Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “methods: 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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Mathematical nuances of Gaussian process-driven autonomous experimentation

Abstract The fields of machine learning (ML) and artificial intelligence (AI) have transformed almost every aspect of science and engineering. The excitement for AI/ML methods is in large part due to their perceived novelty, as compared to traditional methods of statistics, computation, and applied mathematics. But clearly, all methods in ML have their foundations in mathematical theories, such as function approximation, uncertainty quantification, and function optimization. Autonomous experimentation is no exception; it is often formulated as a chain of off-the-shelf tools, organized in a closed loop, without emphasis on the intricacies of each algorithm involved. The uncomfortable truth is that the success of any ML endeavor, and this includes autonomous experimentation, strongly depends on the sophistication of the underlying mathematical methods and software that have to allow for enough flexibility to consider functions that are in agreement with particular physical theories. We have observed that standard off-the-shelf tools, used by many in the applied ML community, often hide the underlying complexities and therefore perform poorly. In this paper, we want to give a perspective on the intricate connections between mathematics and ML, with a focus on Gaussian process-driven autonomous experimentation. Although the Gaussian process is a powerful mathematical concept, it has to be implemented and customized correctly for optimal performance. We present several simple toy problems to explore these nuances and highlight the importance of mathematical and statistical rigor in autonomous experimentation and ML. One key takeaway is that ML is not, as many had hoped, a set of agnostic plug-and-play solvers for everyday scientific problems, but instead needs expertise and mastery to be applied successfully. Graphical abstract

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Planck 2015 results: XI. CMB power spectra, likelihoods, and robustness of parameters

This study presents the Planck 2015 likelihoods, statistical descriptions of the 2-point correlationfunctions of the cosmic microwave background (CMB) temperature and polarization fluctuations that account for relevant uncertainties, both instrumental and astrophysical in nature. They are based on the same hybrid approach used for the previous release, i.e., a pixel-based likelihood at low multipoles (ℓ< 30) and a Gaussian approximation to the distribution of cross-power spectra at higher multipoles. The main improvements are the use of more and better processed data and of Planck polarization information, along with more detailed models of foregrounds and instrumental uncertainties. The increased redundancy brought by more than doubling the amount of data analysed enables further consistency checks and enhanced immunity to systematic effects. It also improves the constraining power of Planck, in particular with regard to small-scale foreground properties. Progress in the modelling of foreground emission enables the retention of a larger fraction of the sky to determine the properties of the CMB, which also contributes to the enhanced precision of the spectra. Improvements in data processing and instrumental modelling further reduce uncertainties. Extensive tests establish the robustness and accuracy of the likelihood results, from temperature alone, from polarization alone, and from their combination. For temperature, we also perform a full likelihood analysis of realistic end-to-end simulations of the instrumental response to the sky, which were fed into the actual data processing pipeline; this does not reveal biases from residual low-level instrumental systematics. Even with the increase in precision and robustness, the ΛCDM cosmological model continues to offer a very good fit to the Planck data. The slope of the primordial scalar fluctuations, n s , is confirmed smaller than unity at more than 5σ from Planck alone. We further validate the robustness of the likelihood results against specific extensions to the baseline cosmology, which are particularly sensitive to data at high multipoles. For instance, the effective number of neutrino species remains compatible with the canonical value of 3.046. For this first detailed analysis of Planck polarization spectra, we concentrate at high multipoles on the E modes, leaving the analysis of the weaker B modes to future work. At low multipoles we use temperature maps at all Planck frequencies along with a subset of polarization data. These data take advantage of Planck’s wide frequency coverage to improve the separation of CMB and foreground emission. Within the baseline ΛCDM cosmology this requires τ = 0.078 ± 0.019 for the reionization optical depth, which is significantly lower than estimates without the use of high-frequency data for explicit monitoring of dust emission. At high multipoles we detect residual systematic errors in E polarization, typically at the μK 2 level; we therefore choose to retain temperature information alone for high multipoles as the recommended baseline, in particular for testing non-minimal models. Finally and nevertheless, the high-multipole polarization spectra from Planck are already good enough to enable a separate high-precision determination of the parameters of the ΛCDM model, showing consistency with those established independently from temperature information alone.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Bayesian event categorization matrix approach for explosion monitoring

