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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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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Testing Nesting DXTRAN Spheres for Neutron Transport in Air

The deterministic transport sphere (DXTRAN sphere) is a technology that can be utilized in MCNP for improving sampling in the vicinity of a particular geometry, when the solid angle, between the source and particular geometry, is small. At a collision event, a DXTRAN particle is created and deterministically transported to the DXTRAN sphere, while the colliding particle (NONDXTRAN particle) continues transporting as if no DXTRAN event had occurred. Weight is balanced by killing the NONDXTRAN particle when traversing the DXTRAN sphere. If set up correctly, the DXTRAN particle placed on the DXTRAN sphere has a high probability of interacting with the geometry within the DXTRAN sphere. The Central Limit Theorem (CLT) states that the estimated mean approaches a normal distribution with known variance as the number of random samples grows sufficiently large. In reality the variance is seldom known and therefore must be estimated. In a Monte Carlo calculation, one must determine when the number of random samples is sufficiently large for the CLT to be satisfied. For the CLT to be satisfied, the first two moments (mean and variance) must exist. The second moment, the variance of a normal distribution is estimated. The slope test, which uses the largest 201 history scores of each tally fluctuation chart (TFC) bin fit to a Pareto function, is used to determine if the largest history scores decrease faster than 1/x{sup 3}. If the slope is less than 3, then enough histories have not yet been sampled such to meet the second moment existence requirement of the CLT. Usually, a slope of less than 3 indicates that the effects of high weight scores have not been satisfactorily sampled; such a consequence can also be manifested in examining trends in the 4. moment, the variance of the variance (VOV). For example, when examining the TFC, if the VOV is monotonically decreasing for a few sets of histories and then drastically increases for the next set of histories, chances are a rare high weight score, significantly greater than the average score, was encountered. As a result of the 1/R{sup 2} term, a point detector can achieve an extremely high weight, much greater than the average, as the distance from collision to point detector approaches zero. To remedy this difficulty, a 'radius of exclusion' is applied, where within the radius of exclusion 'the point detector estimate is assumed to be the average flux uniformly distributed'. A DXTRAN sphere can suffer from the same weight fluctuation as collisions in the vicinity of the DXTRAN sphere produce much larger weights than collisions happening far from the DXTRAN sphere. If the rare large weight put on the DXTRAN sphere then scores in a tally, there will be a significant increase in the VOV and the slope may drop below 3. To compliment that analysis, here we utilize an air composition and density that varies as a function of altitude, detailing the trials and tribulations of attempting to pass the 10 statistical checks for a variety of single and nested DXTRAN sphere approaches. The objective here is to illuminate important considerations for radiation transport problems with small solid angles in a scatter medium of few mean free paths (MFPs)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Coherency-Constrained Spectral Clustering for Power Network Reduction

This paper presents a methodology for reducing the complexity of large-scale power network models using spectral clustering, aggregation of electrical components, and cost function approximation. Two approaches are explored using unconstrained and constrained spectral clustering to determine areas for effective system reduction. Once the system areas are determined, both loads and generators by type are aggregated, and their new cost function is approximated through polynomial curve-fitting or statistical methods. The performance of reduced networks is evaluated in terms of their ability to follow the true daily cost of the original system over a 24-hour period considering a set of several days. Two test systems are taken as test beds. Application of the methodology to a modified version of the IEEE 39-bus system reduces it from 17 generators to a 4-bus system and 9 generators with about 93% of accuracy. Similarly, the IEEE 118-bus system is reduced from 19 generators to a 3-bus system with three aggregated units achieving over 99% of accuracy. These findings address scalability challenges and enhance accuracy for high and mid-loading level conditions, and by aggregating thermal units with similar cost functions.

