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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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Rational Design of Novel Biomimetic Sequence-Defined Polymers for Mineralization Applications
Silica biomineralization is a naturally occurring process, wherein organisms use proteins and other biological structures to direct the formation of complex, hierarchical nanostructures. Discovery and characterization of such proteins and their underlying mechanisms spurred significant efforts to identify routes for biomimetic mineralization that reproduce the exquisite shapes and size selectivities found in nature. A common strategy has been the use of short peptide sequences with chemistry mimicking those found in natural systems, such as the use of the silaffin-derived R5 peptide. While progress has been made using this approach, there are many limitations that have prevented breakthroughs in biomimicry. To advance our ability to use charged macromolecules for silica formation, we propose to use sequence-defined synthetic polymers known as peptoids, or N-substituted polyglycines, which present significant capability for the precise tuning of sequence and structure beyond what can often be achieved with peptides alone. This study presents a computationally predicted design of these polymers that leads to the controlled formation of silica nanomaterials. We investigate surface adsorption and the mineralization process through analysis of binding mechanisms and energetics of the R5 system. Next, we synthesized two R5-inspired peptoids and validated our prediction in the design of mineralization polymers through characterization using surface plasmon resonance and electron microscopy. Here, this computationally guided study holds great promise for designing new sequences with unprecedented control of the placement of chemical functional groups, thus allowing for further unraveling of silicification mechanisms and the eventual design of sequence-defined synthetic polymers leading to the predictive synthesis of nanostructured functional materials.
Effect of the Substrate on MoS 2 Monolayer Morphology: An Integrated Computational and Experimental Study
Not provided.
Hydrogen Storage with Aluminum Formate, ALF: Experimental, Computational, and Technoeconomic Studies
Long-duration storage of hydrogen is necessary for coupling renewable H 2 with stationary fuel cell power applications. In this work, aluminum formate (ALF), which adopts the ReO 3 -type structure, is shown to have remarkable H 2 storage performance at non-cryogenic (>120 K) temperatures and low pressures. The most promising performance of ALF is found between 120 K and 160 K and at 10 bar to 20 bar. The study illustrates H 2 adsorption performance of ALF over the 77 K to 296 K temperature range using gas isotherms, in situ neutron powder diffraction, and DFT calculations, as well as technoeconomic analysis (TEA), illustrating ALF’s competitive performance for long-duration storage versus compressed hydrogen and leading metal–organic frameworks. In the TEA, it is shown that ALF’s storage capacity, when combined with a temperature/pressure swing process, has advantages versus compressed H 2 at a fraction of the pressure (15 bar versus 350 bar). In conclusion, given ALF’s performance in the 10 bar to 20 bar regime under moderate cooling, it is particularly promising for use in safe storage systems serving fuel cells.
Hydrogen Storage with Aluminum Formate, ALF: Experimental, Computational, and Technoeconomic Studies
Long-duration storage of hydrogen is necessary for coupling renewable H2 with stationary fuel cell power applications. In this presentation, I will discuss how aluminum formate, Al(HCOO)3 (ALF), which adopts an ReO3-type structure, is shown to have remarkable H2 storage performance at non-cryogenic (> 120 K) temperatures and low pressures. The most promising performance of ALF is found between 120 K and 160 K and at 10 bar to 20 bar. The talk will cover and illustrate the H2 adsorption performance of ALF over the 77 K to 296 K temperature range using gas isotherms, in situ neutron powder diffraction, and DFT calculations, as well as technoeconomic analysis (TEA), illustrating ALF’s competitive performance for long-duration storage versus compressed hydrogen and leading metal–organic frameworks. In the TEA, it is shown that ALF’s storage capacity, when combined with a temperature/pressure swing process, has advantages versus compressed H2 at a fraction of the pressure (15 bar versus 350 bar). Given ALF’s performance in the 10 bar to 20 bar regime under moderate cooling, it is particularly promising for use in safe storage systems serving fuel cells, and is currently the only MOF that works in this moderate temperature range/ low pressure regime to be cost competitive with compressed H2 gas for large scale H2 storage.[1]
