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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.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Planetary atmosphere representation for Monte-Carlo simulation of aerospace vehicle trajectories

Methods of simulating large scale random atmospheric variations are examined for use in Monte Carlo simulations of hypervelocity vehicles. Random density, temperature, and pressure variations have a significant impact on the performance of such vehicles. Methods for generating random atmosphere profiles are described and compared in the context of a Monte Carlo simulation of the Aeroassist Flight Experiment (AFE). It is seen that these techniques result in statistically significant differences in key simulation point statistics.

Dutton, K.↗
DOE OSTI2022

Randomized probe imaging through deep k-learning

Randomized probe imaging (RPI) is a single-frame diffractive imaging method that uses highly randomized light to reconstruct the spatial features of a scattering object. The reconstruction process, known as phase retrieval, aims to recover a unique solution for the object without measuring the far-field phase information. Typically, reconstruction is done via time-consuming iterative algorithms. In this work, we propose a fast and efficient deep learning based method to reconstruct phase objects from RPI data. The method, which we call deep k-learning, applies the physical propagation operator to generate an approximation of the object as an input to the neural network. This way, the network no longer needs to parametrize the far-field diffraction physics, dramatically improving the results. Deep k-learning is shown to be computationally efficient and robust to Poisson noise. The advantages provided by our method may enable the analysis of far larger datasets in photon starved conditions, with important applications to the study of dynamic phenomena in physical science and biological engineering.

Guo, Zhen (ORCID:0000000213473451)↗
DOE OSTI2019

Intrinsic ductility of random substitutional alloys from nonlinear elasticity theory

A method suitable for computing the ideal strength of random substitutional alloys is introduced. The method relies on nonlinear continuum elasticity theory and allows for the high-throughput computation of ideal strength. The method also allows for the high-throughput computation of an intrinsic ductility parameter defined for a given applied stress state as the ratio of the strain associated with the cleavage instability to the strain associated with the first shear instability. The intrinsic ductility parameter is shown to correlate well with the measured elongations to failure for elemental body-centered-cubic and hexagonal close-packed metals. Application to four high-entropy alloys indicates that the intrinsic ductility parameter describes their experimental compressions to failure well. The method is used to argue that the brittle refractory high-entropy alloy Ta-Nb-V-W-Mo could be made much more ductile through replacement of Mo with Nb. Finally, the potential for the high-throughput optimization of high entropy and chemically complex alloys is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Triaxial quadrupole deformation dynamics in sd-shell nuclei around {sup 26}Mg

Large-amplitude dynamics of axial and triaxial quadrupole deformation in {sup 24,26}Mg, {sup 24}Ne, and {sup 28}Si is investigated on the basis of the quadrupole collective Hamiltonian constructed with the use of the constrained Hartree-Fock-Bogoliubov plus the local quasiparticle random-phase approximation method. The calculation reproduces well properties of the ground rotational bands, and {beta} and {gamma} vibrations in {sup 24}Mg and {sup 28}Si. The {gamma} softness in the collective states of {sup 26}Mg and {sup 24}Ne are discussed. Contributions of the neutrons and protons to the transition properties are also analyzed in connection with the large-amplitude quadrupole dynamics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1974

Digital vibration control techniques

Analog vibration control techniques are reviewed and are compared with digital techniques. The advantages of the digital methods over the analog methods are demonstrated. The following topics are covered: (1) methods of computer-controlled random vibration and reverberation acoustic testing; (2) methods of computer-controlled sinewave vibration testing; and (3) methods of computer-controlled shock testing. Basic concepts are stressed rather than specific techniques or equipment. General algorithms are described in the form of block diagrams and flow diagrams. Specific problems and potential problems are discussed. The material is computer sciences oriented but is kept at a level that facilitates an understanding of the basic concepts of computer-controlled induced environmental test systems.

Chapman, P.↗
NASA NTRS1975

Seminar on Understanding Digital Control and Analysis in Vibration Test Systems

The advantages of the digital methods over the analog vibration methods are demonstrated. The following topics are covered: (1) methods of computer-controlled random vibration and reverberation acoustic testing, (2) methods of computer-controlled sinewave vibration testing, and (3) methods of computer-controlled shock testing. General algorithms are described in the form of block diagrams and flow diagrams.

