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DOE OSTI2022

One‐Step Thermal Gradient‐ and Antisolvent‐Free Crystallization of All‐Inorganic Perovskites for Highly Efficient and Thermally Stable Solar Cells

Abstract All‐inorganic perovskites have emerged as promising photovoltaic materials due to their superior thermal stability compared to their heat‐sensitive hybrid organic–inorganic counterparts. In particular, CsPbI 2 Br shows the highest potential for developing thermally‐stable perovskite solar cells (PSCs) among all‐inorganic compositions. However, controlling the crystallinity and morphology of all‐inorganic compositions is a significant challenge. Here, a simple, thermal gradient‐ and antisolvent‐free method is reported to control the crystallization of CsPbI 2 Br films. Optical in situ characterization is used to investigate the dynamic film formation during spin‐coating and annealing to understand and optimize the evolving film properties. This leads to high‐quality perovskite films with micrometer‐scale grain sizes with a noteworthy performance of 17% (≈16% stabilized), fill factor (FF) of 80.5%, and open‐circuit voltage ( V OC ) of 1.27 V. Moreover, excellent phase and thermal stability are demonstrated even after extreme thermal stressing at 300 °C.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Deep learning closure models for large-eddy simulation of flows around bluff bodies

Near-wall flow simulation remains a central challenge in aerodynamics modelling: Reynolds-averaged Navier–Stokes predictions of separated flows are often inaccurate, and large-eddy simulation (LES) can require prohibitively small near-wall mesh sizes. A deep learning (DL) closure model for LES is developed by introducing untrained neural networks into the governing equations and training in situ for incompressible flows around rectangular prisms at moderate Reynolds numbers. The DL-LES models are trained using adjoint partial differential equation (PDE) optimization methods to match, as closely as possible, direct numerical simulation (DNS) data. They are then evaluated out-of-sample – for aspect ratios, Reynolds numbers and bluff-body geometries not included in the training data – and compared with standard LES models. The DL-LES models outperform these models and are able to achieve accurate LES predictions on a relatively coarse mesh (downsampled from the DNS mesh by factors of four or eight in each Cartesian direction). We study the accuracy of the DL-LES model for predicting the drag coefficient, near-wall and far-field mean flow, and resolved Reynolds stress. A crucial challenge is that the LES quantities of interest are the steady-state flow statistics; for example, a time-averaged velocity component $\langle {u}_i\rangle (x) = \lim _{t \rightarrow \infty } ({1}/{t}) \int _0^t u_i(s,x)\, {\rm d}s$ . Calculating the steady-state flow statistics therefore requires simulating the DL-LES equations over a large number of flow times through the domain. It is a non-trivial question whether an unsteady PDE model with a functional form defined by a deep neural network can remain stable and accurate on $t \in [0, \infty )$ , especially when trained over comparatively short time intervals. Our results demonstrate that the DL-LES models are accurate and stable over long time horizons, which enables the estimation of the steady-state mean velocity, fluctuations and drag coefficient of turbulent flows around bluff bodies relevant to aerodynamics applications.

Mechanics↗

NASA NTRS2009

A Method for Combining Experimentation and Molecular Dynamics Simulation to Improve Cohesive Zone Models for Metallic Microstructures

Fracture processes within a material begin at the nanometer length scale at which the formation, propagation, and interaction of fundamental damage mechanisms occur. Physics-based modeling of these atomic processes quickly becomes computationally intractable as the system size increases. Thus, a multiscale modeling method, based on the aggregation of fundamental damage processes occurring at the nanoscale within a cohesive zone model, is under development and will enable computationally feasible and physically meaningful microscale fracture simulation in polycrystalline metals. This method employs atomistic simulation to provide an optimization loop with an initial prediction of a cohesive zone model (CZM). This initial CZM is then applied at the crack front region within a finite element model. The optimization procedure iterates upon the CZM until the finite element model acceptably reproduces the near-crack-front displacement fields obtained from experimental observation. With this approach, a comparison can be made between the original CZM predicted by atomistic simulation and the converged CZM that is based on experimental observation. Comparison of the two CZMs gives insight into how atomistic simulation scales.