Structural and electronic properties of rare-earth chromites: A computational and experimental study
Here, in this work, the structural, optical, and electronic properties of rare-earth perovskites of the general formula R CrO 3 , where R represents the rare-earth Gd, Tb, Dy, Ho, Er, and Tm, have been studied in detail. These compounds were synthesized through a facile citrate route. X-ray diffraction, Raman spectroscopy, and UV-Visible spectroscopy were utilized to reveal the structural evolutions in R CrO 3 . The lattice parameters, Cr 3+ –O 2 –Cr 3+ bond angle, and CrO 6 octahedral distortions were found to strongly depend on the ionic radii of R . First-principles calculations based on density-functional theory within the generalized gradient approximation (GGA) of Perdew-Burke-Ernzerhof (PBE) and strongly constrained and appropriately normed (SCAN) meta-GGA were also employed to calculate the structural and electronic properties of R CrO 3 . The ground-state energy, lattice constants, electronic structures, and density of states of R CrO 3 were calculated. These provide some insights into the electronic characteristics of the R CrO 3 compounds. The calculated values of lattice parameters and band gaps with Hubbard U correction (SCAN+ U ) agree well with values measured experimentally and show more accuracy in predicting the ground-state crystal structure and band structure compared to PBE+ U approximation. The band gap of R CrO 3 is found to be independent of the ionic radii of R from both experiments and calculations.
Computational and Experimental Study of Permeability Changes under Confinement in Fractured Porous Media.
Abstract not provided.
Computational and Experimental Study on Film Cooling Effectiveness with and without Downstream Vortex Generators
Final technical paper for 2022 ASME Turbo Expo Conference
Experimental and Computational Heat Transfer Study of sCO2 Single Jet Impingement
The present study experimentally investigates the heat transfer capability of supercritical carbon dioxide (sCO2) single-jet impingement. The evaluated jet Reynolds number range is between 80,000 and 1,000,000, with a non-dimensional jet-to-target surface spacing of 2.8. CO2- impinging jet stagnation conditions were maintained at approximately 20 MPa and a temperature of 673 K for most experiments. The goal is to understand how changes in the aforementioned parameters influence heat transfer between the working fluid and the heated surface. Additionally, due to the elevated Reynolds numbers and difference in thermodynamic properties between air and CO2, air-derived impingement correlations may not be appropriate for CO2 impingement; these correlations will be evaluated against experimental sCO2 impingement data. At the time of this study, no sCO2 impingement data was available relevant to sCO2 power cycles. The target surface is a 1.5-inch diameter copper block centered on the 3mm jet orifice. A mica heating element bolted to the bottom of the copper block provides a uniform heat flux. Thermocouples embedded in the copper block are used to determine the surface temperature. Nusselt numbers obtained from experimental sCO2 data are compared to area-averaged Nusselt numbers from air-derived correlations. The comparisons showed that air correlations drastically underpredict the heat transfer when sCO2 is used as the working fluid. A modified sCO2 correlation using experimental data at discussed conditions is derived based on an existing air correlation.
Computational thermodynamics-guided alloy design and phase stability in CoCrFeMnNi-based medium-and high-entropy alloys: An experimental-theoretical study
A computational thermodynamics approach has been employed to design CoCrFeMnNi-based medium- and high-entropy alloys (M/HEAs) with systematically varied compositions (Co(( 80-X )/ 2 )Cr(( 80-X )/ 2 )Fe X Mn 10 Ni 10 with x = 30, 40, and 50 at.%) and phase stability. Since the formation of sigma phase, usually brittle and undesirable, is a common concern, when this class of alloys is subjected to elevated temperatures (600–1000 °C), predicting its formation becomes essential. Thus, its formation and the phase equilibria were studied using the CALPHAD method, and two empirical methods, namely, valence electron concentration (VEC) and paired sigma-forming element (PSFE). Isothermal aging treatments at 900–1100 °C for 20 h were performed, since CALPHAD and VEC/PSFE predictions diverged. Both prediction methods were compared with experimental characterization by a combination of scanning electron microscopy and high-energy synchrotron X-ray diffraction. In conclusion, the predictions from the VEC/PSFE and CALPHAD calculations (depending on the database used) were shown to be quite accurate.