42 ENGINEERING↗
DOE OSTI2024

Temperature-dependent mechanical properties and crystal plasticity parameters for additively manufactured Haynes-214 alloy: Experiments and numerical modeling

Our experimental mechanical testing data demonstrated that the additively manufactured (AM) laser powder bed fusion (L-PBF) Haynes-214 alloy exhibits non-linear mechanical properties as the temperature rises from ambient to 870 °C. Crystal plasticity (CP) simulations provide an effective approach to gaining deeper insights into microstructure-property linkages under thermomechanical loading. This method can reduce the need for costly high-temperature mechanical testing while accounting for the effects of crystallographic texture and grain morphology on the mechanical behavior of AM materials. However, calibrating a CP model is time-consuming because individual simulations are computationally expensive and hundreds (or more) of iterations over parameter sets may be required. To address this issue, we have designed a machine learning-differential evolution (ML-DE) CP framework that can accurately interpolate the tensile properties of AM L-PBF Haynes-214 alloy across a wide temperature range from ambient to 870 °C, with minimal reliance on experimental data. The framework uses electron backscatter diffraction (EBSD) measurements to generate statistically equivalent microstructural volume elements to serve as inputs to the CP modeling framework. Stress–strain curves were generated from 1000 CP simulations, which serve as the training data set for the three ML regression algorithms explored: linear, extra-trees, and multi-layer perceptron. These three regression models were independently evaluated to compare their efficiency and identify the most suitable algorithm for the given problem. Results revealed that the extra-trees ML regressor outperforms the other models in both qualitative and quantitative aspects with an R 2 of 0.98. Subsequently, the differential evolution optimization approach is employed to calibrate the ML-based CP material parameters with experimental results obtained at various temperatures. Finally, temperature-dependent CP material parameters are formulated. The effectiveness and efficiency of the designed framework are validated through comparison with experimental results, demonstrating a high degree of agreement. These calibrated parametric constitutive equations enable further use of the CP model to study the deformation behavior of this alloy under a wide range of thermo-mechanical loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advanced Signal Decomposition Analysis and Anomaly Detection in Photovoltaic Systems

With the rapid expansion of large-scale photovoltaic (PV) plants, it is paramount for solar stakeholders to understand the reliability and efficiency of their plants to inform maintenance decisions, increase production, and understand the design factors that impact performance. Diagnosing underperformance in PV plants is challenging due to the relatively few monitoring points with respect to the large geographic footprint of the plant. This work introduces a cutting-edge method that transforms the analysis and management of key factors influencing PV plant performance, including performance loss rate (PLR), recoverable soiling, and major system changes. Identifying these factors is critical for deriving actionable insights. Leveraging advanced analytical techniques such as wavelet transformation, robust regression, and extreme point analysis, this approach provides a nuanced understanding of these factors. This method has been tested across two synthetic datasets and one real dataset, consistently surpassing existing benchmarks by achieving a lower median mean absolute error and reduced error variability across all comparable components.

14 SOLAR ENERGY↗
DOE OSTI2020

Sensitive neutron transverse polarization analysis using a 3 He spin filter

Here we report an experimental implementation for neutron transverse polarization analysis that is capable of detecting a small angular change ($\ll$10 –3 rad) in neutron spin orientation. This approach is demonstrated for monochromatic beams, and we show that it could be extended to polychromatic neutron beams. Our approach employs a 3 He spin filter inside a solenoid with an analyzing direction perpendicular to the incident neutron polarization direction. The method was tested with polarized neutron beams and a spin rotator placed inside a μ -metal shield just upstream of the analyzer. No cryogenic superconducting shields or additional neutron spin manipulations are needed. With a counting detector, we experimentally show that the angular resolution δ θ = 1 / ( P n A N ) rad is only determined by the counting statistics for the total counts N and the product of the neutron polarization P n and the analyzing power A . With a high-flux neutron beam, 10 –6 rad angular sensitivity is feasible within a day. This simple, classical-quantum-limited transverse polarization analysis scheme may reduce the overall complexity of experimental implementation for applications requiring sensitive neutron polarimetry and improve the precision in fundamental science studies and polarized neutron imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Angular systematics-free cosmological analysis of galaxy clustering in configuration space