Hochhalter, J. D.↗

NASA NTRSDate not supplied

Tessellation and Numerical Simulation of the in-Space Assembled Telescope (iSAT) Reflector

A novel tessellation method has been developed to allow the use of uniform truss modules to form curved or doubly curved surfaces. To illustrate the method, the structural support for the doubly curved in-Space Assembled Telescope primary reflector is developed. The method intentionally leaves gaps at the nodes between identical connecting modules; the gaps are relatively small, on the order of 0.5% of the lattice characteristic cell size. Uniquely shaped nodal connectors called multi-nuts are generated to fill in the nodal gaps and connect adjacent modules. With optimization techniques, the size of these nodal gaps can be minimized to thereby minimize the incurred structural efficiency losses. Finite element analyses of the primary reflector were performed, and the structural efficiency losses amounted to a natural frequency reduction of 5%. The benefits of having uniform truss modules outweigh the structural penalties as it will greatly simplify assembly and lower manufacturing costs.

Tessellation↗

NASA NTRSDate not supplied

Tessellation and Numerical Simulation of the in-Space Assembled Telescope (iSAT) Reflector

Tessellation↗

NASA NTRS2004

New Material for Surface-Enhanced Raman Spectroscopy

A chemical method of synthesis and application of coating materials that are especially suitable for surface-enhanced Raman spectroscopy (SERS) has been developed. The purpose of this development is to facilitate the utilization of the inherently high sensitivity of SERS to detect chemicals of interest (analytes) in trace amounts, without need for lengthy sample preparation. Up to now, the use of SERS has not become routine because the methods available have not been able to reproduce sampling conditions and provide quantitative measurements. In contrast, the coating materials of the present method enable analysis with minimum preparation of samples, and SERS measurements made using these materials are reproducible and reversible. Moreover, unlike in methods investigated in prior efforts to implement SERS, sampling is not restricted to such specific environments as electrolytes or specific solvents. The coating materials of this method are porous glasses, formed in sol-gel processes, that contain small particles of gold or silver metal. Materials of this type can be applied to the sample-contact surfaces of a variety of sampling and sensing devices, including glass slides, glass vials, fiber-optic probes, and glass tubes. Glass vials with their insides coated according to this method are particularly convenient for SERS to detect trace chemicals in solutions: One simply puts a sample solution containing the analyte(s) into a vial, then puts the vial into a Raman spectrometer for analysis. The chemical ingredients and the physical conditions of the sol-gel process have been selected so that the porous glass formed incorporates particles of the desired metal with size(s) to match the wavelength(s) of the SERS excitation laser in order to optimize the generation of surface plasmons. The ingredients and processing conditions have further been chosen to tailor the porosity and polarity of the glass to optimize the sample flow and the interaction between the analyte(s) and the plasmon field that generates Raman photons. The porous silica network of a sol-gel glass creates a unique environment for stabilizing SERS-active metal particles. Relative to other material structures that could be considered for SERS, the porous silica network offers higher specific surface area and thus greater interaction between analyte molecules and metal particles. Efforts to perform SERS measurements with the help of sampling devices coated by this method have been successful. In tests, numerous organic and inorganic chemicals were analyzed in several solvents, including water. The results of the tests indicate that the SERS measurements were reproducible within 10 percent and linear over five orders of magnitude. One measure of the limits of detectability of chemicals in these tests was found to be a concentration of 300 parts per billion. Further development may eventually make it possible to realize the full potential sensitivity of SERS for detecting some analytes in quantities as small as a single molecule.

