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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

Neural Network for Principle of Least Action

The principle of least action is the cornerstone of classical mechanics, theory of relativity, quantum mechanics, and thermodynamics. Here, we describe how a neural network (NN) learns to find the trajectory for a Lennard-Jones (LJ) system that maintains balance in minimizing the Onsager–Machlup (OM) action and maintaining the energy conservation. The phase-space trajectory thus calculated is in excellent agreement with the corresponding results from the “ground-truth” molecular dynamics (MD) simulation. Furthermore, we show that the NN can easily find structural transformation pathways for LJ clusters, for example, the basin-hopping transformation of an LJ 38 from an incomplete Mackay icosahedron to a truncated face-centered cubic octahedron. Unlike MD, the NN computes atomic trajectories over the entire temporal domain in one fell swoop, and the NN time step is a factor of 20 larger than the MD time step. The NN approach to OM action is quite general and can be adapted to model morphometrics in a variety of applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Introducing a Markov chain-based time calibration procedure for multi-channel particle detectors: application to the SuperFGD and ToF detectors of the T2K experiment

Inter-channel mis-synchronisation can be a limiting factor to the time resolution of high performance timing detectors with multiple readout channels and independent electronics units. In these systems, time calibration methods employed must be able to efficiently correct for minimal mis-synchronisation between channels and achieve the best detector performance. We present an iterative time calibration method based on Markov Chains, suitable for detector systems with multiple readout channels. Starting from correlated hit pairs alone, and without requiring an external reference time measurement, the method solves for fixed per-channel offsets, with precision limited only by the intrinsic single-channel resolution. A mathematical proof that the method is able to find the correct time offsets to be assigned to each detector channel in order to achieve inter-channel synchronisation is given, and it is shown that the number of iterations to reach convergence within the desired precision is controllable with a single parameter. Numerical studies are used to confirm unbiased recovery of true offsets. Finally, the application of the calibration method to the Super Fine-Grained Detector (SuperFGD) and the Time of Flight (TOF) detector at the upgraded T2K near detector (ND280) shows good improvement in overall timing resolution, demonstrating the effectiveness in a real-world scenario and scalability.

calibration and fitting methods↗
DOE OSTI2020

Predicting Solar Plant Generation with Machine Learning Techniques

Accurately predicting power generation for PV sites is critical for prioritizing relevant operations & maintenance activities, thereby extending the lifetime of a system and improving profit margins. A number of factors influence power generation at PV sites, including local weather, shading and soiling losses, design of modules, DC mismatches, and degradation over time. Other external factors such as curtailment and grid outages can also have a notable impact on power generation. Machine learning techniques can be used to provide more accurate predictions of PV power production by accounting for important weather and climate information neglected by current industry methods. This article will cover the deficiencies of those methods and will show how machine learning can dramatically improve power generation predictions.

14 SOLAR ENERGY↗
DOE OSTI2022

Multi-tool optimization for computer controlled optical surfacing

With the rapid development of precision technologies, the demand of high-precision optical surfaces has drastically increased. These optical surfaces are mainly fabricated with computer controlled optical surfacing (CCOS). In a CCOS process, a target surface removal profile is achieved by scheduling the dwell time for a set of machine tools. The optimized dwell time should be positive and smooth to ensure convergence to the target while considering CNC dynamics. The total run time of each machine tool is also expected to be balanced to improve the overall processing efficiency. In the past few decades, dwell time optimization for a single machine tool has been extensively developed. While the methods are applicable to multi-tool scenarios, they fail to consider the overall contributions of multiple tools simultaneously. In this paper, we conduct a systematic study on the strategies for multi-tool dwell time optimization and propose an innovative method for simultaneously scheduling dwell time for multiple tools for the first time. First, the influential factors to the positiveness and smoothness of dwell time solutions for a single machine tool are analyzed. The compensation strategies that minimize the residual while considering the CNC dynamics limit are then proposed. Afterwards, these strategies are extended to the proposed multi-tool optimization that further balances the run time of machine tools. Finally, the superiority of each strategy is carefully studied via simulation and experiment. The experiment is performed by bonnet polishing a 60 mm × 60 mm mirror with three tools of different diameters ( i.e., 12 mm, 8 mm, and 5 mm). The figure error of the mirror is reduced from 45.42 nm to 11.18 nm root mean square in 13.28 min. Moreover, the measured polishing result well coincides with the estimation, which proves the effectiveness of the proposed method.

