Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model exploration”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The encapsulin from Thermotoga maritima is a flavoprotein with a symmetry matched ferritin-like cargo protein

Abstract Bacterial nanocompartments, also known as encapsulins, are an emerging class of protein-based ‘organelles’ found in bacteria and archaea. Encapsulins are virus-like icosahedral particles comprising a ~ 25–50 nm shell surrounding a specific cargo enzyme. Compartmentalization is thought to create a unique chemical environment to facilitate catalysis and isolate toxic intermediates. Many questions regarding nanocompartment structure–function remain unanswered, including how shell symmetry dictates cargo loading and to what extent the shell facilitates enzymatic activity. Here, we explore these questions using the model Thermotoga maritima nanocompartment known to encapsulate a redox-active ferritin-like protein. Biochemical analysis revealed the encapsulin shell to possess a flavin binding site located at the interface between capsomere subunits, suggesting the shell may play a direct and active role in the function of the encapsulated cargo. Furthermore, we used cryo-EM to show that cargo proteins use a form of symmetry-matching to facilitate encapsulation and define stoichiometry. In the case of the Thermotoga maritima encapsulin, the decameric cargo protein with fivefold symmetry preferentially binds to the pentameric-axis of the icosahedral shell. Taken together, these observations suggest the shell is not simply a passive barrier—it also plays a significant role in the structure and function of the cargo enzyme.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

GRB2 dimerization mediated by SH2 domain-swapping is critical for T cell signaling and cytokine production

GRB2 is an adaptor protein required for facilitating cytoplasmic signaling complexes from a wide array of binding partners. GRB2 has been reported to exist in either a monomeric or dimeric state in crystal and solution. GRB2 dimers are formed by the exchange of protein segments between domains, otherwise known as “domain-swapping”. Swapping has been described between SH2 and C-terminal SH3 domains in the full-length structure of GRB2 (SH2/C–SH3 domain-swapped dimer), as well as between α-helixes in isolated GRB2 SH2 domains (SH2/SH2 domain-swapped dimer). Interestingly, SH2/SH2 domain-swapping has not been observed within the full-length protein, nor have the functional influences of this novel oligomeric conformation been explored. We herein generated a model of full-length GRB2 dimer with an SH2/SH2 domain-swapped conformation supported by in-line SEC–MALS–SAXS analyses. This conformation is consistent with the previously reported truncated GRB2 SH2/SH2 domain-swapped dimer but different from the previously reported, full-length SH2/C-terminal SH3 (C–SH3) domain-swapped dimer. Our model is also validated by several novel full-length GRB2 mutants that favor either a monomeric or a dimeric state through mutations within the SH2 domain that abrogate or promote SH2/SH2 domain-swapping. GRB2 knockdown and re-expression of selected monomeric and dimeric mutants in a T cell lymphoma cell line led to notable defects in clustering of the adaptor protein LAT and IL-2 release in response to TCR stimulation. These results mirrored similarly-impaired IL-2 release in GRB2-deficient cells. These studies show that a novel dimeric GRB2 conformation with domain-swapping between SH2 domains and monomer/dimer transitions are critical for GRB2 to facilitate early signaling complexes in human T cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Augmenting machine learning of energy landscapes with local structural information

We present a machine learning approach for accurately predicting formation energies of binary compounds in the context of crystal structure predictions. The success of any machine learning model depends significantly on the choice of representation used to encode the relevant physical information into machine-learnable data. We test different representation schemes based on partial radial and angular distribution functions (RDF+ADF) on Al–Ni and Cd–Te structures generated using our genetic algorithm for structure prediction. We observe a remarkable improvement in predictive accuracy upon transitioning from global to atom-centered representations, resulting in a threefold decrease in prediction errors. We show that a support vector regression model using a combination of atomic radial and angular distribution functions performs best at the formation energy prediction task, providing small root mean squared errors of 3.9 meV/atom and 10.9 meV/atom for Al–Ni and Cd–Te, respectively. We test the performance of our models against common traditional descriptors and find that RDF- and ADF-based representations significantly outperform many of those in the prediction of formation energies. The high accuracy of predictions makes our machine learning models great candidates for the exploration of energy landscapes.