Source record↗
DOE OSTI2019

Randomized prospective phase III trial of 68 Ga-PSMA-11 PET/CT molecular imaging for prostate cancer salvage radiotherapy planning [PSMA-SRT]

Salvage radiotherapy (SRT) for prostate cancer (PCa) recurrence after prostatectomy offers long-term biochemical control in about 50–60% of patients. SRT is commonly initiated in patients with serum PSA levels < 1 ng/mL, a threshold at which standard-of-care imaging is insensitive for detecting recurrence. As such, SRT target volumes are usually drawn in the absence of radiographically visible disease. 68 Ga-PSMA-11 (PSMA) PET/CT molecular imaging is highly sensitive and may offer anatomic localization of PCa biochemical recurrence. However, it is unclear if incorporation of PSMA PET/CT imaging into the planning of SRT could improve its likelihood of success. The purpose of this trial is to evaluate the success rate of SRT for recurrence of PCa after prostatectomy with and without planning based on PSMA PET/CT. Methods We will randomize 193 patients to proceed with standard SRT (control arm 1, n = 90) or undergo a PSMA PET/CT scan (free of charge for patients) prior to SRT planning (investigational arm 2, n = 103). The primary endpoint is the success rate of SRT measured as biochemical progression-free survival (BPFS) after initiation of SRT. Biochemical progression is defined by PSA ≥ 0.2 ng/mL and rising. The randomization ratio of 1:1.13 is based on the assumption that approximately 13% of subjects randomized to Arm 2 will not be treated with SRT because of PSMA-positive extra-pelvic metastases. These patients will not be included in the primary endpoint analysis but will still be followed. The choice of treating the prostate bed alone vs prostate bed and pelvic lymph nodes, with or without androgen deprivation therapy (ADT), is selected by the treating radiation oncologist. The radiation oncologist may change the radiation plan depending on the findings of the PSMA PET/CT scan. Any other imaging is allowed for SRT planning in both arms if done per routine care. Patients will be followed until either one of the following conditions occur: 5 years after the date of initiation of randomization, biochemical progression, diagnosis of metastatic disease, initiation of any additional salvage therapy, death. Discussion This is the first randomized phase 3 prospective trial designed to determine whether PSMA PET/CT molecular imaging can improve outcomes in patients with PCa early BCR following radical prostatectomy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

A note on the logistic equation subject to uncertainties in parameters

This paper discusses the logistic equation subject to uncertainties in the intrinsic growth rate, α, in the initial population density, N{sub 0}, and in the environmental carrying capacity, K. These parameters are treated as independent random variables. The random variable transformation method is applied to compute the first probability density function of the time–population density, N(t), and of its inflection point, t{sup ∗}. Results for the density functions of N(t), for a fixed t>0, and t{sup ∗} are also provided for α, N{sub 0} and K uniformly distributed. Finally, numerical experiments illustrate the proposed theoretical results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Self-oscillating AB diblock copolymer developed by post modification strategy

We prepared AB diblock copolymer composed of hydrophilic poly(ethylene oxide) segment and self-oscillating polymer segment. In the latter segment, ruthenium tris(2,2′-bipyridine) (Ru(bpy){sub 3}), a catalyst of the Belousov-Zhabotinsky reaction, is introduced into the polymer architecture based on N-isopropylacrylamide (NIPAAm). The Ru(bpy){sub 3} was introduced into the polymer segment by two methods; (i) direct random copolymerization (DP) of NIPAAm and Ru(bpy){sub 3} vinyl monomer and (ii) post modification (PM) of Ru(bpy){sub 3} with random copolymer of NIPAAm and N-3-aminopropylmethacrylamide. For both the diblock copolymers, a bistable temperature region (the temperature range; ΔT{sub m}), where the block copolymer self-assembles into micelle at reduced Ru(bpy){sub 3}{sup 2+} state whereas it breaks-up into individual polymer chain at oxidized Ru(bpy){sub 3}{sup 3+} state, monotonically extends as the composition of the Ru(bpy){sub 3} increases. The ΔT{sub m} of the block copolymer prepared by PM is larger than that by DP. The difference in ΔT{sub m} is rationalized from the statistical analysis of the arrangement of the Ru(bpy){sub 3} moiety along the self-oscillating segments. By using the PM method, the well-defined AB diblock copolymer having ΔT{sub m} (ca. 25 °C) large enough to cause stable self-oscillation can be prepared. The periodic structural transition of the diblock copolymer in a dilute solution ([Polymer] = 0.1 wt. %) is closely investigated in terms of the time-resolved dynamic light scattering technique at constant temperature in the bistable region. A macroscopic viscosity oscillation of a concentrated polymer solution (15 wt. %) coupled with the periodic microphase separation is also demonstrated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗
DOE OSTI2020

Efficient 3-D velocity model building using joint inline and crossline plane-wave wave-equation migration velocity analyses

