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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Autopiquer - a Robust and Reliable Peak Detection Algorithm for Mass Spectrometry

We present a simple algorithm for robust and unsupervised peak detection by determining a noise threshold in isotopically resolved mass spectrometry data. Solving this problem will greatly reduce the subjective and time-consuming manual picking of mass spectral peaks and so will prove beneficial in many research applications. The Autopiquer approach uses autocorrelation to test for the presence of (isotopic) structure in overlapping windows across the spectrum. Within each window, a noise threshold is optimized to remove the most unstructured data, whilst keeping as much of the (isotopic) structure as possible. This algorithm has been successfully demonstrated for both peak detection and spectral compression on data from many different classes of mass spectrometer and for different sample types, and this approach should also be extendible to other types of data that contain regularly spaced discrete peaks. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Statistical Estimation of Strain Using Spatial Correlation Functions

Ex-situ estimation of strains from deformed micrographs is not possible as there are no persistent features which can be tracked. Two point spatial statistics enable the rigorous quantification of spatial patterns in heterogeneous media. In this paper, we propose a novel method for estimating strains directly from dissimilar micrographs using a continuum mechanics approach. Rather than operating directly on images from sequential frames, as is done in digital image correlation, we operate on different microstructure realizations. This is made possible by comparing the spatial autocorrelation maps of deformed and undeformed micrographs rather than direct comparison of images. Additionally, a Bayesian framework is proposed for quantifying uncertainty. We first illustrate the efficacy of this method on speckle pattern images from digital image correlation experiments. Then, we demonstrate that the method is capable of operating on dissimilar micrographs using deformed synthetic binary microstructures. Finally, we present a case study on polycrystalline additively manufactured 316L deformed via tension. The proposed method works well and we discuss implications and limitations of the presented work.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Modeling longitudinal INMA(1) with COM–Poisson innovation under non-stationarity: application to medical data

This paper introduces an observation-driven (OD) longitudinal integer-valued moving average model of order 1 (INMA(1)) with COM–Poisson innovations under non-stationary moment conditions. This new longitudinal model provides lot of practical flexibility in terms of modeling a wide range of over-, under-dispersion and any mixed level of dispersion. In this set-up, the model parameters of primary interest consist of the regression and dispersion effects while the serial autocorrelation parameters are treated as nuisance. A robust Generalized Quasi-Likelihood approach is formulated to estimate the different set of parameters. The performance of the estimating algorithm is assessed via Monte Carlo experiments under various combination of the serial and dispersion values and is compared with the existing adaptive generalized method of moments. Application to two medical data: epileptic seizures and polyposis counts are also illustrated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Prediction of annual soil respiration from its flux at mean annual temperature

Accurately scaling soil respiration (SR, the soil-to-atmosphere flow of CO2) across time and space is important to constrain and understand ecosystem to global scale SR, a major terrestrial carbon flux to the atmosphere. Year-round SR measurements are however expensive and sometimes impossible to perform. Bahn et al. (2010) developed an approach to estimate annual SR (SRannual) from the flux measured at mean soil temperature (SRMAST), but the robustness of this approach needs to be evaluated in diverse ecosystem types and climatic conditions globally. We used a global soil respiration database (SRDB-V4, with 823 SR observations worldwide) to test the capability of SRMAST to predict SRannual. SRMAST estimated using a variety of methods all showed clear relationships with annual SRannual. Two single-rate methods (i.e., using the single SR rate most close to mean annual soil temperature, or the single SR rate most close to mean annual air temperature) showed the most pronounced divergence from the true SRannual, but errors significantly decreased when using multiple SR rates within 1 ? of the mean annual soil temperature to estimate SRMAST. SRannual was most closely correlated with SRMAST estimated via a Q10 relationship, but this method has a potential autocorrelation issue that we explore and discuss. Air temperature data are much more widely available than is soil temperature, and we found that SR at mean annual air temperature (SRMAAT) can be used to predict SRannual as well. This study builds on Bahn et al. (2010) to demonstrate that SR measured at both annual mean soil and air temperature can be used to predict annual SR, with well-quantified errors. This capability could be used to reduce SR measurement frequency required for estimating SRannual and greatly decrease cost, factors that are generally important but especially in lower-income countries and cold, inaccessible regions.

