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At least 721 records · Page 40

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Method for active sensor signal optimization

A system and method for optimizing a fiber optic sensor by properly clocking or rotationally orienting a window with a fiber optic focuser. This method may include shining light through the focuser into the window and receiving with a reflectometer a first reflection from a first surface of the window and a second reflection from a second surface of the window. Next, the method may include calculating a delta of intensities between the first and second reflections, and then rotating an orientation of the window to a next rotational orientation. The steps of rotating the orientation of the window, receiving reflections, and calculating their delta may be repeated any plurality of times to solve for a plurality of deltas. Then, the method may include the steps of determining which one of the deltas is the largest and fixing the window to the focuser at a rotational orientation associated therewith.

42 ENGINEERING↗

DOE OSTI2014

Control and optimization system and method for chemical looping processes

A control system for optimizing a chemical loop system includes one or more sensors for measuring one or more parameters in a chemical loop. The sensors are disposed on or in a conduit positioned in the chemical loop. The sensors generate one or more data signals representative of an amount of solids in the conduit. The control system includes a data acquisition system in communication with the sensors and a controller in communication with the data acquisition system. The data acquisition system receives the data signals and the controller generates the control signals. The controller is in communication with one or more valves positioned in the chemical loop. The valves are configured to regulate a flow of the solids through the chemical loop.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Control and optimization system and method for chemical looping processes

47 OTHER INSTRUMENTATION↗

DOE OSTI2019

Tensor optimized Fermi sphere method for nuclear matter—Power series correlated wave function and a cluster expansion

A new formalism, called “tensor optimized Fermi sphere (TOFS) method”, is developed to treat the nuclear matter using a bare interaction among nucleons. In this method, the correlated nuclear matter wave function is taken to be a power series type, Ψ{sub N}=[∑{sub n=0}{sup N}(1∕n!)F{sup n}]Φ{sub 0} and an exponential type, Ψ{sub ex}=exp(F)Φ{sub 0}, with the uncorrelated Fermi-gas wave function Φ{sub 0}, where the correlation operator F can induce central, spin–isospin, tensor, etc. correlations, and Ψ{sub ex} corresponds to a limiting case of Ψ{sub N} (N→∞). In the TOFS formalism based on Hermitian form, it is shown that the energy per particle in nuclear matter with Ψ{sub ex} can be expressed in terms of a linked-cluster expansion. On the basis of these results, we present the formula of the energy per particle in nuclear matter with Ψ{sub N}. We call the Nth-order TOFS calculation for evaluating the energy with Ψ{sub N}, where the correlation functions are optimally determined in the variation of the energy. The TOFS theory is applied for the study of symmetric nuclear matter using a central NN potential with short-range repulsion. The calculated results are fairly consistent to those of other theories such as the Brueckner–Hartree–Fock approach etc.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Simulation and Optimizing Groundwater Investigations, Conceptual Design Analysis, Model Calibration and Remedial Design Using Parallel Numerical Methods: Theory and Application - 16578

Optimization of environmental restoration efforts at complex groundwater sites involves the development of a conceptual and often numerical flow and transport models. Typically, a limited number of simulations are done to assess the impact of parameter uncertainty, to evaluate remedial alternatives and develop a remedy, or to modify an existing remedy. A limited number of simulations are performed because each simulation can take upwards of several hours per simulation and until now the ability to conduct the 100's to 1000's of simulations needed to derive an optimal remedy was beyond the reach of the everyday practitioner. HGL has developed and implemented new parallel simulation and optimization methods in their software and has successfully deployed applications on high performance Cloud-based computing platforms. This advancement removes the impediment of burdensome and unpredictable runtime requirements these types of projects have typically exhibited using heuristic trial-and-error or even automated serial computation approaches. A parallel numerical solver has been added to the subsurface flow and transport simulator MODFLOW-SURFACT{sup TM} and has demonstrated reductions in solution time by as much as a factor of 9.8. This is accomplished by distributing the numerical computations of every time step over multiple processors. The integrated parallel simulation and optimization method uses the breadth and depth search approach; iteratively producing sets of tens or 100's of candidate solutions that are evaluated concurrently instead sequentially (i.e. one model evaluation at a time). Solutions within 80-100% of the globally optimal solution are regularly found expending only 5-10% of the usual computational effort using this new parallel optimization method (reducing computation effort by 10 to 20 times). Distributing optimal search runs across multiple processors additionally reduces calendar time to solutions. Practitioners evaluating multiple conceptual site models, model calibration by auto-calibration methods and remedial design by heuristics can enjoy significant reductions in model solution time by using the parallel numerical matrix solver in the physics-based simulator. Combining investigation and design analysis with breadth and depth parallel optimization methods, projects at the fringe of practical optimization, along with projects that were beyond the reach of formal optimization methods are now wholly feasible and tractable optimization problems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Determining biomolecular structures near room temperature using X-ray crystallography: concepts, methods and future optimization

