Search NASA⌕ Search

SEARCH · Search NASA

Results for “Randomized methods”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

An advanced understanding of the specific effects of xylan and surface lignin contents on enzymatic hydrolysis of lignocellulosic biomass

A deep understanding of biomass recalcitrance has been hampered by the intricate and heterogeneous nature of pretreated biomass substrates obtained from random deconstruction methods. In this study, we established a unique methodology based on chemical pulping principles to create "reference substrates" with intact cellulose fibers and controlled morphological and chemical properties that enable us to investigate the individual effect of xylan, bulk, and surface lignin content on enzymatic hydrolysis. We also developed and demonstrated an X-ray photoelectron spectroscopy (XPS) technique for quantifying surface lignin content on biomass substrates. The results from this study show that, apart from its hindrance effect, xylan can facilitate cellulose fibril swelling and thus create more accessible surface area, which improves enzyme and substrate interactions. Surface lignin has a significant impact on enzyme adsorption kinetics and hydrolysis rate. Advanced understanding of xylan, bulk, and surface lignin effects provides critical information for an effective biomass conversion process.

biomass↗
DOE OSTI2023

Machine learning assisted phase and size-controlled synthesis of iron oxide particles

Synthesis of iron oxides with specific phases and particle sizes is a crucial challenge in various fields, including materials science, energy storage, biomedical applications, environmental science, and earth science. However, despite significant advances in this area, much of the current palette of particle outcomes has been based on time-consuming trial-and-error exploration of synthesis conditions. The present study was designed to explore a very different approach to 1) predict the outcome of synthesis from specified reaction parameters based on using machine learning (ML) techniques, and 2) correlate sets of parameters to obtain products with desired outcomes by a newly designed recommendation algorithm. To achieve this, four ML algorithms were tested, namely random forest, logistic regression, support vector machine, and k-nearest neighbor. Among the models, random forest outperformed the others, attaining 96% and 81% accuracy when predicting the phase and size of iron oxide particles in the test dataset. Surprisingly, the permutation feature importance analysis revealed that volume, which may strongly relate to pressure, was one of the important features, along with precursor concentration, pH, temperature, and time, influencing the phase and size of iron oxide particles during synthesis. To verify the robustness of the random forest models, prediction and experimental results were compared based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models agreed well with the experimental results. Furthermore, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with the desired phase and particle size from previous studies in the dataset. Furthermore, this study lays the foundation for a closed-loop approach in materials synthesis and preparation, beginning with suggesting potential reaction parameters from the dataset and predicting potential outcomes, followed by conducting experiments and analyses, and ultimately enriching the dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Stochastic AC optimal power flow: A data-driven approach

There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow (AC-OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. Herein this paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has extremely low sample requirement. The novelty behind the algorithm’s performance involves an iterative scenario design approach that merges information regarding constraint violations in the system with data-driven sparse regression. Compared to conventional methods with random scenario sampling, our approach is able to provide feasible operating points for realistic systems with much lower sample requirements. Furthermore, multiple sub-tasks in our approach can be easily paralleled and based on historical data to enhance its performance and application. We demonstrate the computational improvements of our approach through simulations on different test cases in the IEEE PES PGLib-OPF benchmark library.

42 ENGINEERING↗
DOE OSTI2015

Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet

The majority of research on efficient and scalable algorithms in computational science and engineering has focused on the forward problem : given parameter inputs, solve the governing equations to determine output quantities of interest. In contrast, in this paper, we consider the broader question: given a (large-scale) model containing uncertain parameters, (possibly) noisy observational data, and a prediction quantity of interest, how do we construct efficient and scalable algorithms to (1) infer the model parameters from the data (the deterministic inverse problem ), (2) quantify the uncertainty in the inferred parameters (the Bayesian inference problem), and (3) propagate the resulting uncertain parameters through the model to issue predictions with quantified uncertainties (the forward uncertainty propagation problem )? We present efficient and scalable algorithms for this end-to-end, data-to-prediction process under the Gaussian approximation and in the context of modeling the flow of the Antarctic ice sheet and its effect on loss of grounded ice to the ocean. The ice is modeled as a viscous, incompressible, creeping, shear-thinning fluid. The observational data come from satellite measurements of surface ice flow velocity, and the uncertain parameter field to be inferred is the basal sliding parameter, represented by a heterogeneous coefficient in a Robin boundary condition at the base of the ice sheet. The prediction quantity of interest is the present-day ice mass flux from the Antarctic continent to the ocean. We show that the work required for executing this data-to-prediction process—measured in number of forward (and adjoint) ice sheet model solves—is independent of the state dimension, parameter dimension, data dimension, and the number of processor cores. The key to achieving this dimension independence is to exploit the fact that, despite their large size, the observational data typically provide only sparse information on model parameters. This property can be exploited to construct a low rank approximation of the linearized parameter-to-observable map via randomized SVD methods and adjoint-based actions of Hessians of the data misfit functional.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Spatial and temporal prediction of radiation dose rates near Fukushima Daiichi Nuclear Power Plant

