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All sources NASA NTRS DOE OSTI

DOE OSTI2019

Conceptual Analysis of Aviation Infrastructure

In determining optimal transportation methods, the great distances between the European region of Russia and industrial centers must be taken into account. The geographical features and potential growth of Russian infrastructure are analyzed in comparison with other countries. Besides traditional airplanes, more innovative aviation options are considered. More active use of such new technologies is recommended. State support is needed for the development of both technology and ground-transportation infrastructure.

42 ENGINEERING↗

DOE OSTI2016

Electromagnetic analysis of the plasma chamber of an ECR-based charge breeder

The optimization of the efficiency of an ECR-based charge breeder is a twofold task: efforts must be paid to maximize the capture of the injected 1+ ions by the confined plasma and to produce high charge states to allow post-acceleration at high energies. Both tasks must be faced by studying in detail the electrons heating dynamics, influenced by the microwave-to-plasma coupling mechanism. Numerical simulations are a powerful tools for obtaining quantitative information about the wave-to-plasma interaction process: this paper presents a numerical study of the microwaves propagation and absorption inside the plasma chamber of the PHOENIX charge breeder, which the selective production of exotic species project, under construction at Legnaro National Laboratories, will adopt as charge breeder. Calculations were carried out with a commercial 3D FEM solver: first, all the resonant frequencies were determined by considering a simplified plasma chamber; then, the realistic geometry was taken into account, including a cold plasma model of increasing complexity. The results gave important information about the power absorption and losses and will allow the improvement of the plasma model to be used in a refined step of calculation reproducing the breeding process itself.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Island scanning pattern optimization for residual deformation mitigation in laser powder bed fusion via sequential inherent strain method and sensitivity analysis

Laser powder bed fusion (L-PBF) has emerged as one of the mainstream additive manufacturing approaches for fabricating metal parts with complex geometries and intricate internal structures. However, large deformation associated with rapid heating and cooling can lead to build failure and requires post-processing which may increase manufacturing cost and prolong the production period. Here in this work, an island scanning pattern design method is proposed to optimize the scanning direction of each island in order to reduce part deformation after cutting off the build platform. The objective of this optimization is to minimize the upward bending of the part after sectioning, which allows the part deformation to satisfy the tolerance requirement or reduce the post heat treatment time. Inherent strain method is employed in the sequential finite element analysis consisting of layer-by-layer activations and sectioning for fast residual distortion prediction. Full sequential sensitivity analysis for the formulated optimization is provided to update the island scanning directions. To show the feasibility and effectiveness of the proposed method, the scanning patterns of a block structure and a connecting rod were designed and parts were fabricated using an open architecture L-PBF machine. The fabrication experiments demonstrated that the residual deformation of both parts fabricated by optimized scanning pattern can be reduced by over 50% compared to the initial scanning patterns, which demonstrate the effectiveness of the proposed method.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Direction-optimizing Label Propagation Framework for Structure Detection in Graphs: Design, Implementation, and Experimental Analysis

Label Propagation is not only a well-known machine learning algorithm for classification but also an effective method for discovering communities and connected components in networks. We propose a new Direction-optimizing Label Propagation Algorithm (DOLPA) framework that enhances the performance of the standard Label Propagation Algorithm (LPA), increases its scalability, and extends its versatility and application scope. As a central feature, the DOLPA framework relies on the use of frontiers and alternates between label push and label pull operations to attain high performance. It is formulated in such a way that the same basic algorithm can be used for finding communities or connected components in graphs by only changing the objective function used. Additionally, DOLPA has parameters for tuning the processing order of vertices in a graph to reduce the number of edges visited and improve the quality of solution obtained. We present the design and implementation of the enhanced algorithm as well as our shared-memory parallelization of it using OpenMP. We also present an extensive experimental evaluation of our implementations using the LFR benchmark and real-world networks drawn from various domains. Compared with an implementation of LPA for community detection available in a widely used network analysis software, we achieve at most five times the F-Score while maintaining similar runtime for graphs with overlapping communities. We also compare DOLPA against an implementation of the Louvain method for community detection using the same LFR-graphs and show that DOLPA achieves about three times the F-Score at just 10% of the runtime. For connected component decomposition, our algorithm achieves orders of magnitude speedups over the basic LP-based algorithm on large-diameter graphs, up to 13.2× speedup over the Shiloach-Vishkin algorithm, and up to 1.6× speedup over Afforest on an Intel Xeon processor using 40 threads.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Electrically-Assisted Tubing Processes for Enhancing Manufacturability of Oxide Dispersion Strengthened Structural Materials for Nuclear Reactor Applications

