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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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We present a supervised learning method to perform photoelectron counting in PMT- based dark matter search experiments and show that it is possible to achieve better accuracy and precision compared to standard Bayesian statistics methods.
iGeoT v1.0 automates into a stand-alone computer program the multicomponent chemical geothermometry code GeoT v2.1 and the numerical optimization engine of iTOUGH2. iGeoT allows for optimizations of GeoT runs using multiple water chemical analyses. The underlying geothermometry method is that previously developed by Reed and Spycher (1984) using the computed saturation indices of multiple minerals. GeoT automatically reconstitutes deep fluid compositions and estimates reservoir temperature from statistical evaluation of computed mineral saturation indices. The output include estimated temperatures following various statistical methods and their range of uncertainty. The computer program also outputs temperatures predicted using classical geothermometers. Input parameters include water composition and, optionally, gas composition, fraction of gas discharge, dilution/evaporation factor (in case of boiling or mixing with dilute waters), and end-member water composition (in case of mixing with other non-dilute waters). The dilution/evaporation factor, fraction of gas discharge, and concentration of various aqueous and gas species can be automatically estimated by numerical optimization. This program was designed for geothermal applications.
The statistical methods used for antineutrino detection will need to be improved to effectively monitor the inventory of next-generation nuclear reactors. In this sensitivity study, we evaluate machine learning models compared to previously used statistical approaches to identify diversion scenarios in a simulated Advanced Fast Reactor (AFR)-100. A chi-square goodness-of-fit technique, which individually compares the simulated antineutrino yields to the expected antineutrino yield, resulted in precise but low diversion detection probability. Various support vector machine (SVM) models were applied with diverse training datasets to evaluate the robustness of the method towards unexpected or “unseen” diversion scenarios. Furthermore, our results indicate that while the SVM models significantly improved the detection probability of near-field antineutrino-based safeguards, up to a probability of ~0.04, for the simulated small reactor, the detection system still needs improvements to reach the 0.2 detection limit established by the International Atomic Energy Agency.
Purpose: A recent retrospective study found 53 patients previously treated to 78Gy/39 using flattened filtered (FF) 6X-VMAT at GRRCC had rectal DVH more than one standard deviation higher than the average. This study was to investigate if using 6FFFor10FFF beams could reduce these DVHs without compromising target coverage. Methods: Twenty patients’ plans were re-planed with 2-arc 6X-VMAT, 6FFF-VMAT and 10FFF-VMAT using the Eclipse TPS following departmental protocol. All plans had the same optimization and normalization, and were evaluated against the acceptance criteria from the QUANTEC and Emami. Statistical differences in the mean dose to OARs (D{sub m}) and PTV homogeneity index (HI) between energies were tested using the paired sample Wilcoxon signed rank statistical method (p<0.05). Beam delivery accuracy was checked on five patients using portal dosimetry (PD). Results: The PTV HI for the 10FFF shows no statistical difference from the 6X. All the OARs, except left femoral head with 6FFF, have significantly lower Dm using 6FFF and 10FFF .There is no difference in the maximum doses to rectum and bladder and are limited by the prescribed doses. Measurements show good agreements in the gamma evaluation (3%/3mm) for all energies. Conclusion: This preliminary study shows that doses to the OARs are reduced using 10FFF for the same target coverage. The plans using 6FFF result in lower doses to some OARs, and statistically different PTV HI. All plans showed very good agreement with measurements.
