Simulating precipitation in the Northeast United States using a climate-informed K -nearest neighbour algorithm
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Water Resources↗
SEARCH · Engineering Papers
Results for “statistical”
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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Building materials genome from ground‐state configuration to engineering advance
Individual phases are commonly considered as the building blocks of materials. However, the accurate theoretical prediction of properties of individual phases remains elusive. The top-down approach by decoding genomic building blocks of individual phases from experimental observations is nonunique. The density functional theory (DFT), as a state-of-the-art solution of quantum mechanics, prescribes the existence of a ground-state configuration at 0 K for a given system. It is self-evident that the ground-state configuration alone is insufficient to describe a phase at finite temperatures as symmetry-breaking non-ground-state configurations are excited statistically at temperatures above 0 K. Our multiscale entropy approach (recently terms as Zentropy theory) postulates that the entropy of a phase is composed of the sum of the entropy of each configuration weighted by its probability plus the configurational entropy among all configurations. Consequently, the partition function of each configuration in statistical mechanics needs to be evaluated by its free energy rather than total energy. The combination of the ground-state and symmetry-breaking non-ground-state configurations represents the building blocks of materials and can be used to quantitatively predict free energy of individual phases with the free energy of each configuration predicted from DFT as well as all properties derived from free energy of individual phases.
CALPHAD↗
Physics‐based iterative reconstruction for dual‐source and flying focal spot computed tomography
Purpose For single‐source helical Computed Tomography (CT), both Filtered‐Back Projection (FBP) and statistical iterative reconstruction have been investigated. However, for dual‐source CT with flying focal spot (DS‐FFS CT), a statistical iterative reconstruction that accurately models the scanner geometry and acquisition physics remains unknown to researchers. Therefore, our purpose is to present a novel physics‐based iterative reconstruction method for DS‐FFS CT and assess its image quality. Methods Our algorithm uses precise physics models to reconstruct from the native cone‐beam geometry and interleaved dual‐source helical trajectory of a DS‐FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture. In addition, we design forward system models to compute the locations of deflected focal spots, the dimension, and sensitivity of voxels and detector units, as well as the length of intersection between x‐rays and voxels. The forward system models further represent the coordinated movement between the dual sources by computing their x‐ray coverage gaps and overlaps at an arbitrary helical pitch. With the above models, we reconstruct images by an advanced Consensus Equilibrium (CE) numerical method to compute the maximum a posteriori estimate to a joint optimization problem that simultaneously fits all models. Results We compared our reconstruction with Siemens ADMIRE, which is the clinical standard hybrid iterative reconstruction (IR) method for DS‐FFS CT, in terms of spatial resolution, noise profile, and image artifacts through both phantoms and clinical scan datasets. Experiments show that our reconstruction has a higher spatial resolution, with a Task‐Based Modulation Transfer Function (MTF task ) consistently higher than the clinical standard hybrid IR. In addition, our reconstruction shows a reduced magnitude of image undersampling artifacts than the clinical standard. Conclusions By modeling a precise geometry and avoiding data rebinning or interpolation, our physics‐based reconstruction achieves a higher spatial resolution and fewer image artifacts with smaller magnitude than the clinical standard hybrid IR.
Wang, Xiao↗
Probabilistic learning and updating of a digital twin for composite material systems
This paper presents an approach for characterizing and estimating statistical dependence between a large number of observables in a composite material system. Conditional regression is carried out using the estimated joint density function, permitting a systematic exploration of interdependence between fine scale and coarse observables that can be used for both prognosis and design of complex material systems. An example demonstrates the integration of experimental data with a computational database. The statistical approach is based on the probabilistic learning on manifolds recently developed by the authors. This approach leverages intrinsic structure detected through diffusion on graphs with projected stochastic differential equations to generate samples constrained to that structure.
42 ENGINEERING↗
International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)
Abstract The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high‐quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.
14 SOLAR ENERGY↗
Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems
Abstract Fault detection and classification in photovoltaic (PV) systems through real‐time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend‐based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change‐point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.
