Accelerated Convergence of Contracted Quantum Eigensolvers through a Quasi-Second-Order, Locally Parameterized Optimization
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
SEARCH · Engineering Papers
Results for “optimization 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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Π4U: A high performance computing framework for Bayesian uncertainty quantification of complex models
We present Π4U,{sup 1} an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation (UQ+P) of complex and computationally demanding physical models, that can exploit massively parallel computer architectures. The framework incorporates Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task-based parallelism for heterogeneous clusters. Sampling is based on the Transitional Markov Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic approximations are treated via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). A modified subset simulation method is used for posterior reliability measurements of rare events. The framework accommodates scheduling of multiple physical model evaluations based on an adaptive load balancing library and shows excellent scalability. In addition to the software framework, we also provide guidelines as to the applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical and computational developments are demonstrated with applications drawn from molecular dynamics, structural dynamics and granular flow.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Using standard calibrated geometries to characterize a coaxial high purity germanium gamma detector for Monte Carlo simulations
A detector model optimization procedure based on matching Monte Carlo simulations with measurements for two experimentally calibrated sample geometries which are frequently used in radioactivity measurement laboratories results in relative agreement within 5% between simulated and measured efficiencies for a high purity germanium detector. The optimization procedure indicated that the increase in dead layer thickness is largely responsible for a detector efficiency decrease in time. The optimized detector model allows Monte Carlo efficiency calibration for all other samples of which the geometry and bulk composition is known. The presented method is a competitive and economic alternative to more elaborate detector scanning methods and results in a comparable accuracy.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Evaluation of partial volume correction and analysis of longitudinal [ 18 F]GTP1 tau PET imaging in Alzheimer's disease using linear mixed-effects models
Purpose: We evaluated the impact of partial volume correction (PVC) methods on the quantification of longitudinal [ 18 F]GTP1 tau positron-emission tomography (PET) in Alzheimer's disease and the suitability of describing the tau pathology burden temporal trajectories using linear mixed-effects models (LMEM). Methods: We applied van Cittert iterative deconvolution (VC), 2-compartment, and 3-compartment, and the geometric transfer matrix plus region-based voxelwise methods to data acquired in an Alzheimer's disease natural history study over 18 months at a single imaging site. We determined the optimal PVC method by comparing the standardized uptake value ratio change (%ΔSUVR) between diagnostic and tau burden–level groups and the longitudinal repeatability derived from the LMEM. The performance of LMEM analysis for calculating %ΔSUVR was evaluated in a natural history study and in a multisite clinical trial of semorinemab in prodromal to mild Alzheimer's disease by comparing results to traditional per-visit estimates. Results: The VC, 2-compartment, and 3-compartment PVC methods had similar performance, whereas region-based voxelwise overcorrected regions with a higher tau burden. The lowest within-subject variability and acceptable group separation scores were observed without PVC. The LMEM-derived %ΔSUVR values were similar to the per-visit estimates with lower variability. Conclusion: The results indicate that the tested PVC methods do not offer a clear advantage or improvement over non-PVC images for the quantification of longitudinal [ 18 F]GTP1 PET data. LMEM offers a robust framework for the longitudinal tau PET quantification with low longitudinal test–retest variability. Clinical trial registration: NCT02640092 and NCT03289143.
60 APPLIED LIFE SCIENCES↗
Optimization of turbine design in wind farms with multiple hub heights, using exact analytic gradients and structural constraints
Wind farms are generally designed with turbines of all the same hub height. If wind farms were designed with turbines of different hub heights, wake interference between turbines could be reduced, lowering the cost of energy (COE). This paper demonstrates a method to optimize onshore wind farms with two different hub heights using exact, analytic gradients. Gradient-based optimization with exact gradients scales well with large problems and is preferable in this application over gradient-free methods. Our model consisted of the following: a version of the FLOw Redirection and Induction in Steady-State wake model that accommodated three-dimensional wakes and calculated annual energy production, a wind farm cost model, and a tower structural model, which provided constraints during optimization. Structural constraints were important to keep tower heights realistic and account for additional mass required from taller towers and higher wind speeds. We optimized several wind farms with tower height, diameter, and shell thickness as coupled design variables. Our results indicate that wind farms with small rotors, low wind shear, and closely spaced turbines can benefit from having two different hub heights. A nine-by-nine grid wind farm with 70-meter rotor diameters and a wind shear exponent of 0.08 realized a 4.9% reduction in COE by using two different tower sizes. If the turbine spacing was reduced to 3 diameters, the reduction in COE decreased further to 11.2%. Allowing for more than two different turbine heights is only slightly more beneficial than two heights and is likely not worth the added complexity.
