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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Optimal resource allocation for flexible-grid entanglement distribution networks

We use a genetic algorithm (GA) as a design aid for determining the optimal provisioning of entangled photon spectrum in flex-grid quantum networks with arbitrary numbers of channels and users. After introducing a general model for entanglement distribution based on frequency-polarization hyperentangled biphotons, we derive upper bounds on fidelity and entangled bit rate for networks comprising one-to-one user connections. Simple conditions based on user detector quality and link efficiencies are found that determine whether entanglement is possible. We successfully apply a GA to find optimal resource allocations in four different representative network scenarios and validate features of our model experimentally in a quantum local area network in deployed fiber. Our results show promise for the rapid design of large-scale entanglement distribution networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Density-Functional Tight-Binding for Platinum Clusters and Bulk: Electronic vs Repulsive Parameters

We present a general purpose Pt-Pt density-functional tight-binding (DFTB) parameter for Pt clusters as well as bulk, using a genetic algorithm (GA) to automatize the parameterization effort. First we quantify the improvement possible by only optimizing the repulsive potential alone, and second we investigate the effect of improving the electronic parameter as well. During both parameterization efforts we employed our own training set and test sets, with one set containing ~20,000 spin-polarized DFT structures. We analyze the performance of our two DFTB Pt-Pt parameter sets against density functional theory (DFT) as well as an earlier DFTB Pt-Pt parameters. Our study sheds light on the role of both repulsive and electronic parameters with regards to DFTB performance.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Joint optimal scheduling for electric vehicle battery swapping-charging system based on wind farms

Insufficiencies in charging facilities limit the broad application of electric vehicles (EVs). In addition, EV can hardly represent a green option if its electricity primarily depends on fossil energy. Considering these two problems, this paper studies a battery swapping-charging system based on wind farms (hereinafter referred to as W-BSCS). In a W-BSCS, the wind farms not only supply electricity to the power grid but also cooperate with a centralized charge station (CCS), which can centrally charge EV batteries and then distribute them to multiple battery swapping stations (BSSs). The operational framework of the W-BSCS is analyzed, and some preprocessing technologies are developed to reduce complexity in modeling. Then, a joint optimal scheduling model involving a wind power generation plan, battery swapping demand, battery charging and discharging, and a vehicle routing problem (VRP) is established. Then a heuristic method based on the exhaustive search and the Genetic Algorithm is employed to solve the formulated NP-hard problem. Numerical results verify the effectiveness of the joint optimal scheduling model, and they also show that the W-BSCS has great potential to promote EVs and wind power.

17 WIND ENERGY↗
DOE OSTI2019

Study of Integrable and Quasi-Integrable Sextupole Lattice

In order to maximize beam lifetime in circular particle accelerators, the nonlinear beam optics are optimized to maximize the dynamic aperture of the beam. The dynamic aperture (DA), which is a 6-D phase space volume of stable trajectories, depends on the strength of the nonlinearities in the machine, and is calculated via particle tracking. Current DA optimization processes include multi-objective genetic algorithm optimizers, and relies on minimizing the magnitudes of resonance driving terms (RDT), which are calculated from the nonlinear contribution to the one-turn-map. The process of searching through the parameter space for an ideal combination that maximizes DA is computationally strenuous. By setting up the sextupole channel such that it is resembles a symplectic integrator of a smooth Hamiltonian, with only a few sextupoles we are able to closely reproduce phase space trajectories of a smooth Hamiltonian up to the hyperbolic point. No chaos and resonances are observed if phase advance per one sextupole magnet in the channel does not exceed ~0.12x2 pi. Therefore, an important property of the suggested approach is the intrinsic elimination of the resonances, and minimization of corresponding RDTs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Optimization of a High Bunch Charge ERL Injection Merger for PERLE

Delivery of high charge electron bunches into the main loop of an ERL (energy recovery linac) while preserving the emittance is challenging. This is because at the typical injection momentum, space charge forces still have a significant effect on the beam dynamics. In this work we consider the design of the merger for PERLE, an ERL test facility to be based at IJCLab in France. Previous simulations have shown that the baseline DC gun based injector can achieve the required emittance at the booster linac exit. The quality of the 500 pC bunches must then be preserved with space charge through the merger at total beam energy of 7 MeV keeping the emittance below 6 mm mrad. The beam dynamics in the merger were simulated using the code OPAL and optimised using a genetic algorithm. Three possible merger schemes were investigated. The goal of the optimisation was to minimise the emittance growth while also achieving the required Twiss parameters to match onto the spreader at the main linac exit. A three dipole solution is then examined in more detail.

