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At least 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

New Tabu Algorithm for Positioning Mining Drillholes with Blocks Uncertainty

This paper proposes a new approach to the mining exploration drillholes positioning problem (DPP) that incorporates both geostatistical and optimization techniques. A metaheuristic was developed to solve the DPP taking into account an uncertainty index that quantifies the reliability of the current interpretation of the mineral deposit. The uncertainty index was calculated from multiple deposit realizations obtained by truncated Gaussian simulations conditional to the available drillholes samplings. A linear programming model was defined to select the subset of future drillholes that maximizes coverage of the uncertainty. A Tabu Search algorithm was developed to solve large instances of this set partitioning problem. The proposed Tabu Search algorithm is shown to provide good quality solutions approaching 95% of the optimal solution in a reasonable computing time, allowing close to optimal coverage of uncertainty for a fixed investment in drilling.

58 GEOSCIENCES↗
DOE OSTI2020

Hyper-Differential Sensitivity Analysis of Uncertain Parameters in PDE-Constrained Optimization

Many problems in engineering and sciences require the solution of large scale optimization constrained by partial differential equations (PDEs). Though PDE-constrained optimization is itself challenging, most applications pose additional complexity, namely, uncertain parameters in the PDEs. Uncertainty quantification (UQ) is necessary to characterize, prioritize, and study the influence of these uncertain parameters. Sensitivity analysis, a classical tool in UQ, is frequently used to study the sensitivity of a model to uncertain parameters. In this article, we introduce "hyper-differential sensitivity analysis" which considers the sensitivity of the solution of a PDE-constrained optimization problem to uncertain parameters. Our approach is a goal-oriented analysis which may be viewed as a tool to complement other UQ methods in the service of decision making and robust design. We formally define hyper-differential sensitivity indices and highlight their relationship to the existing optimization and sensitivity analysis literatures. Assuming the presence of low rank structure in the parameter space, computational efficiency is achieved by leveraging a generalized singular value decomposition in conjunction with a randomized solver which converts the computational bottleneck of the algorithm into an embarrassingly parallel loop. Two multi-physics examples, consisting of nonlinear steady state control and transient linear inversion, demonstrate efficient identification of the uncertain parameters which have the greatest influence on the optimal solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The Leading Modes of Asian Summer Monsoon Variability as Pulses of Atmospheric Energy Flow

Monsoon rainfall anomalies are often organized into dynamical modes that are more predictable than rainfall at specific locations. Here a linear response function (LRF) is constructed to extract the neutral modes (NM) of the dynamical atmospheric system and their optimal forcing that govern precipitation variability, providing a dynamical underpinning for the empirically derived, recurrent rainfall patterns. The leading mode (NM1) features a shift in the Atlantic ITCZ, a drying over the Indian Ocean and Indian subcontinent, and a “South-Flood-North Drought (SFND)” pattern over eastern China, a pattern inferable from changes in the atmospheric energy flow forced by the corresponding optimal forcing and further enhanced by the water vapor and cloud feedbacks. Moreover, the slow evolving component of the NM1 in response to CO2 forcing can be skillfully predicted by running the ocean heat uptake through the LRF, implicating a deterministic ocean dynamic source for the NM1-like trend under climate warming.

monsoon mode of variability, EBM, climate change, ↗
DOE OSTI2014

WE-G-17A-09: Novel Magnetic Shielding Design for Inline and Perpendicular Integrated 6 MV Linac and 1.0 T MRI Systems