36 MATERIALS SCIENCE↗
DOE OSTI2024

MicroBooNE investigations on the photon interpretation of the MiniBooNE low energy excess

The MicroBooNE experiment is a liquid argon time projection chamber with 85-ton active volume at Fermilab, operated from 2015 to 2020 to collect neutrino data from Fermilab's Booster Neutrino Beam. One of MicroBooNE's physics goals is to investigate possible explanations of the low-energy excess observed by the MiniBooNE experiment in $\nu_{\mu}\rightarrow \nu_{e}$ neutrino oscillation measurements. MicroBooNE has performed searches to test hypothetical interpretations of the MiniBooNE low-energy excess, including the underestimation of the photon background or instrinic $\nu_{e}$ background. This thesis presents MicroBooNE's searches for two neutral current (NC) single-photon production processes that contribute to the photon background of the MiniBooNE measurement: NC $\Delta$ resonance production followed by $\Delta$ radiative decay: $\Delta \rightarrow N\gamma$, and NC coherent single-photon production. Both searches take advantage of boosted decision trees to yield efficient background rejection, and a high-statistic NC $\pi^0$ measurement to constrain dominant background, and make use of MicroBooNE's first three years of data. The NC $\Delta \rightarrow N\gamma$ measurement yielded a bound on the $\Delta$ radiative decay process at 2.3 times the predicted nominal rate at 90\% confidence level(C.L.), disfavoring a candidate photon interpretation of the MiniBooNE low-energy excess as a factor of 3.18 times the nominal NC Δ radiative decay rate at the 94.8\% C.L. The NC coherent single photon measurement leads to the world's first experimental limit on the cross-section of this process below 1 GeV, of $1.49 \times 10^{-41} \text{cm}^2$ at 90\% C.L., corresponding to 24.0 times the nominal prediction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Time-temperature-superposition analysis of diverse datasets by the minimum-arclength method: long-term prediction with uncertainty margins

In a recent publication, we carried out an extensive analysis of an unsupervised method of determining optimum shift factors in time-temperature-superposition of accelerated-aging data that involves minimizing the vertical arclength to obtain the master curve. For synthetic Arrhenius data with a variety of noise distributions, the work showed that, in conjunction with bootstrap-resampling, the method can produce reliable estimates of the mean activation energy along with uncertainty quantification. Here, we apply the above method to six different datasets taken from the published literature and demonstrate accurate prediction of mean activation energy from the data as-is without the need for any pre-processing or fitting. We also compare uncertainty margins computed by second-order bootstrap with that by linear regression theory and show that the former appears to provide consistent margins in the presence of common noise types in real data, including intra-isotherm measurement-errors, sample-to-sample variations, and intrinsic deviation from perfect Arrhenius behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2022

What Determines the Number and the Timing of Pulses in Afternoon Precipitation in the Green Ocean Amazon (GoAmazon) Observations?

Using GoAmazon observations over the central Amazon, we investigate double-pulse events of afternoon precipitation for the first time. Relative humidity is the dominant factor in determining the onset timing and the number of pulses of precipitation. A moister free troposphere on single-pulse days features heavy precipitation in the early afternoon, which stabilizes the atmosphere and prohibits further convection. On early–onset double-pulse days, weaker precipitation from shallower convection in the first pulse leads to less consumption of atmospheric instability, making the environment favorable for a second pulse of stronger precipitation. A cluster tracking algorithm using the scanning radar reflectivity is developed to examine the relationship between precipitation pulses. Through explicitly tracking convective clusters that exist from the first to the second pulse, convective aggregation plays a more dominant role to the survival of clusters with larger final size, and on average around 20% of cluster survivals are due to natural growth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Tomography of pions and kaons in the QCD vacuum: Transverse momentum dependent parton distribution functions