Honrao, Shreyas J. (ORCID:0000000166155257)↗
DOE OSTI2021

Correction and verification of x-ray imaging crystal spectrometer analysis on Wendelstein 7-X through x-ray ray tracing

X-ray ray tracing is used to develop ion-temperature corrections for the analysis of the X-ray Imaging Crystal Spectrometer (XICS) used at Wendelstein 7-X (W7-X) and perform verification on the analysis methods. The XICS is a powerful diagnostic able to measure ion-temperature, electron-temperature, plasma flow, and impurity charge state densities. While these systems are relatively simple in design, accurate characterization of the instrumental response and validation of analysis techniques are difficult to perform experimentally due to the requirement of extended x-ray sources. For this reason, a ray tracing model has been developed that allows characterization of the spectrometer and verification of the analysis methods while fully considering the real geometry of the XICS system and W7-X plasma. Through the use of ray tracing, several important corrections have been found that must be accounted for in order to accurately reconstruct the ion-temperature profiles. The sources of these corrections are described along with their effect on the analyzed profiles. The implemented corrections stem from three effects: (1) effect of sub-pixel intensity distribution during de-curving and spatial binning, (2) effect of sub-pixel intensity distribution during forward model evaluation and generation of residuals, and (3) effect of defocus and spherical aberrations on the instrumental response. Possible improvements to the forward model and analysis procedures are explored, along with a discussion of trade-offs in terms of computational complexity. Finally, the accuracy of the tomographic inversion technique in stellarator geometry is investigated, providing for the first time a verification exercise for inversion accuracy in stellarator geometry and a complete XICS analysis tool-chain.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗
DOE OSTI2021

Neural network potential for Zr–Rh system by machine learning

Zr–Rh metallic glass has enabled its many applications in vehicle parts, sports equipment and so on due to its outstanding performance in mechanical property, but the knowledge of the microstructure determining the superb mechanical property remains yet insufficient. Here, we develop a deep neural network potential of Zr–Rh system by using machine learning, which breaks the dilemma between the accuracy and efficiency in molecular dynamics simulations, and greatly improves the simulation scale in both space and time. Here, the results show that the structural features obtained from the neural network method are in good agreement with the cases in ab initio molecular dynamics simulations. Furthermore, we build a large model of 5400 atoms to explore the influences of simulated size and cooling rate on the melt-quenching process of Zr 77 Rh 23 . Our study lays a foundation for exploring the complex structures in amorphous Zr 77 Rh 23 , which is of great significance for the design and practical application.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

MHD mode tracking using high-speed cameras and deep learning

Abstract We present a new algorithm to track the amplitude and phase of rotating magnetohydrodynamic (MHD) modes in tokamak plasmas using high speed imaging cameras and deep learning. This algorithm uses a convolutional neural network (CNN) to predict the amplitudes of the n = 1 sine and cosine mode components using solely optical measurements from one or more cameras. The model was trained and tested on an experimental dataset consisting of camera frame images and magnetic-based mode measurements from the High Beta Tokamak - Extended Pulse (HBT-EP) device, and it outperformed other, more conventional, algorithms using identical image inputs. The effect of different input data streams on the accuracy of the model’s predictions is also explored, including using a temporal frame stack or images from two cameras viewing different toroidal regions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Effects beyond ideal MHD on stability of wide and enhanced pedestal regimes in NSTX

Stability of edge-localized modes (ELMs) in spherical tokamaks is explored using the extended MHD model. Linear NIMROD simulations have been performed for three NSTX discharges 132543, 132588, and 141133, to investigate the role of resistivity, diffusivity, and shear flows on the onset of ELMs. The first discharge represents the wide pedestal regime and the later two discharges represent ELM-free enhanced pedestal H-mode. We first present the effect of toroidal rotation shear and find a flow shear destabilizing effect in these NSTX discharges. Simulations are also extended to include the two-fluid and ion gyroviscosity effects. Simulations show that the flow shear can shift the mode spectrum and alter the critical condition of ELM onset. We also uncover that ELM onset prediction in spherical tokamaks requires effects beyond MHD, in particular gyroviscosity and diamagnetic terms could stabilize Peeling-Ballooning modes consistent with the experimental observation of ELM-free regimes in NSTX. The findings give new insight into the nature of the interplay between resistivity, flows, and diamagnetic stabilization in ELM suppression and have potential applications to ELM control schemes in NSTX-U and next-generation spherical tokamaks. This study identifies the essential physical effects that must be included in future predictive and validation simulations.