Current efforts to correctly categorize natural events from suspected explosion sources with data that is collected by ground- or space-based sensors presents historical challenges that remain unaddressed by the Event Categorization Matrix (ECM) model. Smaller historical events (lower yield explosions) may have data available from fewer measurement techniques than are available today, and therefore, a historical event record can lack a complete set of discriminants. The covariance structures can also differ between such observations of event (source-type) categories. Both obstacles are problematic for the classic ECM model. Our work addresses this gap and presents a Bayesian update to the previous ECM model, termed the Bayesian Event Categorization Matrix model, which can be trained on partial observations and does not rely on a pooled covariance structure. We further augment the ECM model with Bayesian Decision Theory so that false negative or false positive rates of an event categorization can be reduced in an intuitive manner. To demonstrate improved categorization rates for the Bayesian Event Categorization Matrix model, we compare an array of Bayesian and classic models with multiple performance metrics using Monte Carlo experiments. We use both synthetic and real data. Our Bayesian models show consistent gains in overall accuracy and lower false negative rates relative to the classic ECM model. Here, we propose future avenues to improve Bayesian Event Categorization Matrix models’ decision making and predictive capability.

58 GEOSCIENCES↗
DOE OSTI2016

LUNGx Challenge for computerized lung nodule classification

The purpose of this work is to describe the LUNGx Challenge for the computerized classification of lung nodules on diagnostic computed tomography (CT) scans as benign or malignant and report the performance of participants’ computerized methods along with that of six radiologists who participated in an observer study performing the same Challenge task on the same dataset. The Challenge provided sets of calibration and testing scans, established a performance assessment process, and created an infrastructure for case dissemination and result submission. We present ten groups that applied their own methods to 73 lung nodules (37 benign and 36 malignant) that were selected to achieve approximate size matching between the two cohorts. Area under the receiver operating characteristic curve (AUC) values for these methods ranged from 0.50 to 0.68; only three methods performed statistically better than random guessing. The radiologists’ AUC values ranged from 0.70 to 0.85; three radiologists performed statistically better than the best-performing computer method. The LUNGx Challenge compared the performance of computerized methods in the task of differentiating benign from malignant lung nodules on CT scans, placed in the context of the performance of radiologists on the same task. Lastly, the continued public availability of the Challenge cases will provide a valuable resource for the medical imaging research community.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1992

On the Yakhot-Orszag renormalization group method for deriving turbulence statistics and models

An independent, comprehensive, critical review of the 'renormalization group' (RNG) theory of turbulence developed by Yakhot and Orszag (1986) is provided. Their basic theory for the Navier-Stokes equations is confirmed, and approximations in the scale removal procedure are discussed. The YO derivations of the velocity-derivative skewness and the transport equation for the energy dissipation rate are examined. An algebraic error in the derivation of the skewness is corrected. The corrected RNG skewness value of -0.59 is in agreement with experiments at moderate Reynolds numbers. Several problems are identified in the derivation of the energy dissipation rate equations which suggest that the derivation should be reformulated.

Smith, L. M.↗
NASA NTRS2008

Assessment of Current Jet Noise Prediction Capabilities

An assessment was made of the capability of jet noise prediction codes over a broad range of jet flows, with the objective of quantifying current capabilities and identifying areas requiring future research investment. Three separate codes in NASA s possession, representative of two classes of jet noise prediction codes, were evaluated, one empirical and two statistical. The empirical code is the Stone Jet Noise Module (ST2JET) contained within the ANOPP aircraft noise prediction code. It is well documented, and represents the state of the art in semi-empirical acoustic prediction codes where virtual sources are attributed to various aspects of noise generation in each jet. These sources, in combination, predict the spectral directivity of a jet plume. A total of 258 jet noise cases were examined on the ST2JET code, each run requiring only fractions of a second to complete. Two statistical jet noise prediction codes were also evaluated, JeNo v1, and Jet3D. Fewer cases were run for the statistical prediction methods because they require substantially more resources, typically a Reynolds-Averaged Navier-Stokes solution of the jet, volume integration of the source statistical models over the entire plume, and a numerical solution of the governing propagation equation within the jet. In the evaluation process, substantial justification of experimental datasets used in the evaluations was made. In the end, none of the current codes can predict jet noise within experimental uncertainty. The empirical code came within 2dB on a 1/3 octave spectral basis for a wide range of flows. The statistical code Jet3D was within experimental uncertainty at broadside angles for hot supersonic jets, but errors in peak frequency and amplitude put it out of experimental uncertainty at cooler, lower speed conditions. Jet3D did not predict changes in directivity in the downstream angles. The statistical code JeNo,v1 was within experimental uncertainty predicting noise from cold subsonic jets at all angles, but did not predict changes with heating of the jet and did not account for directivity changes at supersonic conditions. Shortcomings addressed here give direction for future work relevant to the statistical-based prediction methods. A full report will be released as a chapter in a NASA publication assessing the state of the art in aircraft noise prediction.

