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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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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Theoretical Insights into Reaction-Induced Transformation and Tuning of Catalytic Behavior in Heterogenous Catalysis

Reaction-induced transformations in heterogenous catalysis represent diverse phenomena that challenge traditional views of static catalyst surfaces. From surface adsorbate dynamics, atomic rearrangements, to composition and phase transitions, these processes reveal the profound differences between idealized model systems under ultrahigh vacuum and the complex, evolving interfaces that govern real catalytic behaviors under reaction conditions. Here, this perspective reviews recent theoretical efforts to provide atomic-level mechanistic insights into significant reaction-induced transformations and their impact on catalytic activity and selectivity. It underscores the need for an integrated framework that combines predictive simulations with operando characterization to uncover active sites and mechanisms under realistic operating conditions. Achieving this requires accelerating existing simulations to fully capture diverse reaction-induced surface dynamics, enabling scalable and accurate modeling of catalysts as condition-dependent, dynamically evolving systems. Such approaches are critical to bridge the gap between theory and practice, offering a pathway to more impactful and predictive catalyst design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Monte carlo simulations and experiments of all-silica zeolite LTA assembly combining structure directing agents that match cage sizes

In this work, we investigated the influence of organic structure-directing agents (OSDAs) on the formation rates of all-silica zeolite LTA using both simulations and experiments, to shed light on the crystallization process. We compared syntheses using one OSDA with a diameter close to the size of the large cavity in LTA, and two OSDAs of diameters matching the sizes of both the small and large LTA cavities. Reaction-ensemble Monte Carlo (RxMC) simulations predict a speed up of LTA formation using two OSDAs matching the LTA pore sizes; this qualitative result is confirmed by experimental studies of crystallization kinetics, which find a speedup in all-silica LTA crystallization of a factor of 3. Analyses of simulated rings and their Si–O–Si angular energies during RxMC crystallizations show that all ring sizes in the faster crystallization exhibit lower angular energies, on average, than in the slower crystallization, explaining the origin of the speedup through packing effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Kinetic modeling of anaerobic degradation of plant-derived aromatic mixtures by Rhodopseudomonas palustris

Abstract Rhodopseudomonas palustris is a model microorganism for studying the anaerobic metabolism of aromatic compounds. While it is well documented which aromatics can serve as sole organic carbon sources, co-metabolism of other aromatics is poorly understood. This study used kinetic modeling to analyze the simultaneous degradation of aromatic compounds present in corn stover hydrolysates and model the co-metabolism of aromatics not known to support growth of R. palustris as sole organic substrates. The simulation predicted that p -coumaroyl amide and feruloyl amide were hydrolyzed to p -coumaric acid and ferulic acid, respectively, and further transformed via p -coumaroyl-CoA and feruloyl-CoA. The modeling also suggested that metabolism of p -hydroxyphenyl aromatics was slowed by substrate inhibition, whereas the transformation of guaiacyl aromatics was inhibited by their p -hydroxyphenyl counterparts. It also predicted that substrate channeling may occur during degradation of p -coumaroyl-CoA and feruloyl-CoA, resulting in no detectable accumulation of p -hydroxybenzaldehyde and vanillin, during the transformation of these CoA ligated compounds to p- hydroxybenzoic acid and vanillic acid, respectively. While the simulation correctly represented the known transformation of p -hydroxybenzoic acid via the benzoyl-CoA pathway, it also suggested co-metabolism of vanillic acid and syringic acid, which are known not to serve as photoheterotrophic growth substrate for R. palustris .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Random sub-diffusion and capture of genes by the nuclear pore reduces dynamics and coordinates inter-chromosomal movement