ABSTRACT Galaxy redshift surveys are subject to incompleteness and inhomogeneous sampling due to the various constraints inherent to spectroscopic observations. This can introduce systematic errors on the summary statistics of interest, which need to be mitigated in cosmological analysis to achieve high accuracy. Standard practices involve applying weighting schemes based on completeness estimates across the survey footprint, possibly supplemented with additional weighting schemes accounting for density-dependent effects. In this work, we concentrate on pure angular systematics and describe an alternative approach consisting in analysing the galaxy two-point correlation function where angular modes are nulled. By construction, this procedure removes all possible known and unknown sources of angular observational systematics, but also part of the cosmological signal. We use a modified Landy–Szalay estimator for the two-point correlation function that relies on an additional random catalogue where angular positions are randomly drawn from the galaxy catalogue, and provide an analytical model to describe this modified statistic. We test the model by performing an analysis of the full anisotropic clustering in mock catalogues of luminous red and emission-line galaxies at 0.43 < z < 1.1. We find that the model fully accounts for the modified correlation function in redshift space, without introducing new nuisance parameters. The derived cosmological parameters from the analysis of baryon acoustic oscillations and redshift-space distortions display slightly larger statistical uncertainties, mostly for the growth rate of structure parameter fσ8 that exhibits a $50{{\ \rm per\ cent}}$ statistical error increase, but free from angular systematic error.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

The Role of Adjuvant Radiotherapy in the Treatment of Pleural Mesothelioma

Background: Pleural mesothelioma is a rare but aggressive form of cancer. Local recurrence represents the majority of treatment failures and overall survival (OS) outcomes remain dismal. Adding locoregional treatment with radiotherapy after surgical resection has been considered but its role remains uncertain. Objective: The purpose of this study was to evaluate the outcomes of adjuvant radiation therapy (RT) for patients with malignant pleural mesothelioma. Methods: The National Cancer Data Base (NCDB) was queried (2004–2013) for patients with malignant mesothelioma. Patients were divided into three groups: observation, surgery alone, and surgery followed by adjuvant RT. Statistics included Fisher’s exact or Chi square tests to analyze categorical proportions between groups, Kaplan–Meier analysis to evaluate OS, and Cox proportional hazards modeling to determine variables associated with OS. Propensity matching was performed to make comparisons between homogenous groups. Results: Overall, the surgery plus radiotherapy group had a higher median survival (21.4 months) compared with surgery alone (16.59 months) [p < 0.001]. RT was more likely to be delivered after extrapleural pneumonectomy than with lung-sparing surgical approaches. On multivariable analysis, receipt of surgery plus radiotherapy, chemotherapy administration, and higher socioeconomic status were associated with improved OS (p < 0.0001). After propensity matching, receipt of surgery plus radiotherapy and chemotherapy administration were still associated with improved OS (p < 0.05). Conclusions: In the treatment of malignant pleural mesothelioma, adjuvant radiotherapy after surgical intervention was associated with improved OS. This study is the largest study of adjuvant radiotherapy to date, and our findings highlight the need for additional prospective data.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Next Generation Hydrogen Storage Vessels Enabled by Carbon Fiber Infusion with a Low Viscosity, High Toughness System

Onboard hydrogen storage for fuel cell vehicles holds considerable commercial promise which could be accelerated by lower cost storage vessels. In past years, the use of lower cost reinforcing fiber had been studied as an intuitive avenue to reduce vessel costs. Although the matrix resin does not play a large role in bearing stress loads in many areas of a filament wound pressure vessel, the matrix is relied upon for stress transfer between fibers and appears to play an important role in certain regions of the vessel construction such as the “tangent region” where the cylinder section transitions to the dome. Also, the matrix resin can play a role in the damage tolerance of vessels after impact events. Furthermore, the effect of dry spots or voids in the laminate can affect stress transfer between fibers in a manner that is challenging to quantify. Therefore, excess fiber must be used to account for this variability in performance. Unfortunately, in the case hydrogen pressure vessels the cost of the vessel is strongly dependent on the amount of carbon-fiber used in the design and manufacturing. An extensive investigation on the use of non-traditional fabrication processes enabled by an ultra-low viscosity thermoset resin (<10 cP) with high fracture toughness was conducted to determine the opportunity to reduce carbon-fiber usage and the associated high cost. The hypothesis was based on enabling a lower variation in vessel performance from a reduction in void defects (often found with traditional wet-winding) and a more defect-tolerant vessel resulting from use of a matrix resin with high fracture toughness. These two features were anticipated to justify a more aggressive design approach to the vessel winding pattern. From the work in this project, the potential use of the vacuum infusion approach to produce high quality vessels was validated. However, the team recognized the need to re-optimize the design, the dry-process, and the vacuum infusion process at each scale-up stage. During the planning phase, the critical need to re-optimize at this frequency was under-estimated. Also, the team identified the lack of industry testing standards on subscale parts for providing predictive value of vessel performance. Therefore, significant effort is needed to produce statistically significant numbers of vessels. To produce such numbers of vessels the project needed in retrospect to partner closely with a series vessel manufactures to develop a proof of concept production line to make the vessels. However, without the appropriate data, a manufacturing partner was challenging to fully engage during this timeline. New technologies relying on changing raw materials and manufacturing processes carry inherent risks. This project did not reach its ultimate objectives of demonstrated a vessel with lower carbon-fiber. But some companies expressed interest in learning from the partial successes of this project for future challenges in pressure vessel applications.