For roughly two decades, cryocrystallography has been the overwhelmingly dominant method for determining high-resolution biomolecular structures. Competition from single-particle cryo-electron microscopy and micro-electron diffraction, increased interest in functionally relevant information that may be missing or corrupted in structures determined at cryogenic temperature, and interest in time-resolved studies of the biomolecular response to chemical and optical stimuli have driven renewed interest in data collection at room temperature and, more generally, at temperatures from the protein–solvent glass transition near 200 K to ∼350 K. Fischer has recently reviewed practical methods for room-temperature data collection and analysis [Fischer (2021), Q. Rev. Biophys. 54 , e1]. Here, the key advantages and physical principles of, and methods for, crystallographic data collection at noncryogenic temperatures and some factors relevant to interpreting the resulting data are discussed. For room-temperature data collection to realize its potential within the structural biology toolkit, streamlined and standardized methods for delivering crystals prepared in the home laboratory to the synchrotron and for automated handling and data collection, similar to those for cryocrystallography, should be implemented.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Method of Moving Asymptotes Optimization Algorithm in Python

Python implementation of the Method of Moving Asymptotes optimization algorithm described in[Svanberg, K., The method of moving asymptotes- a new method for structural optimization. International journal for numerical methods in engineering, 1987. 24(2): p.359 (https://onlinelibrary.wiley.com/doi/abs/10.1002/nme.1620240207). Originally implemented in[GetDP](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=717799) [project](https://gitlab.onelab.info/getdp/getdp), although it has been deleted in their repository.

Salazar De Troya, Miguel↗

DOE OSTI2023

Generalized method for the optimization of pulse shape discrimination parameters

Organic scintillators exhibit fast timing, high detection efficiency for fast neutrons and pulse shape discrimination (PSD) capability. PSD is essential in mixed radiation fields, where different types of radiation need to be detected and discriminated. In neutron measurements for nuclear security and non proliferation effective PSD is crucial, because a weak neutron signature needs to be detected in the presence of a strong gamma-ray background. Here, the most commonly used deterministic PSD technique is charge integration (CI). This method requires the optimization of specific parameters to obtain the best gamma-neutron separation. These parameters depend on the scintillating material and light readout device and typically require a lengthy optimization process and a calibration reference measurement with a mixed source. In this paper, we propose a new method based on the scintillation fluorescence physics that enables to find the optimum PSD integration gates using only a gamma-ray emitter. We demonstrate our method using three organic scintillation detectors: deuterated trans-stilbene, small-molecule organic glass, and EJ-309. In all the investigated cases, our method allowed finding the optimum PSD CI parameters without the need of iterative optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

A Multi-Objective Bayesian Optimization Approach Using the Weighted Tchebycheff Method