Hundreds of genes interact with the yeast nuclear pore complex (NPC), localizing at the nuclear periphery and clustering with co-regulated genes. Dynamic tracking of peripheral genes shows that they cycle on and off the NPC and that interaction with the NPC slows their sub-diffusive movement. Furthermore, NPC-dependent inter-chromosomal clustering leads to coordinated movement of pairs of loci separated by hundreds of nanometers. We developed fractional Brownian motion simulations for chromosomal loci in the nucleoplasm and interacting with NPCs. These simulations predict the rate and nature of random sub-diffusion during repositioning from nucleoplasm to periphery and match measurements from two different experimental models, arguing that recruitment to the nuclear periphery is due to random sub-diffusion and transient capture by NPCs. Finally, the simulations do not lead to inter-chromosomal clustering or coordinated movement, suggesting that interaction with the NPC is necessary, but not sufficient, to cause clustering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Kiwi-TNT Simulation Model [Slides]

The Kiwi-TNT Test (Toonerville Trolley) was performed on January 12, 1965. LANL has the objective: "LANL will develop a defensible methodology for establishing the maximum number of fissions for postulated inadvertent criticality events. The methodology may include several methods, such as hand calculations, sophisticated modeling, and the use of historical data.” The historic test, Kiwi TNT, posed a starting point for predictive simulations to determine the number of fissions and excursion understanding. This presentation gives results of that effort.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Nanoprecipitates to Enhance Radiation Tolerance in High-Entropy Alloys

The growth of advanced energy technologies for power generation is enabled by the design, development, and integration of structural materials that can withstand extreme environments, such as high temperatures, radiation damage, and corrosion. High-entropy alloys (HEAs) are a class of structural materials in which suitable chemical elements in four or more numbers are mixed to typically produce single-phase concentrated solid solution alloys (CSAs). Many of these alloys exhibit good radiation tolerance like limited void swelling and hardening up to relatively medium radiation doses (tens of displacements per atom (dpa)); however, at higher radiation damage levels (>50 dpa), some HEAs suffer from considerable void swelling limiting their near-term acceptance for advanced nuclear reactor concepts. In this study, we developed a HEA containing a high density of Cu-rich nanoprecipitates distributed in the HEA matrix. The Cu-added HEA, NiCoFeCrCu 0.12 , shows excellent void swelling resistance and negligible radiation-induced hardening upon irradiation up to high radiation doses (i.e., higher than 100 dpa). The void swelling resistance of the alloy is measured to be significantly better than NiCoFeCr CSA and austenitic stainless steels. Density functional theory simulations predict lower vacancy and interstitial formation energies at the coherent interfaces between Cu-rich nanoprecipitates and the HEA matrix. The alloy maintained a high sink strength achieved via nanoprecipitates and the coherent interface with the matrix at a high radiation dose (~50 dpa). From our experiments and simulations, the effective recombination of radiation-produced vacancies and interstitials at the coherent interfaces of the nanoprecipitates is suggested to be the critical mechanism responsible for the radiation tolerance of the alloy. Furthermore, the materials design strategy based on incorporating a high density of interfaces can be applied to high-entropy alloy systems to improve their radiation tolerance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Molecular Modes Elucidate the Nuclear Magnetic Resonance Relaxation of Viscous Fluids

The Bloembergen, Purcell, and Pound (BPP) theory of nuclear magnetic resonance (NMR) relaxation in fluids dating back to 1948 continues to be the linchpin in interpreting NMR relaxation data in applications ranging from characterizing fluids in porous media to medical imaging (MRI). The BPP theory is founded on assuming molecules are hard spheres with 1 H– 1 H dipole pairs reorienting randomly; assumptions that are severe in light of modern understanding of liquids. Nevertheless, it is intriguing to this day that the BPP theory was consistent with the original experimental data for glycerol, a hydrogen-bonding molecular fluid for which the hard-sphere-rigid-dipole assumption is inapplicable. To better understand this incongruity, atomistic molecular simulations are used to compute 1 H NMR T 1 relaxation dispersion (i.e., frequency dependence) in two contrasting cases: glycerol, and a (non hydrogen-bonding) viscosity standard. At high viscosities, simulations predict distinct functional forms of T 1 for glycerol compared to the viscosity standard, in agreement with modern measurements, yet both in contrast to BPP theory. The cause of these departures from BPP theory is elucidated, without assuming any relaxation models and without any free parameters, by decomposing the simulated T 1 response into dynamic molecular modes for both intramolecular and intermolecular interactions. The decomposition into dynamic molecular modes provides an alternative framework to understand the physics of NMR relaxation for viscous fluids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Variations in northeast Asian summer precipitation driven by the Atlantic multidecadal oscillation