We evaluate the pion and kaon transverse momentum dependent parton distribution functions in the instanton liquid model, a model of the QCD vacuum at low resolution. The relevant transverse momentum distributions (TMDs) are factored into a constituent quark distribution times a rapidity dependent soft factor from staple-shaped Wilson lines, for fixed parton longitudinal momentum and transverse separation. The results are evolved to higher rapidities using the Collins-Soper kernel and higher resolution using renormalization group evolution. The comparison to existing extractions of pion TMDs from Drell-Yan data is briefly discussed.

classical solutions in field theory↗
DOE OSTI2021

Search for Neutrino-Induced Neutral Current Delta Radiative Decay in MicroBooNE and a First Test of the MiniBooNE Low Energy Excess Under a Single Photon Hypothesis (submitted to PRL)

We report results from a search for neutrino-induced neutral current (NC) resonant Δ(1232) baryon production followed by Δ radiative decay, with a h0.8i GeV neutrino beam. Data corresponding to MicroBooNE’s first three years of operations (6.80×10 20 protons on target) are used to select single-photon events with one or zero protons and without charged leptons in the final state (1γ1p and 1γ0p, respectively). The background is constrained via an in-situ high-purity measurement of NC π 0 events, made possible via dedicated 2γ1p and 2γ0p selections. A total of 16 and 153 events are observed for the 1γ1p and 1γ0p selections, respectively, compared to a constrained background prediction of 20.5 ± 3.65(sys.) and 145.1 ± 13.8(sys.) events. The data lead to a bound on an anomalous enhancement of the normalization of NC Δ radiative decay of less than 2.3 times the predicted nominal rate for this process at the 90% confidence level (CL). The measurement disfavors a candidate photon interpretation of the MiniBooNE low-energy excess as a factor of 3.18 times the nominal NC Δ radiative decay rate at the 94.8% CL, in favor of the nominal prediction, and represents a greater than 50-fold improvement over the world’s best limit on single-photon production in NC interactions in the sub-GeV neutrino energy range.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A laser parameter study on enhancing proton generation from microtube foil targets

Abstract The interaction of an intense laser with a solid foil target can drive $$\sim$$ ∼ TV/m electric fields, accelerating ions to MeV energies. In this study, we experimentally observe that structured targets can dramatically enhance proton acceleration in the target normal sheath acceleration regime. At the Texas Petawatt Laser facility, we compared proton acceleration from a $$1\, {\upmu }\hbox {m}$$ 1 μ m flat Ag foil, to a fixed microtube structure 3D printed on the front side of the same foil type. A pulse length (140–450 fs) and intensity ((4–10) $$\times 10^{20}$$ × 10 20 W/cm $$^2$$ 2 ) study found an optimum laser configuration (140 fs, 4 $$\times 10^{20}$$ × 10 20 W/cm $$^2$$ 2 ), in which microtube targets increase the proton cutoff energy by 50% and the yield of highly energetic protons ( $$>10$$ > 10 MeV) by a factor of 8 $$\times$$ × . When the laser intensity reaches $$10^{21}$$ 10 21 W/cm $$^2$$ 2 , the prepulse shutters the microtubes with an overcritical plasma, damping their performance. 2D particle-in-cell simulations are performed, with and without the preplasma profile imported, to better understand the coupling of laser energy to the microtube targets. The simulations are in qualitative agreement with the experimental results, and show that the prepulse is necessary to account for when the laser intensity is sufficiently high.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

CFL Optimized Forward–Backward Runge–Kutta Schemes for the Shallow-Water Equations

Abstract We present the formulation and optimization of a Runge–Kutta-type time-stepping scheme for solving the shallow-water equations, aimed at substantially increasing the effective allowable time step over that of comparable methods. This scheme, called FB-RK(3,2), uses weighted forward–backward averaging of thickness data to advance the momentum equation. The weights for this averaging are chosen with an optimization process that employs a von Neumann–type analysis, ensuring that the weights maximize the admittable Courant number. Through a simplified local truncation error analysis and numerical experiments, we show that the method is at least second-order in time for any choice of weights and exhibits low dispersion and dissipation errors for well-resolved waves. Further, we show that an optimized FB-RK(3,2) can take time steps up to 2.8 times as large as a popular three-stage, third-order strong stability-preserving Runge–Kutta method in a quasi-linear test case. In fully nonlinear shallow-water test cases relevant to oceanic and atmospheric flows, FB-RK(3,2) outperforms SSPRK3 in admittable time step by factors roughly between 1.6 and 2.2, making the scheme approximately twice as computationally efficient with little to no effect on solution quality. Significance Statement The purpose of this work is to develop and optimize time-stepping schemes for models relevant to oceanic and atmospheric flows. Specifically, for the shallow-water equations we optimize for schemes that can take time steps as large as possible while retaining solution quality. We find that our optimized schemes can take time steps between 1.6 and 2.2 times larger than schemes that cost the same number of floating point operations, translating directly to a corresponding speedup. Our ultimate goal is to use these schemes in climate-scale simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