SUMMARY Wave-equation migration velocity analysis (WEMVA) is an image-domain inversion method for velocity model building. Automatic plane-wave WEMVA (PWEMVA) calculates the moveouts of plane-wave common-image gathers (CIGs) by searching a best-fitting parabola with semblance analysis and backprojects residual CIG moveouts into wavefield wave paths with a reflection tomographic kernel. However, 3-D PWEMVA is very computationally expensive because 3-D reflection tomographic inversion requires at least five 3-D reverse-time migrations per iteration and stores two types of source wavefields at model boundaries. We develop a joint inline and crossline PWEMVA method for efficient 3-D velocity model building. We alternatively implement the inline and crossline PWEMVAs with a constraint for each other, in which we iteratively construct the 3-D velocity model update through 1-D spline interpolation of 2-D gradients. The inline and crossline joint inversion is practical since PWEMVA only inverts for low-wavenumber velocity perturbations along wave paths, and the method can take less than 1 per cent of the computational cost of full 3-D PWEMVA. To construct unaliased plane waves for our joint inline and crossline PWEMVA, we develop a 3-D data interpolation method in the frequency–wavenumber (FK) domain to recover regularly and randomly missing traces. The method minimizes the misfit on sufficiently localized data subsets with iterative optimal step lengths and a gradient preconditioner that iteratively selects dominant dips along different azimuths. In numerical experiments, we use a 3-D synthetic seismic data set and a land 3-D field seismic data set acquired at the Farnsworth CO2-EOR (enhanced oil recovery) field to demonstrate the efficacy of our velocity model building and data interpolation methods.

Liu, Xuejian↗
DOE OSTI2015

FEM: Feature-enhanced map

A method is presented that modifies a 2 m F obs - D F model σ A -weighted map such that the resulting map can strengthen a weak signal, if present, and can reduce model bias and noise. The method consists of first randomizing the starting map and filling in missing reflections using multiple methods. This is followed by restricting the map to regions with convincing density and the application of sharpening. The final map is then created by combining a series of histogram-equalized intermediate maps. In the test cases shown, the maps produced in this way are found to have increased interpretability and decreased model bias compared with the starting 2 m F obs - D F model σ A -weighted map.

36 MATERIALS SCIENCE↗
NASA NTRS1975

A random search algorithm for laboratory computers

The small laboratory computer is ideal for experimental control and data acquisition. Postexperimental data processing is often performed on large computers because of the availability of sophisticated programs, but costs and data compatibility are negative factors. Parameter optimization can be accomplished on the small computer, offering ease of programming, data compatibility, and low cost. A previously proposed random-search algorithm ('random creep') was found to be very slow in convergence. A method is proposed (the 'random leap' algorithm) which starts in a global search mode and automatically adjusts step size to speed convergence. A FORTRAN executive program for the random-leap algorithm is presented which calls a user-supplied function subroutine. An example of a function subroutine is given which calculates maximum-likelihood estimates of receiver operating-characteristic parameters from binary response data. Other applications in parameter estimation, generalized least squares, and matrix inversion are discussed.

Curry, R. E.↗
NASA NTRS1993

Dynamic two-dimensional beam-pattern steering technique

A dynamic two-dimensional laser-beam-pattern steering technique using photorefractive holograms in conjunction with electrically addressed spatial light modulators is proposed and investigated. The experimental results demonstrate the dynamic steering of random combinations of basis beam patterns. The proposed method has the advantages of random beam-pattern combination, good beam intensity uniformity, and higher diffraction efficiency compared with conventional methods.

Zhou, Shaomin↗
DOE OSTI2015

Multi-element least square HDMR methods and their applications for stochastic multiscale model reduction

Stochastic multiscale modeling has become a necessary approach to quantify uncertainty and characterize multiscale phenomena for many practical problems such as flows in stochastic porous media. The numerical treatment of the stochastic multiscale models can be very challengeable as the existence of complex uncertainty and multiple physical scales in the models. To efficiently take care of the difficulty, we construct a computational reduced model. To this end, we propose a multi-element least square high-dimensional model representation (HDMR) method, through which the random domain is adaptively decomposed into a few subdomains, and a local least square HDMR is constructed in each subdomain. These local HDMRs are represented by a finite number of orthogonal basis functions defined in low-dimensional random spaces. The coefficients in the local HDMRs are determined using least square methods. We paste all the local HDMR approximations together to form a global HDMR approximation. To further reduce computational cost, we present a multi-element reduced least-square HDMR, which improves both efficiency and approximation accuracy in certain conditions. To effectively treat heterogeneity properties and multiscale features in the models, we integrate multiscale finite element methods with multi-element least-square HDMR for stochastic multiscale model reduction. This approach significantly reduces the original model's complexity in both the resolution of the physical space and the high-dimensional stochastic space. We analyze the proposed approach, and provide a set of numerical experiments to demonstrate the performance of the presented model reduction techniques. - Highlights: • Multi-element least square HDMR is proposed to treat stochastic models. • Random domain is adaptively decomposed into some subdomains to obtain adaptive multi-element HDMR. • Least-square reduced HDMR is proposed to enhance computation efficiency and approximation accuracy in certain conditions. • Integrating MsFEM and multi-element least square HDMR can significantly reduce computation complexity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Transperineal Prostate Core Needle Biopsy: A Comparison of Coaxial Versus Noncoaxial Method in a Randomised Trial