By analysing a 113–year (1900–2012) observational dataset, it is shown in this work that the interdecadal fluctuation of the summer precipitation over Northeast Asia differs from that in central East Asia during the 20th century, and has experienced three interdecadal shifts in the 1920s, mid-1960s and late 1990s. That fluctuation coincides well with the multidecadal fluctuation of the sea surface temperature in the North Atlantic, known as the Atlantic Multidecadal Oscillation (AMO). The AMO affects Northeast Asia via a circumglobal teleconnection pattern extending from the North Atlantic to North America. Corresponding with the positive phase of this teleconnection pattern, a tripolar pattern emerges in the Asian–North Pacific sector, with anomalous low pressure over Northeast Asia and high pressure over Lake Baikal and the mid-latitude western North Pacific. These results suggest that the positive phase of the AMO favours the occurrence of cold vortex over Northeast Asia and anomalous highs over Lake Baikal and the mid-latitude western North Pacific in summer, which enhances the East Asian summer monsoon and southward intrusion of high-latitude cold air, and eventually increases the summer precipitation in Northeast Asia. Furthermore, initialized decadal prediction simulations using the CCSM4 model reproduce well the observed variations of the AMO and its associated atmospheric teleconnection but with slightly shifted geographic locations. The simulated anomalous Northeast Asia cold vortex and strong East Asian summer monsoon, lead to above-normal summer precipitation over Northeast Asia. The modelling results confirm that the multidecadal variability in the North Atlantic can cause the observed interdecadal variations of Northeast Asia summer precipitation. Our results suggest that to understand and predict interdecadal climate change over Northeast Asia, it is important to consider the key role of the AMO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Material Characterization for Large Scale Additive Manufacturing (AM)

The objective of this project was to enable prediction of residual stresses and warpage for extrusion deposition additive manufacturing (EDAM) fabricated carbon fiber thermoplastic matrix parts with three different materials systems produced by Techmer. The materials selected include Polyphenylene sulfide reinforced with 50% by weight of carbon fiber (CF-PPS), Polyethersulfone reinforced with 25% by weight of carbon fiber (CF-PESU), Polysulfone reinforced with 25% by weight of carbon fiber (CF-PSU). ADDITIVE3D © , a physics-based simulation workflow for EDAM, provided the simulation capabilities required for this project. Simulation predictions were validated against measurements carried out during and after the printing process carried out in the CAMRI and LSAM systems . Predictions for temperature, degree of crystallinity and deformation were carried out for the three material systems and two different geometries. Predictions for temperature were correlated very well with the experimental measurements for the two geometries printed using the three-material systems. The crystallinity level was verified for CF-PPS. The predictions for part deformation were in good agreement with the experimental measurements. In the best-case, predictions were within 8% of the maximum displacement observed in the 3-direction whereas predictions were within 14% for the worst-case. The adoption of this technology in commercial large-scale EDAM production processes would dramatically reduce the costs associated with producing articles by that process. Many thousands of dollars in materials, energy, and machine time could be saved by utilizing this simulation technology to develop articles rather than iterative printings to arrive at the optimal or correct design. The avoidance of a single failed print has the possibility of saving tens of thousands of dollars involved in the cost of material, machine and operators’ time.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Why Are There so Few Reports of High-Energy Electron Drift Resonances? Role of Radial Phase Space Density Gradients