Design of High Efficient Mid-Wavelength Infrared Polarizer on ORMOCHALC Polymer
While an organically modified chalcogenide (ORMOCHALC) can be used to fabricate a polymeric mid-wavelength infrared (MWIR) polarizer with competitive extinction ratio compared to the commercial wire-grid polarizers, which are composed of fragile inorganic materials, there is still a knowledge gap regarding the systematic design process to obtain high transmission efficiency and extinction ratio. As such, a computational parameter study for design optimization is conducted with the geometric parameters of the bilayer grating ORMOCHALC polarizer. The computational study shows that the Fabry–Pérot cavity is the primary mechanism that determines the transmission behaviors and the extinction ratio. A bilayer grating design, guided by the parameter study, is realized through the thermal nanoimprint and metal deposition processes. The extinction ratios measured with the Fourier-transform infrared are 245, 304, and 351 at the wavelength of 3, 4, and 5 μm, respectively. Compared to the state-of-the-art of the polymeric MWIR linear polarizers, the extinction ratio is improved by 1.4 times, and the transmission efficiency is increased by 2.5 times. Theoretical analysis with the multiple-layer model based on the transfer matrix method predicts a matched transmission behavior with the experiment and a full-wave electromagnetic simulation.
Machine-Learning Accelerated Studies of Materials with High Performance and Edge Computing
In the studies of materials, experimental measurements often serve as the reference to verify physics theory and modeling; while theory and modeling provide a fundamental understanding of the physics and principles behind. However, the interactions and cross validation between them have long been a challenge even to-date. Not only that inferring a physics model from experimental data is itself a difficult inverse problem, another major challenge is the orders-of-magnitude longer wall-clock time required to carry out high-fidelity computer modeling to match the timescale of experiments. We envisage that by combining high performance computing, data science, and edge computing technology, the current predicament can be alleviated, and a new paradigm of data-driven physics research will open up. For example, we can accelerate computer simulations by first performing the large-scale modeling on high performance computers and train a machine-learned surrogate model. This computationally inexpensive surrogate model can then be transferred to the computing units residing closely to the experimental facilities to perform high-fidelity simulations at a much higher throughout. The model will also be more amenable to analyzing and validating experimental observations in comparable time scales at a much lower computational cost. Further integration of these accelerated computer simulations with an outer machine learning loop can also inform and direct future experiments, while making the inverse problem of physics model inference more tractable. We will demonstrate a proof-of-concept by using a quantum Monte Carlo application, Dynamical Cluster Approximation (DCA++), to machine-learn a surrogate model and accelerate the study of quantum correlated materials.
Operational Analytics Studies for ATLAS Distributed Computing: Data Popularity Forecast and Utilization of the WLCG Centers
Operational analytics is the direction of research related to the analysis of the current state of computing processes and the prediction of future states in order to anticipate imbalances and take timely measures to stabilize a complex system. There are two relevant areas in ATLAS Distributed Computing that are currently the focus of studies: user physics analysis including the forecast of popularity of data samples among users, and evaluating WLCG centers for their readiness to process user analysis payloads. Studying these areas is challenging due to the complexity involved, as it requires a comprehensive understanding of numerous boundary conditions typically found in large-scale distributed computing infrastructures. Forecasts of data popularity are problematic without the categorization of user tasks by their types (data transformation or physics analysis), which do not always appear on the surface but may induce noise, which introduces significant distortions for predictive analysis. Evaluating the WLCG resources by their analysis workloads is also a challenging task as it is necessary to find a balance between the workload of the resource, its performance, the waiting time for jobs on it, as well as the volume of jobs that it processes. This is especially difficult in a heterogeneous computing environment, where legacy resources are used along with modern high-performance machines. We will look at these areas of research in detail and discuss what tools and methods are used in our work, demonstrating results already obtained.
