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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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Hydration water dynamics, structure, and thermodynamics are crucially important to understand and predict water-mediated properties at molecular interfaces.
Polymers are widely implemented materials often used for safety and security in our daily lives. However, we do not have a thorough understanding of the phase transformations of these materials when exposed to extreme condition, high strain rate, events. To study polymers in these severe environments, we performed plate impact experiments that impart a one-dimensional shock wave, at high pressures and temperatures, to observe the materials response. Generally, velocimetry is the experimental diagnostic used to capture the movement of high velocity interfaces to then calculate the mechanical variables associated with shock compression. However, when a material undergoes a phase transition, such as between two crystalline phases or melting to decomposition during shock compression, velocimetry does not explicitly indicate what structural change occurred. At the Dynamic Compression Sector at the Advanced Photon Source gas gun driven compression has been paired with x-ray diffraction, providing the ability to study both the shock parameters and structural changes concurrently. We explored high density polyethylene (HDPE) both on its reactants Hugoniot (<10.12 GPa) and within its transition region (24.3 GPa) where HDPE is hypothesized to decompose from reactants to products. With x-ray diffraction we observed both polymer chain compression and crystal structure reorientation from orthorhombic to monoclinic above 6 GPa, as well as either a possible phase change or chemical decomposition at 24.3 GPa. With these experiments we were able to determine the crystalline and amorphous Hugoniot loci at pressures below the polymer transition region.
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Ice Ih, the common form of ice in the biosphere, contains proton disorder. Its proton-ordered counterpart, ice XI, is thermodynamically stable below 72 K. However, the formation of ice XI is kinetically hindered, and experimentally it is obtained by doping with KOH. Doping creates ionic defects that promote the migration of protons and the associated change in proton configuration. In this article, we mimic the effect of doping with a bias potential that enhances the formation of ionic defects in molecular dynamics simulations. The recombination of the ions thus formed proceeds through fast migration of the hydroxide along hydrogen bond loops, providing a physical and expedite way to change the proton configuration. Here, a key ingredient of this approach is a machine learning potential trained with density functional theory data and capable of modelling molecular dissociation. We exemplify the usefulness of this idea by studying the order-disorder transition using an appropriate order parameter that distinguishes the proton environments in ice Ih and XI. We calculate the changes in free energy, enthalpy, and entropy associated with the transition. Our estimated entropy agrees with experiment within the error bars of the calculation.
A high-fidelity, low-Mach computational fluid dynamics simulation tool that includes evaporating droplets and variable-density turbulent flow coupling is well-suited to ascertain transmission probability and supports risk mitigation methods development for airborne infectious diseases such as COVID-19. A multi-physics large-eddy simulation-based paradigm is used to explore droplet and aerosol pathogen transport from a synthetic cough emanating from a kneeling humanoid. For an outdoor configuration that mimics the recent open-space social distance strategy of San Francisco, maximum primary droplet deposition distances are shown to approach 8.1 m in a moderate wind configuration with the aerosol plume transported in excess of 15 m. In quiescent conditions, the aerosol plume extends to approximately 4 m before the emanating pulsed jet becomes neutrally buoyant. A dose–response model, which is based on previous SARS coronavirus (SARS-CoV) data, is exercised on the high-fidelity aerosol transport database to establish relative risk at eighteen virtual receptor probe locations.
In (Mn, Fe) 2 (P, Si) alloys crystallographic first-order phase transformations enable strong coupling of magnetic and entropic properties, potentially leading to high-efficiency energy generation and refrigeration applications. Although hysteresis losses that limit these applications can be reduced through careful control of alloy composition, compositional tuning can also unfavorably influence transformation temperatures and magnetocaloric coupling strength. Hence, exploration of additional processing variables enabling independent control of transformation properties is crucial. In this work, we investigate the role of thermal history as an additional processing variable, exploiting thermally-activated mechanisms to control properties of non-diffusive transformations in (Mn, Fe) 2 (P, Si) alloys. In so doing, we report an unusual transformation-splitting phenomenon following annealing at intermediate times, where a single well-defined magneto-structural transformation evolves towards a multi-step transformation with individual steps occurring at multiple distinct temperatures. Here, on longer annealing at the same temperatures, single-step transformation behavior is recovered. Through additional magnetic and crystallographic characterization, we show that the thermal history-controlled multi-step behavior results from sluggish thermally-activated diffusion. The two-step transformation corresponds to non-equilibrium bimodal composition distributions in the transforming phase, and these develop through a dynamic re-equilibration process as the alloy passes relatively slowly between different thermal equilibria. Together, these results suggest that thermal history primarily controls the transformation properties of (Mn, Fe) 2 (P, Si) alloys indirectly through the composition of one or more transforming phases. Additional investigations are needed to develop thermal history processing for decoupling hysteresis control from other transformation properties.