Purpose: The influence of fringe magnetic fields delivered by magnetic resonance imaging (MRI) on the beam generation and transportation in Linac is still a major challenge for the integration of linear accelerator and MRI (Linac-MRI). In this study, we investigated an optimal magnetic shielding design for Linac-MRI and further characterized the beam trajectory in electron gun. Methods: Both inline and perpendicular configurations were analyzed in this study. The configurations, comprising a Linac-MRI with a 100cm SAD and an open 1.0 T superconductive magnet, were simulated by the 3D finite element method (FEM). The steel shielding around the Linac was included in the 3D model, the thickness of which was varied from 1mm to 20mm, and magnetic field maps were acquired with and without additional shielding. The treatment beam trajectory in electron gun was evaluated using OPERA 3d SCALA with and without shielding cases. Results: When Linac was not shielded, the uniformity of diameter sphere volume (DSV) (30cm) was about 5 parts per million (ppm) and the fringe magnetic fields in electron gun were more than 0.3 T. With shielding, the magnetic fields in electron gun were reduced to less than 0.01 T. For the inline configuration, the radial magnetic fields in the Linac were about 0.02T. A cylinder steel shield used (5mm thick) altered the uniformity of DSV to 1000 ppm. For the perpendicular configuration, the Linac transverse magnetic fields were more than 0.3T, which altered the beam trajectory significantly. A 8mm-thick cylinder steel shield surrounding the Linac was used to compensate the output losses of Linac, which shifted the magnetic fields' uniformity of DSV to 400 ppm. Conclusion: For both configurations, the Linac shielding was used to ensure normal operation of the Linac. The effect of magnetic fields on the uniformity of DSV could be modulated by the shimming technique of the MRI magnet. NIH/NIGMS grant U54 GM104944, Lincy Endowed Assistant Professorship.

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

On the Design, Development, and Analysis of Optimized Matrix-Vector Multiplication Routines for Coprocessors

The manycore paradigm shift, and the resulting change in modern computer architectures, has made the development of optimal numerical routines extremely challenging. In this work, we target the development of numerical algorithms and implementations for Xeon Phi coprocessor architecture designs. In particular, we examine and optimize the general and symmetric matrix-vector multiplication routines (gemv/symv), which are some of the most heavily used linear algebra kernels in many important engineering and physics applications. We describe a successful approach on how to address the challenges for this problem, starting with our algorithm design, performance analysis and programing model and moving to kernel optimization. Our goal, by targeting low-level and easy to understand fundamental kernels, is to develop new optimization strategies that can be effective elsewhere for use on manycore coprocessors, and to show significant performance improvements compared to existing state-of-the-art implementations. Therefore, in addition to the new optimization strategies, analysis, and optimal performance results, we finally present the significance of using these routines/strategies to accelerate higher-level numerical algorithms for the eigenvalue problem (EVP) and the singular value decomposition (SVD) that by themselves are foundational for many important applications.

Kabir, Khairul↗
DOE OSTI2016

Volume of interest CBCT and tube current modulation for image guidance using dynamic kV collimation

Purpose: The focus of this work is the development of a novel blade collimation system enabling volume of interest (VOI) CBCT with tube current modulation using the kV image guidance source on a linear accelerator. Advantages of the system are assessed, particularly with regard to reduction and localization of dose and improvement of image quality. Methods: A four blade dynamic kV collimator was developed to track a VOI during a CBCT acquisition. The current prototype is capable of tracking an arbitrary volume defined by the treatment planner for subsequent CBCT guidance. During gantry rotation, the collimator tracks the VOI with adjustment of position and dimension. CBCT image quality was investigated as a function of collimator dimension, while maintaining the same dose to the VOI, for a 22.2 cm diameter cylindrical water phantom with a 9 mm diameter bone insert centered on isocenter. Dose distributions were modeled using a dynamic BEAMnrc library and DOSXYZnrc. The resulting VOI dose distributions were compared to full-field CBCT distributions to quantify dose reduction and localization to the target volume. A novel method of optimizing x-ray tube current during CBCT acquisition was developed and assessed with regard to contrast-to-noise ratio (CNR) and imaging dose. Results: Measurements show that the VOI CBCT method using the dynamic blade system yields an increase in contrast-to-noise ratio by a factor of approximately 2.2. Depending upon the anatomical site, dose was reduced to 15%–80% of the full-field CBCT value along the central axis plane and down to less than 1% out of plane. The use of tube current modulation allowed for specification of a desired SNR within projection data. For approximately the same dose to the VOI, CNR was further increased by a factor of 1.2 for modulated VOI CBCT, giving a combined improvement of 2.6 compared to full-field CBCT. Conclusions: The present dynamic blade system provides significant improvements in CNR for the same imaging dose and localization of imaging dose to a predefined volume of interest. The approach is compatible with tube current modulation, allowing optimization of the imaging protocol.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