The overall goal of this project is to enhance the formability of Nanostructured Ferritic Alloys (NFAs) such that high-strength thin-walled claddings can be successfully and economically manufactured for use in future nuclear reactors. Specifically, four objectives were identified: Objective #1 – to achieve a good understanding of the mechanical behavior and resulting microstructure of 14YWT made from the powder form subjected to various Electrically- Assisted (EA) loading conditions. Objective #2 – to conduct EA-flat rolling of 14YWT completed and the prioritization/selection of two alternative designs of EA tube rolling processes. Objective #3 – to design and assemble a new EA tube rolling machine. Objective #4 – to conduct rolling using the new EA rolling machine. The above objectives were achieved and the findings can be further applied to establish other innovative fabrication processes. Specifically, the major accomplishments are summarized as: The use of an EA forming process was investigated to reduce the forming force of ODS ferritic steels. The effect of a continuous electric current on 14YWT during plastic deformation was evaluated. EA uniaxial tension tests show that the electric current is able to reduce the flow stress of 14YWT. 14YWT exhibits a ductility minimum at a current density of 34.5 A/mm2 which is consistent with fractography results. Hardness measurements and TEM results indicate that oxide particles are stable during the EA process. By following the tests, microstructure analysis was performed on the sample in its as-received state and after current treatment. The microstructures are stable when subjected to high current density. The related thermal model was developed to predict the Joule heating temperatures. The temperatures were compared with the experimental results. At last, a temperature-dependent hardening model was used to explore the mechanisms acting during EA deformation. Since the model can accurately predict the yield strength of 14YWT under EA conditions without the incorporation of non-thermal effects, it can be concluded that the reduction in yield strength is due to the Joule heating generated under continuous current loading conditions. An oven-heated pure thermal tension test was conducted to confirm this conclusion. The new EA tube rolling machine has been completely developed. The related work includes: (1) forming process design; (2) analysis of the effect of roll-tube contact geometry on rolling force; (3) analysis of machine performance under mechanical loading; (4) design of machine electrical setting path and insulation; (5) multi-pass process analysis; (6) design of power transmission system (axial/radial/torque feeding mechanisms), roll bearing system, cooling system, workpiece feeding mechanism, and other related mechanical connection support components; (7) design and optimization of roll profiles/dimensions; (8) analysis and simulation of the rolling process; (9) development of engineering drawings and fabrication; (10) assembly and calibration of the EA tube rolling machine; (11) design and integration of the electrical control system; (12) hardware integration for high power supply electrically-assisted rolling process; (13) induction heating system integration; (14) experimental tests for the new rolling process. Through the process from conceptual design to final machine calibration and experimental tests, there are many technical and physical works that have been implemented and improved for the best performance of the innovative prototype.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Simulation-Based Design and Optimization of Waste Heat Recovery Systems

This fact sheet features an R&D project that will develop a modeling platform that can quantify the value of a district energy system and its potential for waste heat recovery. The new software analysis platform will evaluate and optimize district energy systems to better utilize low-temperature waste heat from nearby commercial and industrial buildings. The platform will help project developers and engineers easily quantify the potential value and cost savings of community energy systems for both producers and consumers of waste heat.

