Statistics, My Past and Your Future [Slides]
This report documents a STEM presentation on the subject of Statistics that was presented at a High School.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
SEARCH · Engineering Papers
Results for “statistics”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Statistically Significant Evidence of a Release of Zinc from Pit 1 at the Lawrence Livermore National Laboratory Experimental Test Site 300 (90-Day Report)
Lawrence Livermore National Laboratory (LLNL) is submitting this 90-Day Report in accordance with Waste Discharge Requirements Order No. 93-100, Provision 4. This letter report is provided in reference to several seven-day letters in which LLNL reported that ground water sample analytical results from monitoring wells Kl-05, Kl-08, Kl-09, and W-PITl-2326 indicated "statistically significant evidence of a release" of zinc from LLNL's Site 300 Experimental Test Facility landfillPit 1 (Figure 1).
54 ENVIRONMENTAL SCIENCES↗
Statistics of the Charge Spectrum of Photo-Multipliers and Methods for Absolute Calibration
In this paper, we derive the full expression for the shape of the charge distribution that results from the illumination of a photo-multiplier tube. The derivation assumes that the photo-multiplier is operated with low intensity illumination with constant gain. Under these conditions, it is shown that an analytic expression can be formulated that describes details of the spectrum including the pedestal and dark noise with excellent fidelity to allow statistical fits to data. The full formula and its limiting forms under various simplifying assumptions are presented with strategies on their use. The analytic description can be used to formulate data acquisition strategies to perform precise absolute calibration of photo-multipliers, the digitizers, and the data acquisition system.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Evaluation of Component Reliability in Photovoltaic Systems using Field Failure Statistics
Ongoing operations and maintenance (O&M) are needed to ensure photovoltaic (PV) systems continue to operate and meet production targets over the lifecycle of the system. Although average costs to operate and maintain PV systems have been decreasing over time, reported costs can vary significantly at the plant level. Estimating O&M costs accurately is important for informing financial planning and tracking activities, and subsequently lowering the levelized cost of electricity (LCOE) of PV systems. This report describes a methodology for improving O&M planning estimates by using empirically-derived failure statistics to capture component reliability in the field. The report also summarizes failure patterns observed for specific PV components and local environmental conditions observed in Sandia's PV Reliability, Operations & Maintenance (PVROM) database, a collection of field records across 800+ systems in the U.S. Where system-specific or fleet-specific data are lacking, PVROM-derived failure distribution values can be used to inform cost modeling and other reliability analyses to evaluate opportunities for performance improvements.
14 SOLAR ENERGY↗
Measurement of Spontaneous Undulator Radiation Statistics Generated by a Single Electron (URSSE)
As previously stated in the experiment proposal [1], the goal of the URSSE experiment was to study statistical properties of the undulator radiation generated by a single electron, circulating inthe IOTA storage ring. Namely, the idea was to study the distribution of the number of photons detected in a certain time window, and the distribution of the inter-arrival times of the photons. We wanted to see if the photostatistics was exactly Poissonian, as predicted or if there was some deviation towards Sub-Poissonian light, similar to what was observed in another study.
43 PARTICLE ACCELERATORS↗
The Idiot's Guide to the Statistical Theory of Fission Chains
What follows are my personal notes developed as I’ve struggled to understand the Statistical Theory of Fission Chains. The theoretical work detailed here is principally the work of R. P. Feynman, G. I. Bell, K. Boehnel, W. Hage, D. M. Cifarelli, M. Prasad, and N. Snyderman, plus a little of my own dablings around the edges.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Statistical Hauser-Feshbach model calculation for (α, n) reaction cross section and discrete level population
We calculate the α-particle induced reactions on 17,18 O, 19 F, and 23 Na, in the energy range 0 ≤ E α ≤ 10 MeV, with a particular attention to the branching ratios of populated discrete levels in the (α,n) channels. Since there are too many open channels to employ the R-matrix theory, we apply the statistical Hauser-Feshbach model to calculate these reaction cross sections and branching ratios. The branching ratio is defined as b n = $\frac{σ_n}{Σ_iσ_i + σ_c}$ , where σ n is the n-th level production cross section after the neutron emission, and σ c is the production of the continuum state. In the cases of our target nuclei and the energy range of interest, the residual nuclei of the neutron emission channel are always in their discrete states, so that σ c can be negligible.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Statistical Distributions for Mesh Independent Solutions in ALEGRA
The representation of material heterogeneity (also referred to as "spatial variation") plays a key role in the material failure simulation method used in ALEGRA. ALEGRA is an arbitrary Lagrangian-Eulerian shock and multiphysics code developed at Sandia National Laboratories and contains several methods for incorporating spatial variation into simulations. A desirable property of a spatial variation method is that it should produce consistent stochastic behavior regardless of the mesh used (a property referred to as "mesh independence"). However, mesh dependence has been reported using the Weibull distribution with ALEGRA's spatial variation method. This report describes efforts towards providing additional insight into both the theory and numerical experiments investigating such mesh dependence. In particular, we have implemented a discrete minimum order statistic model with properties that are theoretically mesh independent.