JuliaOceanWaves

SAND2023-05107O JuliaOceanWaves is a collection of packages and functions for modeling wave energy converters. The open-source software is designed to be used by the wave energy community to model new concepts, perform design optimization, and more. Capabilities include: • A representation of ocean wave spectra and input/output (e.g. read from National Oceanic and Atmospheric Administration buoys) and post-processing functionalities; • A solver for the linear potential flow radiation and diffraction equations for wave forcing on floating structures. • Solving the floating body linear dynamics in the frequency domain. Future additions to the software will include solving non-linear floating body dynamics in the time-domain through time-marching Ordinary Differential Equation solvers and the pseudo-spectral optimization method. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Michelen Strofer, Carlos↗
DOE OSTI2014

Economic model predictive control of nonlinear process systems using empirical models

Economic model predictive control (EMPC) is a feedback control technique that attempts to tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first principles or through system identification techniques. In industrial practice, it may be difficult in general to obtain an accurate first‐principles model of the process. Motivated by this, in the present work, Lyapunov‐based EMPC (LEMPC) is designed with a linear empirical model that allows for closed‐loop stability guarantees in the context of nonlinear chemical processes. Specifically, when the linear model provides a sufficient degree of accuracy in the region where time varying economically optimal operation is considered, conditions for closed‐loop stability under the LEMPC scheme based on the empirical model are derived. The LEMPC scheme is applied to a chemical process example to demonstrate its closed‐loop stability and performance properties as well as significant computational advantages. © 2014 American Institute of Chemical Engineers AIChE J , 61: 816–830, 2015

Alanqar, Anas↗
DOE OSTI2023

On unifying randomized methods for inverse problems

This work unifies the analysis of various randomized methods for solving linear and nonlinear inverse problems with Gaussian priors by framing the problem in a stochastic optimization setting. By doing so, we show that many randomized methods are variants of a sample average approximation (SAA). More importantly, we are able to prove a single theoretical result that guarantees the asymptotic convergence for a variety of randomized methods. Additionally, viewing randomized methods as an SAA enables us to prove, for the first time, a single non-asymptotic error result that holds for randomized methods under consideration. Another important consequence of our unified framework is that it allows us to discover new randomization methods. Here, we present various numerical results for linear, nonlinear, algebraic, and PDE-constrained inverse problems that verify the theoretical convergence results and provide a discussion on the apparently different convergence rates and the behavior for various randomized methods.

42 ENGINEERING↗
DOE OSTI2011

Measurements of charge state breeding efficiency at BNL test EBIS

Charge breeding of singly charged ions is required to efficiently accelerate rare isotope ion beams for nuclear and astrophysics experiments, and to enhance the accuracy of low-energy Penning trap-assisted spectroscopy. An efficient charge breeder for the Californium Rare Isotope Breeder Upgrade (CARIBU) to the ANL Tandem Linear Accelerator System (ATLAS) facility is being developed using the BNL Test Electron Beam Ion Source (Test EBIS) as a prototype. Parameters of the CARIBU EBIS charge breeder are similar to those of the BNL Test EBIS except the electron beam current will be adjustable in the range from 1 to 2 {angstrom}. The electron beam current density in the CARIBU EBIS trap will be significantly higher than in existing operational charge state breeders based on the EBIS concept. The charge state breeding efficiency is expected to be about 25% for the isotope ions extracted from the CARIBU. For the success of our EBIS project, it is essential to demonstrate high breeding efficiency at the BNL Test EBIS tuned to the regime close to the parameters of the CARIBU EBIS at ANL. The breeding efficiency optimization and measurements have been successfully carried out using a Cs{sup +} surface ionization ion source for externally pulsed injection into the BNL Test EBIS. A Cs{sup +} ion beam with a total number of ions of 5 x 10{sup 8} and optimized pulse length of 70 {mu}s has been injected into the Test EBIS and charge-bred for 5.3 ms for two different electron beam currents 1 and 1.5 {angstrom}. In these experiments we have achieved 70% injection/extraction efficiency and breeding efficiency into the most abundant charge state 17%.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Stochastic Learning Approach for Binary Optimization: Application to Bayesian Optimal Design of Experiments

