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Optimization of the application of electroacoustic coatings is considered, taking account of their structure, properties, and environmental impact.
To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.
Nuclear reactor fuel performance analysis studies the thermo-mechanical behavior of fuel rods and verify their compliance with safety criteria under both normal operation and accidental conditions. One of the major concerns in fuel performance analysis is the behavior of the fission gases xenon and krypton in uranium dioxide fuel, which significantly affect the thermo-mechanical performance of the nuclear fuel rods employed in current LWRs. Previously, the modeling of fission gas release (FGR) in nuclear fuel performance codes have been relying on empirical models which are not applicable beyond their range of calibration. Recently a more efficient and flexible physics-based FGR and swelling model that can describe a wider range of reactor operation conditions have been implemented in fuel performance code BISON. In this work, we performed Bayesian calibration to inversely quantify the uncertainties associated with the 5 tuning parameters in BISON FGR model, based on the time-dependent FGR measurement data from Riso-AN3 benchmark. It takes around 1 hour and 40 minutes for BISON to simulate the Riso-AN3 benchmark with a moderate mesh size using 32 processors. As hundreds of thousands of BISON simulations are needed during Markov Chain Monte Carlo (MCMC) sampling, running BISON directly is not a viable solution. Therefore we propose to use Kriging-based metamodels to replace BISON during MCMC sampling. Surrogate model is an approximation of the input/output relation of a computer code/model. It is also called metamodel, response surface, emulator. Surrogate models usually take much less computational time than the original model (like BISON) while maintaining the input/output relation of the original model to a desirable accuracy. Constructing the metamodels normally requires small number of executions of the original model. Once validated, metamodels can be used to perform uncertainty and sensitivity analysis, Bayesian calibration, optimization, etc.
Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material behavior is known and reliable, this often requires search over multidimensional function space to identify low-dimensional manifold corresponding to the required Pareto front. In this work, we introduce the multi-objective Bayesian optimization (MOBO) workflow for the ferroelectric/antiferroelectric performance optimization for memory and energy storage applications based on the numerical solution of the Ginzburg–Landau equation with electrochemical or semiconducting boundary conditions. MOBO is a low computational cost optimization tool for expensive multi-objective functions, where we update posterior surrogate Gaussian process models from prior evaluations and then select future evaluations from maximizing an acquisition function. Using the parameters for a prototype bulk antiferroelectric (PbZrO 3 ), we first develop a physics-driven decision tree of target functions from the loop structures. We further develop a physics-driven MOBO architecture to explore multidimensional parameter space and build Pareto-frontiers by maximizing two target functions jointly—energy storage and loss. This approach allows for rapid initial materials and device parameter selection for a given application and can be further expanded toward the active experiment setting. The associated notebooks provide both the tutorial on MOBO and allow us to reproduce the reported analyses and apply them to other systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).
This article provides a detailed design guide, optimization, and performance assessment for air–water separation of an axial flow cyclone. Axial flow cyclones (also known as swirl tube demisters, mist eliminators, or Austin–Write cyclones) have a range of applications in several different industries. This method of gas–liquid separation offers many benefits. Among these are high liquid separation efficiencies (near 99%) and an inline design that allows them to be more easily fitted into existing piping structures. Despite these benefits, there are several design parameters that have not been optimized for performance in wastewater purification applications. This research fills the gap in the literature by quantifying the effect of new design parameters on water collection efficiency, η water collection , and the air bypass efficiency, η air bypass , defined as the ratio of the air mass flowrate exiting through the desired air outlet over the inlet air mass flowrate. A set of wide-ranging experiments were conducted to study the effects of gas–liquid flow rates, tube geometry, and relative injection angles to optimize the water collection and air bypass efficiencies. The water collection efficiency exceeded 99.8% when the liquid streamline came in direct contact with the water drainage exit. An empirical correlation was developed to predict the swirl pitch as a function of the above design parameters. Predictions from the correlation were within 10% of the experimental results. The correlation can be used to design highly efficient in-line gas–liquid separators.