PurposeTo compare the procedural time and complication rate of coaxial technique with those of noncoaxial technique in transperineal prostate biopsy.Materials and MethodsTransperineal prostate biopsy with coaxial (first group, n = 120) and noncoaxial (second group, n = 120) methods was performed randomly in 240 patients. The procedural time was recorded. The level of pain experienced during the procedure was assessed on a visual analogue scale (VAS), and the rate of complications was evaluated in comparison of the two methods.ResultsThe procedural time was significantly shorter in the first group (p < 0.001). In the first group, pain occurred less frequently (p = 0.002), with a significantly lower VAS score being experienced (p < 0.002). No patient had post procedural fever. Haematuria (p = 0.029) and haemorrhage from the site of biopsy (p < 0.001) were seen less frequently in the first group. There was no significant difference in the rate of urethral haemorrhage between the two groups (p = 0.059). Urinary retention occurred less commonly in the first group (p = 0.029). No significant difference was seen in the rate of dysuria between the two groups (p = 0.078).ConclusionsTransperineal prostate biopsy using a coaxial needle is a faster and less painful method with a lower rate of complications compared with conventional noncoaxial technique.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Automated classification of periodic variable stars detected by the wide-field infrared survey explorer

We describe a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases. This will assist in the future construction of a WISE Variable Source Database that assigns variables to specific science classes as constrained by the WISE observing cadence with statistically meaningful classification probabilities. We have analyzed the WISE light curves of 8273 variable stars identified in previous optical variability surveys (MACHO, GCVS, and ASAS) and show that Fourier decomposition techniques can be extended into the mid-IR to assist with their classification. Combined with other periodic light-curve features, this sample is then used to train a machine-learned classifier based on the random forest (RF) method. Consistent with previous classification studies of variable stars in general, the RF machine-learned classifier is superior to other methods in terms of accuracy, robustness against outliers, and relative immunity to features that carry little or redundant class information. For the three most common classes identified by WISE: Algols, RR Lyrae, and W Ursae Majoris type variables, we obtain classification efficiencies of 80.7%, 82.7%, and 84.5% respectively using cross-validation analyses, with 95% confidence intervals of approximately ±2%. These accuracies are achieved at purity (or reliability) levels of 88.5%, 96.2%, and 87.8% respectively, similar to that achieved in previous automated classification studies of periodic variable stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Machine learning for disruption warnings on Alcator C-Mod, DIII-D, and EAST

In this paper, we report on disruption prediction using a shallow machinelearning method known as Random Forests, trained on large databases containingonly plasma parameters that are available in real-time on Alcator C-Mod, DIII-D, and EAST. The database for each tokamak contains parameters sampled ~10 6 times throughout ~10 4 discharges (disruptive and non-disruptive) over the last 4years of operation. It is found that a number of parameters (e.g.P rad /P input ,l i ,n/n G ,B n=1 /B 0 ) exhibit changes in aggregate as a disruption is approached on one ormore of these tokamaks. However, for each machine, the most useful parameters,as well as the details of their precursor behaviors, are markedly different. When the prediction problem is framed using a binary classification scheme to discriminate between time slices "close to disruption" and "far from disruption," it is found that the prediction algorithms differ substantially in performance among the three machines ona time slice-by-time slice basis, but have similar disruption detection rates (~80-90%) on a shot-by-shot basis after appropriate optimisation. This could have important implications for disruption prediction and avoidance on ITER, for which developmentof a training database of disruptions may be infeasible. The algorithm's output is interpretable using a method that identifies the most strongly contributing inputsignals, which may have implications for avoiding disruptive scenarios. Lastly, to further support its real-time capability, successful applications in inter-shot and real-time environments on EAST and DIII-D are also discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