Performance Loss Rate Estimation of Fielded Photovoltaic Systems Based on Statistical Change-Point Techniques .
Abstract not provided.
Livera, Andreas↗
SEARCH · Engineering Papers
Results for “statistical techniques”
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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Simulation of Particle-Material Interactions
This paper gives an overview of the particle transport theory essentials, the basics of particle-material interaction simulation, physical quantities needed to simulate particle transport and interactions in materials, Monte Carlo simulation flow, response of additive detectors, statistical weights and other techniques to minimize statistical errors. Effects in materials under irradiation, materials response related to component lifetime and performance are considered with a focus on high-energy and high-power accelerator applications. Implementation of simulation of particle-material interactions in the modern Monte Carlo codes along with the code s main features and results of recent benchmarking are described.
43 PARTICLE ACCELERATORS↗
Hydrogen Dispersion Modeling for Development of Smart Distributed Monitoring
Studying hydrogen dispersion is crucial for ensuring the safe and effective deployment of hydrogen as an energy carrier. This study presents a comprehensive CFD modeling framework for simulating hydrogen dispersion at a real-world hydrogen production, storage, and utilization facility. Utilizing the Hydrogen Research Facility under the Advanced Research on Integrated Energy Systems (ARIES) at the National Renewable Energy Laboratory's (NREL) Flatirons campus, controlled hydrogen releases at 27 kg-H2/hr were simulated. The model incorporated site-specific atmospheric conditions, including hourly wind speeds and temperatures recorded between 8 AM and 8 PM from October to December 2023. To reduce computational demands, a statistical reduction technique was applied to condense the dataset to 100 representative scenarios, validated by statistical tests for wind speeds and power law coefficients. Simulations were conducted using the Reynolds-Averaged Navier-Stokes equations. Results demonstrated that wind speed substantially influences hydrogen dispersion, with low wind conditions forming concentrated clouds and higher wind speeds stretching the plume. Additionally, clustering analysis informed optimal sensor placement at various elevations with up to 10 sensor locations on each elevation. This framework offers a robust approach for understanding hydrogen behavior in ambient conditions and informing detection strategies.
08 HYDROGEN↗
Evaluating downscaled products with expected hydroclimatic co-variances
Abstract. There has been widespread adoption of downscaled products amongst practitioners and stakeholders to ascertain risk from climate hazards at the local scale (e.g., ∼ 5 km resolution). Such products must nevertheless be consistent with physical laws to be credible and of value to users. Here we evaluate statistically and dynamically downscaled products by examining local co-evolution of downscaled temperature and precipitation during convective and frontal precipitation events (two mechanisms testable with just temperature and precipitation). We find that two widely used statistical downscaling techniques (Localized Constructed Analogs version 2, LOCA2, and Seasonal Trends and Analysis of Residuals Empirical Statistical Downscaling Model, STAR-ESDM) generally preserve expected co-variances during convective precipitation events over the historical and future projected intervals as compared to European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and two observation-based data products (Livneh and nClimGrid-Daily). However, both techniques dampen future intensification of frontal precipitation that is otherwise robustly captured in global climate models (i.e., prior to downscaling) and with process-based dynamical downscaling across five different regional climate models. In the case of LOCA2, this leads to appreciable underestimation of future frontal precipitation event intensity. This study is one of the first to quantify a likely ramification of the stationarity assumption underlying statistical downscaling methods and identify a phenomenon where projections of future change diverge depending on data production method employed. Finally, our work proposes expected co-variances during convective and frontal precipitation as useful evaluation diagnostics that can be universally applied to a wide range of statistically downscaled products.
54 ENVIRONMENTAL SCIENCES↗
Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins
Here, we outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self-organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure-function relationships - however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.
59 BASIC BIOLOGICAL SCIENCES↗
Quantifying Graph Uncertainty from Communication Data
Graphs are a widely used abstraction for representing a variety of important real-world problems including emulating cyber networks for situational awareness, or studying social networks to understand human interactions or pandemic spread. Communication data is often converted into graphs to help understand social and technical patterns in the underlying communication data. However, prior to this project, little work had been performed analyzing how best to develop graphs from such data. Thus, many critical, national security problems were being performed against graph representations of questionable quality. Herein, we describe our analyses that were precursors to our final statistically grounded technique for creating static graph snapshots from a stream of communication events. The first analyzes the statistical distribution properties of a variety of real-world communication datasets generally fit best by Pareto, log normal, and extreme value distributions. The second derives graph properties that can be estimated given the expected statistical distribution for communication events and the communication interval to be viewed node observability, edge observability, and expected accuracy of node degree. Unfortunately, as that final process is under review for publication, we can't publish it here at this time.