Pump and Treat systems are being utilized to remediate contaminated groundwater in the Hanford 100 Areas adjacent to the Columbia River in Eastern Washington. Design of the systems was supported by a three-dimensional (3D) fate and transport model. This model provided sophisticated simulation capabilities but requires many hours to calculate results for each simulation considered. Many simulations are required to optimize system performance, so a two-dimensional (2D) model was created to reduce run time. The 2D model was developed as a equivalent-property version of the 3D model that derives boundary conditions and aquifer properties from the 3D model. It produces predictions that are very close to the 3D model predictions, allowing it to be used for comparative remedy analyses. Any potential system modifications identified by using the 2D version are verified for use by running the 3D model to confirm performance. The 2D model was incorporated into a comprehensive analysis system (the Pumping Optimization Model, POM) to simplify analysis of multiple simulations. It allows rapid turnaround by utilizing a graphical user interface that: 1 allows operators to create hypothetical scenarios for system operation, 2 feeds the input to the 2D fate and transport model, and 3 displays the scenario results to evaluate performance improvement. All of the above is accomplished within the user interface. Complex analyses can be completed within a few hours and multiple simulations can be compared side-by-side. The POM utilizes standard office computing equipment and established groundwater modeling software. (authors)
We propose a generalized framework which performs an optimal partitioning of a limited budget into various organizational sectors in order to improve the cybersecurity of a smart device or component in the Cyber Physical Energy System (CPS). The framework identifies the adversarial threats and possible attack sequences which can be performed to exploit cyber vulnerabilities of the component. Thereafter, we formulate an Mixed Integer Linear Programming (MILP) optimization problem which aims to evaluate the optimal budget partitions in order to minimize the number of highly likely attack sequences. Though we provide results for using the framework in CPES, the proposed methodology can be extended for multiple domains with a set of known adversarial and mitigation actions.
Here, this work outlines an optimized process for converting 2,3-butanediol (BDO) into sustainable aviation fuel (SAF) and C4 chemicals. BDO is reactively separated from fermentation broth by forming dioxolanes, which are converted to isobutyraldehyde, methyl ethyl ketone (MEK), and 1,3-butadiene. These intermediates are reduced and dehydrated over Cu/ZSM-5 to form alkenes, which can be oligomerized and hydrotreated to jet-range alkanes. Previous BDO-dioxolane-alkene processes are limited by the requirement for a continuous aldehyde source for dioxolane formation. Brønsted acidic zeolites catalyze dioxolane deacetalization to form isobutyraldehyde and MEK in a >2:1 molar ratio, providing an internal, recyclable aldehyde source. Dioxolane formation optimization was performed to achieve >95% dioxolane yields over Amberlyst-15 and minimize isobutyraldehyde recycle. The overall BDO-dioxolane-fuel process yields an alkane mixture that enables at least a 50% v/v blend with Jet-A. Techno-economic analyses and life cycle assessments for this BDO-dioxolane-fuel process yield scenarios with <$2.50 per gallon gas equivalent and >58% reduction in CO2 emissions.
Organosulfur electrolytes are promising candidates for enabling high-voltage cathodes due to their superior oxidative stability compared to conventional carbonate-based systems. However, their viscous nature and inability to passivate the anode necessitate the use of passivating agents and diluents to achieve meaningful charge/discharge rates. In this study, we investigate the use of cyclic fluorinated carbonates (CFCs) to form passivating interphases on electrode surfaces, aiming to optimize electrolyte performance in high-voltage systems. Electrolyte formulations containing 1.2 M LiPF 6 in a CFC:sulfone:1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropylether (TTE) mixture (2/5/3 V/V/V) were assessed in full-cell configurations with a LiNi 0.8 Mn 0.1 Co 0.1 O 2 (FCG-NMC) cathode and a 4.5 V upper cutoff potential. The results reveal that 3-3-3-trifluoropropylene carbonate (TFPC) combined with ethyl methyl sulfone (EMS) optimizes electrolyte performance, resulting in lower resistance buildup and reduced capacity loss compared to commercial electrolytes. We also explore the failure mechanisms of several carbonate/sulfone mixtures and identify key considerations for electrolyte compatibility in high-voltage systems. TFPC enhances cycling stability and lowers overall cell resistance, while fluoroethylene carbonate (FEC) formulations, despite higher ionic conductivity, fail to form adequate passivation layers, leading to rapid capacity loss. Additionally, EMS provides superior physical properties that avoid common failure modes seen with other sulfone solvents like methyl isopropyl sulfone and tetramethylene sulfone. Furthermore, this study underscores the importance of carefully selecting passivating carbonates and sulfone solvents to improve electrolyte performance and expand the viability of high-voltage electrolyte systems.