NSTX↗
DOE OSTI2023

The DESI One-Percent survey: exploring the Halo Occupation Distribution of Emission Line Galaxies with AbacusSummit simulations

The One-Percent survey of the Dark Energy Spectroscopic Instrument collected ~ 270k emission line galaxies (ELGs) at 0.8 < z < 1.6. The high completeness of the sample allowed the clustering to be measured down to scales never probed before, 0.04 Mpc/h in rp for the projected 2-point correlation function (2PCF) and 0.17 Mpc/h in galaxy pair separation s for the 2PCF monopole and quadrupole. The most striking feature of the measurements is a strong signal at the smallest scales, below 0.2 Mpc/h in rp and 1 Mpc/h in s. We analyse these data in the halo occupation distribution framework. We consider different distributions for central galaxies, a standard power law for satellites with no condition on the presence of a central galaxy and explore several extensions of these models. For all considered models, the mean halo mass of the sample is found to be log10 $\langle$Mh$\rangle$ ~ 11.9. We obtain a satellite mean occupation function which agrees with physically motivated ELG models only if we introduce central-satellite conformity, meaning that the satellite occupation is conditioned by the presence of central galaxies of the same type. To achieve in addition a good modelling of the clustering between 0.1 and 1 Mpc/h in rp, we allow for ELG positioning outside of the halo virial radius and find 0.5% of ELGs residing in the outskirts of halos. Furthermore, the satellite velocity dispersion inside halos is found to be ~ 30% larger than that of the halo dark matter particles. These are the main findings of our work. Here we investigate assembly bias as a function of halo concentration, local density or local density anisotropies and observe no significant change in our results. We split the data sample in two redshift bins and report no significant evolution with redshift. Lastly, changing the cosmology in the modelling impacts only slightly our results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Modeling electron temperature profiles in the pedestal with simple formulas for ETG transport

Abstract This paper reports on the refinement (building on (Hatch D.R. et al 2022 Phys. Plasmas 29 062501)) and application of simple formulas for electron heat transport from electron temperature gradient (ETG) driven turbulence in the pedestal. The formulas are improved by (1) improving the parameterization for certain key parameters and (2) carefully accounting for the impact of geometry and shaping in the underlying gyrokinetic simulation database. Comparisons with nonlinear gyrokinetic simulations of ETG transport in the MAST pedestal demonstrate the model’s applicability to spherical tokamaks in addition to standard aspect ratio tokamaks. We identify bounds for model applicability: the model is accurate in the steep gradient region, where the ETG turbulence is largely slab-like, but accuracy decreases as the temperature gradient becomes weaker in the pedestal top. We use the formula to model the electron temperature profile in the pedestal for four experimental scenarios while extensively varying input parameters to represent uncertainties. In all cases, the predicted electron temperature profile exhibits extreme sensitivity to separatrix temperature and density, which has implications for core-edge integration. The model reproduces the electron temperature profile for high η e = L n e / L T e scenarios but not for low η e scenarios in which microtearing modes (MTMs) have been identified. We develop a proof-of-concept model for MTM transport and explore the relative roles of ETG and MTM in setting the electron temperature profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

CMIP6 skill at predicting interannual to multi-decadal summer monsoon precipitation variability