Hounsell, B.↗
DOE OSTI2017

MULTI-OBJECTIVE ONLINE OPTIMIZATION OF BEAM LIFETIME AT APS

In this paper, online optimization of beam lifetime at the APS (Advanced Photon Source) storage ring is presented. A general genetic algorithm (GA) is developed and employed for some online optimizations in the APS storage ring. Sextupole magnets in 40 sectors of the APS storage ring are employed as variables for the online nonlinear beam dynamics optimization. The algorithm employs several optimization objectives and is designed to run with topup mode or beam current decay mode. Up to 50\% improvement of beam lifetime is demonstrated, without affecting the transverse beam sizes and other relevant parameters. In some cases, the top-up injection efficiency is also improved.

APS↗
DOE OSTI2017

ONLINE MINIMIZATION OF VERTICAL BEAM SIZES AT APS

In this paper, online minimization of vertical beam sizes along the APS (Advanced Photon Source) storage ring is presented. A genetic algorithm (GA) was developed and employed for the online optimization in the APS storage ring. A total of 59 families of skew quadrupole magnets were employed as knobs to adjust the coupling and the vertical dispersion in the APS storage ring. Starting from initially zero current skew quadrupoles, small vertical beam sizes along the APS storage ring were achieved in a short optimization time of one hour. The optimization results from this method are briefly compared with the one from LOCO (Linear Optics from Closed Orbits) response matrix correction.

APS↗
DOE OSTI2017

COMPARISON OF NONLINEAR DYNAMICS OPTIMIZATION METHODS FOR APS-U

Many different objectives and genetic algorithms have been proposed for storage ring nonlinear dynamics performance optimization. These optimization objectives include nonlinear chromaticities and driving/detuning terms, on-momentum and off-momentum dynamic acceptance, chromatic detuning, local momentum acceptance, variation of transverse invariant, Touschek lifetime, etc. In this paper, the effectiveness of several different optimization methods and objectives are compared for the nonlinear beam dynamics optimization of the Advanced Photon Source upgrade (APS-U) lattice. The optimized solutions from these different methods are preliminarily compared in terms of the dynamic acceptance, local momentum acceptance, chromatic detuning, and other performance measures.

APS-U∗↗
DOE OSTI2026

Design update on the transition beamline for the CEBAF Energy Upgrade

For Jefferson Lab’s 22GeV upgrade, two new permanent-magnet Fixed-Field Alternating Gradient (FFA) arcs will be integrated to serve the accelerator’s six highest-energy recirculation passes. Connecting these FFA arcs to the existing linear accelerator (linac) requires a carefully engineered transition section. The current design has two parts where the first part adiabatically matches the beam dispersion and orbit trajectories, while the second part aligns the Twiss parameters (alpha and beta functions) with those at the linac entrance. Given the tight spatial constraints and multiple matching requirements, a genetic algorithm is being explored to optimize the beam optics matching. This paper presents the current progress in developing and optimizing this transition.

Accelerator Physics↗
DOE OSTI2025

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗
DOE OSTI2014

Design Optimization of Structural Health Monitoring Systems

Sensor networks drive decisions. Approach: Design networks to minimize the expected total cost (in a statistical sense, i.e. Bayes Risk) associated with making wrong decisions and with installing maintaining and running the sensor network itself. Search for optimal solutions using Monte-Carlo-Sampling-Adapted Genetic Algorithm. Applications include structural health monitoring and surveillance.

42 ENGINEERING↗
DOE OSTI2015

TAOI B- Computational Microstructural Optimization Design Tool for High Temperature Structural Materials