97 MATHEMATICS AND COMPUTING↗
How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections?
Abstract We present an intercomparison of a suite of high‐resolution downscaled climate projections based on a six‐member General Circulation Model (GCM) ensemble from Coupled Models Intercomparison Project (CMIP6). The CMIP6 GCMs have been downscaled using dynamical and statistical downscaling techniques based on two meteorological reference observations over the conterminous United States. We use the regional climate model, RegCM4, for dynamical downscaling, double bias correction constructed analogs method for statistical downscaling, and Daymet and Livneh datasets as the reference observations for statistical training and bias‐correction. We evaluate the performances of downscaled data in both historical and future periods under the SSP585 scenario. While dynamical downscaling improves the simulation of some performance evaluation indices, it adds an extra bias in others, highlighting the need for statistical correction before its use in impact assessments. Downscaled datasets after bias‐correction compare exceptionally well with observations. However, the choice of downscaling techniques and the underlying reference observations influence the hydroclimate characteristics of downscaled data. For instance, the statistical downscaling generally preserves the GCMs climate change signal but overestimates the frequency of hot extremes. Similarly, simulated future changes are sensitive to the choice of reference observations, particularly for precipitation extremes that exhibit a higher projected increase in the ensembles trained and/or corrected by Daymet than Livneh. Overall, these results demonstrate that multiple factors, including downscaling techniques and reference observations, can substantially influence the outcome of downscaled climate projections and stress the need for a comprehensive understanding of such method‐based uncertainties.
54 ENVIRONMENTAL SCIENCES↗
Comparative Study of Differentially Private Data Synthesis Methods
When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set. Data synthesis is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional data synthesis techniques often rely on strong assumptions of a data intruder’s behaviors and background knowledge to assess disclosure risk. Differential privacy (DP) formulates a theoretical approach for a strong and robust privacy guarantee in data release without having to model intruders’ behaviors. Efforts have been made aiming to incorporate the DP concept in the data synthesis process. Here, we examine current DIfferentially Private Data Synthesis (DIPS) techniques for releasing individual-level surrogate data for the original data, compare the techniques conceptually and evaluate the statistical utility and inferential properties of the synthetic data via each DIPS technique through extensive simulation studies. Our work sheds light on the practical feasibility and utility of the various DIPS approaches, and suggests future research directions for DIPS.
97 MATHEMATICS AND COMPUTING↗
Misclassification in Workers’ Telecommuting Frequency Choices Using a Generalized Extreme Value Model
Telecommuting frequency is a response variable collected in travel surveys and is, therefore, prone to errors leading to mismeasurements or misclassification. Misclassification of explanatory variables is a common risk when using statistical modeling techniques. We define “misclassification” as a response reported or recorded in the wrong category; for example, a variable is recorded as a 1 when it should be 0. Here, in this context, this study aims to develop a statistical model to analyze telecommuting data which accounts for potential misclassification errors by building on existing literature in econometrics. The empirical analysis was undertaken using the 2017 National Household Travel Survey (NHTS) and the general extreme value (GEV) models available in the literature. Specifically, the frequency of telecommuting days was analyzed using the negative binomial (NB) model recast as the multinomial logit (MNL) model. By nature—and consistent with other studies—NHTS data are prone to errors that can be classified as intentional or unintentional misinformation provided by the person being interviewed. Ignoring these errors while modeling telecommuting frequencies using standard discrete count models can result in biased parameter estimates. The misclassification parameter was calculated for both over-reporting and under-reporting scenarios. The misclassification errors can be as high as 14% over-reported and 10% under-reported, particularly for the neighboring values. Statistical fit comparison between the models shows that models that ignore misclassification have worse data fit and biased parameter estimates with significant policy implications.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Going off topics to demix quark and gluon jets in α S extractions
Quantum chromodynamics is the theory of the strong interaction between quarks and gluons; the coupling strength of the interaction, α S , is the least precisely-known of all interactions in nature. An extraction of the strong coupling from the radiation pattern within jets would provide a complementary approach to conventional extractions from jet production rates and hadronic event shapes, and would be a key achievement of jet substructure at the Large Hadron Collider (LHC). Presently, the relative fraction of quark and gluon jets in a sample is the limiting factor in such extractions, as this fraction is degenerate with the value of αS for the most well-understood observables. To overcome this limitation, we apply recently proposed techniques to statistically demix multiple mixtures of jets and obtain purified quark and gluon distributions based on an operational definiton. We illustrate that studying quark and gluon jet substructure separately can significantly improve the sensitivity of such extractions of the strong coupling. We also discuss how using machine learning techniques or infrared- and collinear-unsafe information can improve the demixing performance without the loss of theoretical control. While theoretical research is required to connect the extract topics with the quark and gluon objects in cross section calculations, our study illustrates the potential of demixing to reduce the dominant uncertainty for the α S extraction from jet substructure at the LHC.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing
A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. Finally, the validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.