Farquharson, Stuart↗

DOE OSTI2020

Pore-scale simulation of gas flow in microscopic permeable media with complex geometries

Lattice Boltzmann method (LBM) is an efficient tool to perform direct numerical simulation of gas flow in micro-size pores. Despite great advances in LBM and its use in describing gas flow in micro-size channels in slip and transitional flow regimes, exploring formulations that are able to capture flow behavior in domains with complex boundary geometries remains a challenging task. As a result, the impact of complexities in pore structures on gas flow may not be fully explored using LBM. In this study, we propose to use the combined bounce-back and Maxwellian diffusive reflection scheme to capture the slip velocity with a new set of slip coefficients. Optimal values of slip coefficients are determined using a gradient based method, where the results from the linearized Boltzmann equation is used as the reference solution. The proposed formulation is further validated against predictions from molecular dynamics simulation in the presence of complex geometries by introducing obstacles of different shapes in straight channels. LBM with the proposed boundary treatment is then used to investigate gas flow in a synthetic microscopic permeable medium. Here, results indicate that gas exhibits different flow configurations as Knudsen number varies, and apparent gas permeability appears to have up to a second-order dependency on the reciprocal mean pressure in slip and transitional flow regimes.

03 NATURAL GAS↗

DOE OSTI2022

Toward a QUBO-Based Density Matrix Electronic Structure Method

Density matrix electronic structure theory is used in many quantum chemistry methods to “alleviate” the computational cost that arises from directly using wave functions. Although density matrix based methods are computationally more efficient than wave function based methods, significant computational effort is involved. Because the Schrödinger equation needs to be solved as an eigenvalue problem, the time-to-solution scales cubically with the system size in mean-field type approaches such as Hartree–Fock and density functional theory and is solved as many times in order to reach charge or field self-consistency. We hereby propose and study a method to compute the density matrix by using a quadratic unconstrained binary optimization (QUBO) solver. This method could be useful to solve the problem with quantum computers and, more specifically, quantum annealers. Our proposed approach is based on a direct construction of the density matrix using a QUBO eigensolver. We explore the main parameters of the algorithm focusing on precision and efficiency. We show that, while direct construction of the density matrix using a QUBO formulation is possible, the efficiency and precision have room for improvement. Moreover, calculations performed with quantum annealing on D-Wave’s new Advantage quantum computer are compared with results obtained with classical simulated annealing, further highlighting some problems of the proposed method. Finally, we also suggest alternative methods that could lead to a more efficient QUBO-based density matrix construction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Direct implicit and explicit energy-conserving particle-in-cell methods for modeling of capacitively coupled plasma devices

Achieving large-scale kinetic modeling is a crucial task for the development and optimization of modern plasma devices. With the trend of decreasing pressure in applications, such as plasma etching, kinetic simulations are necessary to self-consistently capture the particle dynamics. The standard, explicit, electrostatic, momentum-conserving particle-in-cell method suffers from restrictive stability constraints on spatial cell size and temporal time step, requiring resolution of the electron Debye length and electron plasma period, respectively. This results in a very high computational cost, making the technique prohibitive for large volume device modeling. We investigate the direct implicit algorithm and the explicit energy conserving algorithm as alternatives to the standard approach, both of which can reduce computational cost with a minimal (or controllable) impact on results. These algorithms are implemented into the well-tested EDIPIC-2D and LTP-PIC codes, and their performance is evaluated via 2D capacitively coupled plasma discharge simulations. The investigation reveals that both approaches enable the utilization of cell sizes larger than the Debye length, resulting in a reduced runtime, while incurring only minor inaccuracies in plasma parameters. The direct implicit method also allows for time steps larger than the electron plasma period; however, care must be taken to avoid numerical heating or cooling. It is demonstrated that by appropriately adjusting the ratio of cell size to time step, it is possible to mitigate this effect to an acceptable level.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1982

Robust Kalman filter design for active flutter suppression systems

Additional insight is provided into the use of the Doyle-Stein (1979, 1981) technique in aeroelastic control problems by examining the application of the method to a flutter control problem. The system to be controlled consists of a full-size wind tunnel model of a wing, plus an aileron, an actuator, and an accelerometer used to sense the motion of the wing. A full-state feedback controller was designed using linear optimal control theory, and a Kalman filter was used in the feedback loop for state estimation. The filter design procedure is explained along with that to improve closed-loop properties of the system. The locus of the poles of the filter is examined as a scalar design parameter is varied. The Doyle-Stein design procedure is shown to substantially improve the stability properties of an active flutter controller designed using the linear quadratic Gaussian control theory.

