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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Experimental Investigations for Optimizing the Extrusion Parameters on FDM PLA Printed Parts

Fused deposition modeling (FDM) has become one of the most extensively used additive manufacturing technologies in recent years because of its wide adaptability, simple mechanism and low cost. It is difficult, however, to achieve an equitable trade-off among mechanical properties, surface finish quality and production time, which is an area seldom explored. This paper concentrates on the optimization of the parameters to achieve higher tensile strength and lower surface roughness with less build time during the FDM process based on central composite design for the tensile specimen forming process. The effects of five extrusion parameters (nozzle diameter, liquefier temperature, extrusion velocity, filling velocity and layer thickness) on the three outputs of tensile strength (TS), surface roughness (SR) and build time (BT) are investigated. Response surface methodology combined with nondominated sorting genetic algorithm II is developed to optimize the process parameters to achieve the maximum TS, minimum SR and BT, as verified by subsequent experiments. The predicted results are found to be very close to the experimental data, illustrating that the presented approach in this paper is effective for improving mechanical properties, surface finish and efficiency of the FDM process.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Calibrated Blade-Element/Momentum Theory Aerodynamic Model of the MARIN Stock Wind Turbine: Preprint

In this paper, a calibrated blade-element/momentum theory aerodynamic model of the MARIN stock wind turbine is developed and documented. The model is created using open-source software and calibrated to closely emulate experimental data obtained by the DeepCwind Consortium using a genetic algorithm optimization routine. The provided model will be useful for those interested in validating interested in validating floating wind turbine numerical simulators that rely on experiments utilizing the MARIN stock wind turbine—for example, the International Energy Agency Wind Task 30’s Offshore Code Comparison Collaboration Continued, with Correlation project.

17 WIND ENERGY↗
DOE OSTI2021

EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics

Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Optimization of an individual re-identification modeling process using biometric features

We present results from the optimization of a re-identification process using two sets of biometric data obtained from the Civilian American and European Surface Anthropometry Resource Project (CAESAR) database. The datasets contain real measurements of features for 2378 individuals in a standing (43 features) and seated (16 features) position. A genetic algorithm (GA) was used to search a large combinatorial space where different features are available between the probe (seated) and gallery (standing) datasets. Results show that optimized model predictions obtained using less than half of the 43 gallery features and data from roughly 16% of the individuals available produce better re-identification rates than two other approaches that use all the information available.

CAESAR↗
DOE OSTI2020

A novel active optimization approach for rapid and efficient design space exploration using ensemble machine learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (pGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.

Machine learning, active learning, design optimiza↗
DOE OSTI2024

Application of Wireless Charging at Seaports for Range Extension of Drayage Battery Electric Trucks

Even though heavy-duty battery electric trucks (BETs) have become commercially available, their range limitation still hinders widespread adoption. Drayage has been regarded as a suitable application for early BETs due to typically having limited daily mileage. However, drayage operation can vary widely and some form of range extension may still be needed for BETs operating in this application. Here, in this paper, wireless charging at port terminals is proposed for this purpose. Potential wireless charging zones at port terminals are identified, and efficacy of wireless charging to extend BET range in drayage operation is verified by simulating the activity of 20 BETs from a drayage operator serving the ports of Los Angeles and Long Beach, using a microscopic BET energy consumption model. Furthermore, an optimization problem is formulated for optimal wireless charging zone planning from the port authority's perspective, considering subsets of the identified zones, and charging power options to choose from, for different budget ranges. In this context, zone planning means determining which areas of the port terminals should be selected for installing wireless charging systems, and what level of charging power should be for each selected zone's system. For each budget range, the optimization problem is solved using genetic algorithm to determine an optimal zone plan that provides the maximum amount of energy through wireless charging per unit cost of installation. The results show that wireless charging can aid improving activity completion of the simulated fleet by 5%, and further optimizing the zone plan can achieve similar performance with lower cost.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Data-Driven Mathematical Modeling of the Effect of Particle Size Distribution on the Transitory Reaction Kinetics of Hot Metal Desulfurization