The objectives of this research were two-fold: (a) develop a methodology for microstructural optimization of alloys - genetic algorithm approach for alloy microstructural optimization using theoretical models based on fundamental micro-mechanisms, and (b) develop a new computationally designed Ni-Cr alloy for coal-fired power plant applications. The broader outcome of these objectives is expected to be creation of an integrated approach for ‘structural materials by microstructural design’. Three alloy systems were considered for computational optimization and validation, (i) Ni-20Cr (wt.%) base alloy using only solid solution strengthening, (ii) nano-Y 2 O 3 containing Ni-20Cr-1.2Y 2 O 3 (wt.%) alloy for dispersion strengthening and (iii) a sub-micron Al 2 O 3 for composite strengthening, Ni-20Cr-1.2Y 2 O 3 -5.0Al 2 O 3 (wt.%). The specimens were synthesized by mechanical alloying and consolidated using spark plasma sintering. Detailed microstructural characterization was done along with initial mechanical properties to validate the computational prediction. A key target property is to have creep rate of 1x10 -9 s -1 at 100 MPa and 800°C. The initial results were quite promising and require additional quantification of strengthening contributions from dislocation-particle attractive interaction and load transfer. The observed creep rate was in order of 10 -9 s -1 for longer time creep test of Ni-20Cr -1.2Y 2 O 3 -5Al 2 O 3 , lending support to the overall approach pursued in this project.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Design and simulation of the nuSTORM facility

This thesis provides a full design and simulation of the nuSTORM beam line facility including the pion beam line , the pion and muon orbit combination section, and the muon decay ring. The study also includes the development of numerical optimization methods and their application to the design. At nuSTORM, one proton batch from the Main Injector (MI) is extracted and transported to the target station to bombard a solid target. A magnetic horn is used as the collection device for the secondary particles such as pions and kaons. A pion beam line is designed to transport the secondary particles downstream to the muon decay ring. In order to avoid the use of a fast kicker with a large aperture for the injection, an Orbit Combination Section (OCS) was designed to combine the reference pion orbit with the circulating muon orbit. This ful lls the stochastic injection scheme proposed in the 1980s. It is shown through the simulation that the pion beam line is able to deliver 0.011 muons per proton on target within the acceptance of the ring. In order to improve the circulating muon ux, an optimization of the magnetic collection horn was studied. A Multi-Objective Genetic Algorithm (MOGA), that was modi ed to integrate Message Passing Interface (MPI) in the process, is applied to the optimization. The number of muons within the ring acceptance from the pion decay is estimated by the pions after the collection horn. This new method was proposed to lower the requirement on the computing resources for the optimization. The study gives a new and better target and horn con guration after a full investigation of the system. The nuSTORM ring is examined with two optics designs. With di erent features, the two designs are optimized via correcting the nonlinearities with sextupolar eld distribution in the ring. A modi ed MPI-based Simulated Annealing (SA) algorithm was applied to obtain the optimal ring acceptance. The comparison with the GA shows good agreements on the achieved optimal ring acceptance. The pion and muon beams at nuSTORM were simulated and yield avor-pure and precisely measurable neutrino beams, which can be used to provide a de nite statement about the existence of a sterile neutrino.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Microgrid Design Toolkit (MDT) Technical Documentation and Component Summaries

The Microgrid Design Toolkit (MDT) is a decision support software tool for microgrid designers to use during the microgrid design process. The models that support the two main capabilities in MDT are described. The first capability, the Microgrid Sizing Capability (MSC), is used to determine the size and composition of a new microgrid in the early stages of the design process. MSC is a mixed-integer linear program that is focused on developing a microgrid that is economically viable when connected to the grid. The second capability is focused on refining a microgrid design for operation in islanded mode. This second capability relies on two models: the Technology Management Optimization (TMO) model and Performance Reliability Model (PRM). TMO uses a genetic algorithm to create and refine a collection of candidate microgrid designs. It uses PRM, a simulation based reliability model, to assess the performance of these designs. TMO produces a collection of microgrid designs that perform well with respect to one or more performance metrics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Operational Excellence through Schedule Optimization and Production Simulation of Application Specific Integrated Circuits.

Upcoming weapon programs require an aggressive increase in Application Specific Integrated Circuit (ASIC) production at Sandia National Laboratories (SNL). SNL has developed unique modeling and optimization tools that have been instrumental in improving ASIC production productivity and efficiency, identifying optimal operational and tactical execution plans under resource constraints, and providing confidence in successful mission execution. With ten products and unprecedented levels of demand, a single set of shared resources, highly variable processes, and the need for external supplier task synchronization, scheduling is an integral part of successful manufacturing. The scheduler uses an iterative multi-objective genetic algorithm and a multi-dimensional performance evaluator. Schedule feasibility is assessed using a discrete event simulation (DES) that incorporates operational uncertainty, variability, and resource availability. The tools provide rapid scenario assessments and responses to variances in the operational environment, and have been used to inform major equipment investments and workforce planning decisions in multiple SNL facilities.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2016