14 SOLAR ENERGY↗
The Role of Pentaerythritol Tetranitrate (PETN) Aging in Determining Detonator Firing Characteristics
Pentaerythritol tetranitrate (PETN) is one of the most commonly used explosives for both military and mining applications. PETN is used in EBW (exploding bridge wire) and EFI (exploding foil initiator) detonator devices. Due the widespread usage of PETN, studies focusing on how the explosive changes with age, primarily its function in detonators has received particular interest. Aging studies have identified several factors that affect PETN detonator function over time, most notably the specific surface area of the explosive powder. Though small-scale studies have been performed on specific detonator systems with isolated batches of material, what is missing is a large statistically-significant study focusing on PETN aging characteristics - such as powder coarsening - and their influence on detonator performance. Herein we report the first large statistically viable PETN aging study focusing on four batches of PETN powder from the same stock of PETN using two stabilizers, polysaccharide and TriPEON. PETN was aged both as a free-flowing powder as well as in modified EBW RP-2 detonators, for one month at 75 °C. The PETN powder has been analyzed chemically using Fisher specific surface area analysis, SEM imaging and light scattering particle size analysis. Additionally the detonators were tested for performance through voltage-sweep threshold testing and function time measurements. Findings from the study indicate that aging at 75 °C for one month significantly changes the specific surface area and particle size of unstabilized PETN, leading to increases in detonator function time, but not detonator threshold values. Powders stabilized with TriPEON displayed less significant increases in function time, while powder stabilized with polysaccharide exhibited no aging effects, despite the high temperature aging.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Image Distinguishability Analysis Testing Through Principal Components and Its Application to Hot Spot Scale Invariance
Hot spots are spatial regions of intense energy localization that govern initiation of secondary high explosives. Studies that characterize or compare simulated hot spots are frequently either qualitatively descriptive or resort to quantitative distribution functions that neglect stochastic variations and spatial correlations—effects that are also neglected in common comparison tests like the Kolmogorov–Smirnov test. To this end, we develop an image distinguishability analysis (IDA) test based on principal component (PC) analysis that makes pixel-by-pixel comparisons between small, for example, O(<10), image data sets. The IDA test makes comparisons through a generalized distance metric in the PC space and a test statistic that is derived to calculate mathematical equation-values. Here, we derive a statistical distribution and criticality criterion to determine whether images are distinguishable from established baselines. We apply the IDA test on images generated from molecular dynamics simulations of hot spots from pore collapse in TATB to assess scale invariance in the complex patterns of hot spots that form in a representative high explosive crystal. The IDA test shows that TATB hot spot spatial temperature fields and their derived temperature histograms exhibit scale-invariant features over specific intervals of shock orientation, strength, and initial pore diameter. However, the IDA test also shows that qualitatively different conclusions regarding invariance can be reached depending on whether the hot spot is treated as a spatially correlated field as opposed to a distribution function that lacks spatial information.
organic↗
Improvement of Drop‐Hammer Impact Testing for Safety Assessment of High Explosives Using 10‐mg Samples
Here, in this study, we established an improved method for drop-hammer impact testing of small quantities of high explosives (10 mg). We performed about seven hundred impact tests under various experimental conditions (e.g., sandpaper vs bare anvil, different sample masses, drop-weights, and striker diameters) to determine an optimal set of conditions and reaction detection methods (e.g., gas analysis, video, and sound recordings) that give the most statistically reliable results with 10 mg samples. We used both Frequentist and Bayesian statistical approaches to compare estimates of the drop height (DH50) that initiates a reaction 50% of the time, and to quantify the associated uncertainty. Gas analysis proved to be the most reliable reaction detection method, showing unambiguous rises in HE decomposition products (e.g., CO 2 ) even when the other indicators (e.g., sound, video) were inconclusive. The impact tests performed with a bare anvil showed much better reproducibility than those conducted with sandpaper, reducing the largest uncertainty observed in the data sets by a factor of 1.7. The DH 50 values obtained from three different sample masses (10, 20, and 35 mg) fell within the uncertainties of the measurements. We demonstrated the improved procedure (i.e., 10-mg samples, gas analysis, bare anvil, and Bayesian approach) on a variety of PETN samples having different surface areas and thermal histories.