Soil respiration, temperature, modeling↗
DOE OSTI2019

Causal hydrodynamic fluctuations in non-static and inhomogeneous backgrounds

Highlights: •The fluctuation–dissipation relation is modified by inhomogeneous backgrounds. •The constitutive equations are restricted by causality and fluctuation properties. •The structure of constitutive equation gives a natural cutoff scale of the theory. •The memory function with tensor structures in inhomogeneous backgrounds is obtained. -- Abstract: To integrate hydrodynamic fluctuations, namely thermal fluctuations of hydrodynamics, into dynamical models of high-energy nuclear collisions based on relativistic hydrodynamics, the property of the hydrodynamic fluctuations given by the fluctuation–dissipation relation should be carefully investigated. The fluctuation–dissipation relation for causal dissipative hydrodynamics with the finite relaxation time is naturally given in the integral form of the constitutive equation by the linear-response theory. While, the differential form of the constitutive equation is commonly used in analytic investigations and dynamical calculations for practical reasons. We give the fluctuation–dissipation relation for the general linear-response differential form and discuss the restrictions to the structure of the differential form, which comes from the causality and the positive semi-definiteness of the noise autocorrelation, and also the relation of those restrictions to the cutoff scale of the hydrodynamic fluctuations. We also give the fluctuation–dissipation relation for the integral form in non-static and inhomogeneous background by introducing new tensors, the pathline projectors. We find new modification terms to the fluctuation–dissipation relation for the differential form in non-static and inhomogeneous background which are particularly important in dynamical models to describe rapidly expanding systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

PYSIMFRAC: A Python library for synthetic fracture generation and analysis

In this paper, we introduce PYSIMFRAC, an open-source python library for generating 3-D synthetic fracture realizations, integrating with fluid simulators, and performing analysis. PYSIMFRAC allows the user to specify one of three fracture generation techniques (Box, Gaussian, or Spectral) and perform statistical analysis including the autocorrelation, moments, and probability density functions of the fracture surfaces and aperture. This analysis and accessibility of a python library allows the user to create realistic fracture realizations and vary properties of interest. In addition, PYSIMFRAC includes integration examples to two different pore-scale simulators and the discrete fracture network simulator, dfnWorks. The capabilities developed in this work provides opportunity for quick and smooth adoption and implementation by the wider scientific community for accurate characterization of fluid transport in geologic media. We present PYSIMFRAC along with integration examples and discuss the ability to extend PYSIMFRAC from a single complex fracture to complex fracture networks.

58 GEOSCIENCES↗
DOE OSTI2026

Extending SLUSCHI for Automated Diffusion Calculations

We present an extension of the SLUSCHI package (Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces) to enable automated diffusion calculations from first-principles molecular dynamics. While the original SLUSCHI workflow was designed for melting temperature estimation via solid-liquid coexistence, we adapt its input and output handling to isolate the volume search stage and generate one production trajectory suitable for diffusion analysis. Post-processing tools parse VASP outputs, compute mean-square displacements (MSD), and extract tracer diffusivities using the Einstein relation with robust error estimates through block averaging. Diagnostic plots, including MSD curves, running slopes, and velocity autocorrelations, are produced automatically to help identify diffusive regimes. The method has been validated through representative case studies: self-diffusion in Al-Cu liquid alloys, sublattice melting in Li7La3Zr2O12 and Er2O3, interstitial oxygen transport in bcc and fcc Fe, and oxygen diffusivity in Fe-O liquids with variable Si and Al contents. Viscosity and diffusivity are linked through the Stokes-Einstein relation, with composition dependence assessed via simple linear mixing. This capability broadens SLUSCHI from melting-point predictions to transport property evaluation, enabling high-throughput, fully first-principles datasets of diffusion coefficients and viscosities across metals and oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. Overall, we focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ensemble Monte Carlo calculations with five novel moves

We introduce five novel types of Monte Carlo (MC) moves that brings the number of moves of ensemble MC calculations from three to eight. So far such calculations have relied on affine invariant stretch moves that were originally introduced by Christen (2007), walk moves by Goodman and Weare (2010) and quadratic moves by Militzer (2023). Ensemble MC methods have been very popular because they harness information about the fitness landscape from a population of walkers rather than relying on expert knowledge. Here we modified the affine method and employed a simplex of points to set the stretch direction. We adopt the simplex concept to quadratic moves. We also generalize quadratic moves to arbitrary order. Finally, we introduce directed moves that employ the values of the probability density while all other types of moves rely solely on the location of the walkers. We apply all algorithms to the Rosenbrock density in 2 and 20 dimensions and to the ring potential in 12 and 24 dimensions. We evaluate their efficiency by comparing error bars, autocorrelation time, travel time, and the level of cohesion that measures whether any walkers were left behind. Our code is open source.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Socio-economic exposure to natural disasters