Combined heat and power, CHP, District energy, Was↗

DOE OSTI2017

Development of a Multiphase Adjoint Capability in OpenFOAM

Thermal hydraulic predictions of flow regimes within nuclear reactor cores require significant computational resources and accuracy to ensure that the core design does not violate the thermal limits of materials. Multiphase computational fluid dynamics (CFD) has become a valuable computational capability for predicting flow characteristics as they relate to applications such as nuclear reactor safety and mixing phenomena around spacer grids within fuel assemblies. Multiphase CFD requires closure relationships for both the effects of turbulence as well as the transfer of mass, momentum, and energy between phases. Understanding the sensitivity of the parameters used in closure relationships is important for improving accuracy, but performing rigorous sensitivity analysis is computationally expensive. Adjoint mathematics have been utilized for reactor physics and fluid dynamics applications with respect to verification, sensitivity analysis, adaptive mesh refinement, and optimization. The usage of adjoint methods to predict the sensitivities of a variety of closure relationship parameters within a given problem has been shown to return sensitivity responses with significantly fewer computations than normal 'large-scale' methods. As the complexity of multiphase CFD methods increases, the demand for accurate verification tools and improvements in computational efficiency also increases, making the development of adjoint capabilities an attractive option. Closure relationships that describe interphase momentum physics in multiphase CFD modelling rely heavily on prescribed constants determined from experimental data. Due to the computational efficiency of an adjoint approach, the time to derive these closure relationships could be dramatically reduced, and data assimilation techniques could be used to return probability density functions of closure models derived using experimental data. This work develops a multiphase adjoint capability for the Eulerian-Eulerian two field, two phase equations. Specifically, the adjoint capability has been developed for the sub-cooled nucleate boiling solver boilEulerFoam developed by Dr. Nam Dinh of NC State University within the open-source CFD library OpenFOAM.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2015

In-line chemical sensor deployment in a tritium plant

The Savannah River Tritium Plant (TP) relies on well understood but aging sensor technology for process gas analysis. The use of alternative sensing and detection technologies for in-line and real-time analysis would aid process control and optimization. The TP upgrading follows a 2-phase projects. In the first phase, TP sensing requirements were determined by a team of process experts. Meanwhile, Savannah River National Laboratory sensor experts identified candidate technologies and related them to the TP processing requirements. The resulting road-map links the candidate technologies to actual plant needs. In the second phase an instrument demonstration station was established within a TP glove box in order to provide accurate assessments of how a candidate sensor technology would perform in a contaminated process environment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Distribution Grid Impacts of Community Solar [Slides]

Community solar (CS) projects often face uncertain interconnection costs and fees associated with distribution grid infrastructure upgrades required to connect the project. These costs can determine the economic viability of a CS project, but they are difficult to assess. Cost uncertainty can discourage new projects and prevent communities from accessing the benefits of community solar projects. At the same time, CS deployment strategies hold potential to defer or avoid some distribution costs due to new loads. To mitigate CS interconnection costs, it is important to find least-cost combinations of distribution system infrastructure solutions (transformer upgrades, reconductoring, voltage regulators, storage), and to understand how location of CS projects within a feeder impact distribution grid upgrade costs. This study aims to quantify CS impacts on the distribution grid and provide policy and regulatory insights and CS deployment strategies to address them. It is the first analysis that has systematically studied the technical impacts of community solar projects on a wide range of distribution feeders using state-of-the-art optimization and power flow tools. The analysis employs Berkeley Lab’s novel Least-cost Optimal Distribution Grid Expansion (LODGE) model, a deterministic version of the REPAIR model, that optimally upgrades hundreds or even thousands of distribution circuits or feeders. This is the first application of the LODGE model. LODGE finds the least-cost portfolio of traditional distribution system upgrades to integrate CS in combination with alternative solutions, such as utility-owned storage and downsizing CS capacity. Working with a set of least-cost solutions per feeder allows us to benchmark, compare and find techno-economic trends in CS interconnection.

14 SOLAR ENERGY↗

DOE OSTI2014

ARRAY OPTIMIZATION FOR TIDAL ENERGY EXTRACTION IN A TIDAL CHANNEL – A NUMERICAL MODELING ANALYSIS

This paper presents an application of a hydrodynamic model to simulate tidal energy extraction in a tidal dominated estuary in the Pacific Northwest coast. A series of numerical experiments were carried out to simulate tidal energy extraction with different turbine array configurations, including location, spacing and array size. Preliminary model results suggest that array optimization for tidal energy extraction in a real-world site is a very complex process that requires consideration of multiple factors. Numerical models can be used effectively to assist turbine siting and array arrangement in a tidal turbine farm for tidal energy extraction.