PETN↗
Single-Filament Imaging Mass Spectrometry Lipidomics in Arthrospira platensis
Elucidating intra-organismal biochemical and lipid organization in photosynthetic biological cell factories of filamentous cyanobacteria, i.e., Arthrospira platensis (Spirulina), is important to track physiological response mechanisms during growth. Little is known about the filaments' biochemical organization and cellular structure and no label-free imaging techniques exist that provide molecular mapping. We applied ultra-high resolution mass spectrometry (7T FT-ICR-MS) matrix-assisted laser desorption ionization (MALDI) imaging to immobilized Spirulina filaments to investigate the localization of lipids across distinct physiological regions. We optimized matrix selection and deposition methods with the goal of facilitating high spatial, and intra-filament, resolution using untargeted multivariate statistical spectral deconvolution across MS pixels. Our results demonstrate an improved two step application with an optimized procedure for intra-organismal lipid profiling to improve analyte sensitivity and achieve higher spatial resolution, whereby we evaluate three conventional matrices 2,5-dihydroxybenzoic acid (DHB), a 9:1 ratio of DHB:superDHB (sDHB), 1,5- diaminonaphthalene (DAN) and a 50:50 mix of DHB:sDHB and compare delineation and pixel-based elucidation of intra-filament lipidomics. We identified a total of 1,626 features that could be putatively assigned a lipid-like formula based on database query and 46 unique features, with associated lipid assignments that were significantly distinct in their intra-filament location. MALDI- imaging MS with untargeted statistical spectral deconvolution was used to visualize intra-filament lipidomics organization in Spirulina filaments. Improvements in matrix deposition, including sequential sublimation and pneumatic spraying, increased signal abundance at high spatial resolution and allowed for identification of distinct lipid composition regions. Here in this work outlines a methodology that may be used for micro-ecological untargeted molecular phenotyping.
09 BIOMASS FUELS↗
Modern chemical graph theory
Abstract Graph theory has a long history in chemistry. Yet as the breadth and variety of chemical data is rapidly changing, so too do graph encoding methods and analyses that yield qualitative and quantitative insights. Using illustrative cases within a basic mathematical framework, we showcase modern chemical graph theory's utility in Chemists' analysis and model development toolkit. The encoding of both experimental and simulation data is discussed at various levels of granularity of information. This is followed by a discussion of the two major classes of graph theoretical analyses: identifying connectivity patterns and partitioning methods. Measures, metrics, descriptors, and topological indices are then introduced with an emphasis upon enhancing interpretability and incorporation into physical models. Challenging data cases are described that include strategies for studying time dependence. Throughout, we incorporate recent advancements in computer science and applied mathematics that are propelling chemical graph theory into new domains of chemical study. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Molecular Structures
Leite, Leonardo S. G.↗
RANS simulations of neutral atmospheric boundary layer flow over complex terrain with comparisons to field measurements
Micro-scale Reynolds-averaged Navier-Stokes (RANS) simulations of the neutral atmospheric boundary layer (ABL) over complex terrain and a comparison of the results with conditionally averaged met-tower data are presented. A robust conditional sampling procedure for the meteorological tower (met-tower) data to identify near-neutral conditions based on a criterion for the turbulence intensity is developed. The conditionally averaged wind data on 14 met-towers are used for the model validation. The ABL flow simulations are conducted over complex terrain which includes a prominent hill using the OpenFOAM-based simulator for on/offshore wind farm applications (SOWFA) with the k-ϵ and the SST k-ω turbulence models. The discretization of the production term in the transport equation for the turbulent kinetic energy (TKE) is modified to greatly reduce the commonly observed nonphysical near surface TKE peak. The driving inflow is generated through an iterative approach using a precursor method to reproduce the measured wind statistics at the reference tower. Both of the RANS models are able to capture the flow behavior windward of the hill. The SST k-ω model predicts more intense flow separation than the k-ϵ model downstream of the steepest sections of the hill. The wind statistics predicted at the location of the met-towers by both of the RANS models are fairly consistent. Overall, the comparisons of the direction, mean, and standard deviation of the wind between the simulations and the tower data show reasonable agreement except for the differences of the mean wind speeds at four met-towers located closer to the main ridge of the hill in a region of strong terrain variations.
