dependence on the choice of event-dependent basis leading us to adopt “fictitious states,” rather than genuine quantum states. The basis dependence of fictitious states allows for an optimization procedure, which makes the usage of fictitious states advantageous in measuring entanglement and Bell inequality violation. In this work, we show analytically that the basis that diagonalizes the spin-spin correlations is optimal for maximizing spin correlations, entanglement, and Bell inequality violation. We show that the optimal basis is approximately the same as the fixed beam basis (or the rotated beam basis) near the t t ¯ production threshold, while it approaches the helicity basis far above threshold. Using this basis, we present the sensitivity for entanglement and Bell inequality violation in t t ¯ events at the Large Hadron Collider (LHC) and a future e + e − collider. Since observing Bell inequality violation appears to be quite challenging experimentally, and requires a large dataset in collider experiments, choosing the optimal basis is crucially important to observe Bell inequality violation. Our method and general approach are equally applicable to other systems beyond t t ¯ , including interactions beyond the Standard Model. Published by the American Physical Society 2025

Cheng, Kun (ORCID:0000000249592997)↗
DOE OSTI2024

Collaborative Research: Enhancing Laser-Based Ion Sources with High Data Rate Techniques

This collaborative research project focuses on leveraging advanced machine learning techniques to analyze and optimize data from high-repetition-rate laser experiments. The main goal is to apply modern computing hardware, customized data acquisition firmware/software, and machine learning approaches to improve data analysis and experimental control. The project also explores how methodology can be developed on smaller-scale experimental setups and then translated to larger facilities within DOE's LaserNetUS network. With extensive data collection and modeling, the research aims to predict and optimize experimental parameters to enhance performance and efficiency.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Preliminary Design of a New Pulsed-Neutron Die-Away Experimental Testbed for Thermal Scattering Law Benchmarks (PNDA) (IER-501 CED-1)

This report presents the preliminary design (CED-1) of IER-501, a new testbed for pulsed-neutron die-away (PNDA) experiments at Lawrence Livermore National Laboratory (LLNL). The PNDA experiments will be high-quality benchmarks that serve to optimize and validate thermal scattering laws (TSLs), which are high-priority nuclear data for the Department of Energy’s Criticality Safety Program (NCSP). PNDA experiments can be used to generate nuclear data validation benchmarks quickly and cheaply, as they do not require fissile material or a nuclear facility to conduct them.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1988

Effect of Transition Aerodynamics on Aeroassist Flight Experiment Trajectories

Various transition methods are used here to study the viscous effects encountered in low density, hypersonic flight, through the transition from free molecular to continuum flow. Methods utilizing Viking data, Shuttle Orbiter data, a Potter number parameter, and a Shock Reynolds number were implemented in the Program to Optimize Simulated Trajectories (POST). Simulations of the Aeroassist Flight Experiment (AFE) using open loop guidance were used to assess the aerodynamic performance of the vehicle. A bank angle was found for each transition method that would result in a 200 nautical-mile apogee. Once this was done, the open loop guidance was replaced by the proposed guidance algorithm for the AFE. Simulations were again conducted using that guidance and the different transitions for comparison. For the gains used, the guidance system showed some sensitivity in apogee altitude to the transition method assumed, but the guidance was able to successfully complete the mission.

Minier, Elizabeth A.↗
DOE OSTI2023

Enhancing strontium clock atom interferometry using quantum optimal control

Strontium clock atom interferometry is a promising new technology, with multiple experiments under development around the world to explore its potential for dark matter and gravitational wave detection. In these detectors, large momentum transfer using sequences of many laser pulses is necessary, and thus high-fidelity pulses are important since small errors become magnified. Quantum optimal control (QOC) is a framework for developing control pulse waveforms that achieve high fidelity and are robust against experimental imperfections. Resonant single-photon transitions using the narrow clock transition of strontium involve significantly different quantum dynamics than more established atom interferometry methods based on far-detuned two-photon Raman or Bragg transitions, which leads to new opportunities and challenges when applying QOC. Here, we study in simulation QOC pulses for strontium clock interferometry and demonstrate their advantage over basic square pulses (primitive pulses) and composite pulses in terms of robustness against multiple noise channels. In conclusion, this could improve the scale of large momentum transfer in Sr clock interferometers, paving the way to achieving these scientific goals.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Insights into Constraining Rate Coefficients in Fuel Oxidation Mechanisms Using Genetic Algorithm Optimization