Numerical weather prediction (NWP) models have been widely used for wind resource assessment. Model runs with higher spatial resolution are generally more accurate, yet extremely computational expensive. An alternative approach is to use data generated by a low resolution NWP model, in conjunction with statistical methods. In order to analyze the accuracy and computational efficiency of different types of NWP-based wind resource assessment methods, this paper performs a comparison of three deterministic and probabilistic NWP-based wind resource assessment methodologies: (i) a coarse resolution (0.5 degrees x 0.67 degrees) global reanalysis data set, the Modern-Era Retrospective Analysis for Research and Applications (MERRA); (ii) an analog ensemble methodology based on the MERRA, which provides both deterministic and probabilistic predictions; and (iii) a fine resolution (2-km) NWP data set, the Wind Integration National Dataset (WIND) Toolkit, based on the Weather Research and Forecasting model. Results show that: (i) as expected, the analog ensemble and WIND Toolkit perform significantly better than MERRA confirming their ability to downscale coarse estimates; (ii) the analog ensemble provides the best estimate of the multi-year wind distribution at seven of the nine sites, while the WIND Toolkit is the best at one site; (iii) the WIND Toolkit is more accurate in estimating the distribution of hourly wind speed differences, which characterizes the wind variability, at five of the available sites, with the analog ensemble being best at the remaining four locations; and (iv) the analog ensemble computational cost is negligible, whereas the WIND Toolkit requires large computational resources. Future efforts could focus on the combination of the analog ensemble with intermediate resolution (e.g., 10-15 km) NWP estimates, to considerably reduce the computational burden, while providing accurate deterministic estimates and reliable probabilistic assessments.
Homogenization heat treatment is performed to attain uniformity in microstructure of metals and metal alloys, which is helpful to achieve the desired workability and microstructure in final products, and eventually to gain predictable and consistent performance. Fabrication of fuel plates made from a low-enriched uranium alloy with 10 wt% Mo (U-10Mo) involves multiple thermomechanical processing steps. It is well known that the molybdenum homogeneity in the final formed product affects the fuel performance in the nuclear reactor. To make sure these materials are uniformly homogenized, a statistical method was proposed to quantify and characterize the Mo concentration variation in U-10Mo fuel plates by analyzing the Mo concentration measurement data from scanning electron microscopy–energy dispersive spectroscopy line scans. Statistical tolerance intervals were employed to determine the qualification of U-10Mo fuel plates. We formulate an argument for the minimum number of independent samples required to define fuel plate qualification if no Mo measurement data are available in advance, and demonstrate that the given tolerance interval requirements can be equivalently reduced to a sample variance criterion in this application. The outcome of the statistical analysis can be used to optimize casting design and eventually increase productivity and reduce fabrication cost. The statistical strategy developed in this report can be implemented for other applications, especially in the field of material manufacturing to assess qualification requirements and monitor and improve process design.
Surface topography and surface finish are two significant factors for evaluating the quality of products in additive manufacturing (AM). AM parts are fabricated layer by layer, which is quite different from traditional formative or subtractive methods. Despite rapid progress in additive manufacturing and associated optical metrology for quality control and in-situ monitoring, limited research has been conducted to investigate the reliability of 3D surface measurement data. The surface topologies scanned by multiple optical systems demonstrated significant differences due to varying sampling mechanisms, resolutions, system noises, etc. The 3D datasets should be trustworthy in order to extract parameters for quality assurance or feedback control from 3D surface measurements. In this paper, we set up new standards to evaluate the reliability of 3D surface measurement data and analyze the variation in the topographical profile. In this study, two non-contact optical methods based on Focus Variation Microscopy (FVM) and Structured Light System (SLS) were adopted to measure the surface topography of the target components. The two optical metrology systems generated two entirely different point cloud datasets. Statistical methods were applied to test the difference between the data obtained from the two systems. By using a data analytics approach for comparison, it was found that the surface roughness estimated from the point cloud data sets of FVM and SLS has no significant difference, though the point cloud data sets were completely different. This paper provides standard validation approach to evaluate the plausibility of metrology data from in-situ real-time surface analysis for process planning of AM.
We provide a unified theoretical framework for recently emerging experiments that retrieve fixed-in-space molecular information through time-domain rotational coherence spectroscopy. Unlike a previous approach by Makhija et al. (V. Makhija et al., arXiv:1611.06476), our method can be applied to the retrieval of both real-valued (e.g., ionization yield) and complex-valued (e.g., induced dipole moment) molecular response information. It is also a direct retrieval method without using iterations. We also demonstrate that experimental parameters, such as the fluence of the aligning laser pulse and the rotational temperature of the molecular ensemble, can be quite accurately determined using a statistical method.