Here, we present a novel stochastic approach to binary optimization suited for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial differential equations. The OED utility function, namely, the regularized optimality criterion, is cast into a stochastic objective function in the form of an expectation over a multivariate Bernoulli distribution. The probabilistic objective is then solved by using a stochastic optimization routine to find an optimal observational policy. This formulation (a) is generally applicable to binary optimization problems with soft constraints and is ideal for OED and sensor placement problems; (b) does not require differentiability of the original objective function (e.g., a utility function in OED applications) with respect to the design variable, and thus it enables direct employment of sparsity-enforcing penalty functions such as $\ell_0$, without needing to utilize a continuation procedure or apply a rounding technique; (c) exhibits much lower computational cost than traditional gradient-based relaxation approaches; and (d) can be applied to both linear and nonlinear OED problems with proper choice of the utility function. The proposed approach is analyzed from an optimization perspective with detailed convergence analysis of the optimization approach and is also analyzed from a machine learning perspective with correspondence to policy gradient reinforcement learning. The approach is demonstrated numerically by using an idealized two-dimensional Bayesian linear inverse problem and validated by extensive numerical experiments carried out for sensor placement in a parameter identification setup.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Sensor Placement Optimization Software Applied to Site-Scale Methane-Emissions Monitoring

Advances in sensor technology have increased our ability to monitor a wide range of environments. However, even as the cost of sensors decline, only a limited number of sensors can be installed at any given site. The physical placement of sensors, along with the sensor technology and operating conditions, can have a large impact on our ability to adequately monitor environmental change. This paper introduces a new open-source Python package, called Chama, that determines optimal sensor placement and technology to improve a sensor network’s detection capabilities. Additionally, the methods are demonstrated using site-specific methane emission scenarios that capture uncertainty in wind conditions and emission characteristics. Mixed-integer linear programming formulations are used to determine sensor locations and detection thresholds that maximize detection of the emission scenarios. The optimized sensor networks consistently increase the ability to detect leaks, as compared to sensors placed near each potential emission source or along the perimeter of the site.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Disjunctive optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems

This paper proposes a new optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems. The model optimizes both investment decisions (e.g., size, location, and time to install, retire and decommission facilities) and operation decisions (e.g., on/off status, operating capacity, and expected power output). It is also able to optimize reserve systems (or backup systems), as well as the main systems, to improve power systems reliability. An impact of operational strategies of generators (i.e., participating in electricity production vs. remaining as idle units during operation) on power systems reliability is considered. Probability of equipment failures and capacity failure states are used to rigorously estimate the power systems reliability depending on design and operation strategies. The optimization model is formulated with Generalized Disjunctive Programming (GDP), which is reformulated as a mixed-integer linear programming (MILP) model using the Hull relaxation. Two reliability-related penalties, such as downtime penalty and unmet demand penalty, are included in the objective function to maximize reliability while minimizing the total net present cost. Furthermore, a bilevel decomposition with tailored cuts is developed to reduce computational times of the multi-scale optimization model. The effectiveness of the proposed model is shown by comparing the results with the results obtained from the expansion planning models that do not explicitly consider reliability. In conclusion, we also show that the proposed bilevel decomposition is computationally efficient for solving large scale problems through 5-years and 10-years planning case studies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

WE-H-207A-03: The Universality of the Lognormal Behavior of [F-18]FLT PET SUV Measurements

Purpose: Log transforming [F-18]FDG PET standardized uptake values (SUVs) has been shown to lead to normal SUV distributions, which allows utilization of powerful parametric statistical models. This study identified the optimal transformation leading to normally distributed [F-18]FLT PET SUVs from solid tumors and offers an example of how normal distributions permits analysis of non-independent/correlated measurements. Methods: Forty patients with various metastatic diseases underwent up to six FLT PET/CT scans during treatment. Tumors were identified by nuclear medicine physician and manually segmented. Average uptake was extracted for each patient giving a global SUVmean (gSUVmean) for each scan. The Shapiro-Wilk test was used to test distribution normality. One parameter Box-Cox transformations were applied to each of the six gSUVmean distributions and the optimal transformation was found by selecting the parameter that maximized the Shapiro-Wilk test statistic. The relationship between gSUVmean and a serum biomarker (VEGF) collected at imaging timepoints was determined using a linear mixed effects model (LMEM), which accounted for correlated/non-independent measurements from the same individual. Results: Untransformed gSUVmean distributions were found to be significantly non-normal (p<0.05). The optimal transformation parameter had a value of 0.3 (95%CI: −0.4 to 1.6). Given the optimal parameter was close to zero (which corresponds to log transformation), the data were subsequently log transformed. All log transformed gSUVmean distributions were normally distributed (p>0.10 for all timepoints). Log transformed data were incorporated into the LMEM. VEGF serum levels significantly correlated with gSUVmean (p<0.001), revealing log-linear relationship between SUVs and underlying biology. Conclusion: Failure to account for correlated/non-independent measurements can lead to invalid conclusions and motivated transformation to normally distributed SUVs. The log transformation was found to be close to optimal and sufficient for obtaining normally distributed FLT PET SUVs. These transformations allow utilization of powerful LMEMs when analyzing quantitative imaging metrics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Kernel Manifolds: Nonlinear‐Augmentation Dimensionality Reduction Using Reproducing Kernel Hilbert Spaces