HolosGen, LLC is proposing a highly innovative micro-reactor concept targeting both civilian and military applications. It consists of an advanced helium-cooled gas reactor using a turbojet-type turbine and compressor to achieve a highly condensed reactor that fits into a commercial ISO container. The Holos-Quad concept being considered for civilian applications will generate 22MWt, using four Subcritical Power Modules (SPMs) that fit into one 40-foot ISO container. It is a high-temperature gas-cooled reactor concept using TRISO fuel distributed in graphite hexagonal blocks, cooled with helium gas at 7MPa and a high outlet temperature. In FY2019, HolosGen, LLC received an ARPA-E MEITNER grant to team up with the Argonne National Laboratory (ANL) design team to demonstrate the feasibility of the Holos-Quad concept in terms of neutronics. In particular, the evaluation study of the Holos design features and alternative options for reactivity control was considered. Through these tasks, the ANL design team has been working on demonstrating the feasibility of the Holos-Quad concept in terms of neutronics to help advance the core design of the Holos-Quad. A rigorous design approach was employed for the Holos-Quad in order to ensure that the input space is fully investigated, and that the best solutions are considered. The first step of this approach consisted of properly defining the Holos-Quad design problem by identifying the competitive design objectives: its economic performance is maximized with longer lifetime and its transportability to remote locations is maximized with reduced weight. The design problem also required to identify the operational constraints (in terms of thermal hydraulics, reactivity feedback and shutdown margins) and the input parameters that could be varied. In the second step, a sensitivity analysis was performed to enable preliminary investigation of the input space to identify the correlations among input and output variables. It allowed, for instance, to find input parameters that have relatively poor impact on the core performance and that could be removed from the optimization problem to accelerate its convergence. This sensitivity analysis showed that the design problem defined is highly constrained since only 117 solutions out of 1,000 simulated cores met all the design constraints. The design optimization was performed in the third step employing a genetic algorithm to effectively explore this highly constrained input space and find global optimal solutions. More than 2,500 cores were fully evaluated to converge to a Pareto Frontier of optimum solutions providing best compromise between the life-time target and the total core weight, as shown in Figure 1. The main benefit of this approach was to reduce the human effort and to be able to automatically re-run the optimization problem when changing the design problem. The multi-objective optimization identified various core solutions that would be optimum solutions for different types of applications.
Polymer aggregation plays a critical role in the miscibility of materials and the performance of all-polymer solar cells (APSCs). However, many aspects of how polymer texturing and aggregation affect photoactive blend film microstructure and photovoltaic performance are poorly understood. Here the effects of aggregation in donor–acceptor blends are studied, in which the number-average molecular weights (Mns) of both an amorphous donor polymer, poly[4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b;4,5-b]dithiophene-2,6-diyl-alt-(4-(2-ethylhexyl)-3-fluorothieno[3,4-b]thiophene-)-2-carboxylate-2-6-diyl)] (PBDTT-FTTE) and a semicrystalline acceptor polymer, poly{[N,N'-bis(2-octyldodecyl)naphthalene-1,4,5,8-bis(dicarboximide)-2,6-diyl]-alt-5,5'-(2,2'-bithiophene)} (P(NDI2OD-T2)) are systematically varied. The photovoltaic performance is correlated with active layer microstructural and optoelectronic data acquired by in-depth transmission electron microscopy, grazing incidence wide-angle X-ray scattering, thermal analysis, and optical spectroscopic measurements. Coarse-grained modeling provides insight into the effects of polymer aggregation on the blend morphology. Notably, the computed average distance between the donor and the acceptor polymers correlates well with solar cell photovoltaic metrics such as short-circuit current density (Jsc) and represents a useful index for understanding/predicting active layer blend material intermixing trends. Importantly, these results demonstrate that for polymers with different texturing tendencies (amorphous/semicrystalline), the key for optimal APSC performance, photovoltaic blend morphology can be controlled via both donor and acceptor polymer aggregation.