Even though the correct assessment of risks is a key aspect of the risk management analysis, we argue that limited effort has been devoted in the assessment of comprehensive measures of economic exposure at very low scale. For this reason, we aim at providing a series of suitable methodologies to provide a complete and detailed list of the exposure of economic activities to natural disasters. We use Input-Output models to provide information about several socio-economic variables, such as population density, employment density, firms' turnover and capital stock, that can be seen as direct and indirect socio-economic exposure to natural disasters. We then provide an application to the Italian context. These measures can be easily incorporated into risk assessment models to provide a clear picture of the disaster risk for local areas. - Highlights: • Ex ante assessment of economic exposure to disasters at very low geographical scale • Assessment of the cost of natural disasters in ex-post perspective • IO model and spatial autocorrelation to get information on socio-economic variables • Indicators supporting risk assessment and risk management models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Evaluation of methods to assess the uncertainty in estimated energy savings

In this work we present a methodology to evaluate the accuracy of methods used to quantify the uncertainty in estimated total energy savings. We focus on savings measurement and verification (M&V) approaches that use a baseline model to characterize energy use, and that forward-project the model for a counterfactual to determine avoided energy use. These approaches are common to the International Performance Measurement and Verification Protocol's (IPMVP's) Option C and Option B. This methodology can be used to evaluate the uncertainty in savings estimates that are due to model error. It has been applied to evaluate two uncertainty estimation methods, including the industry standard ASHRAE Guideline 14 approach. The evaluation used data from 69 commercial buildings and four different baseline models that span daily and hourly granularity, as well as linear and non-linear/non-parametric forms. The findings of this work indicate that the standard methods that are widely used by the M&V community for estimating the total savings uncertainty over the post-installation period tend to underestimate the uncertainty. The tendency to underestimate the uncertainty is stronger for hourly models than for daily models, due to stronger autocorrelation in model residuals at the hourly time scale.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Synthetic wind speed scenarios generation for probabilistic analysis of hybrid energy systems

Hybrid energy systems consisting of multiple energy inputs and multiple energy outputs have been proposed to be an effective element to enable ever increasing penetration of clean energy. In order to better understand the dynamic and probabilistic behavior of hybrid energy systems, this paper proposes a model combining Fourier series and autoregressive moving average (ARMA) to characterize historical weather measurements and to generate synthetic weather (e.g., wind speed) data. In particular, Fourier series is used to characterize the seasonal trend in historical data, while ARMA is applied to capture the autocorrelation in residue time series (e.g., measurements minus seasonal trends). The generated synthetic wind speed data is then utilized to perform probabilistic analysis of a particular hybrid energy system con guration, which consists of nuclear power plant, wind farm, battery storage, natural gas boiler, and chemical plant. As a result, requirements on component ramping rate, economic and environmental impacts of hybrid energy systems, and the effects of deploying different sizes of batteries in smoothing renewable variability, are all investigated.

17 WIND ENERGY↗
DOE OSTI2018

Spatial associations between social groups and ozone air pollution exposure in the Beijing urban area

Highlights: • Greater percentages of persons living alone and migrants were associated with higher O{sub 3} exposure. • Social groups divided by Hukou status had distinct spatial distributions in the Beijing urban area. • Migrants bore a double burden of social deprivation and O{sub 3} exposure due to city development planning and the Hukou system. • Areas with greater percentages of seniors had lower O{sub 3} exposure but higher NO{sub 2} exposure. Few studies have linked social factors to air pollution exposure in China. Unlike the race or minority concepts in western countries, the Hukou system (residential registration system) is a fundamental reason for the existence of social deprivation in China. To assess the differences in ozone (O{sub 3}) exposure among social groups, especially groups divided by Hukou status, we assigned estimates of O{sub 3} exposure to the latest census data of the Beijing urban area using a kriging interpolation model. We developed simultaneous autoregressive (SAR) models that account for spatial autocorrelation to identify the associations between O{sub 3} exposure and social factors. Principal component regression was used to control the multicollinearity bias as well as explore the spatial structure of the social data. The census tracts (CTs) with higher proportions of persons living alone and migrants with non-local Hukou were characterized by greater exposure to ambient O{sub 3}. The areas with greater proportions of seniors had lower O{sub 3} exposure. The spatial distribution patterns were similar among variables including migrants, agricultural population and household separation (population status with separation between Hukou and actual residences), which fit the demographic characteristics of the majority of migrants. Migrants bore a double burden of social deprivation and O{sub 3} pollution exposure due to city development planning and the Hukou system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Urban morphology from a landscape perspective: How building morphology distribution land models (BMDLM) emulate pattern and process

Urban form (e.g., building morphology such as height or footprint) can be used to predict environmental footprints, such as energy/water consumption and carbon emissions. Although progress has been made in predicting building characteristics to fill gaps in observation or derive 3-D representations, the relationships between morphology and other variables such as land use and population are poorly understood. Understanding these relationships may enable projections for how cities will evolve with landscapes in the future. A suite of random forest models, the Building Morphology Distribution Land Models (BMDLM), was developed to determine how well building morphology for two distinct statistical measures (central tendency and frequency) can be predicted using land use (e.g., zoning) and population at different resolutions. Clark County, Nevada and Los Angeles County, California are explored as case studies. Generally, 1-km models outperformed 30-m models. Frequency distribution models had the best performance, especially in LA County. Frequency models significantly outperformed spatial autocorrelative models using inverse distance weighting (IDW). BMDLM offers a new take on modeling urban form in which generalized landscape patterns are characterized to understand the influence of population and zoning on urban development, as described by urban scaling theory.

