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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Analyzing count data with measurement error

In this article, we analyze observed count data such as the number of defects in a steel product where the observed counts are the true counts measured with errors. We account for the measurement error by using a measurement error model based on a latent lognormal (LLN) distribution. We consider making inference about a single population (e.g., from samples of a production lot) and a regression model (e.g., from runs of a designed experiment), where the measurement system properties are known, that is, the parameters of the LLN distribution are known. Then, we consider simultaneous inference for the single population and regression model as well as the measurement system. We demonstrate the proposed methodology with both simulated and real observed counts.

42 ENGINEERING↗
DOE OSTI2020

Shock‐tube spectroscopic water measurements and detailed kinetics modeling of 1‐pentene and 3‐methyl‐1‐butene

Abstract To understand the effects of the chemical structure of two C 5 alkene isomers on their combustion properties, and to highlight the major chemical reactions occurring during their high‐temperature oxidation, water time histories were measured behind reflected shock waves for the oxidation of 1‐pentene (C 5 H 10 ‐1) and 3‐methyl‐1‐butene (3M1B) in 99.5% Ar. The experiments were carried out at three different equivalence ratios ( φ = 0.5, 1.0, and 2.0) at pressures and temperatures ranging from 1.29 to 1.47 atm and 1 331 to 1 877 K, respectively. The H 2 O quantification extends the database for 1‐pentene and provides new insights for 3M1B. These unique results were used to validate and to develop a new detailed kinetics model. Numerical predictions are presented, and the new model was able to capture the results with suitable accuracy, with 3M1B being notably more reactive than C 5 H 10 ‐1. Sensitivity and rate‐of‐production analyses were performed to help explain the results. Under the present conditions, the reactivity is rapidly initiated by molecular dissociation of a fraction of the pentene isomers. The initiation phase then induces H‐atom abstraction by active radicals (H, OH, O, HO 2 , and CH 3 ) to first produce alkenyl C 5 H 9 radicals (or an alkyl radical and an alkenyl radical by breaking a C─C bond) and subsequent, smaller fragments. The difference in terms of reactivity between the isomers is essentially due to the fact that 3M1B has one particularly weak tertiary allylic C─H bond, which allows for fast H‐atom abstraction compared with 1‐pentene.

Grégoire, Claire M.↗
DOE OSTI2026

Measurement of charged-current quasi elastic-like and inclusive muon (anti-) neutrino interactions using NuMI beam the ICARUS detector

The ICARUS experiment, utilizing Liquid Argon Time Projection Chamber (LArTPC) technology, has been successfully taking physics data at Fermilab since June 2022. The experiment's primary objective is to function as the far detector of the Short Baseline Neutrino program (SBN), searching for hints of physics beyond three-flavour PMNS neutrino oscillations. ICARUS also offers other diverse physics capabilities, including searches beyond the standard model and measurements of cross-sections. In addition to being exposed to the Booster Neutrino (BNB) beamline of the SBN experiment, ICARUS sits 795 m downstream and 5.75 degrees off-axis from the NuMI beam line. Due to the off-axis angle between NuMI and ICARUS, coupled with contributions from both pion and kaon decays to neutrino fluxes, interactions of NuMI neutrinos within ICARUS can be detected over a range of several GeV in energy. Measurements of these interactions present unique opportunities to infer neutrino interaction cross sections on an argon nuclear target within an energy range that overlaps both the SBN oscillation search and a significant portion of the DUNE spectrum. This poster will summarize the current status of muon-neutrino and muon-antineutrino charged current inclusive cross section measurements in ICARUS using the FHC and RHC NuMI sample respectively. Such inclusive measurements provide robust benchmarks for the overall interaction rate and muon kinematics, with reduced dependence on hadronic final-state modeling. The measurement of the muon-antineutrino Argon charged current inclusive cross section is particularly exciting and highly relevant for DUNE, given the scarcity of antineutrino-Argon cross section measurements in the literature. In addition, this poster will highlight ICARUS's first measurement of muon-neutrino Argon charged current mesonless final states using both transverse kinematic imbalance (TKI) variables and muon-proton kinematic variables. These measurements provide sensitivity to nuclear effects, final-state interactions, and nucleon correlations in argon, offering important constraints for improving neutrino interaction modeling for upcoming DUNE.