Filament-based coil optimizations are performed for several quasi-helical stellarator configurations, beginning with the one from Landreman & Paul ( Phys. Rev. Lett. , vol. 128, 2022, 035001), demonstrating that precise quasi-helical symmetry can be achieved with realistic coils. Several constraints are placed on the shape and spacing of the coils, such as low curvature and sufficient plasma–coil distance for neutron shielding. The coils resulting from this optimization have a maximum curvature 0.8 times that of the coils of the Helically Symmetric eXperiment (HSX) and a mean squared curvature 0.4 times that of the HSX coils when scaled to the same plasma minor radius. When scaled up to reactor size and magnetic field strength, no fast particle losses were found in the free-boundary configuration when simulating 5000 alpha particles launched at $3.5\,\mathrm {MeV}$ on the flux surface with a normalized toroidal flux of $s=0.5$ . An analysis of the tolerance of the coils to manufacturing errors is performed using a Gaussian process model, and the coils are found to maintain low particle losses for smooth, large-scale errors up to amplitudes of approximately $0.15\,\mathrm {m}$ . Another coil optimization is performed for the Landreman–Paul configuration with the additional constraint that the coils are purely planar. Visual inspection of the Poincaré plot of the resulting magnetic field-lines reveal that the planar modular coils alone do a poor job of reproducing the target equilibrium. Additional non-planar coil optimizations are performed for the quasi-helical configuration with $5\,\%$ volume-averaged plasma beta from Landreman et al. ( Phys. Plasma , vol. 29, issue 8, 2022, 082501), and a similar configuration also optimized to satisfy the Mercier criterion. The finite beta configurations had larger fast-particle losses, with the free-boundary Mercier-optimized configuration performing the worst, losing approximately $5.5\,\%$ of alpha particles launched at $s=0.5$ .
We report that for the first time an x-ray Talbot-Lau moiré deflectometer is being developed that will use a flash tube source and fast detector for dynamic density gradient measurements. In Talbot-Lau moiré deflectometry, an x-ray grating makes an image of itself on a second grating (the Talbot effect) to produce a moiré pattern on a detector. The test object is placed between these gratings, with variations in index of refraction changing the pattern. A third grating in front of an incoherent x-ray source produces an array of coherent sources. With a 150 kV x-ray flash tube as the source, the gratings are placed in a glancing angle setup for performance at ~60 keV. The detector is a gated CCD with a fast scintillator for x-ray conversion. This diagnostic, designed for the Dual-Axis Radiographic Hydrodynamic Test facility (DARHT) at Los Alamos National Laboratory, measures the density profile of dense plasma plumes ejected from beam-target interactions. DARHT has two high-current, pulsed, inductive linear electron accelerators with bremsstrahlung targets at the end of each beam line to create 2-D radiographic images of hydrodynamic tests. One multi-pulse accelerator has up to four beam pulses striking the same target within 2 μs. Computer simulations that model target evolution and ejected material between pulses are used to design these targets for optimal radiographic performance; the x-ray deflectometer will directly measure density gradients in the ejected plumes and provide the first experimental constraints to these models. During the first year, currently underway, the diagnostic systems are being designed. In year two, the flash tube and fast detector will be deployed at DARHT for radiographic imaging while the deflectometer is built and tested on the bench with a continuous source. In year three, the fast deflectometer will be installed on DARHT and density measurements will be performed.