36 MATERIALS SCIENCE↗
Studying Rate of Fire Spread Using Landscape Statistics and FIRETEC
Wildfires are multi-system and multi-scale processes that are influenced by the land and atmosphere. Here, we study the influence of land, in terms of topography and vegetation, on uphill and downhill fire spread using data of the state of California as a testbed. The California landscape is first quantified by reclassifying its dominant vegetation types into grass, trees, and shrubs. Next, statistics of slope, vegetation height, and live fuel moisture percentage for each dominant vegetation type are gathered to create ensembles of idealized computational fluid dynamics (CFD) model simulations representative of the landscape. Rate of spread values for each CFD simulation are recorded to observe the differences in perturbing slope and fuel moisture for each vegetation type. We have found that all vegetation types prefer slopes of less than 30 degrees. Trees in the California data are 10 to 40 meters high, whereas shrub heights are less than 3 meters and grass heights are less than 1 meter. The live fuel moisture percentage of both trees and shrubs fluctuates around approximately 100% over one year (2019-2020). Using these parameters as inputs into the CFD model, fire spread rate is found to be fastest in the grass ensemble (maximum 12 m s -1 ), followed by shrubs (maximum 10 m s -1 ) and lastly by trees (maximum 7 m s -1 ). Increasing slope increases the spread rate of the uphill fire but the downhill spread decreases for slopes greater than 15 degrees. Increasing fuel moisture decreases the spread rate both up and down the hill. This study highlights the impacts that topography and vegetation have on fire spread.
54 ENVIRONMENTAL SCIENCES↗
Quantifying Uncertainty in All-to-All Estimates of Space Object Conjunction Probabilities using U-Statistics
Predicting space object conjunctions is inherently probabilistic due to initial state and orbit model uncertainty. A commonly considered Monte Carlo estimator of the conjunction probability is the ’all-to-all’ estimator. Given independent random samples of the trajectories of both objects, the estimator is the percentage of all pairs of trajectories that result in a conjunction. Intuitively, the all-to-all estimator is the best possible estimator of the conjunction probability since it considers all pairs of Monte Carlo samples. However, its distribution is not available in closed-form, which limits its use in practice and makes this intuition difficult to make rigorous. In this paper, the all-to-all estimator is identified as a U-statistic, which implies that it has several favorable properties. Specifically, the estimator is the minimum variance unbiased estimator of the conjunction probability and is asymptotically Gaussian distributed. An approximate confidence interval for the conjunction probability is obtained from an estimate of the asymptotic Gaussian distribution. We show how to efficiently compute the confidence interval and demonstrate that the interval has the nominal coverage level. The confidence intervals are also seen to be narrower than those based on the commonly-used each-to-each estimator. Furthermore, the all-to-all estimator is shown to allow different Monte Carlo sample sizes, whereas the each-to-each estimator requires equal sample sizes.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
The STAT7 Code for Statistical Propagation of Uncertainties In Steady-State Thermal Hydraulics Analysis of Plate-Fueled Reactors
STAT7 was written to automate many of the steady-state thermal hydraulic safety calculations for the MIT research reactor, both for conversion of the reactor from highly enriched uranium fuel to low-enriched uranium fuel and for future fuel re-loads after the conversion. A Monte-Carlo statistical propagation approach is used to treat uncertainties in important parameters in the analysis. These safety calculations are ultimately intended to protect against high fuel plate temperatures due to critical heat flux or departure from nucleate boiling or onset of flow instability; but additional margin is obtained by basing the limiting safety settings on avoiding onset of nucleate boiling. STAT7 can simultaneously analyze all of the axial nodes of all of the fuel plates and all of the coolant channels for one stripe of a fuel element. The stripes run the length of the fuel, from the bottom to the top. Power splits are calculated for each axial node of each plate to determine how much of the power goes out each face of the plate. By running STAT7 multiple times, full core analysis can be performed by analyzing the margin to ONB for each axial node of each stripe of each plate of each element in the core.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Synthesis of observed and simulated rain microphysics to inform a new Bayesian statistical framework for microphysical parameterization in climate models (Final Scientific Report)
This project aimed to investigate a new approach to bulk microphysics schemes. We identified the need to move beyond the fixed structural assumptions and approximation of existing schemes. For example, most schemes assume some functional form for the rain drop size distribution (e.g. a gamma or exponential distribution). Most schemes then evolve some number of statistical moments of that distribution via various processes, such as evaporation, sedimentation, collision-coalescence, and collisional breakup. These microphysical processes, in turn, are typically some fixed functional form derived from either some other (more detailed) model, or using some single-particle rates that are then integrated over the size distribution. While these process rate functions may have some free parameters to adjust, in other cases doing so is impossible (e.g. when the process rates are analytical or piecewise solutions to some target function). Our goal was to build and test a scheme that assumed no size distribution form, and used a series of power laws as the basis for the process rates. Moments of the size distribution would be predicted, but no underlying size distribution would be specified. Any number or choice of size distribution moments could be used, and any number of power laws could be employed to model the process rates. Thus, our approach is flexible, and can seamlessly scale across levels of complexity, as demanded by the data. Bayesian inference then would provide the formalism to estimate the model parameters and structure, allowing for robust uncertainty quantification. We proposed testing this framework in idealized simulations using bin microphysical schemes as a data source. We also proposed using real data to inform our microphysics scheme, and also that we would integrate our scheme into WRF.