Roy, Promita [Fermilab; Virginia Tech.]↗
DOE OSTI2020

Ignition Delay Measurements of Four Component Model Gasolines Exploring the Impacts of Biofuels and Aromatics

This study explores the impacts of combinations of biofuel (ethanol, isobutanol and 2-methyl furan) and aromatic (toluene) compounds in a four component fuel blend, at fixed research octane number (RON) on ignition delay measured in an advanced fuel ignition delay analyzer (AFIDA 2805). Ignition delay measurements were performed over a range of temperatures from 400 to 725 °C (673 to 998 K) and two chamber pressures of 10 and 20 bar. The four component mixtures are compared to primary reference fuels at RON values of 90 and 100. The ignition delay measurements show that as the aromatic and biofuel concentrations increased, two stage ignition behavior was suppressed, at both initial chamber pressures. But both RON 100 (isooctane) and RON 90 reference fuels showed two stage ignition behavior, as did fuel mixtures with low biofuel and aromatic content. RON 90 fuels showed stronger two stage ignition behavior than RON 100 fuels, as expected. Depending on the type of biofuel in the mixture, the ignition delay at low chamber temperatures could be far greater than for the reference fuels. In particular, for the RON 100 mixtures at either 10 or 20 bar initial chamber pressure, the ignition delay at 400 °C (673 K) for the high level blend of 2-methyl furan and toluene (30 vol% of each) exhibited an ignition delay that was 10 times longer than for neat isooctane. The results show the strong non-linear octane blending response of these three biofuel compounds, especially in concert with the kinetic antagonism that toluene is known to display in mixtures with isooctane. These results have implications for the formulation of biofuel mixtures for spark ignition and advanced compression ignition engines, where this non-linear octane blending response could be exploited to improve knock resistance, or modulate the autoignition process.

2-methyl furan↗
DOE OSTI2024

CMB lensing and Ly⁢ α forest cross bispectrum from DESI’s first-year quasar sample

The squeezed cross-bispectrum B κ,Ly α between the gravitational lensing in the cosmic microwave background and the 1D Ly α forest power spectrum can constrain bias parameters and break degeneracies between σ8 and other cosmological parameters. We detect B κ,Ly ⁢α with 4.8⁢σ significance at an effective redshift z eff =2.4 using Planck PR3 lensing map and over 280,000 quasar spectra from the Dark Energy Spectroscopic Instrument’s first-year data. We test our measurement against metal contamination and foregrounds such as Galactic extinction and clusters of galaxies by deprojecting the thermal Sunyaev-Zeldovich effect. Finally, we compare our results to a tree-level perturbation theory calculation and find reasonable agreement between the model and measurement.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Projecting Permafrost Thaw of Sub-Arctic Tundra With a Thermodynamic Model Calibrated to Site Measurements

Northern circumpolar permafrost thaw affects global carbon cycling, as large amounts of stored soil carbon becomes accessible to microbial breakdown under a warming climate. The magnitude of carbon release is linked to the extent of permafrost thaw, which is locally variable and controlled by soil thermodynamics. Soil thermodynamic properties, such as thermal diffusivity, govern the reactivity of the soil-atmosphere thermal gradient, and are controlled by soil composition and drainage. In order to project permafrost thaw for an Alaskan tundra experimental site, we used seven years of site data to calibrate a soil thermodynamic model using a data assimilation technique. The model reproduced seasonal and interannual temperature dynamics for shallow (5–40 cm) and deep soil layers (2–4 m), and simulations of seasonal thaw depth closely matched observed data. The model was then used to project permafrost thaw at the site to the year 2100 using climate forcing data for three future climate scenarios (RCP 4.5, 6.0, and 8.5). Minimal permafrost thawing occurred until mean annual air temperatures rose above the freezing point, after which we measured over a 1 m increase in thaw depth for every 1 °C rise in mean annual air temperature. Under no projected warming scenario was permafrost remaining in the upper 3 m of soil by 2100. We demonstrated an effective data assimilation method that optimizes parameterization of a soil thermodynamic model. The sensitivity of local permafrost to climate warming illustrates the vulnerability of sub-Arctic tundra ecosystems to significant and rapid soil thawing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Linear Modelling from Betatron Phase Measurements at the Fermilab Recycler NOvA ring