Homogenization heat treatment is performed to attain uniformity in microstructure which is helpful to achieve the desired workability and microstructure in final products and, eventually, to gain predictive and consistent performance. Fabrication of low-enriched uranium alloys with 10 wt% molybdenum (U-10Mo) fuel plates involves multiple thermomechanical processing steps. It is well known that the molybdenum homogeneity in the final formed product affects the performance in the nuclear reactor. Here, to ensure uniform homogenization, a statistical method is proposed to quantify and characterize the molybdenum concentration variation in U-10Mo fuel plates by analyzing the molybdenum concentration measurement data from scanning electron microscopy energy dispersive spectroscopy line-scan. Statistical tolerance intervals (TI) are employed to determine the qualification of the U-10Mo fuel plate. We formulate an argument for the minimum number of independent samples to define fuel plate qualification if no molybdenum measurement data are available in advance and demonstrate that the given TI requirements can be equivalently reduced to a sample variance criterion in this application. The outcome of the statistical analysis can be used to optimize casting design and eventually increase productivity and reduce fabrication costs. The statistical strategy developed in this paper can be implemented for other applications especially in the field of material manufacturing to assess qualification requirements and monitor and improve the process design.
Abstract Cyclic variations in conventional diesel combustion engines can lead to large differences in engine out emissions even at steady operation. This study uses an optically accessible constant-pressure flow chamber to acquire fuel injections in quick succession to analyze mixing, auto-ignition, and combustion of diesel-surrogate n-heptane using multiple high-speed optical diagnostics. Prior studies have utilized fewer injections and/or they rely on analysis of ensemble average behavior. These approaches do not yield information on injection-to-injection variation or provide confidence in utilizing individual injection measurements for high-fidelity computational fluid dynamics(CFD) model validation. In this study, a large set of 500 injections is used to obtain global parameters including liquid length, vapor penetration length, ignition delay time, and lift-off length. Results for multiple injections are presented to illustrate large injection to injection variations. Potential sources for these variations are analyzed to conclude localized, small scale turbulence and rate of injection variations as the likely sources. Then, a statistical method based on z-scores is proposed and implemented to identify instantaneous injections that best represent the bulk data-set of jet boundaries measured independently by three different diagnostics. This synthesis of statistics-guided screening of data set and ensemble-average analysis offers higher confidence for CFD model validation relying upon both a representative single and average injection results.
Despite widespread claims of power laws across the natural and social sciences, evidence in data is often equivocal. Modern data and statistical methods reject even classic power laws such as Pareto’s law of wealth and the Gutenberg–Richter law for earthquake magnitudes. We show that the maximum-likelihood estimators and Kolmogorov–Smirnov (K-S) statistics in widespread use are unexpectedly sensitive to ubiquitous errors in data such as measurement noise, quantization noise, heaping and censorship of small values. This sensitivity causes spurious rejection of power laws and biases parameter estimates even in arbitrarily large samples, which explains inconsistencies between theory and data. We show that logarithmic binning by powers of λ > 1 attenuates these errors in a manner analogous to noise averaging in normal statistics and that λ thereby tunes a trade-off between accuracy and precision in estimation. Binning also removes potentially misleading within-scale information while preserving information about the shape of a distribution over powers of λ, and we show that some amount of binning can improve sensitivity and specificity of K-S tests without any cost, while more extreme binning tunes a trade-off between sensitivity and specificity. We therefore advocate logarithmic binning as a simple essential step in power-law inference.
Machine learning methods are being increasingly used in condensed matter physics and materials science to classify crystals structures and predict material properties. However, the reliability of these methods for a given problem, especially when large data sets are unavailable, has not been well studied. By addressing the tasks of classifying crystal structure and predicting melting temperatures of the octet subset of AB solids, we performed such a study and found potential problems with using machine learning methods on relatively small data sets. At the same time, however, we can reaffirm the potential power of such methods for these tasks. In particular, we uncovered an important new material feature, the excess Born effective charge, that significantly increased the accuracy of the predictions for the classification problem we defined. This discovery leads us to propose a new scale for the degree of ionicity and covalency in these solids. More specifically, we partitioned the crystal structures of a set of 75 octet solids into those that are ionic and covalent bonded and thus performed a binary classification task. We found that using the standard indices (r σ ,r π ), suggested by St. John and Bloch several decades ago, enabled an average success in classification of 92%. Using just r σ and the excess Born effective charge ΔZ A of the A atom enabled an average success of 97%, but we also found relatively large variations about these averages that were dependent on how certain machine learning methods were used and for which a standard deviation was not a proper measure of the degree of confidence we can place in either average. Instead, we calculated and report with 95 % confidence that the traditional classification pair predicts an accuracy in the interval [ 89%, 95%] and the accuracy of the new pair lies in the interval [96 %, 99%]. For melting temperature predictions, the size of our data set was 46. We estimate the root-mean-squared error of our resulting model to be 11% of the mean melting temperature of the data, but we note that if the accuracy of this predicted error is itself measured, our estimated fitting error itself has a root-mean-square error of 50%. In short, what we illustrate is that classification and regression predictions can vary significantly, depending on the details of how machine learning methods are applied to small data sets. This variation makes it important, if not essential, to average the predictions and compute confidence intervals about these averages to report results meaningfully. However, when properly used, these statistical methods can advance our understanding and improve predictions of material properties even for small data sets.