On the Impact of Income, Age, and Travel Distance on the Value of Time

The value of time (VOT) is a fundamental component used in transportation modeling, policy analysis, and economic appraisal. Decades of research and practice have empirically estimated the VOT across many factors (e.g., mode, purpose, time, comfort, etc.), yet little is known about its underlying form. Although it is well established that VOT can vary, it is still unclear whether patterns exist in this variation. The objective of this paper is not to merely estimate the VOT, but to model the VOT across multiple continuous and interacting variables. The purpose is to reveal its functional form with respect to mode, age, gender, purpose, income, and time of day to provide a generalizable understanding for future research and practice. Such an understanding can help develop simpler models and reduce the need for bespoke estimations for every conceivable variable perturbation. This research utilized a household travel survey containing 14,159 reported trips with imputed travel time and costs for the alternative mode choices. The average overall estimated VOT is 40.32 $/h, with results showing VOT varying log-linearly with income and trip distance, but following a Gaussian function (normal curve) with age. Overall, the results show that travel distance dominates VOT variation, which increases exponentially at a rate that is 3.61 times higher per mile of distance than per $10,000 of income, and that VOT by age peaks at age 54. This basic understanding of how the VOT varies sets the foundation for answering the subsequent question for why it might vary.

Engineering↗
DOE OSTI2021

Evolving CO 2 rather than SST leads to a factor of ten decrease in GCM convergence time

The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the CO 2 mixing ratio to equilibrate climate, has been implemented in a cloud resolving model. In this paper, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global-mean SST. We find that InvCM produces the same climate as normal slab-ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium CO 2 for SSTs ranging from 290 K to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quarkonium semiclassical transport in quark-gluon plasma: factorization and quantum correction

We study quarkonium transport in the quark-gluon plasma by using the potential nonrelativistic QCD (pNRQCD) effective field theory and the framework of open quantum systems. We argue that the coupling between quarkonium and the thermal bath is weak using separation of scales, so the initial density matrix of the total system factorizes and the time evolution of the subsystem is Markovian. We derive the semiclassical Boltzmann equation for quarkonium by applying a Wigner transform to the Lindblad equation and carrying out a semiclassical expansion. We resum relevant interactions to all orders in the coupling constant at leading power of the nonrelativistic and multipole expansions. The derivation is valid for both weakly coupled and strongly coupled quark-gluon plasmas. We find reaction rates in the transport equation factorize into a quarkonium dipole transition function and a chromoelectric gluon distribution function. For the differential reaction rate, the definition of the momentum dependent chromoelectric gluon distribution function involves staple-shaped Wilson lines. For the inclusive reaction rate, the Wilson lines collapse into a straight line along the real time axis and the distribution becomes momentum independent. The relation between the two Wilson lines is analogous to the relation between the Wilson lines appearing in the gluon parton distribution function (PDF) and the gluon transverse momentum dependent parton distribution function (TMDPDF). The centrality dependence of the quarkonium nuclear modification factor measured by experiments probes the momentum independent distribution while the transverse momentum dependence and measurements of the azimuthal angular anisotropy may be able to probe the momentum dependent one. We discuss one way to indirectly constrain the quarkonium in-medium real potential by using the factorization formula and lattice calculations. The leading quantum correction to the semiclassical transport equation of quarkonium is also worked out. The study can be easily generalized to quarkonium transport in cold nuclear matter, which is relevant for quarkonium production in eA collisions in the future Electron-Ion Collider.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Processing Compressed Expanded Natural Graphite for Phase Change Material Composites