Utilizing the measurement of coherent betatron oscillation phase has emerged as fast and precise approach for identifying and rectifying errors in implementing a desired lattice in CESR (Cornell Electron Storage Ring). One key advantage of betatron phase measurement over β measurement is its sensitivity to phase variations between widely separated points. This methodology has been successfully implemented for the Recycler NOvA Ring at Fermilab, with the adoption of TAO (Tool for Accelerator Optics) developed by Cornell University. By employing this technique, a linear model of the Recycler NOvA ring is established, enabling the correction of quadrupole errors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Photometric Objects Around Cosmic Webs (PAC) Delineated in a Spectroscopic Survey. IV. High-precision Constraints on the Evolution of the Stellar–Halo Mass Relation at Redshift z < 0.7

Taking advantage of the Photometric objects Around Cosmic webs method developed in Paper I, we measure the excess surface density $\overline{n}$ 2 w p of the photometric objects around spectroscopic objects down to stellar masses 10 8.0 M ⊙ , 10 9.2 M ⊙ , and 10 9.8 M ⊙ in the redshift ranges of z s < 0.2, 0.2 < z s < 0.4, and 0.5 < z s < 0.7, respectively, using data from the DESI Legacy Imaging Surveys and spectroscopic samples from the Sloan Digital Sky Survey (i.e., the Main, LOWZ, and CMASS samples). We model the measured in an N-body simulation, using the abundance matching method, and we constrain the stellar–halo mass relations (SHMRs) in the three redshift ranges to percent levels. With the accurate modeling, we demonstrate that the stellar mass scatter for a given halo mass is nearly a constant, and that the empirical form of Behroozi et al. describes the SHMR better than the double-power-law form at low mass. Our SHMR accurately captures the downsizing of massive galaxies from z s = 0.7, while it also indicates that small galaxies are still growing faster than their host halos. The galaxy stellar mass functions (GSMFs) from our modeling are in perfect agreement with the model-independent measurements in Paper III, although the current work extends the GSMFs to a much smaller stellar mass. Based on the GSMFs and the SHMRs, we derive the stellar mass completeness and halo occupation distributions for the LOWZ and CMASS samples, which are useful for correctly interpreting their cosmological measurements, such as galaxy–galaxy lensing and redshift space distortion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

ADMS Test Bed Updates

This webinar will present results from a joint project with utility partner Xcel Energy in which we evaluated their ADMS application for volt-var optimization using different levels of model quality. We were able to help Xcel Energy understand the trade-offs of telemetry measurements and model quality when managing a feeder's voltage profile to maximize energy conservation. We simulated scenarios with varying levels of model quality and measurement density to evaluate Xcel's options for best using its ADMS. The results help Xcel and other utilities understand how network data affects voltage management as grid operations see continued growth in solar photovoltaic (PV) systems and electric vehicles (EVs).

ADMS↗
DOE OSTI2021

Residual stresses and microstructure within Allvac 718Plus laser powder bed fusion bars

The residual stresses within Allvac 718Plus bars built using LBPF with different laser powers, speeds and table displacements were measured using diffraction and mechanical methods and modelled with the baseplate attached and then removed. The residual stress profiles within the bars from the top surface down through the bulk were all quite similar, becoming less tensile due to a strain hardening mechanism. Table displacement has the greatest impact on residual stress, decreasing with increasing displacement/powder layer thickness. There was good agreement amongst the modeling and measurements. The microstructures were examined and varied slightly with energy density with higher densities having larger grains and enhanced post solidification diffusion. We found energy density had minimal impact on the residuals stresses within the parameters to produce dense material.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microstructural evolution and transmutation in tungsten under ion and neutron irradiation

