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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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Goals: What is Analysis of Variance (ANOVA); Extending Analysis of Variance to Function Data (F-ANOVA); The role of stochastic processes in F-ANOVA; One and Two Sample Problems for F-ANOVA; One-Way F-ANOVA.
Characterization of clocks and inertial sensors, such as accelerometers and gyroscopes, typically includes Allan variance analysis. Allan variance is ubiquitous in timing and navigation communities which may appear niche compared with generalized spectral analysis. This note provides some motivation for Allan Variance for audiences more familiar with spectral analysis.
Geophysical data at high time frequency (3-hourly, hourly, etc.) can be resolved into components that include a 24-hour diurnal cycle.
A 3D-printed microresonator based on a quartz tuning fork has been developed. The sensor performance is evaluated by detection of methane with a fiber-coupled distributed feedback diode laser. In combination with wavelength modulation spectroscopy, a minimum detectable concentration of 15.6 ppb by volume is achieved at atmospheric pressure with an 8-mW average optical excitation power and a lock-in amplifier time constant of 1 s, which corresponds to a 1σ normalized noise equivalent absorption coefficient of 5·10{sup –9} cm{sup –1}·W/Hz{sup 1/2}. An Allan variance analysis shows that the system has long-term stability and can serve as the basis for a portable gas analyzer. Approaches to improve the current sensor performance are also discussed. This design greatly reduces the time of manufacturing the microresonator.
Empirical modeling the partition behavior and recovery of a recombinant Pseudomonas putida POS-F84 proline dehydrogenase (ProDH) in aqueous two-phase systems (ATPS) was carried out by response surface methodology (RSM). Polyethylene glycol 1000 (PEG-1000) concentration, sodium carbonate concentration, and pH, which were the most important factors, were chosen for modeling the partition feature of enzyme. The adequacy of the models was investigated by means of variance analysis. Also, to confirm the efficiency of the ATPS in partition and purification of recombinant ProDH, purity and enzymatic activity was studied. After numerical optimization, an optimal ATPS composed of 14.33% PEG-1000 and 11.79% sodium carbonate at pH 7.48 was achieved. Yield, purification factor, and recovery were 81.41%, 60.82, and 270.82%, respectively. Purified recombinant ProDH was found as a single protein band into the upper PEG-rich phase and the specific activity was calculated to be 46.23 ± 2.1 U/mg. Collectively, our data showed that the RSM could be an appropriate and powerful tool to define the best ATPS system for recovery and purification of P. putida ProDH.
In inertial con finement fusion implosion experiments, the presence of residual anisotropic fluid motion within the stagnating hot spot leads to signifi cant variations in ion-temperature measurements using neutron time- of-flight detectors along different lines of sight. The minimum ion-temperature measurement is typically used as representative of the thermal temperature. In the presence of isotropic flows, however, even the minimum DT neutron-inferred ion temperature can be well above the plasma thermal temperature. Using both DD and DT neutron-inferred ion-temperature measurements, we show that it is possible to determine the contribution of isotropic flows and infer the DT burn-averaged thermal ion temperature. The contribution of large isotropic flows on driving the ratio of DD to DT neutron-inferred ion temperatures well below unity and approaching the lower bound of 0.8 is demonstrated in multimode simulations. The minimum DD neutron- inferred ion temperature is determined from the velocity variance analysis, accounting for the presence of isotropic flows. Being close to the DT burn-averaged thermal ion temperature, the inferred DD minimum ion temperatures demonstrate a strong correlation with the experimental yields in the OMEGA implosion database. An analytical expression is also derived to explain the effect of mode ℓ = 1 ion-temperature measurement asymmetry on yield degradations caused by the anisotropic flows.
Here, we performed cyclic loading of photovoltaic laminates with precracked silicon cells to explore if and how loading frequency and contact pressure influence the ensuing gridline wear-out process. A measurement of parallel resistance across cracked gridlines on a laminated cell coupon was used as the metric for gridline electrical contact degradation. A statistical analysis of variance (ANOVA) analysis of the experimental results discerned that loading frequency is a more significant factor than contact pressure for gridline degradation.
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Sensitivity analysis and uncertainty quantification are essential steps for enhancing the accuracy of computational models by identifying and mitigating uncertainties. This study focuses on these steps for the Thermal Energy Delivery System at Idaho National Laboratory, specifically targeting the thermocline tank. Using a Modelica/Dymola simulation model, the study perturbed various design parameters and boundary conditions, including shape factor, porosity, outlet temperature, inlet mass flow rate, and system pressure, to predict and quantify uncertainty in the tank’s ax- ial temperature. A dataset of over 1,000 simulations was generated, and surrogate models were developed using the pyMAISE (Michigan Artificial Intelligence Standard Environment) library, which is an Automatic Machine Learning library for nuclear engineering applications. The optimal model, a feedforward neural network with two hidden layers, achieved an R2 score above 0.99 and a mean absolute error below 1 Kelvin. Sensitivity analyses using Sobol indices and Fourier amplitude sensitivity testing methods on this surrogate model revealed that the inlet mass flow rate at initial timestamps and porosity significantly impacts predicted temperatures across all sensors and time steps.