17 WIND ENERGY↗
Efficient Solution Methods for Large-scale Optimization Problems Constrained by Time-dependent Partial Differential Equations (Final Report)
The objectives of this project are to devise and analyze efficient solvers for several classes of PDE- constrained optimization problems. The project was initially focused on, but not restricted to time- dependent PDE-constraints such as parabolic equations, time-dependent fluid flows (Navier-Stokes equations), hyperbolic equations (Burgers equation, shallow water equations). Other constraints considered are PDEs with stochastic coefficients. Additional elements of interest are the handling of problems with control and/or state inequality constraints or non-differentiable regularization terms, and boundary controls. A direction of work not initially listed among the goals, which was triggered by conversations with DOE scientists from the Sandia National Labs, is to develop efficient solvers for optimization- based domain-decomposition PDE-solvers. Also targeted in the project are a number of technical issues necessary for facilitating the integration of the methods resulted from this project with existing high-performance packages developed at DOE national labs; this integration is expected to further enable the scientific community to use the novel methods. An example of such a task is the need to develop algebraic multigrid (AMG) versions of the algorithms. A list of accomplishments in several areas is detailed as well as a list of publications.
97 MATHEMATICS AND COMPUTING↗
TU-PIS-Exhibit Hall-3: Simultaneous tracking of patient and real time staff dose to optimize interventional workflow
The current clinical standard of organ respiratory imaging, 4D-CT, is fundamentally limited by poor soft-tissue contrast and imaging dose. These limitations are potential barriers to beneficial “4D” radiotherapy methods which optimize the target and OAR dose-volume considering breathing motion but rely on a robust motion characterization. Conversely, MRI imparts no known radiation risk and has excellent soft-tissue contrast. MRI-based motion management is therefore highly desirable and holds great promise to improve radiotherapy of moving cancers, particularly in the abdomen. Over the past decade, MRI techniques have improved significantly, making MR-based motion management clinically feasible. For example, cine MRI has high temporal resolution up to 10 f/s and has been used to track and/or characterize tumor motion, study correlation between external and internal motions. New MR technologies, such as 4D-MRI and MRI hybrid treatment machines (i.e. MR-linac or MR-Co60), have been recently developed. These technologies can lead to more accurate target volume determination and more precise radiation dose delivery via direct tumor gating or tracking. Despite all these promises, great challenges exist and the achievable clinical benefit of MRI-based tumor motion management has yet to be fully explored, much less realized. In this proposal, we will review novel MR-based motion management methods and technologies, the state-of-the-art concerning MRI development and clinical application and the barriers to more widespread adoption. Learning Objectives: Discuss the need of MR-based motion management for improving patient care in radiotherapy. Understand MR techniques for motion imaging and tumor motion characterization. Understand the current state of the art and future steps for clinical integration. Henry Ford Health System holds research agreements with Philips Healthcare. Research sponsored in part by a Henry Ford Health System Internal Mentored Grant.
60 APPLIED LIFE SCIENCES↗
A comparative study of coarse-graining methods for polymeric fluids: Mori-Zwanzig vs. iterative Boltzmann inversion vs. stochastic parametric optimization
We construct effective coarse-grained (CG) models for polymeric fluids by employing two coarse-graining strategies. The first one is a forward-coarse-graining procedure by the Mori-Zwanzig (MZ) projection while the other one applies a reverse-coarse-graining procedure, such as the iterative Boltzmann inversion (IBI) and the stochastic parametric optimization (SPO). More specifically, we perform molecular dynamics (MD) simulations of star polymer melts to provide the atomistic fields to be coarse-grained. Each molecule of a star polymer with internal degrees of freedom is coarsened into a single CG particle and the effective interactions between CG particles can be either evaluated directly from microscopic dynamics based on the MZ formalism, or obtained by the reverse methods, i.e., IBI and SPO. The forward procedure has no free parameters to tune and recovers the MD system faithfully. For the reverse procedure, we find that the parameters in CG models cannot be selected arbitrarily. If the free parameters are properly defined, the reverse CG procedure also yields an accurate effective potential. Moreover, we explain how an aggressive coarse-graining procedure introduces the many-body effect, which makes the pairwise potential invalid for the same system at densities away from the training point. From this work, general guidelines for coarse-graining of polymeric fluids can be drawn.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Optimal Control of a Degenerate PDE for Surface Shape
