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Results for “parameter optimization”
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Optimal Estimation of Snow and Ice Surface Parameters from Imaging Spectroscopy Measurements
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Optimal Estimation of Snow and Ice Surface Parameters from Imaging Spectroscopy Measurements
No abstract provided
High-performance plasmas after pellet injections in Wendelstein 7-X
A significant improvement of plasma parameters in the optimized stellarator W7-X is found after injections of frozen hydrogen pellets. The ion temperature in the post-pellet phase exceeds 3 keV with 5 MW of electron heating and the global energy confinement time surpasses the empirical ISS04-scaling. The plasma parameters realized in such experiments are significantly above those in comparable gas-fuelled discharges. Here, we present details of these pellet experiments and discuss the main plasma properties during the enhanced confinement phases. Local power balance is applied to show that the heat transport in post-pellet phases is close to the neoclassical level for the ion channel and is about a factor of two above that level for the combined losses. In comparable gas-fuelled discharges, the heat transport is by about ten times larger than the neoclassical level, and thus is largely anomalous. It is further observed that the improvement in the transport is related to the peaked density profiles that lead to a stabilization of the ion-scale turbulence.
Optimal Sizing of PV and Energy Storage in an Electric Vehicle Extreme Fast Charging Station
This paper proposes an optimization model for the optimal sizing of photovoltaic (PV) and energy storage in an electric vehicle extreme fast charging station considering the coordinated charging strategy of the electric vehicles. The proposed model minimizes the annualized cost of the extreme fast charging station, including investment and maintenance cost of PV and energy storage, cost of purchasing energy from utility and demand charge. The decision variables are capacity of invested PV and the power and energy ratings of invested energy storage. To further reduce the annualized cost of the extreme fast charging station, the charging strategy of electric vehicles are integrated into the optimization model and coordinated with the power output of PV and charging/discharging of energy storage. Results of numerical simulations indicate that investment of PV and energy storage could help reduce the annualized cost of the extreme fast charging station significantly. Meanwhile, the impacts of various parameters on the optimal solution are investigated by sensitivity analysis.
A combined experimental and numerical approach that eliminates the non-uniqueness associated with the Johnson-Cook parameters obtained using inverse methods
Abstract Johnson-Cook constitutive model is a commonly used material model for machining simulations. The model includes five parameters that capture the initial yield stress, strain-hardening, strain-rate hardening, and thermal softening behavior of the material. These parameters are difficult to determine using experiments since the conditions observed during machining (such as high strain-rates of the order of $$10^5$$ 10 5 /sec - $$10^6$$ 10 6 /sec) are challenging to recreate in the laboratory. To address this problem, several researchers have recently proposed inverse approaches where a combination of experiments and analytical models are used to predict the Johnson-Cook parameters. The errors between the measured cutting forces, chip thicknesses and temperatures and those predicted by analytical models are minimized and the parameters are determined. In this work, it is shown that only two of the five Johnson-Cook parameters can be determined uniquely using inverse approaches. Two different algorithms, namely, Adaptive Memory Programming for Global Optimization (AMPGO) and Particle Swarm Optimization (PSO), are used for this purpose. The extended Oxley’s model is used as the analytical tool for optimization. For determining a parameter’s value, a large range for each parameter is provided as an input to the algorithms. The algorithms converge to several different sets of values for the five Johnson-Cook parameters when all the five parameters are considered as unknown in the optimization algorithm. All of these sets, however, yield the same chip shape and cutting forces in FEM simulations. Further analyses show that only the strain-rate and thermal softening parameters can be determined uniquely and the three parameters present in the strain-hardening term of the Johnson-Cook model cannot be determined uniquely using the inverse method. A combined experimental and numerical approach is proposed to eliminate this determine all parameters uniquely.
Processing behavior evolution of recycled polypropylene: An integrated experimental and Computer-Aided engineering simulation study
Polypropylene (PP) comprises 21% of global plastics production and 18% of plastics waste, yet less than 1% of solid-waste PP is recycled in the United States (U.S.), representing significant environmental and economic challenges. Mechanical recycling, the most prevalent recycling method, subject's materials to thermomechanical stresses, which typically degrade polymer properties, affecting the quality of polymer products. This study replicates the impact of mechanical recycling through multiple extrusion cycles to examine the effects on PP's processing behavior. Dynamic scanning calorimetry (DSC) measurements showed stable melting behavior across all processing conditions, while crystallization analysis exhibited consistent shifts in kinetic parameters. Rheological characterization demonstrated progressive viscosity reductions through successive cycles, particularly pronounced at elevated reprocessing temperatures. Here, the integration of this experimental data into injection molding simulations showed that recycled PP maintains viable processing characteristics. Our findings establish quantitative correlations between processing history and material behavior, enabling optimization of processing parameters directly rather than relying on trial-and-error approaches. While these results reflect idealized recycling conditions with minimal contamination, they provide a framework for understanding fundamental property evolution during mechanical recycling.
Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence
Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.
Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing
Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.
Stepwise Dynamic Calibration of a Hydromechanical Simulation Using Time-Lapse Vertical Seismic Profile
This study aims to develop a methodology for calibrating subsurface stress changes through time-lapse Vertical Seismic Profiling (VSP) integration. The selected study site is the 13-10A injector well within the ongoing CO2-EOR operation of the Farnsworth Field Unit. The Time-lapse VSP dataset carries the combined effects of fluid substitution and mean effective stress changes, thereby providing a dataset amenable for the calibration of production and injection-induced stress changes. The concept is similar to calibrating a reservoir simulation model in that the process honor real field data to set up an inverse problem. The solution optimizes the independent and impactful geomechanical parameters that replicate the observed time-lapse seismic velocity changes. This stress calibration is enabled by 4D geomechanical modeling and the VSP Integration workflow. This calibration benefits from extensive geological, geophysical and geomechanical characterization through 3D seismic data, geophysical well logs, and core assessed as part of the 1D MEM conducted on the 13-10A subject well. These data are used to develop a site-specific rock physics model. The Biot Gassmann workflow combines rock physics and reservoir simulation outputs to determine the fluid substitution contribution to seismic velocity change. Additionally, modeled seismic velocity attributed to mean effective stress are determined from the geomechanical simulation outputs, and the stress-velocity relationship developed from the ultrasonic seismic velocity measurements on the extracted Morrow B core. A penalty function is then formed between the modeled seismic velocities and the observed time-lapse VSP dataset. Four independent and impactful geomechanical parameters have been determined. These are the bulk modulus and shear modulus for zero porosity and the shear and compressional seismic velocity to mean effective stress derivatives. The dataset of numerous coupled hydromechanical- geomechanical simulation realizations is built by combining variations of the four stated geomechanical parameters. A machine learning-assisted workflow comprised of an artificial neural network and a particle swarm optimizer are used to converge on the optimal geomechanical parameters. The successful execution of this workflow has affirmed the suitability of acoustic time-lapse measurements for 4D-VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling.
Grid and aerodynamic sensitivity analyses of airplane components
An algorithm is developed to obtain the grid sensitivity with respect to design parameters for aerodynamic optimization. The procedure is advocating a novel (geometrical) parameterization using spline functions such as NURBS (Non-Uniform Rational B-Splines) for defining the wing-section geometry. An interactive algebraic grid generation technique, known as Two-Boundary Grid Generation (TBGG) is employed to generate C-type grids around wing-sections. The grid sensitivity of the domain with respect to geometric design parameters has been obtained by direct differentiation of the grid equations. A hybrid approach is proposed for more geometrically complex configurations such as a wing or fuselage. The aerodynamic sensitivity coefficients are obtained by direct differentiation of the compressible two-dimensional thin-layer Navier-Stokes equations. An optimization package has been introduced into the algorithm in order to optimize the wing-section surface. Results demonstrate a substantially improved design due to maximized lift/drag ratio of the wing-section.
Effect of emitters on quantum state transfer in coupled cavity arrays
Over the last decade, conditions for perfect state transfer in quantum spin chains have been discovered and their experimental realizations addressed, as have their extensions to more complex geometries of coupled cavity-emitter arrays. In this paper we further consider such studies and situations in which quantum state transfer can occur with high fidelity, even when the cavity-cavity coupling rates and cavity-emitter interaction rates are comparable. This is accomplished through the development and use of a Monte Carlo approach to the inverse eigenvalue problem, which allows the determination of coupling rates which optimize quantum state transfer fidelity and subsequent time evolution of the polariton wave function through exact diagonalization of the resulting Jaynes-Cummings-Hubbard Hamiltonian. The effect of inhomogeneous emitter locations is also evaluated. Furthermore, our key results include the demonstration that our methodology can be used successfully to establish Hamiltonian parameters for high-fidelity state transfer in more general lattice geometries and excitation number sectors, and also a determination of the effects of fluctuations in those parameters about their optimal values.
Improved silicon nitride for advanced heat engines
The results of a four year program to improve the strength and reliability of injection-molded silicon nitride are summarized. Statistically designed processing experiments were performed to identify and optimize critical processing parameters and compositions. Process improvements were monitored by strength testing at room and elevated temperatures, and microstructural characterization by optical, scanning electron microscopes, and scanning transmission electron microscope. Processing modifications resulted in a 20 percent strength and 72 percent Weibull slope improvement of the baseline material. Additional sintering aids screening and optimization experiments succeeded in developing a new composition (GN-10) capable of 581.2 MPa at 1399 C. A SiC whisker toughened composite using this material as a matrix achieved a room temperature toughness of 6.9 MPa m(exp .5) by the Chevron notched bar technique. Exploratory experiments were conducted on injection molding of turbocharger rotors.