The aim of this work was to develop a prediction model for hot metal desulfurization. More specifically, the study aimed at finding a set of explanatory variables that are mandatory in prediction of the kinetics of the lime-based transitory desulfurization reaction and evolution of the sulfur content in the hot metal. The prediction models were built through multivariable analysis of process data and phenomena-based simulations. The model parameters for the suggested model types are identified by solving multivariable least-squares cost functions with suitable solution strategies. One conclusion we arrived at was that in order to accurately predict the rate of desulfurization, it is necessary to know the particle size distribution of the desulfurization reagent. It was also observed that a genetic algorithm can be successfully applied in numerical parameter identification of the proposed model type. It was found that even a very simplistic parameterized expression for the first-order rate constant provides more accurate prediction for the end content of sulfur compared to more complex models, if the data set applied for the modeling contains the adequate information.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Hidden Moving Target Defense against False Data Injection in Distribution Network Reconfiguration

This paper introduces Moving Target Defense (MTD) in distribution system against False Data Injection (FDI) attacks on the supervisory control and data acquisition (SCADA) system. Based on the AC power flow model, a hidden MTD (HMTD) strategy is constructed in combination with network reconfiguration by minimizing the system loss and line power flow differences before and after the HMTD. The proposed HMTD-based network reconfiguration is formulated as a mixed-integer nonlinear programming (MINLP) problem. A refined Genetic Algorithm (GA) is proposed to solve it. Numerical test is conducted in a modified IEEE-66 bus system. The simulation results show that the proposed model reduces the power loss introduced by the HMTD as well as yields a stealthy MTD to the attackers. The impact of HMTD on the system performance is also compared with that of the existing MTD strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Bayesian inference for high confidence signal validation and sensor calibration assessment - 328

Online Monitoring (OLM) systems has been investigated for applications in the nuclear industry for the past two decades. These systems offer economic and technological advantages over the currently implemented time based testing methods for sensors and associated instrumentations. The advantages offered not only augment economic viability of current nuclear reactors but will be an inevitable change called for to suit the technologies of the advanced reactors (Gen III+). Irrespective, the acceptance of OLM into the regulatory structure in the U.S. is still hindered by technical and structural clarity on uncertainty quantification used by these systems. A Bayesian inference technique is implemented in the form of a residual model to quantify uncertainty in OLM predictions. The developed system used Gaussian processes to represent the OLM predictions and model inadequacy, the stationary representation of model based uncertainty. In addition, model selection techniques are used in conjunction with numerical methods such as Genetic Algorithm (GA) and Gibb's sampler to add robustness to the estimation model inadequacy, particularly in the presence of drifts and faulted conditions. The prediction model developed herein find applicability in fault detection, obtaining real values of processes after the onset of faults etc. Further development looks into the extension of current method for a multiple output scale implementation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Broadband mid-IR difference-frequency generation in 5 mol % MgO-doped periodically and aperiodically poled lithium niobate

We have theoretically investigated broadband mid-infrared (mid-IR) difference-frequency generation (DFG) in 5 mol % MgO-doped periodically poled lithium niobate (5 mol % MgO : PPLN) based on quasi-phase-matching (QPM) with group-velocity matching (GVM) between the pump and the idler for the eee interaction of QPM. The acceptance bandwidths are 282.3 nm and 35.4 nm around 3.4 μm with the signal and pump wavelengths fixed at 1.550 μm and 1.064 μm, and the required QPM periods are 30.26 μm and 30.25 μm, respectively. A method which can broaden the flattop QPM DFG acceptance bandwidth (ABW) around the GVM wavelength for the idler wavelength in 5 mol % MgO-doped aperiodically poled lithium niobate (5 mol % MgO : APPLN) is proposed. The structure of 5 mol % MgO : APPLN is optimised using a genetic algorithm by adjusting its parameters. The simulation results show that under the GVM conditions, the maximum ABW for the idler is 1045.9 nm and 96.6 nm at a fixed signal wavelength of 1.550 μm and at a fixed pump wavelength of 1.064 μm, respectively. The tradeoff between the reduced effective nonlinear coefficient and DFG ABW is discussed. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Gains in operational flexibility, safety margins, and cost efficiencies via integrated Plant Reload Optimization platform