Monsoons affect the economy, agriculture, and human health of two thirds of the world’s population. Therefore, predicting variations in monsoon precipitation is societally important. We explore the ability of climate models from the sixth phase of the Climate Model Intercomparison Project to predict summer monsoon precipitation variability by using hindcasts from the Decadal Climate Prediction Project (Component A). The multi-model ensemble-mean shows significant skill at predicting summer monsoon precipitation from one year to 6–9 years ahead. However, this skill is dependent on the model, monsoon domain, and lead-time. In general, the skill of the multi-model ensemble-mean prediction is low in year 1 but increases for longer-lead times and is largely consistent with externally forced changes. The best captured region is northern Africa for the 2–5 and 6–9 year forecast lead times. In contrast, there is no significant skill using the ensemble-mean over East and South Asia and, furthermore, there is significant spread in skill among models for these domains. By sub-sampling the ensemble we show that the difference in skill between models is tied to the simulation of the externally forced response over East and South Asia, with models with a more skilful forced response capable of better predictions. A further contribution is from skilful prediction of Pacific Ocean temperatures for the South Asian summer monsoon at longer lead-times. Therefore, these results indicate that predictions of the East and South Asian monsoons could be significantly improved.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Correlations between triaxial shapes and formation history of dark matter haloes

ABSTRACT The shape of dark matter (DM) haloes plays a critical role in constraining cosmology with upcoming large-scale structure surveys. In this paper, we study the correlations between the triaxial shapes and formation histories in DM haloes in the MultiDark Planck 2 N-body cosmological simulation. We find that halo ellipticity is strongly correlated with halo properties that serve as proxies of halo formation history, such as halo concentration and the normalized peak-centroid offset. These correlations are nearly independent of the halo density peak height. We present a simple model for the correlation between halo ellipticity and concentration using conditional abundance matching, and provide fitting formulae for the multidimensional distributions of triaxial halo shape as a function of halo peak height. We apply our halo shape model to gauge the effects of halo ellipticity and orientation bias on the excess surface mass density profiles in cluster-sized haloes. Our model should be useful for exploring the impact of triaxial halo shape on cosmological constraints in upcoming weak lensing surveys of galaxy clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Angular clustering properties of the DESI QSO target selection using DR9 Legacy Imaging Surveys

ABSTRACT The quasar target selection for the upcoming survey of the Dark Energy Spectroscopic Instrument (DESI) will be fixed for the next 5 yr. The aim of this work is to validate the quasar selection by studying the impact of imaging systematics as well as stellar and galactic contaminants, and to develop a procedure to mitigate them. Density fluctuations of quasar targets are found to be related to photometric properties such as seeing and depth of the Data Release 9 of the DESI Legacy Imaging Surveys. To model this complex relation, we explore machine learning algorithms (random forest and multilayer perceptron) as an alternative to the standard linear regression. Splitting the footprint of the Legacy Imaging Surveys into three regions according to photometric properties, we perform an independent analysis in each region, validating our method using extended Baryon Oscillation Spectroscopic Survey (eBOSS) EZ-mocks. The mitigation procedure is tested by comparing the angular correlation of the corrected target selection on each photometric region to the angular correlation function obtained using quasars from the Sloan Digital Sky Survey (SDSS) Data Release 16. With our procedure, we recover a similar level of correlation between DESI quasar targets and SDSS quasars in two-thirds of the total footprint and we show that the excess of correlation in the remaining area is due to a stellar contamination that should be removed with DESI spectroscopic data. We derive the Limber parameters in our three imaging regions and compare them to previous measurements from SDSS and the 2dF QSO Redshift Survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Photon strength functions and nuclear level densities: invaluable input for nucleosynthesis

The pivotal role of nuclear physics in nucleosynthesis processes is being investigated, in particular the intricate influence of photon strength functions (PSFs) and nuclear level densities (NLDs) on shaping the outcomes of the i-, r- and p-processes. Exploring diverse NLD and PSF model combinations uncovers large uncertainties for (p, γ ), (n, γ ) and ( α , γ ) rates across many regions of the nuclear chart. These lead to potentially significant abundance variations of the nucleosynthesis processes and highlight the importance of accurate experimental nuclear data. Theoretical insights and advanced experimental techniques lay the ground work for profound understanding that can be gained of nucleosynthesis mechanisms and the origin of the elements. Recent results further underscore the effect of PSF and NLD data and its contribution to understanding abundance distributions and refining knowledge of the intricate nucleosynthesis processes. This article is part of the theme issue ‘The liminal position of Nuclear Physics: from hadrons to neutron stars’.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Probing Excited-State Dynamics of Transmon Ionization

