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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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A genetic algorithm approach is used to fit orbital interaction energies of sp3s* tight-binding models for the nine binary compound semiconductors consistent of Ga, Al, In and As, P, Sb at room temperature.
The multiplexing capabilities of superconducting X-ray free-electron lasers (FELs) have gained much attention in recent years. The demanding requirements for photon properties from multiple undulator lines necessitate more flexible beam manipulation techniques to achieve the goal of “beam on demand”. In this paper, we investigate a multiplexed configuration for the photoinjector of high-repetition-rate FELs that aims to simultaneously provide low-emittance electron beams of different charges. A parallel, multi-objective genetic algorithm is implemented for the photoinjector parameter optimization. The proposed configuration could drastically enhance the flexibility of beam manipulation to improve multiplexing capabilities and realize the full potential of the facility.
Active magnetic regenerators (AMR) have the potential for high efficiency cryogen liquefaction. One active magnetic regenerative liquefier (AMRL) configuration consists of dual magnetocaloric regenerators that reciprocate in a persistent-mode superconducting solenoid. Issues with this configuration are the spatial and temporal magnetization gradients that induce large magnetic forces and winding currents. To solve the coupled problem, we present a force minimization approach using passive magnetic material to balance a dual-regenerator AMR. A magnetostatic model is developed and simulated force waveforms are compared with experimental measurements. A genetic algorithm identifies force-minimizing passive structures with virtually ideal balancing characteristics. Finally, implementation details are investigated which affirm the potential of the proposed methodology.
A current-source inverter (CSI) has the natural capability of boosting the output voltage which is a notable advantage over voltage source inverter (VSI) in traction drive applications. This paper investigates the voltage boosting feature of the CSI to improve the overall efficiency of the CSI-based surface permanent magnet (SPM) machine drive system in the constant power region. The effects of the boost function on the total system losses, including the machine copper loss, core loss, and magnet loss, as well as the device conduction and switching loss in the CSI and dc/dc converter, are described using analytical models. The operating characteristics of the machine and CSI are validated by 2-D finite-element analysis (FEA) and simulations. Based on the loss model, the effects of the modulation index on the overall drive system losses and power factor have been analyzed. A genetic algorithm has been used to optimize the CSI's boost ratio, demonstrating that the drive system efficiency can be increased by 1% to 2.5% along the constant-power regime envelope by using the boost function.
Recovering ammonia from anaerobic digestion (AD) effluent offers the opportunity to produce a renewablefertilizer and reduce environmental impact. A novel process developed for such an application was evaluatedduring this study. The feature of the process is integrating biogas stripping and gypsum (CaSO 4 •2H 2 O) absorption. The result revealed that the lower CO 2 content and the absorption temperature contributed the higherefficiency of ammonia recovery. The gypsum solution recovered around 95% of the stripped ammonia when thestripping gas contained 10% CO 2 and it rose to 100% when the gas contained more than 30% CO 2 . Furthermore,a statistical model and genetic algorithm were applied to estimate the total ammonia recovery and optimize theoperating parameters. The experimental and modeling results indicated that integrating biogas stripping withgypsum absorption can effectively harvest ammonia nitrogen and reduce CO 2 emissions while producing(NH 4 ) 2 SO 4 fertilizer from AD effluent.
Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distil empirically. Here, we report an algorithm—the quantum model learning agent (QMLA)—to reverse engineer Hamiltonian descriptions of a target system. We test the performance of QMLA on a number of simulated experiments, demonstrating several mechanisms for the design of candidate Hamiltonian models and simultaneously entertaining numerous hypotheses about the nature of the physical interactions governing the system under study. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information, and control of the experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard families of models in parallel, reliably identifying the family which best describes the system dynamics. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models. The selection of models whose features propagate to the next generation is based upon an objective function inspired by the Elo rating scheme, typically used to rate competitors in games such as chess and football. In all instances, our protocol finds models that exhibit F 1 score ≥ 0.88 when compared with the true model, and it precisely identifies the true model in 72% of cases, whilst exploring a space of over 250 000 potential models. By testing which interactions actually occur in the target system, QMLA is a viable tool for both the exploration of fundamental physics and the characterisation and calibration of quantum devices.
The simultaneous optimization of multiple objective functions is needed in many particle accelerator applications. In this paper, we present a parallel evolution based multi-objective optimizer that uses a variable population from generation to generation and an external storage to save good solutions. Two heuristic optimization methods, one uses the unified differential evolution and the other uses the real-coded genetic algorithm, are included in the optimizer to generate next generation candidate solutions, and are compared in the test examples. Finally, as an application, we applied this optimizer to the beam dynamics design optimization of a photoinjector and attained the optimal front solutions after 200 generations with the unified differential evolution offspring production scheme.