42 ENGINEERING↗
At-wavelength characterization of X-ray wavefronts in Bragg diffraction from crystals
The advent of next-generation synchrotron radiation sources and X-ray free-electron lasers calls for high-quality Bragg-diffraction crystal optics to preserve the X-ray beam coherence and wavefront. This requirement brings new challenges in characterizing crystals in Bragg diffraction in terms of Bragg-plane height errors and wavefront phase distortions. Here, a quantitative methodology to characterize crystal optics using a state-of-the-art at-wavelength wavefront sensing technique and statistical analysis is proposed. The method was tested at the 1-BM-B optics testing beamline at the Advanced Photon Source for measuring silicon and diamond crystals in a self-referencing single-crystal mode and an absolute double-crystal mode. The phase error sensitivity of the technique is demonstrated to be at the λ/100 level required by most applications, such as the characterization of diamond crystals for cavity-based X-ray free-electron lasers.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Analysis of time-dependent strain heterogeneity in shales using X-ray microscopy and digital volume correlation
In this paper, we report the results of a 4-dimensional (3 spatial dimensions plus time) in-situ creep experiment on a milli-core shale plug from Barnett formation in Texas. An outstanding challenge in shale mechanics is connecting observable characteristics to predictable mechanical and hydraulic behavior. We employ novel imaging and analysis techniques to statistically link sample mineralogy and structure to observed deformation. In particular, we focus on mechanisms of porosity reduction during transient response to step changes in stress, analogous to the aseismic deformation that occurs after an episode of fracturing. Our main goal in this study is to take the first steps to visualize and capture the small-scale deformation mechanisms involved in creep of shales, using imaging techniques. To do so, we conducted a uniaxial creep experiment inside an Xradia Versa 520 micro-CT system for 12 h at room temperature under a constant uniaxial stress of 25 MPa. We employed a Digital Volume Correlation (DVC) method to measure micro-scale deforspmation of the sample by comparing the resulting volumetric images with a high-resolution scan of the sample obtained prior to the creep experiment. The results indicate that the time-dependent deformation in the compliant clay plus kerogen matrix constituents facilitates rigid grain rotation and compaction of intergranular porosity at the boundary between rigid grains and matrix.
58 GEOSCIENCES↗
Modelling and computational improvements to the simulation of single vector-boson plus jet processes for the ATLAS experiment
This paper presents updated Monte Carlo configurations used to model the production of single electroweak vector bosons (W, Z/γ$^{∗}$) in association with jets in proton-proton collisions for the ATLAS experiment at the Large Hadron Collider. Improvements pertaining to the electroweak input scheme, parton-shower splitting kernels and scale-setting scheme are shown for multi-jet merged configurations accurate to next-to-leading order in the strong and electroweak couplings. The computational resources required for these set-ups are assessed, and approximations are introduced resulting in a factor three reduction of the per-event CPU time without affecting the physics modelling performance. Continuous statistical enhancement techniques are introduced by ATLAS in order to populate low cross-section regions of phase space and are shown to match or exceed the generated effective luminosity. This, together with the lower per-event CPU time, results in a 50% reduction in the required computing resources compared to a legacy set-up previously used by the ATLAS collaboration. The set-ups described in this paper will be used for future ATLAS analyses and lay the foundation for the next generation of Monte Carlo predictions for single vector-boson plus jets production.[graphic not available: see fulltext]
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings
In data-driven automated fault detection and diagnostics (AFDD) modeling for building energy systems, feature engineering is a critical process of extracting information from high-dimensional and noisy sensor measurement and turning it into informative and representative inputs or features for data-driven modeling. However, few studies specifically discuss the feature engineering, especially the interactions between feature extraction and feature selection in whole-building AFDD. We developed a systematic feature extraction and selection framework for whole-building AFDD. In this framework, features are aggressively extracted from raw sensor data using statistical feature extraction techniques with various window sizes and statistics. With many features extracted, a hybrid feature selection algorithm that combines the filter and wrapper method then selects the best feature set. The framework considers diversity in the duration of fault behavior among fault types in whole-building AFDD, thus achieving high model generalization. We implemented our developed framework in a virtual testbed calibrated with measured data from Oak Ridge National Laboratory's Flexible Research Platform designed to mimic the operation of a typical small commercial building. The AFDD model is trained by the simulation data generated from the virtual testbed. The results show that (1) the developed framework improves the generalization of the AFDD model by 10.7% compared with literature-reported feature extraction and selection methods and (2) features with diverse window sizes and statistics are selected, providing insight into physical systems beyond the current understanding of buildings and faults and improving the detection and diagnostics of multiple fault types.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Entropy-driven melting point depression in fcc HEAs