Sturtevant, Jillian [Baylor Univ., Waco, TX (Unite↗
DOE OSTI2016

Pairwise adaptive thermostats for improved accuracy and stability in dissipative particle dynamics

We examine the formulation and numerical treatment of dissipative particle dynamics (DPD) and momentum-conserving molecular dynamics. We show that it is possible to improve both the accuracy and the stability of DPD by employing a pairwise adaptive Langevin thermostat that precisely matches the dynamical characteristics of DPD simulations (e.g., autocorrelation functions) while automatically correcting thermodynamic averages using a negative feedback loop. In the low friction regime, it is possible to replace DPD by a simpler momentum-conserving variant of the Nosé–Hoover–Langevin method based on thermostatting only pairwise interactions; we show that this method has an extra order of accuracy for an important class of observables (a superconvergence result), while also allowing larger timesteps than alternatives. All the methods mentioned in the article are easily implemented. Numerical experiments are performed in both equilibrium and nonequilibrium settings; using Lees–Edwards boundary conditions to induce shear flow.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Surface fluctuating hydrodynamics methods for the drift-diffusion dynamics of particles and microstructures within curved fluid interfaces

Here we introduce fluctuating hydrodynamics approaches on surfaces for capturing the drift-diffusion dynamics of particles and microstructures immersed within curved fluid interfaces of spherical shape. We take into account the interfacial hydrodynamic coupling, traction coupling with the surrounding bulk fluid, and thermal fluctuations. For fluid-structure interactions, we introduce Immersed Boundary Methods (IBM) and related Stochastic Eulerian-Lagrangian Methods (SELM) for curved surfaces. We use these approaches to investigate the statistics of surface fluctuating hydrodynamics and microstructures. For velocity autocorrelations, we find characteristic power-law scalings $τ^{-1}, τ^{-2}$, and plateaus can emerge. This depends on the physical regime associated with the geometry, surface viscosity, and bulk viscosity. This differs from the characteristic $τ^{-3/2}$ scaling for bulk three dimensional fluids. We develop theory explaining these observed power-laws associated with time-scales for dissipation within the fluid interface and coupling to the surrounding fluid. We then use our introduced methods to investigate a few example systems and roles of hydrodynamic coupling and thermal fluctuations including for the kinetics of passive particles and active microswimmers in curved fluid interfaces.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Spectroscopic features of dissolved iodine in pristine and gamma-irradiated nitric acid solutions

While iodine speciation is important for a wide range of nuclear activities, understanding the mechanisms of the transformations of iodine between chemical forms and the sensitivity of these transitions to solution conditions and exposure to radiation remains an active area of research. This work curates spectroscopic data from several experimental techniques and establishes their sensitivity and limitations in detecting changes in iodine speciation in both neutral and acidic regimes. The techniques include Raman spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy, 127 I NMR spectroscopy, and ultraviolet-visible (UV-Vis) spectroscopy. Analysis of these data indicates that these commonly accessible spectroscopies often have dynamic ranges of measurable concentrations that do not always overlap between all techniques. The experimental techniques are disparately sensitive to iodide (I - ), molecular iodine (I 2 ), iodate (IO 3 - ), and periodate (IO 4 - ) species. Raman, FTIR, and NMR spectra were subsequentially analyzed using two-dimensional correlation analyses to generate high-resolution autocorrelation spectra. Here, the use of these spectroscopies is then extended to tracking acidification-induced and gamma irradiation-induced transformations of dissolved sodium iodate in deionized water and concentrated nitric acid. Both dissolution into nitric acid and irradiation with a gamma source are demonstrated to perturb the iodine speciation promoting their assembly into molecular iodine (I 2 ) and/or triiodide (I 3 - ). While I 2 and I 3 - species are undetectable with FTIR spectroscopy and 127 I NMR spectroscopy, the species can be detected with UV-Vis spectroscopy, and in some instances, I 3 - can be detected with Raman spectroscopy in the low wavenumber region. Ultimately, the results of this work provide a path to designing optimal combinations of techniques to detect forms of iodine across a wide range of concentrations and conditions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