Despite widespread claims of power laws across the natural and social sciences, evidence in data is often equivocal. Modern data and statistical methods reject even classic power laws such as Pareto’s law of wealth and the Gutenberg–Richter law for earthquake magnitudes. We show that the maximum-likelihood estimators and Kolmogorov–Smirnov (K-S) statistics in widespread use are unexpectedly sensitive to ubiquitous errors in data such as measurement noise, quantization noise, heaping and censorship of small values. This sensitivity causes spurious rejection of power laws and biases parameter estimates even in arbitrarily large samples, which explains inconsistencies between theory and data. We show that logarithmic binning by powers of λ > 1 attenuates these errors in a manner analogous to noise averaging in normal statistics and that λ thereby tunes a trade-off between accuracy and precision in estimation. Binning also removes potentially misleading within-scale information while preserving information about the shape of a distribution over powers of λ, and we show that some amount of binning can improve sensitivity and specificity of K-S tests without any cost, while more extreme binning tunes a trade-off between sensitivity and specificity. We therefore advocate logarithmic binning as a simple essential step in power-law inference.
We consider a high-dimensional nonlinear computational model of a dynamical system, parameterized by a vector-valued control parameter, in the presence of uncertainties represented by an uncontrolled parameter modeled by a vector-valued random variable, and possibly with stochastic excitation. The objective is to construct a statistical surrogate model where the input is any deterministic value of the control parameter, and the output is a vector-valued observation of the computational model, which is a random vector whose probability measure is updated using a target dataset. To construct this statistical surrogate model, the stochastic response of the computational model must be built, which is a vector-valued time-discretized stochastic process in high dimension, depending on the control parameter. It is assumed that the computational cost of a single evaluation of the deterministic model is high. For the probabilistic updating, we consider a subset of the components of the observation of the computational model, defined as the “identification observation” of the computational model, for which a small target dataset is available. Therefore, the target dataset is associated with partial observability, corresponding to an incomplete data case. Given a prior probability model of the random control and uncontrolled parameters, a training dataset is constructed, consisting of realizations of the random triplet composed of the stochastic response, the random identification observation, and the random control parameter. Since the computational cost of a single evaluation of the deterministic model is assumed to be large, the training dataset is also of small size. The main challenges in this problem are the high dimensionality, partial observability leading to incomplete data in the target dataset for the identification observation of the computational model (which is not sufficient to identify the computational stochastic responses), and the availability of a small training dataset. To address these challenges, we propose a methodology based on statistical methods for constructing necessary reduced representations, direct probabilistic learning under constraints using probabilistic learning on manifolds (PLoM) constrained by the target dataset, and the use of a weak formulation of the Fourier transform of probability measures. Statistical conditioning is also employed to explore the learned dataset. The constructed predictive statistical surrogate model can be implemented in the context of online computation. Here, we apply this approach to a problem of nonlinear stochastic dynamics in high dimensions within the framework of deformable solids mechanics.