This study aims to compare the effects of neutron and self-ion irradiation on the mechanical properties and microstructural evolution in W. Neutron irradiation at the HFR reactor to 1.67 dpa at 800 °C resulted in the formation of large Re and Os rich clusters and voids. The post-irradiation composition was measured using APT and verfified against FISPACT modelling. The measured Re and Os concentration was used to create alloys with equivalent concentrations of Re and Os. These alloys were exposed to self-ion irradiation to a peak dose of 1.7 dpa at 800 °C. APT showed that self-ion irradiation leads to the formation of small Os clusters, wheras under neutron irradiation large Re/Os clusters form. Voids are formed by both ion and neutron irradiation, but the voids formed by neutron irradiation are larger. By comparing the behaviour of W-1.4Re and W-1.4Re-0.1Os, suppression of Re cluster formation was observed. Irradiation hardening was measured using nanoindentation and was found to be 2.7 GPa, after neutron irradiation and 1.6 GPa and 0.6 GPa for the self-ion irradiated W-1.4Re and W-1.4Re-0.1Os. The higher hardening is attributed to the barrier strength of large voids and Re/Os clusters that are observed after neutron irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A comparison of model validation approaches for echo state networks using climate model replicates

As global temperatures continue to rise, climate mitigation strategies such as stratospheric aerosol injections (SAI) are increasingly discussed, but the downstream effects of these strategies are not well understood. As such, there is interest in developing statistical methods to quantify the evolution of climate variable relationships during the time period surrounding an SAI. Feature importance applied to echo state network (ESN) models has been proposed as a way to understand the effects of SAI using a data-driven model. This approach depends on the ESN fitting the data well. If not, the feature importance may place importance on features that are not representative of the underlying relationships. Typically, time series prediction models such as ESNs are assessed using out-of-sample performance metrics that divide the times series into separate training and testing sets. However, this model assessment approach is geared towards forecasting applications and not scenarios such as the motivating SAI example where the objective is using a data driven model to capture variable relationships. Here, in this paper, we demonstrate a novel use of climate model replicates to investigate the applicability of the commonly used repeated hold-out model assessment approach for the SAI application. Simulations of an SAI are generated using a simplified climate model, and different initialization conditions are used to provide independent training and testing sets containing the same SAI event. The climate model replicates enable out-of-sample measures of model performance, which are compared to the single time series hold-out validation approach. For our case study, it is found that the repeated hold-out sample performance is comparable, but conservative, to the replicate out-of-sample performance when the training set contains enough time after the aerosol injection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Probabilistic Power Consumption Modeling for Commercial Buildings Using Logistic Regression Markov Chain

The total energy consumed by buildings takes up to 40% of U.S. energy use, in which a large portion is contributed by commercial buildings. Building performance optimization is desirable but requires accurate building models with uncertainties taken into account. This paper proposes a novel probabilistic modeling method using Logistic Regression Markov Chain (LRMC). The LRMC model enhances the performance of traditional Markov Chain (MC) models by adopting time-variant transition matrices calibrated using logistic regression with exogenous inputs. Compared with existing building models, the proposed model produces accurate multi-step modeling results with full probability distribution. The proposed probabilistic building model is tested using actual commercial building measurements and modeling performance is evaluated with two probabilisitc metrics. The results show that the LRMC model has higher accuracy than traditional MC model and Logistic Regression (LR) model in that it yields lower error scores under both evaluation metrics.

Building modeling↗
DOE OSTI2021

The Cost of Decarbonization and Energy Upgrade Retrofits for US Homes

Cost is a major barrier when upgrading homes to reduce carbon emissions required to meet DOE’s climate-related goals. This report summarizes a nationwide effort to gather home energy upgrade project cost data along with household energy performance data. The goal was to develop cost benchmarks and to guide future R&D efforts aimed at cost compression and scaling of the residential upgrade market. The cost data were compiled for both total project costs and costs of individual measures. The majority of energy savings were modeled, with some models using measured site data for calibration. The database was analyzed using clustering techniques to find common energy and CO2 reduction approaches. The individual measures were combined into archetypal solutions to determine least-cost approaches to maximizing energy and carbon savings. Several financial analyses were preformed to examine other cost metrics beyond first cost. Project data was obtained for 1,739 projects, from 15 states and 12 energy programs, with a total of 10,512 individual measures. The database includes a wide-array of projects, ranging from single-measure HVAC upgrades to net-zero energy whole home remodels. Projects were predominantly single-family detached dwellings with wood frame construction. Most of the data was obtained from energy programs because they had recorded the necessary information and were willing to share with this study. This sample of convenience can provide broad guidance and national cost benchmarks, but lacks sufficient detail to draw more disaggregated conclusions, such as geographical trends. The majority of data contributions were obtained without compensation from sources where the required data was already in some sort of structured format. We compensated sources to enter data from individual projects into a structured data format for about 500 projects, with an average cost of about $40 per project. The database was highly skewed to lower cost, lower impact projects due to the nature of the sample of convenience. Less than 10% of projects had savings greater than 50%. The cost data for individual measures in the database are being used in other DOE efforts on residential energy use/decarbonization. This data collection effort should continue in order to provide the best-informed guidance for DOE and industry R&D, as well as deployment efforts (including policy and program planning).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability

Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient’s status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Computational models of direct and indirect X‐ray breast imaging detectors for in silico trials

Abstract Background To facilitate in silico studies that investigate digital mammography (DM) and breast tomosynthesis (DBT), models replicating the variety in imaging performance of the DM and DBT systems, observed across manufacturers are needed. Purpose The main purpose of this work is to develop generic physics models for direct and indirect detector technology used in commercially available systems, with the goal of making them available open source to manufacturers to further tweak and develop the exact in silico replicas of their systems. Methods We recently reported on an in silico version of the SIEMENS Mammomat Inspiration DM/DBT system using an open‐source GPU‐accelerated Monte Carlo x‐ray imaging simulation code (MC‐GPU). We build on the previous version of the MC‐GPU codes to mimic the imaging performances of two other Food and Drug Administration (FDA)‐approved DM/DBT systems, such as Hologic Selenia Dimensions (HSD) and the General Electric Senographe Pristina (GSP) systems. In this work, we developed a hybrid technique to model the optical spread and signal crosstalk observed in the GSP and HSD systems. MC simulations are used to track each x‐ray photon till its first interaction within the x‐ray detector. On the other hand, the signal spread in the x‐ray detectors is modeled using previously developed analytical equations. This approach allows us to preserve the modeling accuracy offered by MC methods in the patient body, while speeding up secondary carrier transport (either electron–hole pairs or optical photons) using analytical equations in the detector. The analytical optical spread model for the indirect detector includes the depth‐dependent spread and collection of optical photons and relies on a pre‐computed set of point response functions that describe the optical spread as a function of depth. To understand the capabilities of the computational x‐ray detector models, we compared image quality metrics like modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE), simulated with our models against measured data. Please note that the purpose of these comparisons with measured data would be to gauge if the model developed as part of this work could replicate commercially used direct and indirect technology in general and not to achieve perfect fits with measured data. Results We found that the simulated image quality metrics such as MTF, NNPS, and DQE were in reasonable agreement with experimental data. To demonstrate the imaging performance of the three DM/DBT systems, we integrated the detector models with the VICTRE pipeline and simulated DM images of a fatty breast model containing a spiculated mass and a calcium oxalate cluster. In general, we found that the images generated using the indirect model appeared more blurred with a different noise texture and contrast as compared to the systems with direct detectors. Conclusions We have presented computational models of three commercially available FDA‐approved DM/DBT systems, which implement both direct and indirect detector technology. The updated versions of the MC‐GPU codes that can be used to replicate three systems are available in open source format through GitHub.

Sengupta, Aunnasha↗
DOE OSTI2020

Aeroelastic Modeling and Full-Scale Loads Measurements for Investigation of Single-Axis PV Tracker Wind-Driven Dynamic Instabilities

While wind tunnel testing and proprietary industry modeling tools have been used for years to design and develop PV tracker systems, wind induced dynamic failures are becoming more prevalent and high visibility. Designers and manufactures have reacted by developing new systems or add-on products to improve system dynamics adding to overall system costs. To better understand the physics and aeroelastic behavior that leads to dynamic instabilities NREL researches have taken up a first of a kind study to both develop open source aeroelastic modeling tools and measure tracker loads on a single axis full-scale tracker system at a high wind site. These modeling tools can simulate the fluid-structure interaction driving torsional instabilities under a wide range of turbulent inflow conditions and stow angles. The results of these simulations reveal the flow features associated with panel rotation and the induced loads in the structure. The experimental load measurements can be used to validate models and provide a quantifiable understanding of tracker dynamics, critical loads paths, identify instability markers, inform resilient design, and suggest the most favorable stow approach.

14 SOLAR ENERGY↗