Optimization of multiple target electric propulsion trajectories.
Development of the mathematical basis for the generation of multiple target low-thrust trajectories. The unique trajectory shaping capabilities of electric propulsion make it ideally suited for modifying the path of the spacecraft to effect close encounters of selected asteroids with only slight penalties in performance. Each intermediate encounter with an asteroid involves the optimization of four parameters, the encounter time and the three components of the relative velocity at encounter. A second type of mission amenable to this analysis is that of asteroid or comet sample return missions. For missions of this type, both launch date and division of departure and return flight times are capable of being optimized. Use is made of the traditional calculus of variations to derive the necessary conditions that must be satisfied for both types of missions. In addition, examples are presented of both a Comet Encke rendezvous mission with multiple encounters of the asteroids Aschera and Alekto and also a sample return mission from the asteroid Flora.
Increased Agrobacterium-mediated transformation and rooting efficiencies in canola (Brassica napus L.) from hypocotyl segment explants
An efficient protocol for the production of transgenic Brassica napus cv. Westar plants was developed by optimizing two important parameters: preconditioning time and co-cultivation time. Agrobacterium tumefaciens-mediated transformation was performed using hypocotyls as explant tissue. Two variants of a green fluorescent protein (GFP)-encoding gene--mGFP5-ER and eGFP--both under the constitutive expression of the cauliflower mosaic virus 35S promoter, were used for the experiments. Optimizing the preconditioning time to 72 h and co-cultivation time with Agrobacterium to 48 h provided the increase in the transformation efficiency from a baseline of 4% to 25%. With mGFP5-ER, the transformation rate was 17% and with eGFP it was 25%. Transgenic shoots were selected on 200 mg/l kanamycin. Rooting efficiency was 100% on half-strength Murashige and Skoog medium with 10 g/l sucrose and 0.5 mg/l indole butyric acid in the presence of kanamycin.
Optimization Strategies for Single-Stage, Multi-Stage and Continuous ADRs
Adiabatic Demagnetization Refrigerators (ADR) have many advantages that are prompting a resurgence in their use in spaceflight and laboratory applications. They are solid-state coolers capable of very high efficiency and very wide operating range. However, their low energy storage density translates to larger mass for a given cooling capacity than is possible with other refrigeration techniques. The interplay between refrigerant mass and other parameters such as magnetic field and heat transfer points in multi-stage ADRs gives rise to a wide parameter space for optimization. This paper first presents optimization strategies for single ADR stages, focusing primarily on obtaining the largest cooling capacity per stage mass, then discusses the optimization of multi-stage and continuous ADRs in the context of the coordinated heat transfer that must occur between stages. The goal for the latter is usually to obtain the largest cooling power per mass or volume, but there can also be many secondary objectives, such as limiting instantaneous heat rejection rates and producing intermediate temperatures for cooling of other instrument components.
Improved Productivity with Multilaser Rotary Powder Bed Fusion Additive Manufacturing
Laser powder bed fusion (LPBF) enables the fabrication of intricate, geometrically complex structures with a sufficiently fine surface finish for many engineering applications with a diversity of available feedstock metals. However, the production rate of LPBF systems is not well suited for mass production in comparison to traditional manufacturing methods. LPBF systems measure their deposition rates in 100's of grams per hour, while other processes measure in kilograms per hour or even in the case of processes such as forming, stamping, and casting, 100's of kilograms per hour. To be widely adopted in industry for mass production, LPBF requires a new scalable architecture that enables many orders of magnitude improvement in deposition rate, while maintaining the geometry freedom of additive manufacturing. This article explores concepts that could achieve as much as four orders of magnitude increase in the production rate through the application of (1) rotary table kinematic arrangements; (2) a dramatic number of simultaneously operating lasers; (3) reductions of laser optic size; (4) improved scanning techniques; and (5) an optimization of toroidal build plate size. To theoretically demonstrate the possibilities of production improvements, a productivity analysis is proposed for synchronous reluctance motors with relevance to the electric vehicle industry, given the recent increase in the diversity of printable soft magnetic alloys. The analysis provides insights into the impact of the architecture and process parameters necessary to optimize rotary powder bed fusion for mass production.
Techniques for designing rotorcraft control systems
Over the last two and a half years we have been demonstrating a new methodology for the design of rotorcraft flight control systems (FCS) to meet handling qualities requirements. This method is based on multicriterion optimization as implemented in the optimization package CONSOL-OPTCAD (C-O). This package has been developed at the Institute for Systems Research (ISR) at the University of Maryland at College Park. This design methodology has been applied to the design of a FCS for the UH-60A helicopter in hover having the ADOCS control structure. The controller parameters have been optimized to meet the ADS-33C specifications. Furthermore, using this approach, an optimal (minimum control energy) controller has been obtained and trade-off studies have been performed.