This paper generalizes recent advances on quadratic manifold (QM) dimensionality reduction by developing kernel methods-based nonlinear-augmentation dimensionality reduction. QMs, and more generally feature map-based nonlinear corrections, augment linear dimensionality reduction with a nonlinear correction term in the reconstruction map to overcome approximation accuracy limitations of purely linear approaches. While feature map-based approaches typically learn a least squares optimal polynomial correction term, we generalize this approach by learning an optimal nonlinear correction from a user-defined reproducing kernel Hilbert space. Our approach allows one to impose arbitrary nonlinear structure on the correction term, including polynomial structure, and includes feature map and radial basis function-based corrections as special cases. Furthermore, our method has relatively low training cost and has monotonically decreasing error as the latent space dimension increases. In conclusion, we compare our approach to proper orthogonal decomposition and several recent QM approaches on data from several example problems.

kernel methods↗
DOE OSTI2019

Structural, Magnetic, and Magnetoelastic Properties of High Nd-Content Laves Alloys Prepared by Solid-State Synthesis

The magnetostrictive alloys with composition Tb{sub 0.4}Nd{sub 0.6}(Fe{sub 0.8}Co{sub 0.2}){sub x} (1.50 ≤ × ≤ 1.90) are synthesized using the solid-state mechanical alloying (MA) method followed by sintering. The phase component, structure, thermal stability, and magnetoelastic properties are investigated as functions of composition, milling process, and annealing temperature. An amorphous phase companied by free alpha-iron is formed by MA in a high-energy ball mill, which crystallizes into MgCu{sub 2}-type and PuNi{sub 3}-type structures with different annealing temperatures. A single Laves phase for the high Nd-content Tb{sub 0.4}Nd{sub 0.6}(Fe{sub 0.8}Co{sub 0.2}){sub 1.60} is formed at optimal conditions for heat treating at 700 °C for 30 min. A large linear anisotropic magnetostriction λ{sub a} (=λ{sub ||}–λ{sub ⊥}) as high as 560 ppm at 12 kOe is obtained for the sintered alloy, which may make it a promising magnetostrictive material. Meanwhile, this work may help to open up a new way to search for Laves phase magnetostrictive materials containing Nd.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

The Corrosion Behavior of Zn/Graphene Oxide Composite Coatings Fabricated by Direct Current Electrodeposition

The zinc (Zn) and Zn/graphene oxide (Zn/GO) composite coatings on the mild steel were fabricated by direct current electrodeposition from an acidic chloride bath without GO and that with four different concentrations (50, 100, 200 and 300 mg/L) of GO, respectively. The GO first is synthesized by using the typical Hummer method. Physical characterizations including the surface and cross-sectional morphology, chemical composition and crystal structure of the Zn and Zn/GO composite coatings were done by scanning electron microscope, energy-dispersive x-ray and x-ray diffraction. The crystalline size, texture coefficient, water contact angle and hardness of the Zn and Zn/GO composite coatings were measured. The corrosion behavior of the Zn and Zn/GO composite coatings was studied by the electrochemical corrosion tests including the potentiodynamic polarization test and the electrochemical impedance spectroscopy test and the immersion test. The result reveals that the corrosion resistance of the Zn coating is remarkably increased by the incorporation of GO. Furthermore, it does not show a linear increase with the increase in the amount of incorporated GO and an optimal corrosion resistance is provided by the Zn/GO composite coating attained from the electrolyte containing 100 mg/L GO. The influence of the incorporation of GO on the corrosion resistance of the Zn coating is explained in combination with their physical characterizations.

77 NANOSCIENCE AND NANOTECHNOLOGY↗