Abstract Bayesian optimization (BO) is a low-cost global optimization tool for expensive black-box objective functions, where we learn from prior evaluated designs, update a posterior surrogate Gaussian process model, and select new designs for future evaluation using an acquisition function. This research focuses upon developing a BO model with multiple black-box objective functions. In the standard multi-objective (MO) optimization problem, the weighted Tchebycheff method is efficiently used to find both convex and non-convex Pareto frontiers. This approach requires knowledge of utopia values before we start optimization. However, in the BO framework, since the functions are expensive to evaluate, it is very expensive to obtain the utopia values as a prior knowledge. Therefore, in this paper, we develop a MO-BO framework where we calibrate with multiple linear regression (MLR) models to estimate the utopia value for each objective as a function of design input variables; the models are updated iteratively with sampled training data from the proposed MO-BO. These iteratively estimated mean utopia values are used to formulate the weighted Tchebycheff MO acquisition function. The proposed approach is implemented in two numerical test examples and one engineering design problem of optimizing thin tube geometries under constant loading of temperature and pressure, with minimizing the risk of creep-fatigue failure and design cost, along with risk-based and manufacturing constraints. Finally, the model accuracy with frequentist, Bayesian and without MLR-based calibration are compared to true Pareto solutions.

Engineering↗

DOE OSTI2016

Automated property optimization via ab initio O(N) elongation method: Application to (hyper-)polarizability in DNA

An automated property optimization method was developed based on the ab initio O(N) elongation (ELG) method and applied to the optimization of nonlinear optical (NLO) properties in DNA as a first test. The ELG method mimics a polymerization reaction on a computer, and the reaction terminal of a starting cluster is attacked by monomers sequentially to elongate the electronic structure of the system by solving in each step a limited space including the terminal (localized molecular orbitals at the terminal) and monomer. The ELG-finite field (ELG-FF) method for calculating (hyper-)polarizabilities was used as the engine program of the optimization method, and it was found to show linear scaling efficiency while maintaining high computational accuracy for a random sequenced DNA model. Furthermore, the self-consistent field convergence was significantly improved by using the ELG-FF method compared with a conventional method, and it can lead to more feasible NLO property values in the FF treatment. The automated optimization method successfully chose an appropriate base pair from four base pairs (A, T, G, and C) for each elongation step according to an evaluation function. From test optimizations for the first order hyper-polarizability (β) in DNA, a substantial difference was observed depending on optimization conditions between “choose-maximum” (choose a base pair giving the maximum β for each step) and “choose-minimum” (choose a base pair giving the minimum β). In contrast, there was an ambiguous difference between these conditions for optimizing the second order hyper-polarizability (γ) because of the small absolute value of γ and the limitation of numerical differential calculations in the FF method. It can be concluded that the ab initio level property optimization method introduced here can be an effective step towards an advanced computer aided material design method as long as the numerical limitation of the FF method is taken into account.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Gradient-Based Optimization of Wind Farms with Different Turbine Heights: Preprint

Turbine wakes reduce power production in a wind farm. Current wind farms are generally built with turbines that are all the same height, but if wind farms included turbines with different tower heights, the cost of energy (COE) may be reduced. We used gradient-based optimization to demonstrate a method to optimize wind farms with varied hub heights. Our study includes a modified version of the FLORIS wake model that accommodates three-dimensional wakes integrated with a tower structural model. Our purpose was to design a process to minimize the COE of a wind farm through layout optimization and varying turbine hub heights. Results indicate that when a farm is optimized for layout and height with two separate height groups, COE can be lowered by as much as 5%-9%, compared to a similar layout and height optimization where all the towers are the same. The COE has the best improvement in farms with high turbine density and a low wind shear exponent.