Yang, Zhaoqing↗

DOE OSTI2019

PAO

PAO supports the formulation and analysis of mathematical models for complex optimization applications. This capability is commonly associated with algebraic modeling languages (AMLs), which support the description and analysis of mathematical models with a high-level language. PAO is derived from the Pyomo subpackage pyomo.bilevel, which has been deprecated. PAO supports the formulation and solution of optimization models that explicitly represent adversarial behavior. This includes variances of bilevel programming, including methods that represent challenging problems with discrete decisions and uncertain model parameters.

Hart, William↗

DOE OSTI2023

Scenario optimization for the tokamak ramp-down phase in RAPTOR: Part A. Analysis and model validation on ASDEX Upgrade

We discuss how the combination of experimental observations and rapid modeling has enabled to improve understanding of the tokamak ramp-down phase in ASDEX Upgrade. A series of dedicated experiments has been performed, to disentangle the effect of individual actuators like plasma current, auxiliary heating and plasma shaping. Optimized discharge termination strategies with increased margin with respect to radiative and vertical stability limits are proposed and tested in experiment. Radiative collapse of the edge $T$ $e$ profile after the HL back-transition is avoided by initially maintaining auxiliary heating during L-mode, showing beneficial effects even after the auxiliary heating is turned off. The capability of the RAPTOR code to model the time evolution of the internal inductance $\ell$ $i3$ has been validated, including the effect of a change in the $I$ $p$ ramp-down rate and the HL transition timing. The reduction of $\ell$ $i3$ caused by rapid compression of the plasma cross-section has been quantitatively recovered in simulations. Successful modeling of the $\ell$ $i3$ time evolution is essential to optimize ramp-down scenarios for future fusion reactors, for which vertical stability and power balance control will be more challenging.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Optimization Simulations for a Gamma-Ray Calibration Standard for a Cyclic Neutron Activation Analysis Pneumatic System at the Penn State Breazeale Reactor

For new experimental setups, the initial testing and calibrations can be expensive and time consuming without prior optimization. However, these issues can be mitigated using realistic modeling and simulation studies of the proposed system a priori. Specifically, an experiment can be performed virtually using realistic simulations and the expected results evaluated to inform adjustments of the experimental setup to achieve optimum results. At the Penn State Breazeale Reactor, a new pneumatic transfer system has been developed for the detection and characterization of short-lived fission fragments to enhance and complement existing nuclear data. The system accomplishes this task by transporting samples cyclically between an assortment of gamma-ray/neutron detectors and the reactor-core vicinity with sub-second transit times. The measured neutron flux paired with simulations of detector response using Geant4 and a custom module for estimating cascade summing effects and corrections were used to select sample-material masses for system characterization. With the optimized sample masses, an experimental counting plan for characterizing the repeatability of the newly developed pneumatic system was developed. Finally, the predictions made with these simulations allowed for optimization of the irradiation sample characteristics and gamma-ray detection system.

cyclic neutron activation, gamma-ray spectroscopy↗

DOE OSTI2023

Distributed Rules-Based Deconfliction of ADMS Applications: Part 1: Requirements & Decomposition

This work introduces a rules-based deconfliction methodology for resolving conflicting device control commands issued by advanced power applications considering a range of technical, economic, environmental, and social objectives. The methodology is designed to serve as one of multiple alternative implementations (along with application cooperation and global optimization) for the numerical component of the Deconfliction Pipeline. Development of the methodology is divided into two parts. This first document introduces the requirements, context, and methods for decomposing the deconfliction problem using the Laminar Coordination Framework and Variable Grid Structures. The deconfliction problem is decomposed into a distributed optimization problem based on the concept of quasi-static grid segments, which form independent distributed areas for control and coordination. Selection of the optimal number of decompositions of the deconfliction problem should be made based on a tradeoff analysis between computational speed and global optimality. This second document will define an initial set of technical, economic, and environmental criteria, as well as thirty specific qualitative rules that are used as part of the deconfliction methodology to eliminate non-viable setpoint alternatives. The deconfliction optimization problem is converted into a ranking of individual discrete setpoints, which are scored by the extent to which they satisfy specific decision criteria. The ranking is determined through the concepts of deconfliction exclusivity, priority, and preference. Several multi-criteria decision-making frameworks are examined with the simple multi-attribute rating technique exploiting ranks (SMARTER) recommended as a simple implementation alternative that aligns with the steps of the rules-based deconfliction methodology.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Thermodynamic analysis of a MEMS based differential scanning calorimeter model