In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2019

FVCOM-plume – A Three-dimensional Lagrangian Outfall Plume Dilution and Transport Model for Dynamic Tidal Environments: Model Development

Although numerous steady-state effluent plume dilution models are in use, their application in tidal environments remains a challenge. Three-dimensional dynamic circulation models are also inadequate, often due to the lack of required resolution and simplifying assumptions. To overcome these limitations, we present FVCOM-plume—an outfall plume dilution and transport model that operates within the Finite Volume Coastal Ocean Model (FVCOM) framework. It provides simultaneous inclusion of near-field dilution and far-field plume transport processes. The near-field is based on UM3 model using Lagrangian Control Volume approach to compute buoyant plume trajectory and dilution from multiport diffusers. The far-field uses neutrally buoyant particles with point masses and the random walk method to solve unsteady advection-diffusion processes. A density kernel approach is used to compute concentrations at point locations and analyze far-field plume characteristics. The results demonstrate the ability of FVCOM-plume to simultaneously capture near-field and far-field effluent plume dynamics in tidal environments.

Plume Dilution Models↗
DOE OSTI2020

Improved Fast Randomized Iteration Approach to Full Configuration Interaction

In this work, we present three modifications to our recently introduced fast randomized iteration method for full configuration interaction (FCI-FRI) and investigate their effects on the method’s performance for Ne, H 2 O, and N 2 . The initiator approximation, originally developed for full configuration interaction quantum Monte Carlo, significantly reduces statistical error in FCI-FRI when few samples are used in compression operations, enabling its application to larger chemical systems. The semistochastic extension, which involves exactly preserving a fixed subset of elements in each compression, improves statistical efficiency in some cases but reduces it in others. We also developed a new approach to sampling excitations that yields consistent improvements in statistical efficiency and reductions in computational cost. We discuss possible strategies based on our findings for improving the performance of stochastic quantum chemistry methods more generally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Doppler-resolved kinetics of saturation recovery

Frequency modulated laser transient absorption has been used to monitor the ground state rotational energy transfer rates of CN radicals in a double-resonance, depletion recovery experiment. When a pulsed laser is used to burn a hole in the equilibrium ground state population of one rotational state without velocity selection, the population recovery rate is found to depend strongly on the Doppler detuning of a narrow-band probe laser. Similar effects should be apparent for any relaxation rate process that competes effectively with velocity randomization. Alternative methods of extracting thermal rate constants in the presence of these non-thermal conditions are evaluated. Total recovery rate constants, analogous to total removal rate constants in an experiment preparing a single initial rotational level, are in good agreement with quantum scattering calculations, but are slower than previously reported experiments and show qualitatively different rotational state dependence between Ar and He collision partners. As a result, quasi-classical trajectory studies confirm that the differing rotational state dependence is primarily a kinematic effect.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Evidence of Skewness and Sub-Gaussian Character in Temperature-Dependent Distributions of One Million Electronic Excitation Energies in PbS Quantum Dots

Obtaining statistical distributions by sampling a large number of conformations is vital for an accurate description of temperature-dependent properties of chemical systems. However, constructing distributions with 105??106 samples is computationally challenging because of the prohibitively high computational cost of performing rst-principles quantum-mechanical calculations. In this work, we present a new technique called the Eective Stochastic Potential Conguration Interaction Singles (ESP-CIS) method to obtain excitation energies. The ESP-CIS method uses random matrix theory for the construction of an eective stochastic representation of the Fock operator and combines it with linear-response theory. Here, we perform excitation calculations on PbS quantum dots (0.75-1.75 nm) at temperatures of 200-400 K. Results from a total of 15 million excitation energy calculations (1 million samples for each of the three dots at ve dierent temperatures) revealed the distributions to be sub-Gaussian in nature with negative skewness. The distributions progressively became red-shifted with increasing temperature. The study shows that random distortions in structure from the minimum-energy geometry of these quantum dots are more likely to lower the excitation energies.