High Entropy Alloys (HEAs) are an increasingly dominant alloy design paradigm. The premise of entropic stabilization of single-phase alloys has motivated much of the research on HEAs. Chemical complexity may indeed help stabilize single alloy phases relative to other lower-entropy competing solid phases. Paradoxically, this complexity may de-stabilize these alloys against the liquid phase, potentially limiting the application space of HEAs at elevated temperatures. In this work, we carry out a comprehensive investigation of the phase stability in the fcc CoCrFeMnNiV-Al HEA space using a state of the art CALPHAD database. By using modern visualization techniques and statistical analysis we examine the trade-off between chemical complexity and stability against the liquid state and identify a potentially difficult to overcome barrier for development of high temperature alloys, at least within the conventional fcc HEA space. Here, limited experimental data seem to be consistent with this analysis.
36 MATERIALS SCIENCE↗
Data Science Meets Physical Organic Chemistry
At the heart of synthetic chemistry is the holy grail of predictable catalyst design. In particular, researchers involved in reaction development in asymmetric catalysis have pursued a variety of strategies toward this goal. This is driven by both the pragmatic need to achieve high selectivities and the inability to readily identify why a certain catalyst is effective for a given reaction. While empiricism and intuition have dominated the field of asymmetric catalysis since its inception, enantioselectivity offers a mechanistically rich platform to interrogate catalyst-structure response patterns that explain the performance of a particular catalyst or substrate. In the early stages of an asymmetric reaction development campaign, the overarching mechanism of the reaction, catalyst speciation, the turnover limiting step, and many other details are unknown or posited based on related reactions. Considering the unclear details leading to a successful reaction, initial enantioselectivity data are often used to intuitively guide the ultimate direction of optimization. However, if the conditions of the Curtin-Hammett principle are satisfied, then measured enantioselectivity can be directly connected to the ensemble of diastereomeric transition states (TSs) that lead to the enantiomeric products, and the associated free energy difference between competing TSs (ΔΔ G ‡ = - RT ln[( S )/( R )], where ( S ) and ( R ) represent the concentrations of the enantiomeric products). We, and others, speculated that this important piece of information can be leveraged to guide reaction optimization in a quantitative way. Although traditional linear free energy relationships (LFERs), such as Hammett plots, have been used to illuminate important mechanistic features, we sought to develop data science derived tools to expand the power of LFERs in order to describe complex reactions frequently encountered in modern asymmetric catalysis. Specifically, we investigated whether enantioselectivity data from a reaction can be quantitatively connected to the attributes of reaction components, such as catalyst and substrate structural features, to harness data for asymmetric catalyst design. In this context, we developed a workflow to relate computationally derived features of reaction components to enantioselectivity using data science tools. The mathematical representation of molecules can incorporate many aspects of a transformation, such as molecular features from substrate, product, catalyst, and proposed transition states. Statistical models relating these features to reaction outputs can be used for various tasks, such as performance prediction of untested molecules. Perhaps most importantly, statistical models can guide the generation of mechanistic hypotheses that are embedded within complex patterns of reaction responses. Overall, merging traditional physical organic experiments with statistical modeling techniques creates a feedback loop that enables both evaluation of multiple mechanistic hypotheses and future catalyst design. In this Account, we highlight the evolution and application of this approach in the context of a collaborative program based on chiral phosphoric acid catalysts (CPAs) in asymmetric catalysis.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction
Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.
43 PARTICLE ACCELERATORS↗