08 HYDROGEN↗
DOE OSTI2016

Covariance Structure of Pu Isotopic Compositions Obtained Using FRAM 5.2

In the nondestructive assay of special nuclear materials, isotopic determination based on the analysis of high-purity Ge high-resolution gamma-ray spectra plays a pivotal role. The isotopic results, which are based on a relative measurement of peak intensities, are used to interpret data acquired using quantitative (mass) nondestructive assay methods. In the case of plutonium measurements, this includes both passive and active neutron techniques, and also calorimetry (heat). Historically, precision estimates on the Pu- isotopic composition have been generated, on a case-by-case basis, by least squares fitting algorithms embedded in the analysis codes, but all other sources of uncertainty have generally been estimated using a top-down approach by item category. A largely neglected consideration is the covariance between the reported abundances and derived quantities – notably Pu-239-effective mass, Pu-240-effective mass and specific thermal power. In this work we study the covariance structure by statistically evaluating repeat spectrum measurements of two known low burn-up heavily filtered Pu items reduced using the spectrum analysis code FRAM 5.2 and alternate standard parameter sets. For routine measurements, however, only a single spectrum from an item will typically be acquired. We therefore discuss how to generate the sought after covariance information from an individual spectrum by spectrum re-sampling, an approach which could be added to provide item specific reports, and compare the two approaches to test its reliability.

Croft, Stephen↗
DOE OSTI2022

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗
DOE OSTI2017

Comprehensive Measurement and Analysis of the User-Perceived I/O Performance in a Production Leadership-Class Storage System

With the increase of the scale and intensity of the parallel I/O workloads generated by those scientific applications running on high performance computing facilities, understanding the I/O dynamics, especially the root cause of the I/O performance variability and degradation in HPC environment, have become extremely critical to the HPC community. In this paper, we run extensive I/O measuring tests on a production leadership-class storage system to capture the performance variabilities of large-scale parallel I/O. Analyzing these results and its statistic correlation revealed some valuable insights into the characteristics of the storage system and the root cause of I/O performance variability. Further, we leverage these findings and propose an I/O middleware design refactoring which can improve the performance of the parallel I/O by optimizing the data striping and placement. Our preliminary evaluation results demonstrate the proposed approach can reduce the average per-process write latency by at least 80% and the maximum per-process write latency by at least 20%.

Wan, Lipeng↗
DOE OSTI2017

Massive Associative K-biclustering (MAK) v1.0

Massive Associative K-biclustering (MAK) is a method to address the key limitations in other biclustering approaches. In a formal evaluation against new simulated data sets with more realistic properties MAK improves recovery of biclusters exhibiting common behavior across conditions, with the highest F1 scores, a measure of a test's accuracy, and lowest false positive rates, the probability of falsely rejecting the null hypothesis, compared to established methods. The developers show how MAK can be applied to a popular data set from Saccharomyces cerevisiae to uncover new properties not captured by other methods. MAK biclusters showed increased statistical enrichments for known regulatory associations and gene functions, including for nearly 30% of the novel MAK biclusters not found by other methods. Compared to other methods, MAK results had at least two-fold higher coverage of differential expression in the overall dataset, an overlapping bicluster network with distinct modules for protein expression and energy metabolism, and biologically relevant overlapping biclusters including cases of combinatorial regulation. MAK is suited for large datasets with many potentially noisy and overlapping patterns of varying sizes.