A hard x-ray, high-speed, high dynamic range scientific x-ray imager is described. The imager is based on the mixed-mode pixel array detector (MM-PAD) readout chip coupled to a 750 µm thick cadmium telluride (CdTe) sensor. The full imager is a 2 × 3 tiled array of MM-PAD sensor/readout chip hybrids. CdTe improves detection for high energy x-rays as compared to silicon sensors, enabling efficient x-ray imaging to extend to >100 keV. The detector is capable of 1 kHz imaging and in-pixel circuitry has been designed to allow for well depths of greater than 4 × 10 6 80 keV x-rays. Here, a charge integrating front-end allows for quantitative measurement of high flux x-ray images beyond the capabilities of photon counting detectors. Detector performance is summarized and experimental measurements are presented.
The Hartree-Fock generalized Kadanoff-Baym ansatz (HF-GKBA) offers an approximate numerical procedure for propagating the two-time nonequilibrium Green's function (NEGF). Here, using the GW self-energy, we compare the HF-GKBA to exact results for a variety of systems with long- and short-range interactions, different two-body interaction strengths, and various nonequilibrium preparations. We find excellent agreement between the HF-GKBA and exact time evolution in models when more realistic long-range exponentially decaying interactions are considered. Furthermore, this agreement persists for long times and for intermediate to strong interaction strengths. In large systems, HF-GKBA becomes prohibitively expensive for long-time evolutions. For this reason, we look at the use of dynamical mode decomposition (DMD) to reconstruct long-time NEGF trajectories from a sample of the initial trajectory. Using no more than 16% of the total time evolution, we reconstruct the total trajectory with high fidelity. Our results show the potential for DMD to be used in conjunction with HF-GKBA to calculate long-time trajectories in large-scale systems.
The U.S. Department of Energy has identified exascale-class wind farm simulation as critical to wind energy scientific discovery. A primary objective of the ExaWind project is to build high-performance, predictive computational fluid dynamics (CFD) tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next-generation, exascale-class supercomputers. Here, we report on our efforts in preparing the ExaWind unstructured mesh solver, Nalu-Wind, for exascale-class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is insufficient. To achieve high performance, one needs novel algorithms that are application aware, memory efficient, and optimized for the latest-generation GPU devices. The result of our efforts are unstructured-mesh simulations of wind turbines that can effectively leverage thousands of GPUs. In particular, we demonstrate a first-of-its-kind, incompressible-flow simulation using Algebraic Multigrid solvers that strong scales to more than 4000 GPUs on the Summit supercomputer.
Mamyshev oscillators can generate high-power femtosecond pulses, but starting a mode-locked state has remained a major challenge due to the suppression of continuous-wave lasing. Here, we study the starting dynamics of a linear Mamyshev oscillator designed to generate high-power femtosecond pulses while avoiding component damage. Reliable starting to stable mode-locking is achieved with a combination of modulation of the pump power and shifting of a filter passband. The starting process is automated, with full electronic control. The laser delivers 21-nJ pulses that are dechirped to 65 fs in duration outside the cavity.
While drug resistance mutations can often be attributed to the loss of direct or solvent-mediated protein−ligand interactions in the drug-mutant complex, in this study we show that a resistance mutation for the picomolar HIV-1 capsid (CA)-targeting antiviral (GS-6207) is mainly due to the free energy cost of the drug-induced protein side chain reorganization in the mutant protein. Among several mutations, M66I causes the most suppression of the GS-6207 antiviral activity (up to ~84,000-fold), and only 83- and 68-fold reductions for PF74 and ZW-1261, respectively. To understand the molecular basis of this drug resistance, we conducted molecular dynamics free energy simulations to study the structures, energetics, and conformational free energy landscapes involved in the inhibitors binding at the interface of two CA monomers. To minimize the protein−ligand steric clash, the I66 side chain in the M66I−GS-6207 complex switches to a higher free energy conformation from the one adopted in the apo M66I. In contrast, the binding of GS-6207 to the wild-type CA does not lead to any significant M66 conformational change. Based on an analysis that decomposes the absolute binding free energy into contributions from two receptor conformational states, it appears that it is the free energy cost of side chain reorganization rather than the reduced protein−ligand interaction that is largely responsible for the drug resistance against GS-6207.
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