Making the Grid "Smart" Through "Smart" Microgrids: Real-Time Power Management of Microgrids with Multiple Distributed Generation Sources Using Intelligent Control

In this Project we collaborated with two DOE National Laboratories, Pacific Northwest National Lab (PNNL) and Lawrence Berkeley National Lab (LBL). Dr. Hammerstrom of PNNL initially supported our project and was on the graduate committee of one of the Ph.D. students (graduated in 2014) who was supported by this project. He is also a committee member of a current graduate student of the PI who was supported by this project in the last two years (August 2014-July 2016). The graduate student is now supported be the Electrical and Computer Engineering (ECE) Department at Montana State University (MSU). Dr. Chris Marney of LBL provided actual load data, and the software WEBOPT developed at LBL for microgrid (MG) design for our project. NEC-Labs America, a private industry, also supported our project, providing expert support and modest financial support. We also used the software “HOMER,” originally developed at the National Renewable Energy Laboratory (NREL) and the most recent version made available to us by HOMER Energy, Inc., for MG (hybrid energy system) unit sizing. We compared the findings from WebOpt and HOMER and designed appropriately sized hybrid systems for our case studies. The objective of the project was to investigate real-time power management strategies for MGs using intelligent control, considering maximum feasible energy sustainability, reliability and efficiency while, minimizing cost and undesired environmental impact (emissions). Through analytic and simulation studies, we evaluated the suitability of several heuristic and artificial-intelligence (AI)-based optimization techniques that had potential for real-time MG power management, including genetic algorithms (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and multi-agent systems (MAS), which is based on the negotiation of smart software-based agents. We found that PSO and MAS, in particular, distributed MAS, were more efficient and better suited for our work. We investigated the following: • Intelligent load control - demand response (DR) - for frequency stabilization in islanded MGs (partially supported by PNNL). • The impact of high penetration of solar photovoltaic (PV)-generated power at the distribution level (partially supported by PNNL). • The application of AI approaches to renewable (wind, PV) power forecasting (proposed by the reviewers of our proposal). • Application of AI approaches and DR for real-time MG power management (partially supported by NEC Labs-America) • Application of DR in dealing with the variability of wind power • Real-time MG power management using DR and storage (partially supported by NEC Labs-America) • Application of DR in enhancing the performance of load-frequency controller • MAS-based whole-sale and retail power market design for smart grid A

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Advanced Reactors-Intermediate Heat Exchanger (IHX) Coupling: Theoretical Modeling and Experimental Validation

The overall goal of the research project was to model the behavior of the advanced reactorintermediate heat exchange system and to develop advanced control techniques for off-normal conditions. The specific objectives defined for the project were: 1. To develop the steady-state thermal hydraulic design of the intermediate heat exchanger (IHX); 2. To develop mathematical models to describe the advanced nuclear reactor-IHX-chemical process/power generation coupling during normal and off-normal operations, and to simulate models using multiphysics software; 3. To develop control strategies using genetic algorithm or neural network techniques and couple these techniques with the multiphysics software; 4. To validate the models experimentally The project objectives were accomplished by defining and executing four different tasks corresponding to these specific objectives. The first task involved selection of IHX candidates and developing steady state designs for those. The second task involved modeling of the transient and offnormal operation of the reactor-IHX system. The subsequent task dealt with the development of control strategies and involved algorithm development and simulation. The last task involved experimental validation of the thermal hydraulic performances of the two prototype heat exchangers designed and fabricated for the project at steady state and transient conditions to simulate the coupling of the reactor- IHX-process plant system. The experimental work utilized the two test facilities at The Ohio State University (OSU) including one existing High-Temperature Helium Test Facility (HTHF) and the newly developed high-temperature molten salt facility.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Quantum-Accurate Molecular Dynamics Potential for Tungsten

The purpose of this short contribution is to report on the development of a Spectral Neighbor Analysis Potential (SNAP) for tungsten. We have focused on the characterization of elastic and defect properties of the pure material in order to support molecular dynamics simulations of plasma-facing materials in fusion reactors. A parallel genetic algorithm approach was used to efficiently search for fitting parameters optimized against a large number of objective functions. In addition, we have shown that this many-body tungsten potential can be used in conjunction with a simple helium pair potential1 to produce accurate defect formation energies for the W-He binary system.

74 ATOMIC AND MOLECULAR PHYSICS↗