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A tetrahydrofolate-dependent O -demethylase, LigM, from Sphingobium sp. SYK-6 was crystallized by the hanging-drop vapour-diffusion method. However, the obtained P 3 1 21 or P 3 2 21 crystals, which diffracted to 2.5–3.3 Å resolution, were hemihedrally twinned. To overcome the twinning problem, microseeding using P 3 1 21/ P 3 2 21 crystals as microseeds was performed with optimization of the reservoir conditions. As a result, another crystal form was obtained. The newly obtained crystal diffracted to 2.5–3.0 Å resolution and belonged to space group P 2 1 2 1 2, with unit-cell parameters a = 102.0, b = 117.3, c = 128.1 Å. The P 2 1 2 1 2 crystals diffracted to better than 2.0 Å resolution after optimizing the cryoconditions. Phasing using the single anomalous diffraction method was successful at 3.0 Å resolution with a Pt-derivative crystal. This experience suggested that microseeding is an effective method to overcome the twinning problem, even when twinned crystals are utilized as microseeds.
The opposing requirements for the performance and reliability of a thermoelectric generator (TEG) are investigated with numerical finite element analysis simulations. The parameters that significantly influence the operational performance and structural reliability of a TEG are considered for simultaneous optimization. The design of experiments (DOE) based response surface optimization methodology in conjunction with results from finite element analysis was employed to identify optimal parameters from the structural reliability and performance (power output and efficiency) perspectives. A high temperature bismuth telluride based TEG couple was used in the studies to demonstrate the general applicability of the methodology. The response surface methodology is found to be a robust technique that can expedite the process of geometry optimization of a TE device during the research and development phase of TEG devices fabricated from new thermoelectric materials.
Lithium-ion batteries (LIB) are an important component of electric vehicles and sodium-ion batteries (SIB) are an attractive alternative for grid electric storage. We are investigating a novel approach to synthesizing the carbon anode materials for these batteries from biomass pyrolysis oil as part of conversion processes to produce biofuels. Graphite is used as anode material in LIB and is exclusively produced from petroleum residue or mined mineral carbon. We have synthesized drop-in graphite by coking the heavy residual oil from the distillation of catalytic fast pyrolysis oil and by catalytic graphitizing raw pyrolysis oil. XRD and Raman spectroscopies were used to verify the production of high purity crystalline graphite. Figure 1 shows typical XRD spectra of the bio-graphite compared to commercial material as well as the charge/discharge cycle. Graphite particle sizes and morphologies were adjusted to improve performance using jet milling and carbon coating. The bio-graphite delivered a high capacity (335 mAh/g) when tested in a graphite half-cell. Full cell coin cell experiments were also conducted to verify the observations from half-cell testing. The high value of graphite (>$9/kg) can positively impact the economics of biofuels production and can be an important part of a future circular carbon economy. We have also investigated synthesizing hard carbon from pyrolysis oil for use as anode material in SIB. These batteries are not commercially mature but have great promise in grid storage and use earth abundant elements. We have demonstrated that pyrolysis oil can be used to synthesize hard carbon, which was subsequently be used in SIB experiments. This material achieved a sodium-ion capacity of 250 mAh/g and a first cycle efficiency of 78%, with opportunities to further optimize this performance from optimizing the composition of the pyrolysis oil to shaping the hard carbon particles. As with graphite, hard carbon is a high value (> $10/kg) material that could facilitate biofuels production.
We used density functional and many-body perturbation theory to calculate the quasiparticle band structures and electronic transport parameters of p-type SnSe both for the low-temperature Pnma and high-temperature Cmcm phases. The Pnma phase has an indirect band gap of 0.829 eV, while the Cmcm has a direct band gap of 0.464 eV. Both phases exhibit multiple local band extrema within an energy range comparable to the thermal energy of carriers from the global extrema. We calculated the electronic transport coefficients as a function of doping concentration and temperature for single-crystal and polycrystalline materials to understand the previous experimental measurements. The electronic transport coefficients are highly anisotropic and are strongly affected by bipolar transport effects at high temperature. Our results indicate that SnSe exhibits optimal thermoelectric performance at high temperature when doped in the 10{sup 19}–10{sup 20 }cm{sup −3} range.