Interior permanent magnet synchronous motors (IPMSMs) have been widely used due to their high efficiency and high power density. Minimization of torque pulsation resulting in vibration and acoustic noise is one of the important design considerations for IPMSMs. In this paper, a grid on/off search method for the rotor profile is proposed to mitigate torque pulsation. Selection of the rotor profile is due to the fact that air-gap is the most sensitive parameter in electric machines wherein changes in flux densities can cause substantial differences in the distribution of forces. A layer comprised of 20 partitions with a 0.1 mm thickness and 3 degrees wide grids have been introduced to the rotor surface for each pole and the possible geometries have been analyzed using finite element method (FEM) in ANSYS Maxwell. An optimal design was found that has the lowest torque ripple with a higher average torque compared to the original design. Genetic algorithm (GA) has also been applied to the method to automate the coupling between Maxwell and Matlab thereby saving the simulation time. Finally, complete structural analysis has been done for both of the original and optimal designs to verify the superiority and feasibility of the proposed design.
Optimizing traffic control systems at traffic intersections can reduce network-wide fuel consumption as well as improve traffic flow. While traffic signals have conventionally been controlled based on predetermined schedules, various adaptive control systems have been developed recently using advanced sensors such as cameras, radars, and LiDARs. By utilizing rich traffic information enabled by the advanced sensors, more efficient or optimal traffic signal control is possible in response to varying traffic conditions. This paper proposes an optimal traffic signal control method to minimize network-wide fuel consumption utilizing real-time traffic information provided by advanced sensors. This new method employs a priority metric calculated by a weighted sum of various factors, including the total number of vehicles, total vehicle speed, vehicle waiting time, and road preference. Genetic Algorithm (GA) is used as a global optimization method to determine the optimal weights in the priority metric. In order to evaluate the effectiveness of the proposed method, a traffic simulation model is developed in a high-fidelity traffic simulation environment called SUMO, based on a real-world traffic network. The traffic flow within this model is simulated using actual measured traffic data from the traffic network, enabling a comprehensive assessment of the novel optimal traffic signal control method in realistic conditions. The simulation results show that the proposed priority metric-based real-time traffic signal control algorithm can significantly reduce network-wide fuel consumption compared to the conventional fixed-time control and coordinated actuated control methods that are currently used in the modeled network. Additionally, incorporating truck priority in the priority metric leads to further improvements in fuel consumption reduction.
Supply chain management is monitoring of activities involved in supply chain, for integrating and coordinating between suppliers, manufacturing, inventory and transportation both within and among members. The ultimate aim of supply chains is to reduce costs and increase market coverage. Procuring and purchasing requested items in a timely manner are the two most important issues for supply chain stockholders. Group purchasing is one of the purchasing strategies in supply chains. It offers great potential by ordering large volumes to decrease expenses which can increase services to customers. A clustering optimization approach is employed to model group purchasing for a set of pharmacies in the field of healthcare. The proposed model determines a cooperation strategy based on factors such as distance between pharmacies and procurement expenditure in this network. Moreover, the proposed multi-objective group purchasing model is optimized using both goal programming and non-dominated sorting genetic algorithm. To illustrate the application of the proposed model, designing a purchasing group organization for Chalus city pharmacies is investigated. Purchasing groups are established in the way that sum of pharmacies ordering quantity has been located in the second or the third level of discount rate. Thus, the results show that GPOs can take the advantage of this cooperation.
To make full use of the photocathode material and improve its quantum efficiency lifetime, it can be necessary to operate the laser away from the cathode center in photoinjectors. In RF guns, the off-axis emitted beam will see a time-dependent RF effect, which would generate a significant growth in transverse emittance. It has been demonstrated that such an emittance growth can be almost completely compensated by orienting the beam on a proper orbit in the downstream RF cavities along the injector [1]. In this paper we analyze in detail the simulation techniques used in reference [1] and the issues associated with them. The optimization of photoinjector systems involving off-axis beams is a challenging problem. To solve this problem, one needs advanced simulation tools including both genetic algorithms and an efficient algorithm for 3D space charge. In this paper, we report on simulation studies where the two codes ASTRA and IMPACT-T are used jointly to overcome these challenges, in order to optimize a system designed to compensate for the emittance growth in a beam emitted off axis.
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.
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.
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.
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.
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.
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.
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.