The U.S. Department of Energy Light Water Reactor Sustainability Program Risk-Informed Systems Analysis Pathway Plant Reload Optimization Project aims to develop an integrated, comprehensive framework offering an all-in-one solution for reload evaluations with a special focus on optimizing core design. Optimizing the fuel loading pattern is one of the most important considerations in reducing the amount of new fuel used in the core. Due to thousands of possible core configuration options, finding optimal solutions is an unachievable task for a human. The Plant ReLoad Optimization platform, which supports artificial-intelligence-based reactor core designing, is now fully capable of handling realistic problems. The Plant ReLoad Optimization platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses and uses artificial intelligence to support the optimization of core design solutions. The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimizer was developed and tested within RAVEN (Risk Analysis and Virtual ENvironment) to handle many constraints by using an augmented objectives methodology. The demonstration was performed with constrained multiobjective optimization of a 17 × 17 pressurized-water reactor core loading patterns to minimize fuel cost and maximize fuel cycle length.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Targeted evolution of pinning landscapes for large superconducting critical currents

The ability of type II superconductors to carry large amounts of current at high magnetic fields is a key requirement for future design innovations in high-field magnets for accelerators and compact fusion reactors, and largely depends on the vortex pinning landscape comprised of material defects. The complex interaction of vortices with defects that can be grown chemically, e.g., self-assembled nanoparticles and nanorods, or introduced by postsynthesis particle irradiation precludes a priori prediction of the critical current and can result in highly nontrivial effects on the critical current. Here, we borrow concepts from biological evolution to create a vortex pinning genome based on a genetic algorithm, naturally evolving the pinning landscape to accommodate vortex pinning and determine the best possible configuration of inclusions for two different scenarios: a natural evolution process initiating from a pristine system and one starting with preexisting defects to demonstrate the potential for a postprocessing approach to enhance critical currents. Furthermore, the presented approach is even more general and can be adapted to address various other targeted material optimization problems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

On the Interpretation and Correlation of High-Temperature Ignition Delays in Reactors with Varying Thermodynamic Conditions

Ignition delay time (IDT) is a useful global metric for fuel performance screening and a major target for kinetic modeling. Measurements of IDT are conceptually straightforward, however interpretation could be complicated, especially for systems with changing temperature and pressure. Some experimental conditions in the high repetition rate miniature shock tube (HRRST) exhibit complex temperature and pressure state histories. To better interpret and correlate IDTs, especially those obtained in reactors with varying thermodynamic conditions, an inverse Livengood-Wu (L-W) integral technique is applied to de-convolve the constant condition IDTs from measured IDTs in the HRRST using information on the varying state history. In this paper, the approximate problem is demonstrated using only the measured pressure history leading up to ignition, where the temperature history is estimated based on an isentropic assumption. The IDTs of several fuels were first measured in the HRRST including isooctane and acetone to represent those fuels without strong low temperature chemistry effects. Based on the measured pressure and approximated temperature history, measurements of IDTs in the HRRST compare very favorably with those measured using more conventional techniques, including conventionally sized shock tubes, via the inverse L-W correlation with relaxed Newton iteration and Genetic Algorithm (GA). Furthermore, this study demonstrates the feasibility of using a high throughput ignition testing facility, like the HRRST to extract the constant state IDT measured in regular shock tubes, assisted by the inverse L-W correlation. It is expected with an independent temperature measurement available in the future, IDTs in a broader range of thermodynamic conditions and effects of heat loss could be better resolved.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Evolutionary multi-objective optimization and Pareto-frontal uncertainty quantification of interatomic forcefields for thermal conductivity simulations

Predictive Molecular Dynamics simulations of thermal transport require forcefields that can simultaneously reproduce several structural, thermodynamic and vibrational properties of materials like lattice constants, phonon density of states, and specific heat. This requires a multi-objective optimization approach for forcefield parameterization. Existing methodologies for forcefield parameterization use ad-hoc and empirical weighting schemes to convert this into a single-objective optimization problem. Here, we provide and describe software to perform multi-objective optimization of Stillinger–Weber forcefields (SWFF) for two-dimensional layered materials using the recently developed 3rd generation non-dominated sorting genetic algorithm (NSGA-III). NSGA-III converges to the set of optimal forcefields lying on the Pareto front in the multi-dimensional objective space. This set of forcefields is used for uncertainty quantification of computed thermal conductivity due to variability in the forcefield parameters. We demonstrate this new optimization scheme by constructing a SWFF for a representative two-dimensional material, 2H-MoSe 2 and quantifying the uncertainty in their computed thermal conductivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Breeding Realistic D‐Brane Models