Scher, Jeremy A.↗
DOE OSTI2012

Two Homologous Intermetallic Phases in the Na–Au–Zn System with Sodium Bound in Unusual Paired Sites within 1D Tunnels

We report the Na-Au-Zn system contains the two intermetallic phases Na 0.97(4) Au 2 Zn 4 (I) and Na 0.72(4) Au 2 Zn 2 (II) that are commensurately and incommensurately modulated derivatives of K 0.37 Cd 2 , respectively. Compound I crystallizes in tetragonal space group P 4 /mbm (No. 127), a = 7.986(1) Å, c = 7.971(1) Å, Z = 4, as a 1 × 1 × 3 superstructure derivative of K 0.37 Cd 2 (I 4 /mcm). Compound II is a weakly incommensurate derivative of K 0.37 Cd 2 with a modulation vector q = 0.189(1) along c. Its structure was solved in superspace group P4 /mbm(00g)00ss, a = 7.8799(6) Å, c = 2.7326(4) Å, Z = 2, as well as its average structure in P4/mbm with the same lattice parameters.. The Au-Zn networks in both consist of layers of gold or zinc squares that are condensed antiprismatically along c ([Au 4/2 Zn 4 Zn 4 Au 4/2 ] for I and [Au 4/2 Zn 4 Au 4/2 ] for II) to define fairly uniform tunnels. The long-range cation dispositions in the tunnels are all clearly and rationally defined by electron density (Fourier) mapping. These show only close, somewhat diffuse, pairs of opposed, ≤50% occupied Na sites that are centered on (I)(shown) or between (II) the gold squares. Tight-binding electronic structure calculations via linear muffin-tin-orbital (LMTO) methods, assuming random occupancy of ≤ ~100% of nonpaired Na sites, again show that the major Hamilton bonding populations in both compounds arise from the polar heteroatomic Au-Zn interactions. Clear Na-Au (and lesser Na-Zn) bonding is also evident in the COHP functions. These two compounds are the only stable ternary phases in the (Cs,Rb,K,Na)-Au-Zn systems, emphasizing the special bonding and packing requirements in these sodium structures

36 MATERIALS SCIENCE↗
DOE OSTI2018

Machine-Learning Prediction of CO Adsorption in Thiolated, Ag-Alloyed Au Nanoclusters

Here we propose a machine-learning model, based on the random-forest method, to predict CO adsorption in thiolate protected nanoclusters. Two phases of feature selection and training, based initially on the Au 25 nanocluster, are utilized in our model. One advantage to a machine-learning approach is that correlations in defined features disentangle relationships among the various structural parameters. For example, in Au 25 , we find that features based on the distribution of Ag atoms relative to the CO adsorption site are the most important in predicting adsorption energies. Our machine-learning model is easily extended to other Au-based nanoclusters, and we demonstrate predictions about CO adsorption on Ag-alloyed Au 36 and Au 133 nanoclusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine Learning of Key Variables Impacting Extreme Precipitation in Various Regions of the Contiguous United States

Abstract Amplification in extreme precipitation intensity and frequency can cause severe flooding and impose significant social and economic consequences. Variations in extreme precipitation intensity, frequencies, and return periods can be attributed to many physical variables across spatial and temporal scales. Here we employ ensemble machine learning (ML) methods, namely random forest (RF), eXtreme Gradient Boosting (XGB), and artificial neural networks (ANN), to explore key contributing variables to monthly extreme precipitation intensity and frequency in six regions over the United States. We further establish emulators for return periods. Results show that the ML models for intensity perform better in regions with obvious seasonality (i.e., Northern Great Plains, Southern Great Plains, and West Coast) than the other three regions (Northeast, Southwest, and Rocky Mountains), while for frequency the models perform well for most regions. The Shapley additive explanation is used to help explain the relationships between extreme precipitation characteristics and identify top variables for RF and XGB. We find that latent heat flux, relative humidity, soil moisture, and large‐scale subsidence are key common variables across the regions for both monthly intensity and frequency, and their compound effects are non‐negligible. The developed ML models capture the probability and return period of extreme precipitation well for all regions and may be used for decision making (e.g., infrastructure planning and design).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Barium stars as tracers of s -process nucleosynthesis in AGB stars