17 WIND ENERGY↗
The Data Mine model for accessible partnerships in data science
Abstract The Data Mine at Purdue University is a pioneering experiential learning community for undergraduate and graduate students of any background to learn data science. The first data‐intensive experience embedded in a large learning community, The Data Mine had nearly 1300 students in academic year (AY) 2022–2023 and nearly 1700 students for AY 2023–2024. The Data Mine embodies data‐infused education, research, and collaboration. Students learn Python, R, SQL, and shell‐scripting, while working on weekly projects within a high‐performance computing (HPC) cluster. In the Corporate Partners cohort, students work on teams of 5–15 students, led by a paid student team leader. Each cohort follows an Agile approach, working on data‐intensive projects provided by industry partners and mentored by company employees. Students develop professional and data skills throughout the academic year, from August through April. Many students return in subsequent years to the program, increasing their tenure with a Corporate Partner. Student teams are inherently interdisciplinary; students from 133 different majors are involved in the program, ranging from new incoming students through PhD level students. These interdisciplinary teams of students bring new perspectives to challenging problems in which data science is a key part of the solution. The interdisciplinary teams foster an environment of synthesis with ideas and solutions. Students come together with different life experiences, different levels of technical skill, but also varying ways they navigate paths to solutions because of the variety of majors represented, resulting in a more creative and robust solution than a traditional data science program. This article is categorized under: Applications of Computational Statistics > Education in Computational Statistics
Betz, Margaret A.↗
A Rapid Population Assessment Method for Wild Pigs Using Baited Cameras at 3 Study Sites
ABSTRACT Reliable and efficient population estimates are a critical need for effective management of invasive wild pigs ( Sus scrofa ). We evaluated the use of 10‐day camera grids for rapid population assessment (RPA) of wild pigs at 3 study sites that varied in vegetation communities and wild pig densities. Study areas included Buck Island Ranch, Florida; Tejon Ranch, California; and the Savannah River Site, South Carolina, USA, during 2016–2018. Rapid population assessments grids were composed of baited camera traps spaced approximately 500 or 750 m apart. Two RPA grids were deployed per study site and each grid was deployed twice (4–6 months apart) to assess changes in response to season or population control efforts. We assessed the ability of RPA grids to track population trends, how camera number influenced estimate precision, and how relative abundance indices related to density estimates. We detected changes in occupancy probability, detection probability, and N‐mixture estimates following removal operations and between seasons, but the ability of RPA grids to track population trends was dependent on the statistical method used and number of cameras traps. Increasing the number of cameras traps used in RPA grids increased precision, and these results can be used in determining survey design and estimate choice. We found that estimates of occupancy probability, detection probability, and N‐mixture estimates were positively correlated with spatially explicit capture–recapture density estimates. Thus, these less labor‐intensive estimates from RPA grids showed potential to index the relative abundance of wild pigs in some systems. Our evaluation of RPAs indicates that using study‐specific combinations of statistical method and number of cameras can provide a useful tool for monitoring wild pig presence, tracking population trends, and evaluating the effectiveness of management actions. © 2020 The Wildlife Society.