Garrard, W. L.↗

NASA NTRS1995

Dual-Fuel Propulsion in Single-Stage Advanced Manned Launch System Vehicle

As part of the United States Advanced Manned Launch System study to determine a follow-on, or complement, to the Space Shuttle, a reusable single-stage-to-orbit concept utilizing dual-fuel rocket propulsion has been examined. Several dual-fuel propulsion concepts were investigated. These include: a separate-engine concept combining Russian RD-170 kerosene-fueled engines with space shuttle main engine-derivative engines: the kerosene- and hydrogen-fueled Russian RD-701 engine; and a dual-fuel, dual-expander engine. Analysis to determine vehicle weight and size characteristics was performed using conceptual-level design techniques. A response-surface methodology for multidisciplinary design was utilized to optimize the dual-fuel vehicles with respect to several important propulsion-system and vehicle design parameters, in order to achieve minimum empty weight. The tools and methods employed in the analysis process are also summarized. In comparison with a reference hydrogen- fueled single-stage vehicle, results showed that the dual-fuel vehicles were from 10 to 30% lower in empty weight for the same payload capability, with the dual-expander engine types showing the greatest potential.

Lepsch, Roger A., Jr.↗

NASA NTRS1996

Passive Microwave Remote Sensing of Soil Moisture

Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive Microwave soil moisture sensors currently considered for space operation are in the range 10-20 km. The most useful frequency range for soil moisture sensing is 1-5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations.

Njoku, Eni G.↗

NASA NTRS2015

Continued Water-Based Phase Change Material Heat Exchanger Development

In a cyclical heat load environment such as low Lunar orbit, a spacecraft's radiators are not sized to meet the full heat rejection demands. Traditionally, a supplemental heat rejection device (SHReD) such as an evaporator or sublimator is used to act as a "topper" to meet the additional heat rejection demands. Utilizing a Phase Change Material (PCM) heat exchanger (HX) as a SHReD provides an attractive alternative to evaporators and sublimators as PCM HX's do not use a consumable, thereby leading to reduced launch mass and volume requirements. In continued pursuit of water PCM HX development two full-scale, Orion sized water-based PCM HX's were constructed by Mezzo Technologies. These HX's were designed by applying prior research on freeze front propagation to a full-scale design. Design options considered included bladder restraint and clamping mechanisms, bladder manufacturing, tube patterns, fill/drain methods, manifold dimensions, weight optimization, and midplate designs. Two units, Units A and B, were constructed and differed only in their midplate design. Both units failed multiple times during testing. This report highlights learning outcomes from these tests and are applied to a final sub-scale PCM HX which is slated to be tested on the ISS in early 2017.

Hansen, Scott W.↗

DOE OSTI2022

Calibrating the Classical Hardness of the Quantum Approximate Optimization Algorithm

The trading of fidelity for scale enables approximate classical simulators such as matrix product states (MPSs) to run quantum circuits beyond exact methods. A control parameter, the so-called bond dimension $\mathcal{χ}$ for MPSs, governs the allocated computational resources and the output fidelity. Here, we characterize the fidelity for the quantum approximate optimization algorithm by the expectation value of the cost function that it seeks to minimize and find that it follows a scaling law $\mathscr{F}$(ln $\mathcal{χ}$/N), where N is the number of qubits. With ln $\mathcal{χ}$ amounting to the entanglement that a MPS can encode, we show that the relevant variable for investigating the fidelity is the entanglement per qubit. Importantly, our results calibrate the classical computational power required to achieve the desired fidelity and benchmark the performance of quantum hardware in a realistic setup. For instance, we quantify the hardness of performing better classically than a noisy superconducting quantum processor by readily matching its output to the scaling function. Moreover, we relate the global fidelity to that of individual operations and establish its relationship with $\mathcal{χ}$ and N. We sharpen the requirements for noisy quantum computers to outperform classical techniques at running a quantum optimization algorithm in speed, size, and fidelity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

A method for determining optimal electric range by considering electric vehicle lightweighting on perceived ownership cost