Barium (Ba) stars help to verify asymptotic giant branch (AGB) star nucleosynthesis models since they experienced pollution from an AGB binary companion and thus their spectra carry the signatures of the slow neutron capture process (s process). For a large number (180) of Ba stars, we searched for AGB stellar models that match the observed abundance patterns. We aim to uncover any systematic deviations of the sample abundances from the predictions of the nucleosynthesis models. We employed three machine learning algorithms as classifiers: a Random Forest method, developed for this work, and the two classifiers used in our previous study. Compared to that work, we also expanded our observational sample with 11 Ba stars available in the supersolar metallicity range. We studied the statistical behaviour of the different s-process elements in the observational sample to investigate if the AGB models systematically under- or overpredict the abundances observed in the Ba stars and show the results in the form of violin plots of the residuals between spectroscopic abundances and model predictions. We inspected the correlations between the observed [Fe/H], the s-process elemental abundances, and the residuals. We employed the [Zr/Fe] and [Nb/Fe] abundances as a thermometer to constrain the operational temperature that rules the production of these elements in the sample stars, assuming a steady-state s process. We also investigated the mass distribution of the identified polluter AGB stars and the behaviour of the δ parameter, which describes the fraction of accreted AGB material relative to the Ba star envelope. We find a significant trend in the residuals that implies an underproduction of the elements just after the first s-process peak (Nb, Mo, and Ru) in the models relative to the observations. This may originate from a neutron-capture process (e.g. the intermediate neutron-capture process, i process) not yet included in the AGB models of metallicity from solar to roughly 1/5 solar, corresponding to the range of the Ba stars. Correlations are found between the residuals of these peculiar elements, suggesting a common origin for the deviations from the models. In addition, there is a weak metallicity dependence of the residuals of these elements. The s-process temperatures derived with the [Zr/Fe] – [Nb/Fe] thermometer have an unrealistic value for the majority of our stars. The most likely explanation is that at least a fraction of these elements are not produced in a steady-state s process, and instead may be due to processes not included in the AGB models. The mass distribution of the identified models confirms that our sample of Ba stars was polluted by low-mass AGB stars (< 4 M ⊙ ). Most of the matching AGB models require low accreted mass, but a few systems with high accreted mass are needed to explain the observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Predicting for thermodynamic instabilities in water/oil/surfactant microemulsions: A mesoscopic modelling approach

The thermodynamics and structural properties of flexible and rigid nonionic water/oil/surfactant microemulsions have been investigated using a two level-cut Gaussian random field method based on the Helfrich formalism. Ternary stability diagrams and scattering spectra have been calculated for different surfactant rigidities and spontaneous curvatures. A more important contribution of the Gaussian elastic constants compared to the bending one is observed on the ternary stability diagrams. Furthermore, influence of the spontaneous curvature of the surfactant points out a displacement of the instability domains which corresponds to the difference between the spontaneous and effective curvatures. We enlighten that a continuous transition from a connected water in oil droplets to a frustrated locally lamellar (oil in water in oil droplets) microstructure is found to occur when increasing the temperature for an oil-rich microemulsion. This continuous transition translated in a shift in the scattering functions, points out that the phase inversion phenomenon occurs by a coalescence of the water droplets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

SU-E-T-331: To Evaluate Planning Quality of SBRT with Multiple Lung Metastases Generated with Pinnacle and Tomotherapy

Purpose: To evaluate planning quality of SBRT with multiple lung metastases generated with Pinnacle and Tomotherapy Methods: Nine randomly selected patients diagnosed with non small-cell lung cancer with multiple lesions were planned with Pinnacle (version 9.2) and Tomotherapy (version 4.2). Coplanar and non-coplanar plans were generated on Pinnacle. A total dose of 60 Gy was prescribed to 95% of PTV in 3 fractions. Single isocenter was used. Nine static beams were used for Pinnacle plans. Planning outcomes such as minimum and mean dose, V{sub 9} {sub 5}, D{sub 9} {sub 5} (95% of target volume receives prescription dose), D{sub 5}, and D{sub 1} to PTV, maximum dose to heart, esophagus, cord, trachea, brachial plexus, rib, chest wall, and liver, mean dose to liver, total lung, right and left lung, volume of chest wall receives 30 Gy, volume of lungs receives 5 Gy and 20Gy, conformity index (CI = PIV / PTV) and heterogeneity index (HI = D{sub 5} / D{sub 9} {sub 5}) were reported for evaluation. Results: The mean volume of PTV was 37.77 ± 23.4 cm3. D{sub 9} {sub 5} of PTV with Tomo, coplanar, non-coplanar was 60.2 ± 0.3 Gy, 58.6 ± 1.2 Gy, and 59.1 ± 0.7 Gy, respectively. Mean dose to PTV was lower for Tomo (p < 0.0001), so were D{sub 5} (p < 0.0001) and D{sub 1} (p = 0.001). CI was better with Tomo (p < 0.0001), so was HI (p < 0.0001). Maximum dose to other critical organs were also lower exclusively with Tomo plans. Treatment time was recorded only for Tomo plans (73.0 ± 20.6 min). Conclusion: With 51 beam angles, Tomo plans could generally achieve better tumor coverage while sparing more critical structures for multiple lung lesions study. Non-coplanar also has better tumor coverage with lower dose to critical organs such as lungs, liver, chest wall and cord compare to coplanar plans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-106: An Institutional Review of Using Commercially Available Software to Evaluate Treatment Plan Quality for Various Treatment Sites and Beam Deliveries