JOACHIMIAK, MARCIN↗
DOE OSTI2019

Waste Chemistry Envelope for the Determination of Pitting Corrosion Susceptibility - 19554

Liquid radioactive wastes at the Hanford and Savannah River sites are stored in large underground carbon steel tanks. The high-level wastes are processed in several of the tanks and then transferred by piping to other site facilities for further processing before they are stabilized in a vitrified or grout waste form. Based on waste removal and processing schedules, many of the tanks, will be required to be in service for times exceeding the initial intended life (i.e., greater than 60 years). Until the waste is removed from storage, transferred, and processed, the materials and structures of the tanks must maintain a confinement function by providing a barrier to the environment and by maintaining acceptable structural stability during design basis events, which include loadings from both normal service and abnormal (e.g., earthquake) conditions. Administrative programs are in place to mitigate potential corrosion mechanisms and thereby maintain the structural and leak integrity functions of these waste tanks throughout their intended service life. Waste chemistry and temperature control are the means by which corrosion degradation of the waste tanks is minimized. Chemistry controls are based on in-situ coupon tests, waste tank experience, and laboratory testing. These requirements comprise the technical standards for corrosion control. The standards are utilized then as guidelines for operational procedures. The present waste chemistry controls for the Hanford DSTs were established in the 1980's in response to tank failures caused by stress corrosion cracking (SCC) at the DOE Savannah River Site (SRS). Portions of the SRS chemistry limits, which were developed at the Savannah River National Laboratory (SRNL) were coupled with corrosion testing results from Pacific Northwest National Laboratories (PNNL) and adopted for corrosion control of the double-shell tank (DST) waste at the Hanford Site. Since that time, the temperatures of the waste in the DSTs have decreased to less than 50 deg. C in most cases as the radioactive material has decayed. As a consequence, new chemistry controls were formulated for SCC in 2010. This testing however, did not address that potential for pitting in the supernatant and at the liquid-air interface. Indeed, initial tests indicated that the inhibitor requirements for SCC would be insufficient for the prevention of pitting. In addition to lower waste temperatures, future retrieval and process changes may impact the DST waste chemistry. Initial projections suggest that the waste chemistry of the DSTs may shift to a broader range of pH and higher aggressive anion concentrations (e.g., chloride, sulfate, etc.) than the wastes that are presently stored. Additionally, processes within the vitrification facility may result in streams that are returned to the tank farm for evaporation. These streams may also contain high concentrations of aggressive species. SRNL investigated the propensity for pitting of the carbon steel waste tanks at current and projected DST waste chemistries. A statistically designed series of tests provided a pitting factor that relates the ratio of the inhibitor species to the aggressive species and the probability of observing pitting. An acceptable pitting factor was determined and was utilized for the development of a new chemistry envelope. The new chemistry envelope was compared to the envelope that was established for SCC and was determined to be bounding. Validation of the new chemistry envelope was performed via model simulations and comparison with historical experimental results and approaches to establishing chemistry envelopes. The new chemistry envelope will eventually be part of an overall assessment of the condition of the tanks and any changes made to the overall structural integrity program for the Hanford DSTs. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation

Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. At inference time, the model outputs a low-rank decomposition of the distribution field of the charged particles, and we demonstrate that this step is faster than the standard linear algebra technique. Numerical experiments show that the method achieves comparable reconstruction accuracy for interpolation tasks, generalizing to unseen test data in a manner beyond just memorizing training data; patterns in factorization also inherently followed the same numerical trend as those within algebraic methods (e.g., truncated singular-value decomposition). However, when training on the first 70% of a time-series data and testing on the remaining 30%, the method fails to meaningfully extrapolate. Despite this limiting result, the technique may have benefits for simulations in a statistical steady-state or otherwise showing temporal stability. These results suggest that while the model offers a computationally efficient alternative for datasets with temporal stability, its current formulation is best suited for interpolation rather than for predicting future states in time-evolving systems. This study thus lays the groundwork for further refinement of neural network-based approaches to low-rank matrix factorization in high-dimensional plasma simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Global Sensitivity Analysis with Small Sample Sizes: Ordinary Least Squares Approach