Accurate fuel oxidation mechanisms can enable predictive capabilities that aid in advancing combustion technologies. High-level computational kinetics can yield reasonable rate coefficients with uncertainties, in some cases, below a factor of 2. Computed rate coefficients can be constrained further by optimizing against experimental data. Here, we explore the application of genetic algorithm (GA) optimization to constrain computed rate coefficients in complex fuel oxidation mechanisms in conjunction with temperature-dependent species mole fractions from jet-stirred reactor (JSR) measurements. Cyclohexane is a model candidate for understanding the reactivity of cyclic fuels. In this work, we optimize the rate coefficients of the most recent literature cyclohexane mechanism, which incorporates theoretically computed rate coefficients for the reaction networks stemming from the first and second O 2 addition pathways, against the experimental results of two separate literature JSR studies. Optimization consistency is evaluated by carrying out three GA optimizations: fitting to the temperature-dependent species mole fractions in each JSR experiment separately and simultaneously fitting the species mole fractions in both experiments. Local sensitivity analyses are used to identify five influential low-temperature oxidation reactions for optimization. Although the three optimizations do not yield identical rate coefficients, the direction of change in all five rate coefficients is consistent among the three optimizations. Performance of the models from the three optimizations is assessed against literature ignition delay times with differences in the level of agreement observed among the different optimizations. In conclusion, comparisons are made with our recent optimization work of a cyclopentane oxidation master-equation model against time-resolved species concentrations, and insights and improvements of the strategy for constraining rate coefficients using GA optimization are discussed.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2007

Improved Modeling of Land-Atmosphere Interactions using a Coupled Version of WRF with the Land Information System

The exchange of energy and moisture between the Earth's surface and the atmospheric boundary layer plays a critical role in many hydrometeorological processes. Accurate and high-resolution representations of surface properties such as sea-surface temperature (SST), vegetation, soil temperature and moisture content, and ground fluxes are necessary to better understand the Earth-atmosphere interactions and improve numerical predictions of weather and climate phenomena. The NASA/NWS Short-term Prediction Research and Transition (SPORT) Center is currently investigating the potential benefits of assimilating high-resolution datasets derived from the NASA moderate resolution imaging spectroradiometer (MODIS) instruments using the Weather Research and Forecasting (WRF) model and the Goddard Space Flight Center Land Information System (LIS). The LIS is a software framework that integrates satellite and ground-based observational and modeled data along with multiple land surface models (LSMs) and advanced computing tools to accurately characterize land surface states and fluxes. The LIS can be run uncoupled to provide a high-resolution land surface initial condition, and can also be run in a coupled mode with WRF to integrate surface and soil quantities using any of the LSMs available in LIS. The LIS also includes the ability to optimize the initialization of surface and soil variables by tuning the spin-up time period and atmospheric forcing parameters, which cannot be done in the standard WRF. Among the datasets available from MODIS, a leaf-area index field and composite SST analysis are used to improve the lower boundary and initial conditions to the LIS/WRF coupled model over both land and water. Experiments will be conducted to measure the potential benefits from using the coupled LIS/WRF model over the Florida peninsula during May 2004. This month experienced relatively benign weather conditions, which will allow the experiments to focus on the local and mesoscale impacts of the high-resolution MODIS datasets and optimized soil and surface initial conditions. Follow-on experiments will examine the utility of such an optimized WRF configuration for more complex weather scenarios such as convective initiation. This paper will provide an overview of the experiment design and present preliminary results from selected cases in May 2004.