The limited driving range due to high costs and low energy densities of batteries constraints the battery electric vehicle (BEV) market growth. Lightweighting in theory can reduce energy consumption rate and extend the driving range. The knowledge gap is to quantitatively link the cost-effectiveness of light-weight technologies with range extension and consumer acceptance of BEVs. In this study, a physics-based energy consumption model of BEVs is constructed and associated with a statistics-based model on the basis of travel surveys. A perceived cost of ownership (PCO) is then developed by adding intangible costs to traditional total cost of ownership models. We estimate, at the disaggregate vehicle model and driver level and the aggregate market level, 1) the extended range due to lightweighting for a given battery size; and 2) the optimal electric range based on lightweighting decisions. The cost-effectiveness of lightweighting for BEV range extension is found to vary with income-dependent daily range limitation value, driving patterns and lightweighting technology costs. In general, adopting lightweighting in BEVs is more cost-effective for consumers with higher daily limitation value, as well as for those with higher driving intensity or suitable daily driving patterns. When the lightweighting involves a higher vehicle production cost, less lightweighting could reduce the overall PCO for BEV owners. 4 of the selected top ten BEV models are found to benefit from additional 2.09%–4.45% lightweighting. Finally, the method built in this study can guide automakers in planning R&D investments in battery and lightweighting technologies.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1976

Space stable thermal control coatings

The MOX method, i.e., the use of zinc and titanium oxalate precursors, has the distinct advantages of simple and rapid processing, and of controlled pigment particle size. The chemical identify of TiOX was determined. The Zn/Ti ratio effect on the reflectance spectra and ultraviolet irradiation stability in vacuum for Zn2TiO4 was examined. Optimized processing parameters are considered for reproducibly obtaining a pigment of the most desirable optical properties and behavior.

Harada, Y.↗

NASA NTRS1977

Application of a multi-level grid method to transonic flow calculations

A multi-level grid method has been studied as a possible means of accelerating convergence in relaxation calculations for transonic flows. The method employs a hierarchy of grids, ranging from very coarse (e.g., 8 x 2 mesh cells) to fine (e.g., 128 x 32); the coarser grids are used to diminish the magnitude of the smooth part of the residuals, hopefully with far less total work than would be required with, say, optimal SLOR iterations on the finest grid. The method was applied to the solution of the transonic small-disturbance equation for the velocity potential in the conservation form. Nonlifting transonic flow past a parabolic-arc airfoil is the example studied, with meshes of both constant and variable step size.

South, J. C., Jr.↗

DOE OSTI2023

Adaptive, Active Learning, and Multifidelity Monte Carlo Methods in the MOOSE Stochastic Tools Module

MOOSE is an open-source computational platform for constructing multi-physics models and executing them in a massively parallel fashion. It has a stochastic tools module (STM) for forward/inverse uncertainty quantification (UQ) and surrogate modeling. This presentation details some recent developments to the STM with respect to the implementation of adaptive, active learning, and multifidelity Monte Carlo methods for forward UQ of computational models. Specifically, the adaptive Monte Carlo methods include Markov Chain Monte Carlo (MCMC)-driven algorithms like adaptive importance sampling and parallelized subset simulation for statistical QoI estimation, rare events analysis, and stochastic gradient-free optimization. The active learning methods include Gaussian Process (GP) surrogates and their training via Adam optimization, design of acquisition functions, and integration with samplers like Monte Carlo, adaptive importance, and parallelized subset simulation. These active learning methods are also designed to work in a batch mode, wherein, the required calls to the full computational model are executed in parallel whenever a user-specified batch size is met. The multifidelity methods in STM are broadly divided into two categories: hierarchical, where a defined hierarchy exists among the low-fidelity models, and peer, where all the low-fidelity models are treated equally. A GP surrogate is used to learn the differences between the low- and high-fidelity models in both multifidelity categories, and acquisition functions from the active learning classes are used to decide whether to rely on a low-fidelity model or call the expensive high-fidelity model. Alongside the software description and usage, applications are also presented to nuclear engineering computational models including a TRISO nuclear fuel particle, a reactor pressure vessel, and a heat-pipe microreactor.

97 MATHEMATICS AND COMPUTING↗