A new version of global sensitivity analysis is developed in this paper. This new version coupled with tools from statistics, machine learning, and optimization can devise small sample sizes that allow for the accurate ordering of sensitivity coefficients for the first 10-30 most sensitive chemical reactions in complex chemical-kinetic mechanisms, and is particularly useful for studying the chemistry in realistic devices. A key part of the paper is calibration of these small samples. Because these small sample sizes are developed for use in realistic combustion devices, the calibration is done over the ranges of conditions in such devices, with a test case being the operating conditions of a compression ignition engine studied earlier. Compression ignition engines operate under low-temperature combustion conditions with quite complicated chemistry making this calibration difficult, leading to the possibility of false positives and false negatives in the ordering of the reactions. So an important aspect of the paper is showing how to handle the trade-off between false positives and false negatives using ideas from the multiobjective optimization literature. The combination of the new global sensitivity method and the calibration are sample sizes a factor of approximately 10 times smaller than were available with our previous algorithm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Accelerated basis adaptation in homogeneous chaos spaces

Polynomial chaos expansions (PCE) provide an efficient approach to uncertainty quantification (UQ) and have been adapted to diverse applications across the spectrum of science and engineering. For situations involving large stochastic parameterizations, the curse of dimensionality renders PCE-based methods computationally prohibitive. A basis adaptation approach for PCE was proposed by Tipireddy and Ghanem (2014) which transforms the input random variables through an isometry such that sufficient probabilistic characterization of specific quantities of interest (QoI) is concentrated in an algebraic manifold embedded in the linear span of the dominant transformed variables. While quite versatile, that original version of the PCE basis adaptation exhibited slow convergence for a number of problems of practical significance. In the present paper, we propose two novel methods to accelerate the convergence of the original basis adaptation approach, thus expanding its reach while also providing insight into its performance. In the first method, information gained from a pilot PCE representation is used to correct the mean and Gaussian coefficients in the adapted space. By taking advantages of probabilistic information in higher dimensional adaptation gleaned from an initial adaptation, the second method updates the rotation matrix used to identify the dominant transformed variables. In this manner, the new rotation matrix concentrates even more probabilistic information in its first few dimensions. These two method can be combined to achieve even better performance, the combined method is referred to as sequentially optimized adaptation method. Here, the methods are demonstrated on an analytical test function and a model of a space structure with several sub-components and a non-smooth quantity of interest representing the maximum acceleration over time. Both methods achieve accelerated convergence of the basis adaptation approach with negligible additional costs.

Accelerated convergence↗
DOE OSTI2019

Community detection and unveiling of hierarchy in networks: a density-based clustering approach

Abstract The unveiling of communities within a network or graph, and the hierarchization of its members that results is of utmost importance in areas ranging from social to biochemical networks, from electronic circuits to cybersecurity. We present a statistical mechanics approach that uses a normalized Gaussian function which captures the impact of a node within its neighborhood and leads to a density-ranking of nodes by considering the distance between nodes as punishment. A hill-climbing procedure is applied to determine the density attractors and identify the unique parent (leader) of each member as well as the group leader. This organization of the nodes results in a tree-like network with multiple clusters, the community tree. The method is tested using synthetic networks generated by the LFR benchmarking algorithm for network sizes between 500 and 30,000 nodes and mixing parameter between 0.1 and 0.9 . Our results show a reasonable agreement with the LFR results for low to medium values of the mixing parameter and indicate a very mild dependence on the size of the network.

Felfli, Zineb (ORCID:0000000275294635)↗
DOE OSTI2020

The effect of school intervention programs on the body mass index of adolescents: a systematic review with meta-analysis

Abstract Effective obesity interventions in adolescent populations have been identified as an immediate priority action to stem the increasing prevalence of adult obesity. The purpose of this meta-analysis was to make a quantitative analysis of the impact of school-based interventions on body mass index during adolescence. Studies were retrieved from PubMed, Scopus, Science Direct and Web of Science databases. Results were pooled using a random-effects model with 95% confidence interval considered statistically significant. Of the 18 798 possible relevant articles identified, 12 articles were included in this meta-analysis. The global result showed a low magnitude effect, though it was statistically significant (N = 14 428), global e.s. = −0.055, P = 0.004 (95% CI = −0.092, −0.017). Heterogeneity was low among the studies (I2 = 9.017%). The funnel plot showed no evidence of publication bias. The rank-correlation test of Begg (P = 0.45641) and Egger’s regression (P = 0.19459) confirmed the absence of bias. This meta-analysis reported a significant effect favoring the interventions; however, future research are needed since the reported the evidence was of low magnitude, with the studies following a substantial range of approaches and mostly had a modest methodological quality.

Saavedra Dias, R.↗