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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Current-Voltage Analysis Tool for Solar Fuel Production (CATS) v0.1

The program is expected to be a useful tool during operation of a range of solar-driven electrochemical devices, such as PV-electrolyzers and photoelectrochemical (PEC) devices. It makes use of current-voltage data collected during operation, operation parameters that are easily accessible for many devices of interest. The program captures the time-dependence of loss mechanisms at play during PEC device operation and opens the door for real-time optimizations of operational parameters like the feed humidity to minimize performance losses. Currently, the program is useful for applications where the current-voltage polarization response of the photoabsorber and electrolyzer are pre-recorded, and changes over time can be estimated based on the pre-recorded current-voltage scans. More complicated scenarios that involve permanent degradation of the PV component or complex calculations of the electrolyzer current-voltage characteristics may be included in future versions of this software. Aside from the possibility of deconvoluting loss mechanisms, this software can also reveal possible performance benefits by convective PV cooling when the photoabsorber is integrated into the electrolysis cell. The latter can highlight why certain device architectures may be beneficial over others. No similar technologies are known to the developer.

Kistler, Tobias↗
DOE OSTI2023

One System, Many Models: Designing a Surrogate Model for Sulfur Thermal Energy Storage: Preprint

Industrial process heating (IPH) relies primarily on thermal energy generated by fossil fuel combustion to produce, treat, and alter manufactured goods. Thermal energy storage (TES) helps reduce the carbon footprint of IPH systems by facilitating the utilization of renewable and waste heat sources. A promising new TES technology uses elemental sulfur as the heat-storage medium. The design of sulfur TES systems can be evaluated with the aid of computational fluid dynamics (CFD). However, the computational cost of such CFD efforts is prohibitive to comprehensive optimizations over design parameters. To reduce this computational cost, machine learning (ML) models can be developed to act as surrogates for CFD. In this paper, we describe the process of building and evaluating surrogate ML models for facilitating optimization of sulfur TES systems for IPH. To enforce the thermodynamic relationship between the two modeled quantities, we develop a hybrid model for sulfur temperature using both direct predictions of temperature and calculations of temperature from predictions of the heat transfer coefficient. The hybrid model enforces this constraint at the expense of the slightly reduced accuracy compared to two disjoint models. The overall high accuracy observed in our model evaluation demonstrates the usefulness of such surrogate modeling for studying TES systems. This work contributes to the field of TES surrogate modeling by offering a novel accurate hybrid approach to predicting simultaneously the heat transfer coefficient and temperature of the heat storage medium.

computational fluid dynamics↗
DOE OSTI2024

Autonomous Output‐Oriented Aerosol Jet Printing Enabled by Hybrid Machine Learning

Additive manufacturing (AM) is rapidly revolutionizing modern manufacturing with recent progress in advanced printing methods and improved properties of printed materials. However, traditional AM methods are limited by their input‐oriented nature, which demands tedious trial‐and‐error tuning of printing parameters to achieve desired output properties. Here, in this work, an output‐oriented artificial intelligence‐integrated AM (AIAM) method is reported that enables an user to specify desired output properties while the printer autonomously discovers the optimal input printing parameters by integrating hybrid machine learning models and in situ measurements. Based on a predictive mapping between the input printing parameters and the output properties of interests established with <20 experiments designed by active learning, inverse design tasks are performed to intelligently generate the printing parameter settings that lead to desired outcomes using reinforcement learning. This method is demonstrated by autonomous aerosol jet printing (AJP) of conductive polymer films and achieving user‐defined electrical resistances with an ultralow error of 3.7%. The AIAM method, with its output‐oriented nature, holds the potential to significantly improve the autonomy, predictability, efficiency, and accessibility of the AM processes, which will unlock new possibilities in the autonomous and intelligent printing of a broad range of functional materials and devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A numerical approach for detecting switch-like bistability in mass action chemical reaction networks with conservation laws

Theoretical analysis of signaling pathways can provide a substantial amount of insight into their function. One particular area of research considers signaling pathways capable of assuming two or more stable states given the same amount of signaling ligand. This phenomenon of bistability can give rise to switch-like behavior, a mechanism that governs cellular decision making. Investigation of whether or not a signaling pathway can confer bistability and switch-like behavior, without knowledge of specific kinetic rate constant values, is a mathematically challenging problem. Recently a technique based on optimization has been introduced, which is capable of finding example parameter values that confer switch-like behavior for a given pathway. Although this approach has made it possible to analyze moderately sized pathways, it is limited to reaction networks that presume a uniterminal structure. It is this limited structure we address by developing a general technique that applies to any mass action reaction network with conservation laws. In this paper we developed a generalized method for detecting switch-like bistable behavior in any mass action reaction network with conservation laws. The method involves (1) construction of a constrained optimization problem using the determinant of the Jacobian of the underlying rate equations, (2) minimization of the objective function to search for conditions resulting in a zero eigenvalue, (3) computation of a confidence level that describes if the global minimum has been found and (4) evaluation of optimization values, using either numerical continuation or directly simulating the ODE system, to verify that a bistability region exists. The generalized method has been tested on three motifs known to be capable of bistability. We have developed a variation of an optimization-based method for the discovery of bistability, which is not limited to uniterminal chemical reaction networks. Successful completion of the method provides an S-shaped bifurcation diagram, which indicates that the network acts as a bistable switch for the given optimization parameters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Novel and effective surface passivation for high efficiency n- and p-type Silicon solar cell