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Abstract The cluster expansion method (CEM) is a widely used lattice-based technique in the study of multicomponent alloys. Despite its prevalent use, a clear understanding of expansion terms is lacking. We present a modern mathematical formalism of the CEM and introduce thecluster decomposition—a unique and basis-independent decomposition for functions of the atomic configuration in a crystal. We identify the cluster decomposition as an invariant ANOVA decomposition; and demonstrate how functional analysis of variance and sensitivity analysis can be used to interpret interactions among species. Furthermore, we show how the mathematical structure of the cluster decomposition enables numerical evaluation that scales with the number of clusters and is independent of the number of species. Overall, our work enables rigorous interpretations of interactions among species, provides opportunities to explore parameter estimation beyond linear regression, introduces a numerical efficient implementation, and enables analysis of cluster expansions based on established mathematical and statistical principles.
The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market that are available at a low cost. However, their efficacy and accuracy in various applications has not been tested. In this study, three commercially available UAV thermal cameras, including ICI 8640 P-series (Infrared Cameras Inc., USA), FLIR Vue Pro R 640 (FLIR Systems, USA), and thermoMap (senseFly, Switzerland) have been tested and evaluated for their potential for forest monitoring, vegetation stress detection, and plant phenotyping. Mounted on multi-rotor or fixed wing systems, these cameras were simultaneously flown over different experimental sites located in St. Louis, Missouri (forest environment), Columbia, Missouri (plant stress detection and phenotyping), and Maricopa, Arizona (high throughput phenotyping). Thermal imagery was calibrated using procedures that utilize a blackbody, handheld thermal spot imager, ground thermal targets, emissivity and atmospheric correction. A suite of statistical analyses, including analysis of variance (ANOVA), correlation analysis between camera temperature and plant biophysical and biochemical traits, and heritability were utilized in order to examine the sensitivity and utility of the cameras against selected plant phenotypic traits and in the detection of plant water stress. In addition, in reference to quantitative assessment of image quality from different thermal cameras, a non-reference image quality evaluator, which primarily measures image focus that is based on the spatial relationship of pixels in different scales, was developed. Our results show that (1) UAV-based thermal imaging is a viable tool in precision agriculture and (2) the three examined cameras are comparable in terms of their efficacy for plant phenotyping. Overall, accuracy, when compared against field measured ground temperature and estimating power of plant biophysical and biochemical traits, the ICI 8640 P-series performed better than the other two cameras, followed by FLIR Vue Pro R 640 and thermoMap cameras. Our results demonstrated that all three UAV thermal cameras provide useful temperature data for precision agriculture and plant phenotying, with ICI 8640 P-series presenting the best results among the three systems. Cost wise, FLIR Vue Pro R 640 is more affordable than the other two cameras, providing a less expensive option for a wide range of applications.
Sandia National Laboratories (SNL) has developed a novel reduced order modeling approach. Prioritization of inputs is accomplished using Sobo' indices obtained through a more efficient variance-based global sensitivity analysis. To determine the Sobo' functions, simulated input values are aligned to collocation points to permit the use of Gauss-Lobatto integration, thereby reducing the number of simulation trials needed by more than an order of magnitude compared to standard Monte Carlo approaches. Furthermore, by leveraging the orthogonality of Legendre polynomials in conjunction with those same simulations at the collocation nodes, an efficient fitting method is developed to represent the Sobo' functions from which a reduced order model (ROM) is constructed. The developed method is both more efficient computationally, and the resulting ROM is more accurate. The efficacy of this technique is demonstrated on a nonlinear polynomial test function as well as the nonlinear Ishigami and Sobo' g functions.
Human Reliability Analysis predicts accidents caused by human errors and is an important factor in Probabilistic Safety Assessment that comprehensively evaluates the safety of nuclear power plants. This study compares and analyzes the human performance of nuclear power plant operators according to the simulator complexity as part of the HRA data collection support method development project conducted by Idaho National Laboratory. This experiment was conducted by setting two types of simulators and scenarios as independent variables. The data collected by conducting the experiments in two different simulators were analyzed using an analysis of variance test and a correlation analysis, and four human performance charts were derived.
Human reliability analysis (HRA) evaluates human errors and provides human error probabilities (HEPs) for application in probabilistic safety assessment (PSA), which is a comprehensive safety assessment method for nuclear power plants (NPPs). Generally, HRA methods estimate HEPs based on human reliability data collected from actual historical measurement, simulator experiments, or expert judgement. Most recent HRA data collection studies focus on collecting data via full-scope main control room (MCR) simulators with actual licensed reactor operators. Contrary to this, Idaho National Laboratory (INL) has adopted a different approach, which attempts to collect HRA data based on experiment using simplified simulators and student participants by following the Simplified Human Error Experimental Program (SHEEP). This approach has a couple of advantages compared to full-scope data collection. Representatively, it has relatively low entry point for collecting HRA data, and secures large sample sizes with reasonable cost and labor. In the previous studies, we developed the SHEEP framework, then verified whether the data collected through the framework could support a representative full-scope data collection study, i.e., the Human Reliability Data Extraction (HuREX) study. Also, we analyzed human performance measurements depending on participant type (i.e., student vs. operator). In this paper, we analyze human performance data collected from an experiment comparing operator expertise and simulator complexity when using the more simplified simulator developed by INL, i.e., Rancor Microworld and the less simplified simulator, i.e., Compact Nuclear Simulator (CNS) developed by Korea Atomic Energy Research Institute (KAERI). Analysis of variance (ANOVA) tests and correlation analysis are used for analyzing the experimental data.