The fidelity and quantum nondemolition character of the dispersive readout in circuit QED are limited by unwanted transitions to highly excited states at specific photon numbers in the readout resonator. This observation can be explained by multiphoton resonances between computational states and highly excited states in strongly driven nonlinear systems, analogous to multiphoton ionization in atoms and molecules. In this work, we utilize the multilevel nature of high-𝐸 𝐽 /𝐸 𝐶 transmons to probe the excited-state dynamics induced by strong drives during readout. With up to ten resolvable states, we quantify the critical photon number of ionization, the resulting state after ionization, and the fraction of the population transferred to highly excited states. Moreover, using pulse shaping to control the photon number in the readout resonator in the high-power regime, we tune the adiabaticity of the transition and verify that transmon ionization is a Landau-Zener-type transition. We further extend these methods to a typical transmon with 𝐸 𝐽 /𝐸 𝐶 ≈ 55 and probe the offset-charge dependence of ionization dynamics in a timed-resolved manner. Our experimental results agree well with the theoretical prediction from a semiclassical driven transmon model and may guide future exploration of strongly driven nonlinear oscillators.

cavity quantum electrodynamics↗
DOE OSTI2020

Backpropagated frame transformation theory: A reformulation

In this work, the energy-dependent frame transformation theory of Gao and Greene [H. Gao and C. H. Greene, Phys. Rev. A 42, 6946 (1990)] is extended to yield a quantitatively accurate description of the dissociative recombination process. Evidence is presented to show that direct application of the original theory leads to inaccurate cross sections. A major revision, based on an interaction-free backpropagation of the Born-Oppenheimer solutions, markedly improves the frame transformation theory, reducing its average error by orders of magnitude. The original theory and its extension are tested on the previously explored two-dimensional (2D) model that is tailored to describe the singlet ungerade states of molecular hydrogen. The 2D model can be solved exactly (within the numerical accuracy) without implementing the Born-Oppenheimer approximation. These exact results then serve as a benchmark for the frame transformation theory developed in this paper.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Shock revival in core-collapse supernovae assisted by heavy axionlike particles

We report axionlike particles (ALPs) are a class of hypothetical pseudoscalar particles which feebly interact with ordinary matter. The hot plasma of core-collapse supernovae is a possible laboratory to explore physics beyond the standard model including ALPs. Once produced, some of the ALPs can be absorbed by the supernova matter and affect energy transfer. In this study, we calculate the ALP emission in core-collapse supernovae and the backreaction on supernova dynamics consistently. It is found that the stalled bounce shock can be revived if the coupling between ALPs and photons is as high as $g_{aγ}$ ~ 10 -9 GeV -1 and the ALP mass is 40–400 MeV. Most of the models result in more energetic explosions than the average observed supernova. While this can be used to place constraints on those ALPs, long-term simulations across multiple progenitors need to be further investigated to place robust limits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Connecting relativistic density functional theory to microscopic calculations

The development of systematic effective field theories (EFTs) for nuclear forces and advances in solving the nuclear many-body problem have greatly improved our understanding of dense nuclear matter and the structure of finite nuclei. For global nuclear calculations, density functional theories (DFTs) have been developed to reduce the complexity and computational cost required in describing nuclear systems. However, DFT often makes approximations and assumptions about terms included in the functional, which may introduce systematic uncertainties compared to microscopic calculations using EFTs. In this work, we investigate possible avenues of improving nuclear DFT using nonlinear relativistic mean-field (RMF) theory. We explore the impact of RMF model extensions by fitting the nonlinear RMF model to predictions of nuclear matter and selected closed-shell nuclei using four successful chiral EFT Hamiltonians. We find that these model extensions are impactful and important in capturing the physics present within chiral Hamiltonians, particularly for charge radii and neutron skins of closed-shell nuclei. However, there are additional effects that are not captured within the RMF model, particularly within the isoscalar sector of RMF theory. Additional model extensions and the reliability of the nonlinear RMF model are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