58 GEOSCIENCES↗
Solving Statistical Flowgraph Models: A Brief Review
Statistical flowgraph models (SFGMs) have proven useful for analysis and modeling of multistate processes in various fields. In this paper we briefly review flowgraph models, describe how to set up a model, and provide a fully worked example from biostatistics. References are provided for more details.
97 MATHEMATICS AND COMPUTING↗
Statistical Learning for Nonlinear Model Reduction from Local Simulations of Stochastic and Particle- and Agent-Based Systems
Stochastic physical systems across the sciences that have very high-dimensional state spaces, with a large number of fast degrees of freedom that force direct simulators to proceed by integration steps that are orders of magnitude smaller than events of interests (e.g., particle collisions). Examples range from molecular motion to dynamics of large populations of cells. A grand challenge in the simulation and understanding of such systems is the systematic construction of accurate, interpretable, reduced models, enabling faster simulations, revealing fundamental properties of the dynamics, and predicting phenomena of interest that the original simulator could not reached with sufficient accuracy or within a given computational budget. In this projected we developed novel statistical estimation/machine learning techniques for analyzing and building empirical reduced models for important families of high-dimensional stochastic systems, in particular: - we developed techniques for estimating interaction kernels in interacting particle- and agent-based systems, which are ubiquitous in Physics, Biology and many other sciences, given observed trajectories of the system; - we developed techniques for nonlinear model reduction for high-dimensional stochastic systems that have a small number of unknown, nonlinear slow variables, and a large number of fast modes, that are possibly of large magnitude, given observed short trajectories of the system in the form of bursts of trajectories from different initial conditions; - we developed novel techniques for estimating linear dynamical systems on graphs when both the dynamics and the underlying graph are unknown, and we have a sparse set of space-time observations; - we considered the problem of estimating an unknown nonlinear observation function of a standard process (e.g. Brownian motion), so that we can recognized if an observed dynamics is "just" a nonlinear version of a known dynamics; we also developed benchmarks for learning algorithms aimed at learning and classifying diffusion processes.
97 MATHEMATICS AND COMPUTING↗
Monte Carlo Statistical Convergence
This document reviews Monte Carlo Statistical Convergence over the last 60 years, based on the real World state of computers over this time. Stress is placed on providing examples of what was and was not practically possible over this period of time. I present a few examples to illustrate convergence based not only on sample size, but also how cross sections can be modelled.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
RCT Module 2.03: Counting Errors and Statistics [Slides]
This course will present an overview of measurement processes, and statistical evaluation of both measurements and equipment performance. In addition, this course will address some of the actions to take to minimize the sources of error in count room operations. This course will prepare the student with the skills necessary for RCT qualification.
61 RADIATION PROTECTION AND DOSIMETRY↗
Harnessing the power of big omics data: Novel statistical tools to study the role of microbial communities in fundamental biological processes
By harnessing the power of big data through revolutionary new statistical theory in sparse learning, and post selection inference, our project produced tools to better understand the drivers in soil biological phenotypes and to allow new insights into fundamental biological processes.
59 BASIC BIOLOGICAL SCIENCES↗
Chaconne: A Statistical Approach to Nonlocal Compression for Supervised Learning, Semi-Supervised Learning, and Anomaly Detection
This project developed a novel statistical understanding of compression analytics (CA), which has challenged and clarified some core assumptions about CA, and enabled the development of novel techniques that address vital challenges of national security. Specifically, this project has yielded the development of novel capabilities including 1. Principled metrics for model selection in CA, 2. Techniques for deriving/applying optimal classification rules and decision theory to supervised CA, including how to properly handle class imbalance and differing costs of misclassification, 3. Two techniques for handling nonlocal information in CA, 4. A novel technique for unsupervised CA that is agnostic with regard to the underlying compression algorithm, 5. A framework for semisupervised CA when a small number of labels are known in an otherwise large unlabeled dataset. 6. The academic alliance component of this project has focused on the development of a novel exemplar-based Bayesian technique for estimating variable length Markov models (closely related to PPM [prediction by partial matching] compression techniques). We have developed examples illustrating the application of our work to text, video, genetic sequences, and unstructured cybersecurity log files.
99 GENERAL AND MISCELLANEOUS↗