Controlling the shapes of surfaces provides a novel way to direct self-assembly of colloidal particles on those surfaces and may be useful for material design. This motivates the investigation of an optimal control problem for surface shape in this paper. Specifically, we consider an objective (tracking) functional for surface shape with the prescribed mean curvature equation in graph form as a state constraint. The control variable is the prescribed curvature. We prove existence of an optimal control, and using improved regularity estimates, we show sufficient differentiability to make sense of the first order optimality conditions. This allows us to rigorously compute the gradient of the objective functional for both the continuous and discrete (finite element) formulations of the problem. Numerical results are shown to illustrate the minimizers and optimal controls on different domains.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Innovative high-temperature ammonium bifluoride fusion and rapid analysis of elements with nuclear forensic value
High-temperature ammonium bifluoride (ABF) fusions were evaluated for potential use in rapid dissolution of post-detonation nuclear debris. The ABF fusion was carried out in a Pt crucible which allowed evaluation of higher fusion and evaporation temperatures. The high-temperature ABF fusion dissolution method was evaluated using geological reference materials: USGS QLO-1a Quartz Latite, USGS SDC-1 Mica Schist, and NIST 278 Obsidian Rock. The optimized dissolution method involved a 10 min fusion at 540 °C, a 5 min reflux in 8 M HNO 3 , an evaporation at 300 °C and final dilution into 45 mL of 2% (v/v) HNO3. The final solution was filtered after heating at 105 °C using a hotblock. This dissolution method was simple, requiring only a hotplate or hotblock, filtered samples were available for ICP-MS analysis or radiochemical separation within 150 min, and was found to have high (>90%) recovery for many isotopes of interest in nuclear forensics applications. U and Pu in the dissolved material was separated using TEVA and UTEVA extraction chromatography columns, a process which resulted in >90% recovery. An irradiated U tracer was spiked into the material prior to dissolution and analyzed for recovery of major fission products and 239Np. The monitored radionuclides had recoveries of greater than 90%, except for the volatile radioiodine isotopes.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Real Time Optimal Control of Supercapacitor Operation for Frequency Response
Supercapacitors are gaining wider applications in power systems due to fast dynamic response. Utilizing supercapacitors by means of power electronics interfaces for power compensation is a proven effective technique. For applications such as requency restoration if the cost of supercapacitors maintenance as well as the energy loss on the power electronics interfaces are addressed. It is infeasible to use traditional optimization control methods to mitigate the impacts of frequent cycling. This paper proposes a Front End Controller (FEC) using Generalized Predictive Control featuring real time receding optimization. The optimization constraints are based on cost and thermal management to enhance to the utilization efficiency of supercapacitors. A rigorous mathematical derivation is conducted and test results acquired from Digital Real Time Simulator are provided to demonstrate effectiveness.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification
Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for variance reduction to make tractable uncertainty quantification even when numerically simulating the physical systems of interest with high-fidelity models is computationally expensive. This work proposes a context-aware multi-fidelity Monte Carlo method that optimally balances the costs of training low-fidelity models with the costs of Monte Carlo sampling. It generalizes the previously developed context-aware bi-fidelity Monte Carlo method to hierarchies of multiple models and to more general types of low-fidelity models. When training low-fidelity models, the proposed approach takes into account the context in which the learned low-fidelity models will be used, namely for variance reduction in Monte Carlo estimation, which allows it to find optimal trade-offs between training and sampling to minimize upper bounds of the mean-squared errors of the estimators for given computational budgets. This is in stark contrast to traditional surrogate modeling and model reduction techniques that construct low-fidelity models with the primary goal of approximating well the high-fidelity model outputs and typically ignore the context in which the learned models will be used in upstream tasks. Further, the proposed context-aware multi-fidelity Monte Carlo method applies to hierarchies of a wide range of types of low-fidelity models such as sparse-grid and deep-network models. Numerical experiments with the gyrokinetic simulation code Gene show speedups of up to two orders of magnitude compared to standard estimators when quantifying uncertainties in small-scale fluctuations in confined plasma in fusion reactors. This corresponds to a runtime reduction from 72 days to four hours on one node of the Lonestar6 supercomputer at the Texas Advanced Computing Center.