The project objective was to develop a novel Si surface passivation method using chalcogens, sulfur (S) and/or selenium (Se), as passivating elements, to withstand industry-standard high temperature contacting and metallization schemes for p-type Si based passivated emitter and rear contact (p-PERC) solar cells. The back surface passivation of PERC cells has been improved drastically with the invention and successful application of an Al 2 O 3 passivation layer. However, the front n + diffused junction surface is still poorly passivated by the standard amorphous silicon nitride (SiNx) anti-reflection coating (ARC) layer. This project sought to address the passivation challenges of both front n+ emitter and undiffused p-Si back surface. Improved p-PERC solar cell performance with open circuit voltage (V OC ) > 680 mV and efficiency of 22% were targeted to validate superior defect passivation properties as compared to standard SiO 2 / Al 2 O 3 passivation. During this project, we systematically investigated process-structure-properties-performance relationships of this novel advanced defect passivation approach. The S/Se passivation was carried out by reacting industrial Czochralski (Cz) Si wafers in H 2 S and H 2 Se gases in a chemical vapor deposition (CVD) reactor at temperatures up to 700°C. After an exhaustive optimization of the process parameters (temperature, time, and gas concentration), we established an optimized process and demonstrated extremely low surface recombination velocities (SRVs) of 1.5 cm/s and 8 cm/s on n-type and p-type Si, respectively, by S-passivation. In-depth surface and interface characterization were performed using soft x-ray and photoelectron spectroscopies (XPS, UPS, XES), combined with capacitance-voltage-frequency (C-V-f) measurements, to decipher the surface chemical/electronic structure and interface defect state densities. These measurements provided critical understanding of the defect passivation mechanism and elucidated the presence of surface S-Si bonds, a reduction of surface dipoles, and low interface state densities (D it ) < 10 11 cm -2 ev -1 . We also found that the Se-passivation is inferior to the S-passivation (by at least one order of magnitude in SRV). Application of the optimized S-passivation to the n+ diffused emitter surface led to a low surface recombination current density, J0 ≈ 40 fA/cm 2 (~ 1/4 of the industry-standard SiNx-passivation), and high implied V OC (686 mV) in p-PERC solar cell structures. The S-passivation process also was found to improve the bulk quality of the p-type Si, better than the SiO 2 or Al 2 O 3 passivation processes. After successful demonstration of efficient passivation of Si surface defects by S, we extensively studied the air, thermal, and illumination stability of the passivation structure. S-passivation itself degrades in air due to competing reactions with moisture and oxygen to form oxides, which can be eliminated by a SiNx capping layer (also acting as a anti-reflective coating). After SiNx process optimization, we demonstrated illumination and thermally stable S-passivation with SRV < 5 cm/s and J 0 < 80 fA/cm 2 . These enhancements in Si passivation, incorporated into p-PERC cells, achieved an efficiency of 19.93% with V OC = 649 mV, using manufacturing metallization and contacting schemes. The low cell performance (cell V OC is much less than the implied V OC = 686 mV, anticipated from surface passivation) was identified due to degradation of S-passivation during the metal firing step (out-diffusion of S from the Si interface to the SiNx surface). The S-passivation of Si surfaces shows significant promise with excellent passivation quality, essential for high performance (high V OC , high efficiency) solar cells. Integration of this innovative defect passivation into devices, however, demands further development of the capping layer, low temperature (<700°C) metallization process, and/or engineering of advanced device structures. Surface passivation-dominated advanced Si solar cells, such as tunnel oxide passivated contacts and Si heterojunctions, are increasingly of interest due to their high-performance potential and will have a growing photovoltaic market share in the near future.

14 SOLAR ENERGY↗
DOE OSTI2022

Optimum Injector Parameters for Thermoacoustic Stability in a Multi-Nozzle Can Combustion System

Abstract High-frequency transverse instabilities are an important concern in can combustor configurations. In these configurations which are typically operated with multiple injectors around a central injector, each injector is subjected to different parts of the acoustic mode shape and thus respond differently for the same instability mode. Recent work by the author has modeled the response of premixed flames to excitation by natural high-frequency transverse modes in a can combustor both in the center and outer nozzles. The stability of these acoustically non-compact flames was assessed using the Rayleigh criterion (Rayleigh Integral denoted as RI) and not the overall unsteady heat release as is the case for compact flames. Several key control parameters were studied, namely — flame angle, swirling strength, nozzle location. For non-axisymmetric modes such as the commonly occurring 1-T mode, both radial and azimuthal offsets of the nozzle location affected stability. The framework was applied to an optimization study to identify the optimal combination of parameters that minimizes RI for the different nozzles in the multi-nozzle system. In this study, a N-around-1 configuration was studied, and the results indicated that the different nozzles needed to be operated at different flame angles and swirl numbers to result in an overall minimum RI. However, the specific response of the different injectors was not considered. The helical mode distribution at each injector varies as we azimuthally go around the combustor’s injector distribution and thus the most amplified mode and the resulting flame response would be different. To minimize RI, it is important to determine the injector configurations that result in a hydrodynamic profile that minimizes the individual RI for each nozzle. The resulting relationship between the injector’s flow and local hydrodynamics can then be used in a hydrodynamics study of an individual injector so that the most optimal injector is chosen depending on its location in the combustor dump plane.