Hunter, Craid A.↗
DOE OSTI2011

Step-Stress Accelerated Degradation Testing for Solar Reflectors: Preprint

To meet the challenge to reduce the cost of electricity generated with concentrating solar power (CSP) new low-cost reflector materials are being developed including metalized polymer reflectors and must be tested and validated against appropriate failure mechanisms. We explore the application of testing methods and statistical inference techniques for quantifying estimates and improvinglifetimes of concentrating solar power (CSP) reflectors associated with failure mechanisms initiated by exposure to the ultraviolet (UV) part of the solar spectrum. In general, a suite of durability and reliability tests are available for testing a variety of failure mechanisms where the results of a set are required to understand overall lifetime of a CSP reflector. We will focus on the use ofthe Ultra-Accelerated Weathering System (UAWS) as a testing device for assessing various degradation patterns attributable to accelerated UV exposure. Depending on number of samples, test conditions, degradation and failure patterns, test results may be used to derive insight into failure mechanisms, associated physical parameters, lifetimes and uncertainties. In the most complicated casewarranting advanced planning and statistical inference, step-stress accelerated degradation (SSADT) methods may be applied.

14 SOLAR ENERGY↗
NASA NTRS1991

Maximum entropy method applied to deblurring images on a MasPar MP-1 computer

A statistical inference method based on the principle of maximum entropy is developed for the purpose of enhancing and restoring satellite images. The proposed maximum entropy image restoration method is shown to overcome the difficulties associated with image restoration and provide the smoothest and most appropriate solution consistent with the measured data. An implementation of the method on the MP-1 computer is described, and results of tests on simulated data are presented.

Bonavito, N. L.↗
DOE OSTI2014

Data-Driven Benchmarking of Building Energy Efficiency Utilizing Statistical Frontier Models

Frontier methods quantify the energy efficiency of buildings by forming an efficient frontier (best-practice technology) and by comparing all buildings against that frontier. Because energy consumption fluctuates over time, the efficiency scores are stochastic random variables. Existing applications of frontier methods in energy efficiency either treat efficiency scores as deterministic values or estimate their uncertainty by resampling from one set of measurements. Availability of smart meter data (repeated measurements of energy consumption of buildings) enables using actual data to estimate the uncertainty in efficiency scores. Additionally, existing applications assume a linear form for an efficient frontier; i.e.,they assume that the best-practice technology scales up and down proportionally with building characteristics. However, previous research shows that buildings are nonlinear systems. This paper proposes a statistical method called stochastic energy efficiency frontier (SEEF) to estimate a bias-corrected efficiency score and its confidence intervals from measured data. The paper proposes an algorithm to specify the functional form of the frontier, identify the probability distribution of the efficiency score of each building using measured data, and rank buildings based on their energy efficiency. To illustrate the power of SEEF, this paper presents the results from applying SEEF on a smart meter data set of 307 residential buildings in the United States. SEEF efficiency scores are used to rank individual buildings based on energy efficiency, to compare subpopulations of buildings, and to identify irregular behavior of buildings across different time-of-use periods. SEEF is an improvement to the energy-intensity method (comparing kWh/sq.ft.): whereas SEEF identifies efficient buildings across the entire spectrum of building sizes, the energy-intensity method showed bias toward smaller buildings. The results of this research are expected to assist researchers and practitioners compare and rank (i.e.,benchmark) buildings more robustly and over a wider range of building types and sizes. Eventually, doing so is expected to result in improved resource allocation in energy-efficiency programs.

Kavousian, A↗
DOE OSTI2014

MO-G-12A-01: Quantitative Imaging Metrology: What Should Be Assessed and How?