Models of monochromatic Pc5 (2–7 mHz) ultralow frequency (ULF) wave interactions with high energy (greater than ~1 MeV) electrons predict drift resonant interactions that can cause rapid radial transport and acceleration. There are few reports of electron drift resonance at energies greater than ~1 MeV, in contrast to lower energies; moreover, all previous reports occur in the aftermath of interplanetary shocks. These two facts are difficult to reconcile with theory and numerical simulations predicting that greater than ~1 MeV drift resonances should occur more often and in a wider variety of driving conditions. In this study, we show that a combination of observational sampling biases and nominal radial phase space density gradients is one explanation for this discrepancy between theory and observations. In particular, we examine electron dynamics in two case studies with very similar satellite coverage, solar wind conditions, and Pc5 wave properties, yet with different radial phase space density profiles. Using global wave and particle observations, we show that the events have vastly different particle responses despite having similar wave properties. Placing these results in context with past studies, we further show here that nominal radial PSD gradients near geostationary orbit can mask the expected drift resonance particle response and explain (1) the small number of past greater than ~1 MeV drift resonance reports and (2) the restriction of these reports to interplanetary shock events. We argue that future observational studies characterizing radial transport via drift resonance should examine global particle dynamics, including observations of the radial phase space density profile.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

On-lattice kinetic Monte Carlo approaches for modeling molecular anisotropy in resveratrol crystallization

Stilbenes are a class of organic compounds with broad-ranging pharmaceutical and agricultural applications, which are typically isolated and purified through recrystallization. We are motivated by reducing experimental waste and optimizing yield via developing predictive simulations for processing-dependent crystal morphologies. Using resveratrol as a model stilbene system, we have developed an approach for simulating crystallization with molecular resolution using on-lattice kinetic Monte Carlo. In this work, we highlight modifications to the Stochastic Parallel PARticle Kinetic Simulator (SPPARKS) software package, which were essential to this application. Key enhancements include the incorporation of non-orthogonal cell shapes and monomer anisotropy approximations using bound hard spheres. This new SPPARKS application has been applied to resveratrol with attachment energy libraries obtained from density functional theory, resulting in excellent agreement with experimental morphology prediction.

crystallization↗
DOE OSTI2024

Data Science Shows that Entropy Correlates with Accelerated Zeolite Crystallization in Monte Carlo Simulations

We have performed a data science study of Monte Carlo simulation trajectories to understand factors that can accelerate formation of zeolite nanoporous crystals, a process that can take days or even weeks. In previous work, Monte Carlo simulations predicted and experiments confirmed that using a secondary organic structure-directing agent (OSDA) accelerates crystallization of all-silica LTA zeolite, with experiments finding a three-fold speedup [PCCP 24, 142-148 (2022)]. However, it remains unclear what physical factors cause the speed-up. Here, we apply data science to analyze the simulation trajectories to discover what drives accelerated zeolite crystallization in Monte Carlo going from a one-OSDA synthesis (1OSDA) to a two-OSDA version (2OSDA). We encoded simulation snapshots using the Smooth Overlap of Atomic Positions approach, which represents all 2- and 3-body correlations within a given cutoff distance. Principal component analyses failed to discriminate datasets of structures from 1OSDA and 2OSDA simulations, while the Support Vector Machine (SVM) approach succeeded at classifying such structures with an area-under-curve (AUC) score of 0.99 (where AUC = 1 is a perfect classification) with all 3-body correlations, and as high as 0.94 with only 2-body correlations. SVM decision functions reveal relatively broad / narrow histograms for 1OSDA / 2OSDA datasets, suggesting that the two simulations differ strongly in information heterogeneity. Informed by these results, we performed pair (2-body) entropy calculations during crystallization, resulting in entropy differences that semi-quantitatively account for the speedup observed in the previous Monte Carlo simulations. We conclude that altering synthesis conditions in ways that substantially changes the entropy of labile silica networks may accelerate zeolite crystallization, and we discuss possible approaches for achieving such acceleration.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders

From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z = 118), it is estimated that as many as about 8,000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but they play an essential role in astrophysical events such as supernova explosions or neutron star mergers that are presumed to be at the origin of most heavy elements in the Universe. Understanding the structure, reactions, and decays of nuclei across the entire chart of nuclides is an enormous challenge because of the experimental difficulties in measuring properties of interest in such fleeting objects and the theoretical and computational issues of simulating strongly-interacting quantum many-body systems. Nuclear density functional theory (DFT) is a fully microscopic theoretical framework which has the potential of providing such a quantitatively accurate description of nuclear properties for every nucleus in the chart of nuclides. Thanks to high-performance computing facilities, it has already been successfully applied to predict nuclear masses, global patterns of radioactive decay like β or γ decay, and several aspects of the nuclear fission process such as, e.g., spontaneous fission half-lives. Yet, predictive simulations of nuclear spectroscopy—the low-lying excited states and transitions between them—or of nuclear fission, or the quantification of theoretical uncertainties and their propagation to basic or applied nuclear science applications, would require several orders of magnitude more calculations than currently possible. However, most of this computational effort would be spent into generating a suitable basis of DFT wavefunctions. Such a task could potentially be considerably accelerated by borrowing tools from the field of machine learning and artificial intelligence. In this paper, we review different approaches to applying supervised and unsupervised learning techniques to nuclear DFT.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Validation of SOLPS-ITER and EDGE2D-EIRENE simulations for H, D, and T JET ITER-like wall low-confinement mode plasmas

Both experiments and simulations with SOLPS-ITER and EDGE2D-EIRENE show that the onset of detachment for the low-field side (LFS) divertor – defined here as the line-averaged upstream density (〈n e 〉 edge ) at which the plasma flux to the LFS target (I LFS-plate ) starts to decrease with increasing 〈n e 〉 edge – is independent of the isotope mass. However, there are three major simulation-experiment discrepancies: (i) the absolute values of I LFS-plate and the electron density (n e ) in the LFS divertor at the onset of detachment are significantly lower in simulations, i.e., approximately a factor of 2 for I LFS-plate and a factor of 3-4 for n e ; (ii) the degree of detachment – defined here as the difference between I LFS-plate at the onset of detachment and at an 〈n e 〉 edge value close to the density limit – is smaller in simulations compared to experiments; and (iii) the experimentally observed larger degree of detachment for D and T plasmas compared to H plasmas cannot be clearly distinguished from the simulation results. There are strong indications that discrepancy (i) is to a large extent caused by neglecting Lyman-opacity effects in our simulations. The simulations predict a similar net volumetric recombination source for all isotopes due to the fact that molecule-activated recombination (MAR) compensates for the reduced electron–ion recombination (EIR) for H, whereas MAR is negligible for D and T. This similar net volumetric recombination source for all isotopes leads to an isotope-independent degree of detachment in simulations. An analysis of the Balmer-α and Balmer-$γ$ emission confirms the underestimate of MAR in simulations (especially for D and T) for the JET metallic wall, which was previously observed for devices with a carbon wall. The underestimate of MAR is an important cause for discrepancy (ii) and the fact that there is a stronger underestimate of MAR for D and T than for H explains discrepancy (iii). Extending the plasma grid to the vessel wall increases I LFS-plate and n e at the onset of detachment by 25%, and the EIR source increases by 80% in detached conditions. Hence, while the extended grid results are closer to the experimental observations, the previously described qualitative discrepancies still persist.

Balmer emission↗
DOE OSTI2020

ExaWind: A multifidelity modeling and simulation environment for wind energy

We introduce the open-source ExaWind modeling and simulation environment for wind energy. The primary physics codes of ExaWind are Nalu-Wind and OpenFAST. Nalu-Wind is a wind-focused computational fluid dynamics (CFD) code that is coupled to the whole-turbine simulation code OpenFAST. The ExaWind environment was created under U.S. Department of Energy funding to achieve the highest-fidelity simulations of wind turbines and wind farms to date, with the goal of enabling disruptive changes to turbine and plant design and operation. Innovation will be gleaned through better understanding of the complex flow dynamics in wind farms, including wake evolution and the impact of wakes on downstream turbines and turbulent flow from complex terrain. High-fidelity predictive simulations employ hybrid turbulence models, geometry/boundary-layer-resolving CFD meshes, atmospheric turbulence, nonlinear structural dynamics, and fluid-structure interaction. While there is an emphasis on very high-fidelity simulations (e.g., blade resolved with full fluid-structure coupling), the ExaWind environment supports lower-fidelity modeling capabilities including actuator-line and -disk methods. Important in the development of ExaWind codes is that the codes scale well on today's largest petascale supercomputers and on the next-generation platforms that will enable exascale computing.