Primary biological aerosol particles (PBAPs) are microscopic solids suspended in the atmosphere emitted by biological systems and play critical roles in the atmosphere and at the atmosphere-biosphere interface, impacting human health, climate, and the ecosystem function. Understanding the sources of PBAPs is necessary to decipher the mechanistic interactions between aerosols, climate, and distinct ecosystem components. However, the detection of specific PBAPs in complex ambient aerosol samples is challenging. We performed metabolomics analyses of pollen from three pollinating tree species and ambient samples collected during the peak pollination period of each species. Random Forest and sPLS-DA machine learning methods were employed to evaluate whether metabolic signatures of ambient samples can reveal the source of the main pollen particles present in the atmosphere. Our results suggest that atmospheric eco-metabolomics techniques combined with sophisticated statistical methods can decipher the origin of abundant PBAPs from complex ambient samples. Developing complete libraries containing high-resolution metabolomic fingerprints of the major PBAPs present in the atmosphere would significantly advance future research to accurately understand the role of PBAPs in the atmosphere, ecosystems and human health.
Purpose: Stereotactic body radiation therapy (SBRT) for ≥5 cm lesions is poorly defined, largely owing to the low sample sizes in existing studies. The present analysis examined the SBRT outcomes and assessed the effect of chemotherapy in this population. Methods and Materials: The National Cancer Data Base was queried for primary non-small cell lung cancer ≥5 cm treated with SBRT (≤10 fractions). Patient, tumor, and treatment parameters were extracted. The primary outcome was overall survival (OS). Statistical methods involved Kaplan-Meier analysis and multivariable Cox proportional hazards modeling. Results: From 2004 to 2012, data from 201 patients were analyzed. The median follow-up was 41.1 months. The median tumor size was 5.5 cm (interquartile range 5.0-6.0), with cT2a, cT2b, and cT3 disease in 24.9%, 53.2%, and 21.9%, respectively. The median total SBRT dose and fractionation was 50 Gy in 4 fractions, and 92.5% of the patients underwent SBRT with ≤5 fractions. The median OS was 25.1 months. Of the 201 patients, 15% received chemotherapy. The receipt of chemotherapy was associated with longer OS (median 30.6 vs 23.4 months; P=.027). On multivariable analysis, worse OS was seen with increasing age (hazard ratio [HR] 1.03; P=.012), poorly differentiated tumors (HR 2.06; P=.049), and T3 classification (HR 2.13; P=.005). On multivariable analysis, chemotherapy remained independently associated with improved OS (HR 0.57; P=.039). Conclusions: SBRT has utility in the setting of tumors ≥5 cm, with chemotherapy associated with improved OS in this subset. These hypothesis-generating data now raise the necessity of performing prospective analyses to determine whether chemotherapy confers outcome benefits after SBRT.
High Energy Physics (HEP) experiments generally employ sophisticated statistical methods to present results in searches of new physics. In the problem of searching for sterile neutrinos, likelihood ratio tests are applied to short-baseline neutrino oscillation experiments to construct confidence intervals for the parameters of interest. The test statistics of the form Δχ2 is often used to form the confidence intervals, however, this approach can lead to statistical inaccuracies due to the small signal rate in the region-of-interest. In this paper, we present a computational model for the computationally expensive Feldman-Cousins corrections to construct a statistically accurate confidence interval for neutrino oscillation analysis. The program performs a grid-based minimization over oscillation parameters and is written in C++. Our algorithms make use of vectorization through Eigen3, yielding a single-core speed-up of 350 compared to the original implementation, and achieve MPI data parallelism by employing DIY. We demonstrate the strong scaling of the application at High-Performance Computing (HPC) sites. We utilize HDF5 along with HighFive to write the results of the calculation to file.
The idea of creating a recognizable face from a sample of DNA is moving closer to reality as Oak Ridge National Laboratory (ORNL) researchers employ a novel statistical representation of the face and a genome-wide association study (GWAS) to relate facial characteristics to genetic mutations. The reduction in cost of DNA sequencing and genotyping technology is leading to the availability of massive datasets of human genome sequences and human genotypes representing mutational variation among people. These large-scale data provide for the first time the potential to correlate an individual’s specific genetic code with their physical appearance. Using a specialized statistical method for representing the entire 3D shape of faces, ORNL researchers performed a GWAS to discover associations between genetic mutations and aspects of face shape. Data from 3,556 subjects resulted in the discovery of a very large set of statistically significant associations, which is strong evidence for the viability of predicting faces from DNA.