Schlichting, Peter E.↗
Model selection and signal extraction using Gaussian Process regression
We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the Higgs boson signature, from the ATLAS collaboration at the Large Hadron Collider. Our approach is based on Gaussian Process (GP) regression — a powerful and flexible machine learning technique which has allowed us to model the background without specifying its functional form explicitly and separately measure the background and signal contributions in a robust and reproducible manner. Unlike functional fits, our GP-regression-based approach does not need to be constantly updated as more data becomes available. We discuss how to select the GP kernel type, considering trade-offs between kernel complexity and its ability to capture the features of the background distribution. We show that our GP framework can be used to detect the Higgs boson resonance in the data with more statistical significance than a polynomial fit specifically tailored to the dataset. Finally, we use Markov Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of the extracted Higgs signature.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Rotons in anyon superfluids
We consider the problem of calculating the excitation spectrum of a gas of nonrelativistic anyons. When the anyons have statistics close to fermionic and the statistical angle has the form θ = π(1 - 1/k) where k is a large integer, the problem can be solved by employing the method of bosonization, which maps the problem to that of an infinite number of bosonic excitations coupled to a U(1) Chern-Simons gauge field. The spectrum consists of a Goldstone boson branch and a large number of massive branches, each having roton minima and maxima. The dispersion curves asymptote to the Landau levels at large momentum.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
First measurement of the \( {\Lambda}_c^{+} \) → pη ' decay
We present the first measurement of the branching fraction of the singly Cabibbo-suppressed (SCS) decay \( {\Lambda}_c^{+} \) → pη ' with η ' → ηπ + π – , using a data sample corresponding to an integrated luminosity of 981 fb – 1 , collected by the Belle detector at the KEKB e + e – asymmetric-energy collider. A significant \( {\Lambda}_c^{+} \) → pη ' signal is observed for the first time with a signal significance of 5.4 σ . The relative branching fraction with respect to the normalization mode \( {\Lambda}_c^{+} \) → pK – π + is measured to be $$ \frac{\mathcal{B}\left({\Lambda}_c^{+}\to p\eta^{\prime}\right)}{\mathcal{B}\left({\Lambda}_c^{+}\to {pK}^{-}{\pi}^{+}\right)}=\left(7.54\pm 1.32\pm 0.73\right)\times {10}^{-3}, $$ where the uncertainties are statistical and systematic, respectively. Using the world-average value of \( \mathcal{B}\left({\Lambda}_c^{+}\to {pK}^{-}{\pi}^{+}\right) \) = (6 . 28 ± 0 . 32) × 10 – 2 , we obtain $$ \mathcal{B}\left({\Lambda}_c^{+}\to p\eta^{\prime}\right)=\left(4.73\pm 0.82\pm 0.46\pm 0.24\right)\times {10}^{-4}, $$ where the uncertainties are statistical, systematic, and from \( \mathcal{B}\left({\Lambda}_c^{+}\to {pK}^{-}{\pi}^{+}\right) \) , respectively.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Nucleon momentum fraction, helicity and transversity from 2+1-flavor lattice QCD
A detailed analysis of the systematic uncertainties in the calculation of the isovector momentum fraction, ( x ) u - d , helicity moment, ( x ) Δ u - Δ d , and the transversity moment, ( x ) δu - δd , of the nucleon is presented using high-statistics data on seven ensembles of gauge configurations generated by the JLab/W&M/LANL/MIT collaborations using 2 + 1-flavors of dynamical Wilson-clover quarks. The much higher statistics have facilitated better control over all systematics compared to previous lattice calculations. The least understood systematic — excited-state contamination — is quantified by studying the variation of the results as a function of different estimates of the mass gap of the first excited state, obtained from two- and three-point correlation functions, and as a function of the pion mass M π . The final results are obtained using a simultaneous fit in the lattice spacing a , pion mass M π and the finite volume parameter M π L keeping leading order corrections. The data show no significant dependence on the lattice spacing and some evidence for finite-volume corrections. Our final results, in the $ \overline{\mathrm{MS}} $ scheme at 2 GeV, are ( x ) u - d = 0 . 155(17)(20), ( x ) Δ u - Δ d = 0 . 183(14)(20) and ( x ) δu - δd = 0 . 220(18)(20), where the first error is the overall analysis uncertainty assuming excited-state contributions have been removed, and the second is an additional systematic uncertainty due to possible residual excited-state contributions. These results are consistent with phenomenological global fit values.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