Purpose: Use Sun Nuclear Quality Reports™ with PlanIQ™ to evaluate different treatment delivery techniques for various treatment sites. Methods: Fifteen random patients with different treatment sites were evaluated. These include the Head/Neck, prostate, pelvis, lung, esophagus, axilla, bladder and abdomen. Initially, these sites were planned on the Pinnacle {sup 3} V9.6 treatment planning system and utilized nine 6MV step-n-shoot IMRT fields. The RT plan, dose and structure sets were sent to Quality Reports™ where a DVH was recreated and the plans were compared to a unique Plan Algorithm for each treatment site. Each algorithm has its own plan quality metrics and objectives, which include the PTV coverage, PTV maximum dose, the prescription dose outside the target, doses to the critical structures, and the global maximum dose and its location. Each plan was scored base on meeting each objective. Plans may have been reoptimized and reevaluated with Quality Reports™ based on the initial score. PlanIQ™ was used to evaluate if any objective not met was achievable or difficult to obtain. A second plan using VMAT delivery was created for each patient and scored with Quality Reports™. Results: There were a wide range of scores for the different treatment sites with some scoring better for IMRT plans and some better for the VMAT deliveries. The variation in the scores could be attributed to the treatment site, location, and shape of the target. Most deliveries were chosen for the VMAT due to the short treatment times and quick patient throughput with acceptable plan scores. Conclusion: The tools are provided for both physician and dosimetrist to objectively evaluate the use of VMAT delivery versus the step-n-shoot IMRT delivery for various sites. PlanIQ validates if objectives can be met. For the physicist, a concise pass/fail report is created for plan evaluation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-334: Dosimetric Impacts of Organ at Risk (OAR) Contour Errors in Prostate Volumetric Modulated Arc Therapy (VMAT) Planning: A Sensitivity Study

Purpose: To investigate the dosimetric impacts of OAR contour errors in prostate VMAT planning with a sensitivity analysis method. Methods: Ten randomly selected prostate VMAT patients were used to simulate OAR contour error in rectum and bladder. For each OAR 12 even spaced contour control points were selected in every three slices. For each simulation, only one of the control points was simulated to move inwards or outwards by up to 6 voxels and coordinates of adjacent voxels to that moving control point were moved accordingly with a 3D-spline smooth function. An in-house software was used to predict OARs dosimetric endpoints based on geometric relationship between PTV and OAR. In this study, the V75, V70, V65, V60 of rectum and V80, V75, V70, V65 of bladder with and without perturbed contours were calculated and compared. Results: The percentage of OAR dose volume difference around the reference OAR contours were plotted as iso-error lines overlaid on CT images. The significant difference was shown in OAR contours adjacent to the PTV where high dose gradient exists. When one of the 12 points adjacent to the PTV moved up to 6 voxels, maximum errors of 1.99%, 2.46%, 2.89%, and 3.19% were found in V75, V70, V65, and V60 of the rectum, and 1.59%, 1.67%, 1.87%, and 1.90% were found in V80, V75, V70, and V65 of the bladder, respectively. Conclusion: We can quantify the dosimetric impact of OAR contouring error by evaluating the percentage of OAR dose volume changes in prostate VMAT planning. For the adaptive radiation therapy, the iso-error lines can provide the planner a guideline that which portion of contour needed to be checked carefully because dosimetric sensitivity of contour errors are different for different part of contours.

60 APPLIED LIFE SCIENCES↗