Thin film based differential scanning calorimeter (DSC) was developed and utilized for the study of phase-transition kinetics in protein samples. In this paper, we proposed a MEMS based DSC model. The distance between the sample and reference cell was optimized based on the thermal analysis result. The influence of the response time to the shape of the DSC thermograms and the transition temperature was also studied systematically. Meanwhile, Lumry-Eyring model was used to investigate the relationship between the scanning rate and the transition temperature. The modeling result was verified by the lysozyme denaturation tests. This study provided a pathway for the optimization of MEMS based DSC array and the correction of the DSC thermograms.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Cost Reduction of School Bus Fleet Electrification With Optimized Charging and Distributed Energy Resources: Preprint

Considerations for electrifying school buses are presented with an analysis of battery sizing to match bus driving requirements. The charging and vehicle-to-building dispatch of the electric school buses were optimized to evaluate the potential to reduce the impact of the bus charging on the school's electric utility bill. Distributed energy resources (DER) and flexible building loads were also considered with the school bus electrification to evaluate the further reduction in energy costs with enhanced system integration and optimized dispatch. The effect of degradation on the school bus batteries was analyzed to determine if the smart charging and vehicle-to-building battery operation decreases the life of the battery. The results show that there is an opportunity to mitigate the increase of electric utility bills with improved charging controls and bi-directionally operating the school bus batteries to reduce building demand charges. The battery degradation analysis using dispatch with optimized charging and discharging shows acceptable battery life.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Gradient-based Optimization for Regression in the Functional Tensor-Train Format

We consider the task of low-multilinear-rank functional regression, i.e., learning a low-rank parametric representation of functions from scattered real-valued data. Our first contribution is the development and analysis of gradient-based optimization procedures, including stochastic gradient descent and quasi-Newton methods, for learning the parameters of a functional tensor-train (FT) that yields improved accuracy over standard alternating least squares methods. The functional tensor-train uses the tensor-train (TT) representation of low-rank arrays as an ansatz for a class of low-multilinear-rank functions. The FT is represented by a set of matrix-valued functions that contain a set of univariate functions, and the regression task is to learn the parameters of these univariate functions. Our second contribution demonstrates that using nonlinearly parameterized univariate functions, e.g., symmetric kernels with moving centers, within each core can outperform the standard approach of using a linear expansion of basis functions. Our final contributions are new rank adaptation and group-sparsity regularization procedures to minimize overfitting. We use several benchmark problems to demonstrate at least an order of magnitude lower accuracy with gradient-based optimization methods than ALS in the low-sample number regime. We also demonstrate an order of magnitude reduction in accuracy on a test problem resulting from nonlinear parameterizations over linear parameterizations. Finally we compare regression performance with 22 other nonparametric and parametric regression methods on 10 real-world data sets. We achieve top-five accuracy for seven of the data sets and best accuracy for two of the data sets. These rankings are the best amongst parametric models and competetive with the best non-parametric methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Optimization of design parameters for bulk micromachined silicon membranes for piezoresistive pressure sensing application

Finite element analysis study has been carried out to optimize the design parameters for bulk micro-machined silicon membranes for piezoresistive pressure sensing applications. The design is targeted for measurement of pressure up to 200 bar for nuclear reactor applications. The mechanical behavior of bulk micro-machined silicon membranes in terms of deflection and stress generation has been simulated. Based on the simulation results, optimization of the membrane design parameters in terms of length, width and thickness has been carried out. Subsequent to optimization of membrane geometrical parameters, the dimensions and location of the high stress concentration region for implantation of piezoresistors have been obtained for sensing of pressure using piezoresistive sensing technique.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