Abstract Intersecting branes provide a useful mechanism to construct particle physics models from string theory with a wide variety of desirable characteristics. The landscape of such models can be enormous, and navigating towards regions which are most phenomenologically interesting is potentially challenging. Machine learning techniques can be used to efficiently construct large numbers of consistent and phenomenologically desirable models. In this work we phrase the problem of finding consistent intersecting D‐brane models in terms of genetic algorithms, which mimic natural selection to evolve a population collectively towards optimal solutions. For a four‐dimensional supersymmetric type IIA orientifold with intersecting D6‐branes, we demonstrate that unique, fully consistent models can be easily constructed, and, by a judicious choice of search environment and hyper‐parameters, of the found models contain the desired Standard Model gauge group factor. Having a sizable sample allows us to draw some preliminary landscape statistics of intersecting brane models both with and without the restriction of having the Standard Model gauge factor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Estimation of LOCA break size using cascaded support vector regression - 334

A failure of emergency core cooling system (ECCS) during a certain small break loss of coolant accident (SBLOCA) can cause a severe core uncovery and fuel failure. The safety injection systems (SISs) might not function properly in case of a SBLOCA, due to the slight change of pressure in the pipes. Early trend of the major parameters has to be observed and the accurate information has to be given to the nuclear power plant (NPP) operators by precisely identifying the break size to effectively manage accidents. In this study, the objective is to provide to operators the information on the break size in a short time by considering the accident situations such as hot-leg LOCA, cold-leg LOCA, steam generator tube rupture (SGTR). A cascaded support vector regression (CSVR) is used in order to estimate the break size. The simulation data set was obtained from the optimized power plant reactor 1000 (OPR1000) using modular accident analysis program (MAAP) code. And genetic algorithm (GA) was used to select the input variables of the CSVR model and optimize its parameters. As a result of this study, the CSVR model estimate very well the break size of LOCAs. If the operators can predict the break size in the LOCA, they can response quickly and properly to LOCA circumstances to prevent the core uncovery and fuel failure. Also, it will be possible to more efficiently manage beyond design basis accidents. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Phase-Only Beam Broadening of Contiguous Uniform Subarrayed Arrays

In modern antenna systems, beam broadening of subarrayed arrays provides continuous coverage of a wide angular extent in a cost-effective manner. While many methods have been published that address beam broadening of traditional (non-subarrayed) arrays, there is a knowledge gap in the published literature with respect to efficient beam broadening of contiguous uniform subarrayed arrays. This article presents efficient methods for beam broadening of contiguous uniform subarrayed arrays where the excitation of each element is not individually controlled, but the elements of the array are grouped together as subarrays to have the same element excitations. Particularly, this article focuses on phase-only optimization to preserve maximum power output. Additionally, three modilied iterative Fourier transform (IFT) methods and one genetic algorithm (GA) are presented to efliciently search the vast solution space of possible phase settings for a solution that satislies the desired broadened pattern. These methods are evaluated on idealized 1 x 40 and 1 x 80 linear arrays with live element subarrays and 40 x 40 and 80 x 80 element rectangular arrays with 5 x 5 element subarrays. The proposed modilied IFT methods are found to be faster than the GA approach, while the GA approach only offers a few percentage points of better effectiveness.

42 ENGINEERING↗
DOE OSTI2020

Local Phasor-Based Control of DER Inverters for Voltage Regulation on Distribution Feeders

We introduce a new control paradigm termed Phasor-Based Control (PBC) to coordinate Distributed Energy Resources (DER) for improved voltage regulation and other objectives. PBC is a multi-layer control framework where the state of the distribution grid is represented by voltage phasors, and Phasor Measurement Units (PMUs) measure the state at critical points in the distribution grid. In the PBC paradigm, a supervisory controller (S-PBC) sets phasor targets which local controllers track. In this paper, we propose a Proportional Integral (PI) implementation for the local control layer (LPBC), to regulate real and reactive power output of distributed inverters to track a voltage phasor target computed by the supervisory layer. We tune the PI controller gains offline with a genetic algorithm that yields better performance than the Ziegler-Nichols method. We benchmark the proposed L-PBC controller against droop volt-var control (DVVC) and observe improved voltage regulation in simulation on the IEEE 13 node unbalanced test feeder (IEEE 13NF). Furthermore, by means of numerical simulation we observe the response of the L-PBC controllers to small and large grid disturbances. Simulations on larger feeders, IEEE 123-node and 647-node, demonstrate challenges with tuning controllers in setups with different amounts of power-voltage coupling.

42 ENGINEERING↗