Phase change materials (PCMs) are used in various thermal energy storage applications but are limited by their low thermal conductivity. One method to increase conductivity involves impregnating organic PCMs into highly porous conductive matrix materials. Of these materials, compressed expanded natural graphite (CENG) matrices have received the most attention. Despite this attention, the effect that CENG processing has on PCM saturation and overall matrix thermal conductivity has not been fully investigated. Therefore, the effect of the heat treatment process used to expand intercalated graphite flakes is evaluated here. Higher heat treatment temperatures yielded higher saturation rates and overall saturation at similar matrix porosities. For example, increasing temperature from 300 °C to 700 °C resulted in approximately 60%-70% increase in pore saturation after 100 minutes of soaking. The exposure time to heat treatment had less of an effect on PCM saturation. The exposure time had negligible effect above 30 min and above 500 °C heating temperatures. However, because the expanded graphite was found to oxidize around 700 °C, the use of longer exposure time in manufacturing applications can be beneficial if a shortened impregnation time is needed. Heat treatment conditions did not impact thermal conductivity. The composite latent heat of fusion was also reduced approximately proportionally to the PCM mass fraction. A local maximum in axial thermal conductivity was observed at around 83% porosity, which is similar to previous studies. The observed conductivity at this maximum was a factor of 81 times greater than the conductivity of the PCM.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Plastic scintillation detectors for time-of-flight mass measurements

Fast timing detectors are an essential element in the experimental setup for time-of-flight (ToF) mass measurements of unstable nuclei. We have upgraded the scintillator detectors used in experiments at the National Superconducting Cyclotron Laboratory (NSCL) by increasing the number of photomultiplier tubes that read out their light signals to four per detector, and characterized them in a test experiment with 48 Ca beam at the NSCL. The new detectors achieved a time resolution (σ) of 7.5 ps. We systematically investigated different factors that affect their timing performance. In addition, we evaluated the ability of positioning the hitting points on the scintillator using the timing information and obtained a resolution (σ) below 1 mm for well-defined beam spots.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Optimizing the hit finding algorithm for liquid argon TPC neutrino detectors using parallel architectures

Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming even more difficult as the detectors increase in size to reach their physics goals. Liquid argon time projection chamber (LArTPC) neutrino experiments are expected to grow in the next decade to have 100 times more wires than in currently operating experiments, and modernization of LArTPC reconstruction code, including parallelization both at data- and instruction-level, will help to mitigate this challenge. The LArTPC hit finding algorithm is used across multiple experiments through a common software framework. In this paper we discuss a parallel implementation of this algorithm. Using a standalone setup we find speedup factors of two times from vectorization and 30–100 times from multi-threading on Intel architectures. The new version has been incorporated back into the framework so that it can be used by experiments. On a serial execution, the integrated version is about 10 times faster than the previous one and, once parallelization is enabled, more speedups comparable to the standalone program are achieved.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Factors Influencing Willingness to Pool in Ride-Hailing Trips

In the past decade, transportation network companies (TNCs) such as Uber, Lyft, and Via have established themselves as a viable transportation alternative to other modes. However, the popularity of these services has come with a fair share of criticism for their negative externalities such as increasing vehicle miles traveled and congestion in cities. Pooled ride-hailing trips, in which all or a part of two individual (or group) trips are combined in and served by a single vehicle, have the potential to reduce these externalities. Pooling of rides is an effective solution to reduce congestion and travel cost, but pooled rides still represent a small percentage of the total trips served (and miles driven) by TNCs relative to single-occupancy (and without customer) vehicle miles. Both TNCs and cities alike will benefit from understanding what factors encourage or deter pooling a ride-hailing trip. In this study, newly available Chicago transportation network provider data were explored to identify the extent to which different socioeconomic, spatiotemporal, and trip characteristics affect willingness to pool (WTP) in ride-hailing trips. Furthermore, multivariate linear regression and machine-learning models were employed to understand and predict WTP based on location, time, and trip factors. The results show intuitive trends, with income level at drop-off and pickup locations and airport trips as the most important predictors of WTP. Results from this study can help TNCs and cities devise strategies that increase pooled ride-hailing, thereby reducing adverse transportation and energy impacts from ride-hailing modes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