Acharya, Vishal↗
DOE OSTI2022

Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process

Abstract Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces to discover optimal experimental conditions and regions of interest in the image space or parameter space of computational models. The direct grid search of the parameter space tends to be extremely time-consuming, leading to the development of strategies balancing exploration of unknown parameter spaces and exploitation towards required performance metrics. However, classical Bayesian optimization (BO) strategies based on the Gaussian process (GP) do not readily allow for the incorporation of the known physical behaviors or past knowledge. Here we explore a hybrid optimization/exploration algorithm created by augmenting the standard GP with a structured probabilistic model of the expected system’s behavior. This approach balances the flexibility of the non-parametric GP approach with a rigid structure of physical knowledge encoded into the parametric model. The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of a standard univariate test function used to evaluate optimization algorithms and further extended to physical lattice models. This methodology is expected to be universally suitable for injecting prior knowledge in the form of physical models and past data in the BO framework.

42 ENGINEERING↗
DOE OSTI2024

Laser Powder Bed Fusion of Steels for Nuclear Applications

This report provides an update on laser powder bed fusion of steels for nuclear applications. This report is a Milestone 3 deliverable in FY2024, under work package CT-24AN130401 to support research and qualification activities supported by the Advanced Materials and Manufacturing Technologies (AMMT) program here at Argonne National Laboratory (ANL). Continuing from FY23, the focus of FY24 for ANL includes further optimization of process parameters for three alloys: A709, G91, G92. The work package also includes fabricating test samples to conduct a thorough microstructural analysis and perform preliminary mechanical testing. For the same, we have used CALPHAD-based thermodynamic kinetic modeling to optimize the experimental activities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Diffusion, atomic transport, and ordering in Al-Zr alloys: FCC and liquid phases

Additive manufacturing of materials with controlled microstructure demands knowledge of atomic scale properties near the solid-liquid transition state. Many of these properties are not affordable by experimental techniques and computer modeling is the possible solution to the problem. In this paper, we present the results of an extended atomistic study of intrinsic atomic transport due to vacancy diffusion in FCC and L12 solid phases and diffusion in the liquid phase of Al-Zr alloys. A deceleration of the overall self-diffusion was observed when Zr was added to Al. The effect was stronger in the solid and weaker in the liquid. Additionally, the effect was strongly temperature dependent in the solid phases, but not in the liquid. Atomic transport was chemically biased: transport of Zr atoms was significantly slower than that of Al atoms, and this bias effect was stronger in the solid phases. The overall diffusion and chemical ordering processes in the liquid state were five to six orders in magnitude faster than in the solid. Chemical short-range order parameters in the liquid saturated at values close to those in the ordered L12 structure of Al3Zr. Chemical and structural ordering in the solid phases was negligible over the modeled microsecond time scale. Here, the results are discussed in view of optimizing additive manufacturing parameters for the controlled formation of metastable L1 2 precipitates.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Entanglement estimation in non-optimal qubit states

Highlights: • Quantum Fisher Information. • Entanglement Estimation. • Squeezed States. • Husimi Function. - Abstract: In the last years, a relationship has been established between the quantum Fisher information (QFI) and quantum entanglement. In the case of two-qubit systems, all pure entangled states can be made useful for sub-shot-noise interferometry while their QFI meets a necessary and sufficient condition (Hyllus et al., 2010). In M-qubit systems, the QFI provides just a sufficient condition in the task of detecting the degree of entanglement of a generic state (Pezzé and Smerzi, 2009). In our work, we show analytically that, for a large class of one-parameter non-optimal two-qubit states, the maximally entangled states are associated with stationary points of the QFI, as a function of such parameter. We show, via numerical simulations, that this scenario is maintained for the generalisation of this class of states to a generic M-qubit system. Furthermore, we suggest a scheme for an interferometer able to detect the entanglement in a large class of two-spin states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

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.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

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.

Liu, Guodong↗
DOE OSTI2022

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.

42 ENGINEERING↗
DOE OSTI2025

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.

42 ENGINEERING↗
DOE OSTI2025

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.

36 MATERIALS SCIENCE↗
DOE OSTI2022

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.

14 SOLAR ENERGY↗
DOE OSTI2021

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.

02 PETROLEUM↗
DOE OSTI2022

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.

77 NANOSCIENCE AND NANOTECHNOLOGY↗