42 ENGINEERING↗
Final Technical Report for "Applied Mathematics Research: Simulation Based Optimization and Application to Electromagnetic Inverse Problems"
The focus of research was: Developing adaptive mesh for the solution of Maxwell's equations; Developing a parallel framework for time dependent inverse Maxwell's equations; Developing multilevel methods for optimization problems with inequality constraints; A new inversion code for inverse Maxwell's equations in the 0th frequency (DC resistivity); A new inversion code for inverse Maxwell's equations in low frequency regime. Although the research concentrated on electromagnetic forward and in- verse problems the results of the research was applied to the problem of image registration.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Optimizing collimator positions using bayesian optimization in the Fermilab MI-8 transfer line
Collimators are used to minimize losses and to remove particles that would otherwise get lost downstream and irradiate the machine. Finding the optimal jaw positions is time consuming and with the upstream beam properties changing, the collimation settings would need to be readjusted each time. Therefore, a method to optimize collimator positions and to operate them at full capacity in a short time is required for loss control downstream. A study of collimator positions was conducted and a machine learning (ML) model was developed to predict optimal collimator positions. Bayesian Optimization (BO) was used to calculate new jaw positions from the ML model. The results of BO and usage of ML for better performance of the collimation system are presented in this paper.
Babacan, Betiay [Fermilab]↗
Machine Learning for Additive Manufacturing of Functionally Graded Materials
Additive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-by-layer from 3D modeling data. Among AM applications, the fabrication of Functionally Graded Materials (FGMs) has significant importance due to the potential to enhance component performance across several industries. FGMs are manufactured with a gradient composition transition between dissimilar materials, enabling the design of new materials with location-dependent mechanical and physical properties. This study presents a comprehensive review of published literature pertaining to the implementation of Machine Learning (ML) techniques in AM, with an emphasis on ML-based methods for optimizing FGMs fabrication processes. Through an extensive survey of the literature, this review article explores the role of ML in addressing the inherent challenges in FGMs fabrication and encompasses parameter optimization, defect detection, and real-time monitoring. The article also provides a discussion of future research directions and challenges in employing ML-based methods in the AM fabrication of FGMs.
36 - MATERIALS SCIENCE↗
A Grid Intensity Map-Based Source Model for Monte Carlo Modeling of IMRT Field Irradiation - Paper 5
An accurate source model is important for TPS (Treatment Planning System) dose calculation and verification, especially for IMRT (Intensity Modulated Radiotherapy) irregular and inhomogeneous fields. Monte Carlo simulation is the most accurate way to calculate dose distributions in the heterogeneous media like human bodies. A full radiotherapy modeling generally includes Linac modeling and phantom dose calculation. Full Linac modeling is very time-consuming and requires highly detailed knowledge of the accelerator composition and structure, however, which is very difficult to obtain for commercial interests. Often all relevant parameters are not known with sufficient accuracy, and thus a time-consuming tuning of e.g. the electron beam spot size and energy must be performed. A multiple source model (MSM) regards particles coming from different parts of an accelerator as if they are coming from different virtual sources. MSM uses a set of virtual sources representing the treatment beam (e.g. primary photons, head scatter, electron contamination) by a set of analytic functions or histograms with parameters derived from dosimetric measurements or a full MC simulation. Tillikainen et al built a three sub-source model by matching with measurement data using an automatic process to optimize the free model parameters for a Varian 2100 C/D accelerator. Lin et al have built a convenient five-photon-sub-source model for Varian 2300C 6 MV-X beam based on Monte Carlo simulated Linac for ARTS (Accurate Radiotherapy System). The particle generation of MSM is very fast compared to full Linac simulation, and its sampling efficiency is not limited by the size of the phase space file (PSF). However, generally MSM is generated based on regular field PSF and needs an irregular component to form the beam shape meanwhile apt to bringing error Linac modeling and MSM producing from PSF analysis actually both are multi-freedom optimization process by adjusting several parameters (e.g. electron energy and angular divergence). Linac modeling adjusts its parameters from the beginning of electron hitting target through all components to fit the simulation to the measurement. MSM tunes its PSF-derived parameters by optimizing to minimize the deviations between the simulation and the measurement. Since the full Linac modeling and the MSM model both need to fine tune their parameters to match the simulation to the measurement, and sometimes maybe offsets the real accelerator for its multi-parameter optimization process. So, why not fine tune directly all source model parameters just behind all Linac components to fit the simulation to the measurement? A grid intensity-based surface source model was suggested here, which assumed the particle emitting from the bottom surface of Linac last beam trimmer MLC (Multi Leaf Collimator). The particle emitting position is sampled on the grid intensity map optimized by TPS, and the particle weight is adjusted by its original grid intensity. This source model not only takes advantage of the Monte Carlo method in the heterogeneous media, but also avoids effectively the transportation speed shortcoming of Monte Carlo simulation through Linac components. (authors)
61 RADIATION PROTECTION AND DOSIMETRY↗
Optimizing Information Automation Using a New Method Based on System-Theoretic Process Analysis: Tool Development and Method Evaluation