The first two symposia in the Quantitative Imaging Track focused on 1) the introduction of quantitative imaging (QI) challenges and opportunities, and QI efforts of agencies and organizations such as the RSNA, NCI, FDA, and NIST, and 2) the techniques, applications, and challenges of QI, with specific examples from CT, PET/CT, and MR. This third symposium in the QI Track will focus on metrology and its importance in successfully advancing the QI field. While the specific focus will be on QI, many of the concepts presented are more broadly applicable to many areas of medical physics research and applications. As such, the topics discussed should be of interest to medical physicists involved in imaging as well as therapy. The first talk of the session will focus on the introduction to metrology and why it is critically important in QI. The second talk will focus on appropriate methods for technical performance assessment. The third talk will address statistically valid methods for algorithm comparison, a common problem not only in QI but also in other areas of medical physics. The final talk in the session will address strategies for publication of results that will allow statistically valid meta-analyses, which is critical for combining results of individual studies with typically small sample sizes in a manner that can best inform decisions and advance the field. Learning Objectives: Understand the importance of metrology in the QI efforts. Understand appropriate methods for technical performance assessment. Understand methods for comparing algorithms with or without reference data (i.e., “ground truth”). Understand the challenges and importance of reporting results in a manner that allows for statistically valid meta-analyses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Accounting for erroneous model structures in biokinetic process models

In engineering practice, model-based design requires not only a good process-based model, but also a good description of stochastic disturbances and measurement errors to learn credible parameter values from observations. However, typical methods use Gaussian error models, which often cannot describe the complex temporal patterns of residuals. Consequently, this results in overconfidence in the identified parameters and, in turn, optimistic reactor designs. Here, we assess the strengths and weaknesses of a method to statistically describe these patterns with autocorrelated error models. This method produces increased widths of the credible prediction intervals following the inclusion of the bias term, in turn leading to more conservative design choices. However, we also show that the augmented error model is not a universal tool, as its application cannot guarantee the desired reliability of the resulting wastewater reactor design.

42 ENGINEERING↗
DOE OSTI2014

Consistent description of kinetics and hydrodynamics of dusty plasma

A consistent statistical description of kinetics and hydrodynamics of dusty plasma is proposed based on the Zubarev nonequilibrium statistical operator method. For the case of partial dynamics, the nonequilibrium statistical operator and the generalized transport equations for a consistent description of kinetics of dust particles and hydrodynamics of electrons, ions, and neutral atoms are obtained. In the approximation of weakly nonequilibrium process, a spectrum of collective excitations of dusty plasma is investigated in the hydrodynamic limit.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Validation of the intrinsic spatial efficiency method for non cylindrical homogeneous sources using MC simulation

The Monte Carlo simulation of the gamma spectroscopy systems is a common practice in these days. The most popular softwares to do this are MCNP and Geant4 codes. The intrinsic spatial efficiency method is a general and absolute method to determine the absolute efficiency of a spectroscopy system for any extended sources, but this was only demonstrated experimentally for cylindrical sources. Due to the difficulty that the preparation of sources with any shape represents, the simplest way to do this is by the simulation of the spectroscopy system and the source. In this work we present the validation of the intrinsic spatial efficiency method for sources with different geometries and for photons with an energy of 661.65 keV. In the simulation the matrix effects (the auto-attenuation effect) are not considered, therefore these results are only preliminaries. The MC simulation is carried out using the FLUKA code and the absolute efficiency of the detector is determined using two methods: the statistical count of Full Energy Peak (FEP) area (traditional method) and the intrinsic spatial efficiency method. The obtained results show total agreement between the absolute efficiencies determined by the traditional method and the intrinsic spatial efficiency method. The relative bias is lesser than 1% in all cases.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2015

Advanced Bayesian Method for Planetary Surface Navigation

Autonomous Exploration, Inc., has developed an advanced Bayesian statistical inference method that leverages current computing technology to produce a highly accurate surface navigation system. The method combines dense stereo vision and high-speed optical flow to implement visual odometry (VO) to track faster rover movements. The Bayesian VO technique improves performance by using all image information rather than corner features only. The method determines what can be learned from each image pixel and weighs the information accordingly. This capability improves performance in shadowed areas that yield only low-contrast images. The error characteristics of the visual processing are complementary to those of a low-cost inertial measurement unit (IMU), so the combination of the two capabilities provides highly accurate navigation. The method increases NASA mission productivity by enabling faster rover speed and accuracy. On Earth, the technology will permit operation of robots and autonomous vehicles in areas where the Global Positioning System (GPS) is degraded or unavailable.

Center, Julian↗