17 WIND ENERGY↗

DOE OSTI2026

Automated ICRF heating surrogate modeling via machine learning

This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications. The automated framework includes data analysis tools that transform raw datasets into actionable insights in seconds, replacing weeks of manual exploratory effort and ensuring consistent, reproducible dataset characterization. By integrating advanced hyperparameter optimization (HPO) methods including Bayesian optimization via BoTorch and Tree-structured Parzen Estimators (TPE), the framework significantly reduces model development time from weeks to hours, decreasing computational cost and required expertise, while enabling high-accuracy surrogate models. Compared to traditional hyperparameter scanning (HPS) techniques such as methodical, randomized, and grid searches, HPO methods achieve superior convergence and predictive performance, even when compared to already well-tuned reference models. On NSTX High Harmonic Fast Wave (HHFW) heating datasets, both Random Forest Regressor (RFR) and neural network surrogates demonstrate improved accuracy, achieving R 2 values beyond 0.97 and 0.98, respectively. The results show that while HPO gains are modest for robust architectures like RFR, they become essential for more sensitive models such as neural networks, highlighting the trade-offs across optimization strategies. Through automated workflows that eliminate manual hyperparameter tuning and require minimal ML expertise, this work enables widespread adoption of high-fidelity surrogate models across the fusion community for real-time plasma control, uncertainty quantification, rapid experimental scenario development, and integrated system optimization.

Sanchez-Villar, Alvaro [Princeton Plasma Physics L↗

DOE OSTI2020

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2016

First-principle optimal local pseudopotentials construction via optimized effective potential method

The local pseudopotential (LPP) is an important component of orbital-free density functional theory, a promising large-scale simulation method that can maintain information on a material’s electron state. The LPP is usually extracted from solid-state density functional theory calculations, thereby it is difficult to assess its transferability to cases involving very different chemical environments. Here, we reveal a fundamental relation between the first-principles norm-conserving pseudopotential (NCPP) and the LPP. On the basis of this relationship, we demonstrate that the LPP can be constructed optimally from the NCPP for a large number of elements using the optimized effective potential method. Specially, our method provides a unified scheme for constructing and assessing the LPP within the framework of first-principles pseudopotentials. Our practice reveals that the existence of a valid LPP with high transferability may strongly depend on the element.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2013

Multigrid preconditioning of linear systems for semi-smooth Newton methods applied to optimization problems constrained by smoothing operators

This article is concerned here with the question of constructing efficient multigrid preconditioners for the linear systems arising when applying semi-smooth Newton methods to large-scale linear-quadratic optimization problems constrained by smoothing operators with box-constraints on the controls. It is shown that, for certain discretizations of the optimization problem, the linear systems to be solved at each semi-smooth Newton iteration reduce to inverting principal minors of the Hessian of the associated unconstrained problem. As in the case when box-constraints on the controls are absent, the multigrid preconditioner introduced here is shown to increase in quality as the mesh-size decreases, resulting in a number of iterations that decreases with mesh-size. However, unlike the unconstrained case, the spectral distance between the preconditioners and the Hessian is shown to be of suboptimal order in general.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

A Modular Optimal Power Flow Method for Integrating New Technologies in Distribution Grids

This work proposes a modular concept to build optimal power flow (OPF) models for distribution networks containing various emerging technologies under diverse ownership structures, to efficiently deal with evolving technology capabilities and information sharing or privacy constraints. This concept will support any typical OPF application (e.g., optimal dispatch of a given asset without violating grid constraints) by coordinating between grid module and technology module without the need to recreate various modeling elements as technology capability changes due to innovation. Moreover, the modularity of the proposed concept enables achieving system level objectives without sharing detailed information on module level objectives and constraints among modules. To achieve this, the proposed work develops a gradient-descent algorithm which builds upon the literature on the state-of-the-art power flow approximation. The proposed concept is demonstrated with two case studies of i) controllable loads and ii) battery energy storage system (BESS) on an actual large-scale distribution grid.

Hanif, Sarmad↗

DOE OSTI2024

Development of a Method for Shape Optimization for a Gas Turbine Fuel Injector Design Using Metal-Additive Manufacturing