17 WIND ENERGY↗
DOE OSTI2021

In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory

In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analog states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3 × 3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with outer product updates. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance toward developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Pyrolysis of bio-derived dioxolane fuels: A ReaxFF molecular dynamics study

Alkyl-substituted 1,3-dioxolanes, including 4,5-dimethyl-2-pentan-3-yl-1,3-dioxolane (Fuel 1), 4,5-dimethyl-2-pentyl-1,3-dioxolane (Fuel 2), and 2-(heptan-3-yl)-4,5-dimethyl-1,3-dioxolane (Fuel 3), have been recently suggested as potential biodiesels. In this paper, we investigate the initial pyrolysis of the alkyl-substituted 1,3-dioxolanes at high temperatures using ReaxFF molecular dynamics (MD) simulations. We analyze the decomposition rate, reaction mechanism, and product distribution in the pyrolysis of the three alkyl 1,3-dioxlanes. The three fuels primarily decompose to 4,5-dimethyl-1,3-dioxolane radical and hydrocarbons derived from the alkyl side-chains. The further decomposition of 4,5-dimethyl-1,3-dioxolane radical primarily leads to 2-C 4 H 8 and CO 2 within a few decomposition steps. The hydrocarbon product distribution is significantly affected by the molecular structure of the alkyl side-chain, which would have a strong influence on the sooting tendency of these fuels. The ReaxFF simulations predict that the order of sooting tendency would be Fuel 3 > Fuel 1 > Fuel 2, which agrees with the measured sooting tendency trend. Based on the pyrolysis mechanism identified by ReaxFF, we propose a new alkyl dioxolane, 4-hexyl-5-methyl-1,3-dioxolane (Fuel 4), which might produce even less soot, by modifying the molecular structure of Fuel 2. Our ReaxFF simulation shows that Fuel 4 produce much less C 4 H 8 , an effective non-aromatic soot precursor, than Fuel 2. Moreover, more carbon atoms are bonded to each oxygen atom in Fuel 4 than Fuel 2, which would help reduce soot yield by removing more carbon atoms from the soot-producing pool of species. The major decomposition pathways identified in this work can be used to develop chemical kinetic models for 1,3-dioxolane based compounds, as biodiesel components, applicable to combustion engine simulations. We also demonstrate that the chemical kinetic insight offered by ReaxFF simulations can be used to design new fuel molecules with more desired properties.

09 BIOMASS FUELS↗
DOE OSTI2024

Effects of drive pulse shape on graded metal pushered single shell capsule implosions on the National Ignition Facility

Graded metal pushered single shells (PSS) are a viable alternative to low-Z capsules (Z is the atomic number) for indirect drive inertial confinement fusion implosions due to enhanced core tamping and radiation trapping, but they can be compromised by the pusher mixing with the fuel. We compare 2-shock and 3-shock laser pulses for Be/Cr PSS capsules filled with deuterium–tritium gas fuel at 6 mg/cc density. 1D radiation-hydrodynamic simulations predict higher core compression and, hence, ∼2× higher fusion yield for the 3-shock drive than for 2-shock. Nevertheless, we observe similar core ion temperatures and fusion yields for both drives. The implosion burn duration is 25% shorter and the core volume is ∼2.5× smaller for the 3-shock drive than for 2-shock, consistent with a higher compression. 1D LASNEX mix simulations using a buoyancy-drag model matching the measured yields also agree with the observed core sizes and burn durations and suggest ∼40% and ∼70% yield degradations for 2-shock and 3-shock drives due to hydrodynamic instabilities and atomic mix at the pusher–fuel interface. At the same time, 2D HYDRA simulations show that mid-mode (2–250) instability degradations are negligible for the 2-shock implosion (9%) and significant (45%) for 3-shock. Subtracting these from the 1D mix simulations, we infer similar degradations from high-mode instabilities and atomic mix for both drives. Due to its robustness to mid-mode instabilities, future pusher–gas mix studies will use the 2-shock drive.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