Case, Jonathan L.↗
DOE OSTI2020

Effect of Nuclear Data Covariances on Integral Experiment Design with Sensitivity and Uncertainty Analysis

Washington River Protection Solutions (WRPS) uses MCNP6.2 and the Whisper code for criticality safety analyses of the Hanford Tank Farm. Together the codes derive baseline upper subcritical limits (USLs) for the waste models using experimental benchmarks. Whisper returns higher USLs, i.e. , has less of a conservative penalty, when the neutronic similarity of the experimental benchmarks to the application is high. Unfortunately, few critical benchmarks have high similarity to the Hanford tanks. The waste in the tanks is highly dilute in plutonium and contains large masses of weakly neutron-absorbing elements like iron and manganese. Experimental benchmarks typically have low sensitivity to these absorbers because they are present as structural materials. Lacking similar benchmarks, new Thermal Epithermal eXperiment (TEX) configurations with high Pu content and interstitial iron absorbers have been designed for the criticality safety validation. The features of the design have been iterated upon to maximize the similarity between the experiment and different Hanford waste models. The similarity is quantified with sensitivity analysis and uncertainty quantification using the representativity coefficient, or c k . The representativity calculation requires nuclear data covariances, which may differ between nuclear data libraries and between library versions. Because of these variations, the optimal design may depend on the nuclear data covariances library. A scenario can be envisioned where an experiment is designed, and c k is maximized, with one set of covariance data. However, when the covariance data is changed, say from ENDF/B-VII.1 to ENDF/B-VIII.0, and the benchmark is used in a criticality safety evaluation, the experiment becomes suboptimal with respect to c k . In this paper, we present how the optimal design of the new TEX experiments varied depending on the nuclear data covariances used to calculate c k . We compare ENDF/B-VII.1 and ENDF/B-VIII.0, as if the library had been updated since the design of the experiment. Additionally, we use JEFF3.3 to simulate if the covariance data of a different library had been used. The results show that the covariances do have an important effect on the designs, less so for thermal systems (where the data are more consistent between evaluations) and more so for epithermal systems where more differences exist.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Optimization of battery swapping infrastructure for e-commerce drone delivery

Drone delivery is widely-researched to alter the current e-commerce delivery convention for providing short delivery lead times. Yet, flight range of drones, constrained by the available battery technology, sets a milestone toward realizing the sole-drone delivery. To tackle with the flight range limitation, locating automated battery swapping machines (ABSM) have been proposed and a few studies modeled the problem. Using the ABSMs, drones can be loaded with fully-charged batteries along the route to a demand location. Here, we introduce a mixed-integer nonlinear program to model the problem. The objective of the program is to optimally select ABSM locations, determine the delivery-mode choices (drone-only, truck-only, and mixed delivery) of demand locations, find drone delivery routes, and approximate the baseline requirements for the number of drones and batteries needed. The program minimizes the overall delivery system costs including: ABSM, delivery, drone ownership, battery inventory, and service congestion. A cutting-plane method is developed to find exact solutions in finite iterations. Computational experiments showed that the method quickly yields the optimal solution to instances with less than 60 ABSM candidates and 20 demand locations. A case study shows that the optimal drone delivery infrastructure can save almost 20% cost compared to the conventional truck-only delivery. Sensitivity analyses were conducted to reveal the impact of key parameters in the decision-making and found that a decrease in the ABSM and drone costs highly affect the system cost.

25 ENERGY STORAGE↗
NASA NTRS2001

Global Optimization Techniques for Fluid Flow and Propulsion Devices

This viewgraph presentation gives an overview of global optimization techniques for fluid flow and propulsion devices. Details are given on the need, characteristics, and techniques for global optimization. The techniques include response surface methodology (RSM), neural networks and back-propagation neural networks, design of experiments, face centered composite design (FCCD), orthogonal arrays, outlier analysis, and design optimization.

Shyy, Wei↗
DOE OSTI2020

Optimization of a Cyclone Using Multiphase Flow Computational Fluid Dynamics

The U.S. Department of Energy National Energy Technology Laboratory's (NETL) 50 kW th chemical looping reactor (CLR) has an underperforming cyclone, which was designed using empirical correlations. To improve the performance of this cyclone using computational fluid dynamics (CFD)-based modeling simulations, four critical design parameters including the vortex tube radius and length, barrel radius, and the inlet width and height were optimized. Here, NETL's open source multiphase flow with interphase exchange (MFiX) CFD code has been used to model a series of cyclones by systematically varying the geometric design parameters. To perform the optimization process, the surrogate modeling and sensitivity analysis followed by the optimization capability in nodeworks was used. The basic methodology for the process is to employ a statistical design of experiments (DOE) method to generate sampling simulations that fill the design space. Corresponding CFD models are then created, executed, and postprocessed. A response surface is created to characterize the relationship between input parameters and the quantities of interest (QoI). Finally, the CFD-surrogate is used by an optimization method to find the optimal design condition based on the objective and constraints prescribed. The resulting optimal cyclone has a larger diameter and longer vortex tube, a larger diameter barrel, and a taller and narrower solids inlet. The improved design has a predicted pressure drop 11 times lower than the original design while reducing the mass loss by a factor of 2.3.