Adjoint shape optimization has enabled physics-based optimal designs for aerodynamic surfaces. Additive manufacturing (AM) makes it possible to manufacture complex shapes. However, there has been a gap between optimal and manufacturable surfaces due to the inherent limitations of commercial computational fluid dynamics (CFD) codes to implement geometric constraints during adjoint computation. In such cases, the design sensitivities are exported and used to perform constrained shape modifications using parametric information stored in computer aided design (CAD) files to satisfy manufacturability constraints. However, modifying the design using adjoint methods in CFD solvers and performing constrained shape modification in CAD can lead to inconsistencies due to different shape parameterization schemes. This paper describes a method to enable the simultaneous optimization of the fluid domain and impose AM manufacturability constraints, resolving one of the key issues of geometry definition for isogeometric analysis. Similar to a grid convergence study, the proposed method verifies the consistencies between shape parameterization techniques present within commercial CAD and CFD software during mesh movement as a part of the adjoint shape optimization routine. By identifying the appropriate parameters essential to a shape optimization study, the error metric between the different parameterization techniques converges to demonstrate sufficient consistencies for justifiable exchange of data between CAD and CFD. For the identified shape optimization parameters, the error metric to measure the deviation between the two parameterization schemes lies within the AM laser-powder bed fusion (L-PBF) process tolerance. Additionally, comparison for subsequent objective function calculations between iterations of the optimization loop showed acceptable differences within 1% variation between the modified geometries obtained using the two parameterization schemes. This method provides justification for the use of multiphysics guided adjoint design sensitivities computed in CFD software to perform shape modifications in CAD to incorporate AM manufacturability constraints during the shape optimization loop such that optimal designs are also additively manufacturable.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2018

Development and Optimization of Voltammetric Methods for Real Time Analysis of Electrorefiner Salt with High Concentrations of Actinides and Fission Products

This project addresses the problem of achieving accurate material control and accountability (MC&A) around pyroprocessing electrorefiner systems. Spent nuclear fuel pyroprocessing poses a unique challenge with respect to reprocessing technology in that the fuel is never fully dissolved in the process fluid. In this case, the process fluid is molten, anhydrous LiCl-KCl salt. Therefore, there is no traditional input accountability tank. However, electrorefiners (ER) accumulate very large quantities of fissile nuclear material (including plutonium) and should be well safeguarded in a commercial facility. Idaho National Laboratory (INL) currently operates a pyroprocessing facility for treatment of spent fuel from Experimental Breeder Reactor-II with two such ER systems. INL implements MC&A via a mass tracking model in combination with periodic sampling of the salt and other materials followed by destructive analysis. This approach is projected to be insufficient to meet international safeguards timeliness requirements. A real time or near real time monitoring method is, thus, direly needed to support commercialization of pyroprocessing. A variety of approaches to achieving real time monitoring for ER salt have been proposed and studied to date—including a potentiometric actinide sensor for concentration measurements, a double bubbler for salt depth and density measurements, and laser induced breakdown spectroscopy (LIBS) for concentration measurements. While each of these methods shows some promise, each also involves substantial technical complexity that may ultimately limit their implementation. Yet another alternative is voltammetry —a very simple method in theory that has previously been tested for this application to a limited extent. The equipment for a voltammetry system consists of off-the-shelf components (three electrodes and a potentiostat), which results in substantial benefits relative to cost and robustness. Based on prior knowledge of electrochemical reduction potentials for each of the species of interest, voltammetry can be used to quantify concentrations of a variety of elemental species—including uranium, plutonium, minor actinides, and rare earths. Various methods have been tested by other researchers to date—including cyclic voltammetry, square wave voltammetry, normal pulse voltammetry, etc. In most cases, it has been observed that there is a very limited concentration range for which the output can be readily correlated with concentration in the salt. Furthermore, testing to date has been limited to simple ternary salts with only a single element being quantified. While incomplete for application to MC&A for pyroprocessing, these results lead us to believe that voltammetry can be optimized based on salt properties and fundamental electrochemical rate processes to yield a highly accurate and robust method. This project is divided into four tasks jointly executed by three university research groups. This includes experimental measurement of key physical data on the systems of interest, development of a predictive voltammetry model, experimental validation of the voltammetry model, and design/verification of an optimized measurement method. This project supports the goals of the US-ROK Joint Fuel Cycle Study in addition to the NA-24 Office of the National Nuclear Security Agency and the International Atomic Energy Agency (IAEA).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