This report is an update to a prior report that describes progress and findings for a program of research supporting the design and optimization of information automation systems for nuclear power plants. Much of the domestic nuclear fleet is currently focused on modernizing technologies and processes, including transitioning toward digitalization in the control room and throughout the plant, along with a greater interest in the use of automation, artificial intelligence, robotics, and other emerging technologies. While there are significant opportunities to apply these technologies toward greater plant safety, efficiency, and overall cost-effectiveness, optimizing their design and avoiding potential safety and performance risks depends on ensuring that human performance-related organizational and technical design issues are identified and addressed early in the design process. This report describes modeling tools and techniques, based on sociotechnical systems theory, to support these design goals and their application in the current research effort. The report is primarily intended for senior nuclear energy stakeholders, including regulators, corporate management, and senior plant management. We have developed and employed a method to design an optimized information automation ecosystem (IAE) based on the systems-theoretic constructs underlying sociotechnical systems theory in general and the Systems-Theoretic Accident Modeling and Processes (STAMP) approach in particular. We argue that an IAE can be modeled as an interactive information control system whose behavior can be understood in terms of dynamic control, feedback, and communication relationships amongst the system’s technical and organizational components. We have employed two STAMP-based tools in this effort. The first is Causal Analysis based on STAMP (CAST), an accident and incident analysis technique that was used to examine a performance- and safety-related incident at an industry partner’s plant involving the unintentional activation of an emergency diesel generator. This analysis provided insight into the behavior of the plant’s current information control structure within the context of a specific, significant event. The second tool is Systems Theoretic Process Analysis (STPA) which is a proactive risk analysis tool used to examine existing and potential, planned sociotechnical systems. STPA was used to identify risk factors in the current design of a generic nuclear power plant (NPP) preventive maintenance system. Our analyses focused on identifying near-term system improvements and longer-term design requirements for an optimized IAE system. CAST analysis findings indicate an important underlying contributor to the incident under investigation, and a significant risk to information automation system performance, was perceived time and schedule pressure, which exposed weaknesses in interdepartmental coordination between and within responsible plant organizations and challenged the resilience of established plant processes, until a human caused the eventual event. These findings are discussed in terms of their risk to overall system performance and their implications for information automation system resilience and brittleness. STPA findings exposed several areas of concern in the design of current preventive maintenance systems. We also present two preliminary information automation models. The proactive issue resolution (PIR) model is a test case of an information automation concept with significant near-term potential for application and subsequent reduction in significant plant events. The IAE model is a more general representation of a broader, plantwide information automation system and represents an end-state vision for our work. From our results, we have generated an initial set of preliminary system-level requirements and safety constraints for these models. We have also focused on early development of easy to learn, easy to use “transportable” tools for sociotechnical systems analysis. We intend these to be used by NPP personnel as a means of gaining reliable and relatively quick insight into (1) sociotechnical systems factors impacting incidents and accidents, (2) potential sociotechnical risk factors in existing or planned system designs, and (3) potential weaknesses in a system’s safety and/or information control structure. We conclude the report with a set of summary recommendations, a discussion of planned and potential follow-on research and development, and a draft list of system-level requirements and safety constraints for optimized information automation systems.
99 GENERAL AND MISCELLANEOUS↗
MO-G-18A-01: Radiation Dose Reducing Strategies in CT, Fluoroscopy and Radiography
Advances in medical x-ray imaging have provided significant benefits to patient care. According to NCRP 160, there are more than 400 million x-ray procedures performed annually in the United States alone that contributes to nearly half of all the radiation exposure to the US population. Similar growth trends in medical x-ray imaging are observed worldwide. Apparent increase in number of medical x-ray imaging procedures, new protocols and the associated radiation dose and risk has drawn considerable attention. This has led to a number of technological innovations such as tube current modulation, iterative reconstruction algorithms, dose alerts, dose displays, flat panel digital detectors, high efficient digital detectors, storage phosphor radiography, variable filters, etc. that are enabling users to acquire medical x-ray images at a much lower radiation dose. Along with these, there are number of radiation dose optimization strategies that users can adapt to effectively lower radiation dose in medical x-ray procedures. The main objectives of this SAM course are to provide information and how to implement the various radiation dose optimization strategies in CT, Fluoroscopy and Radiography. Learning Objectives: To update impact of technological advances on dose optimization in medical imaging. To identify radiation optimization strategies in computed tomography. To describe strategies for configuring fluoroscopic equipment that yields optimal images at reasonable radiation dose. To assess ways to configure digital radiography systems and recommend ways to improve image quality at optimal dose.
60 APPLIED LIFE SCIENCES↗