42 ENGINEERING↗
DOE OSTI2021

The design of a photoionization front experiment using the Z-Machine as a driving source and estimated measurements

Radiation-driven heat fronts are present in the early universe during reionization, the circumstellar medium of supernovae, and in high-energy-density physics experiments. Dedicated experiments to observe and diagnose the behavior of these types of heat fronts can improve our understanding of these phenomena. A simulation study of photoionization fronts using the HELIOS-CR radiation hydrodynamics code provides an experimental design for the Z-Machine at Sandia National Laboratory using a measurement-calibrated input radiation flux to drive the photoionization front. The simulations use detailed atomic physics and non-diffusive radiation transport in 1D to determine an optimal gas pressure of 0.75 atm for an experiment in N gas as well as the effects of increasing the thickness of the window that seals the gas cell. Post-processing of these simulations demonstrates that ratios of atomic rate coefficients place the heat front in a physics regime where photoionization dominates the energy deposition. Here, to see the sensitivity of the simulations to changes in the model and spatial grid, this analysis performed resolution, atomic model detail, and radiation transport angular grid studies showing less than 10% deviation from the nominal model for increased complexity, when possible. An effort to emulate 3D geometric effects on the radiation flux using an artificial attenuation scheme has shown that, even for conservative estimates of the flux, simulations still produce a photoionization front. Estimations of a streaked, visible spectroscopy measurement using SPECT3D showed that line emission measurements are present early in time and that later in time thermal emission should become dominant.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Challenges and Optimization of Mu2e Proton Target Design with Radiative Cooling

Mu2e, the Muon-to-Electron Conversion Experiment, aims to identify physics beyond the Standard Model, namely, the conversion of muons to electrons without the emission of neutrinos. The muons are produced from pions generated in a production target when it is hit by an 8 GeV proton beam from the Fermilab Booster/Main Injector. The proton target design space is strongly constrained by a one-year operating lifetime and the need for radiative cooling in a vacuum environment. Uncertainties in the lifetime of the existing baseline design – a monolithic, segmented tungsten target – are large, particularly due to unknown impacts of radiation damage at the very high proton fluences expected in the experiment. We have begun evaluation of a new design utilizing Inconel 718 over the WL10 used in the existing target design. As a result, the structural design of the target has evolved significantly. This evolution focuses on lowering the target temperature, minimizing obstruction to muons, increasing structural stability, maximizing fatigue lifetime, simplifying the fabrication process, and more. The thermal management, structural stability and fatigue lifetime are emphasized here. These optimizations have led to a promising new target design for the Mu2e experiment.

Liu, Z. [Fermilab]↗
DOE OSTI2023

A Suite of Constitutive Promoters for Tuning Gene Expression in Plants

The need for convenient tools to express transgenes over a large dynamic range is pervasive throughout plant synthetic biology; however, current efforts are largely limited by the heavy reliance on a small set of strong promoters, precluding more nuanced and refined engineering endeavors in planta . To address this technical gap, we characterize a suite of constitutive promoters that span a wide range of transcriptional levels and develop a GoldenGate-based plasmid toolkit named PCONS, optimized for versatile cloning and rapid testing of transgene expression at varying strengths. We demonstrate how easy access to a stepwise gradient of expression levels can be used for optimizing synthetic transcriptional systems and the production of small molecules in planta . We also systematically investigate the potential of using PCONS as an internal standard in plant biology experimental design, establishing the best practices for signal normalization in experiments. Although our library has primarily been developed for optimizing expression in N. benthamiana , we demonstrate the translatability of our promoters across distantly related species using a multiplexed reporter assay with barcoded transcripts. In conclusion, our findings showcase the advantages of the PCONS library as an invaluable toolkit for plant synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts

Reducing human reliance on inefficient energy systems and fossil fuels has become more urgent due to the consequences of global climate change. However, traditional trial-and-error approaches have hampered our ability to accelerate the discovery and implementation of functional materials for efficient energy conversion devices, such as polymer electrolyte fuel cells (PEFCs). To address this, we develop an adaptive learning framework that integrates machine learning and state-of-the-art capabilities in high-throughput synthesis to achieve expedited optimization of iron-nitrogen-carbon PEFC oxygen reduction reaction (ORR) electrocatalysts. We use statistical inference, uncertainty quantification, and global optimization to build a computational design-of-experiment tool that identifies the optimum compositions to be investigated next to reduce the demands placed on experimental materials discovery. We benchmark the ability of the proposed strategy to discover optimum catalyst synthesis conditions in a six-dimensional search space when starting with a thirty-six-sample database. By following the adaptive learning strategy, we synthesize fourteen new catalysts from approximately ten billion unique compositions and discover four catalysts that outperform all original samples. The best machine learning-optimized catalyst is 33% more active than the highest-performing one in the initial database, showing an ORR activity seven times larger than those typically reported for the same class of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Framework for Optimization, Quantification of Uncertainty, and Surrogates, and CCSI2 Toolset Capabilities

This poster will present key features and capabilities of the CCSI2 software platform to relevant technical and research audience members at the 2024 FECM / NETL Carbon Management Research Project Review Meeting. Attendees will learn about available process models in the CCSI2 Toolset, as well as design of experiments and machine learning features in the Framework for Optimization, Quantification of Uncertainty, and Surrogates (FOQUS) software to support pilot plant work.

Paul, Brandon↗
NASA NTRS1992

Mapping of sea bottom topography

Under suitable conditions the bottom topography of shallow seas is visible in remote sensing radar imagery. Two experiments were performed to establish which remote sensing technique or combination yields optimal imaging of bottom topography and which hydro-meteorological conditions are favorable. A further goal is to gain experience with these techniques. Two experiments were performed over an area in the North Sea near the measuring platform Meetpost Noordwijk (MPN). The bottom topography in the test area is dominated by sand waves. The crests of the sand waves are perpendicular to the coast line and the dominating (tidal-)current direction. A 4x4 sq km wide section of the test area was studied in more detail. The first experiment was undertaken on 16 Aug. 1989. During the experiment the following remote sensing instruments were used: Landsat-Thematic Mapper, and NASA/JPL Airborne Imaging Radar (AIR). The hydro-meteorological conditions; current, wind, wave, and air and water temperature were monitored by MPN, a ship of Rijkswaterstaat (the OCTANS), and a pitch-and-roll WAVEC-buoy. The second experiment took place on 12 July 1992. During this experiment data were collected with the NASA/JPL polarimetric synthetic aperture radar (SAR), and a five-band helicopter-borne scatterometer. Again the hydro-meteorological conditions were monitored at MPN and the OCTANS. Furthermore, interferometric radar data were collected.

Calkoen, C. J.↗
DOE OSTI2024

Nonempirical Prediction of the Length-Dependent Ionization Potential in Molecular Chains

The ionization potential of molecular chains is well-known to be a tunable nanoscale property that exhibits clear quantum confinement effects. State-of-the-art methods can accurately predict the ionization potential in the small molecule limit and in the solid-state limit, but for intermediate, nanosized systems prediction of the evolution of the electronic structure between the two limits is more difficult. Recently, optimal tuning of range-separated hybrid functionals has emerged as a highly accurate method for predicting ionization potentials. This was first achieved for molecules using the ionization potential theorem (IPT) and more recently extended to solid-state systems, based on an ansatz that generalizes the IPT to the removal of charge from a localized Wannier function. Here, we study one-dimensional molecular chains of increasing size, from the monomer limit to the infinite polymer limit using this approach. By comparing our results with other localization-based methods and where available with experiment, we demonstrate that Wannier-localization-based optimal tuning is highly accurate in predicting ionization potentials for any chain length, including the nanoscale regime.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