Basic Physical Processes Involving Dust in Fusion Plasmas
This report presents the main outcomes of our research focused on understanding the behavior and effects of dust particles in fusion plasmas, particularly in the edge regions of tokamaks and stellarators. We conducted computer modeling studies of burst injections of carbon and tungsten dust particles in DIII-D like divertor plasmas. The studies investigated effects of transient influx of the low- and high-Z material plasma contaminants in form of dust on the edge plasma dynamics in a modern mid-size tokamak. We also performed theoretical and computational studies of the forces acting on non-spherical dust grains in magnetized and nonmagnetized plasmas. In addition, we cooperated with General Atomics slag management group on development of DIII-D dust injection experiments to measure trajectories of dust grains in the divertor plasma and compare them with DUSTT code predictions for validation of the dust modeling capabilities. We collaborated with JET experimentalists on evaluation of radiative losses induced by injection of mixed neon-deuterium ice pellets for disruption and run-away electron generation mitigation during thermal and current quench phases. We also cooperated with experimentalists at LHD fusion device (Japan) on modeling support of experiments on injection of boron granules in fusion plasma discharges to assess their dynamics and effectiveness for in situ dynamic wall conditioning.
Probing extended scalar sectors with precision e + e - → Zh and Higgs diphoton studies
We compute the one-loop corrections to σ(e + e - → Zh) arising from representative extended Standard Model scalar sector scenarios. According to the new scalar SU(2)L representations, we consider the inert doublet, real and complex triplet, quintuplet, and septuplet models. With the sub-percent level precision expected for prospective future e + e - collider measurements of σ(e + e - → Zh), studies of the Higgsstrahlung process will probe extended scalar sector particle spectrum and interactions in a manner complementary to direct searches at the Large Hadron Collider and possible future pp colliders. We also compare with the sensitivity of future Higgs diphoton decay rate measurements. We find that the σ(e + e - → Zh) and Γ(h → γγ) complementarity is particularly pronounced for the complex triplet model.
Dynamic Cooling on Contemporary Quantum Computers
We study the problem of dynamic cooling whereby a target qubit is cooled at the expense of heating up N − 1 further identical qubits by means of a global unitary operation. A standard back-of-the-envelope high-temperature estimate establishes that the target qubit temperature can be dynamically cooled by at most a factor of 1 / N . Here we provide the exact expression for the minimum temperature to which the target qubit can be cooled and reveal that there is a crossover from the high initial temperature regime, where the scaling is 1 / N , to a low initial temperature regime, where a much faster scaling of 1 / N occurs. This slow, 1 / N scaling, which was relevant for early high-temperature NMR quantum computers, is the reason dynamic cooling was dismissed as ineffectual around 20 years ago; the fact that current low-temperature quantum computers fall in the fast, 1 / N scaling regime, reinstates the appeal of dynamic cooling today. We further show that the associated work cost of cooling is exponentially more advantageous in the low-temperature regime. We discuss the implementation of dynamic cooling in terms of quantum circuits and examine the effects of hardware noise. We successfully demonstrate dynamic cooling in a three-qubit system on a real quantum processor. Since the circuit size grows quickly with N , scaling dynamic cooling to larger systems on noisy devices poses a challenge. We therefore propose a suboptimal cooling algorithm, whereby relinquishing a small amount of cooling capability results in a drastically reduced circuit complexity, greatly facilitating the implementation of dynamic cooling on near-future quantum computers. Published by the American Physical Society2024
Bayesian optimization for inverse calibration of expensive computer models: A case study for Johnson-Cook model in machining
Inverse model calibration for identifying the constitutive model parameters can be computationally demanding for expensive-to-evaluate simulation models. Here, this paper presents a modified Bayesian optimization (BO) method, denoted as BO-bound, that incorporates theoretical bounds on the quantity of interest. A case study for the inverse calibration of the Johnson Cook (J-C) flow stress model parameters is presented using machining (cutting) force data. The results show fast calibration of the five J-C parameters within 25 simulations. In general, the BO-bound method is applicable for inverse calibration of any expensive simulation models as well as optimization problems with known bounds.