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At least 145 records · Page 8

Radial diffusion in the Uranian radiatian belts - Inferences from satellite absorption loss models

Low-energy charged particle (LECP) phase space density profiles available from the Voyager/1986 Uranus encounter are analyzed, using solutions of the time-averaged radial diffusion equation for charged particle transport in a dipolar planetary magnetic field. Profiles for lower-energy protons and electrons are first analyzed to infer radial diffusion rate as a function of L, assuming that satellite absorption is the dominant loss process and local sources for these particles are negligible. Satellite macrosignatures present in the experimentally derived profiles are approximately reproduced in several cases, lending credence to the loss model and indicating that magnetospheric distributed losses are not as rapid as satellite absorption near the minimum satellite L shells for the particles. Diffusion rates and L dependences are found to be similar to those previously inferred in the inner Jovian magnetosphere (Thomsen et al., 1977) and for the inner Saturnian magnetosphere (Hood, 1985). Profiles for higher energy electrons and protons are also analyzed using solutions that allow for the existence of significant particle sources as well as sinks. Possible implications for radial diffusion mechanisms in the Uranian radiation belts are discussed.

Hood, L. L.↗

Deep operator network surrogate for phase-field modeling of metal grain growth during solidification

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the the Allen-Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400° C at an average rate of 30° C / min , traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. Further, these relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.

36 MATERIALS SCIENCE↗

The shock physics of giant impacts: Key requirements for the equations of state

We discuss major challenges in modeling giant impacts between planetary bodies, focusing on the equations of state (EOS). During the giant impact stage of planet formation, rocky planets are melted and partially vaporized. However, most EOS models fail to reproduce experimental constraints on the thermodynamic properties of the major minerals over the required phase space. Here, we present an updated version of the widely-used ANEOS model that includes a user-defined heat capacity limit in the thermal free energy term. Our revised model for forsterite (Mg 2 SiO 4 ), a common proxy for the mantles of rocky planets, provides a better fit to material data over most of the phase space of giant impacts. We discuss the limitations of this model and the Tillotson equation of state, a commonly used alternative model.

79 ASTRONOMY AND ASTROPHYSICS↗

The Two-Phase Flow Separator Experiment Breadboard Model: Reduced Gravity Aircraft Results

Life support systems in space depend on the ability to effectively separate gas from liquid. Passive cyclonic phase separators use the centripetal acceleration of a rotating gas-liquid mixture to carry out phase separation. The gas migrates to the center, while gas-free liquid may be withdrawn from one of the end plates. We have designed, constructed and tested a breadboard that accommodates the test sections of two independent principal investigators and satisfies their respective requirements, including flow rates, pressure and video diagnostics. The breadboard was flown in the NASA low-gravity airplane in order to test the system performance and design under reduced gravity conditions.

phase separation↗

James Webb Space Telescope Navigation Optimization Challenges

This paper details the orbit determination, solar radiation pressure (SRP) modeling, and station-keeping maneuver planning for the NASA James Webb Space Telescope during the routine science phase of the mission. The complexities of SRP modeling driven by the vehicle’s large area, attitude profile, and attitude constraints for maneuver execution entail unique challenges for spaceflight navigation. The techniques utilized to combine predictive attitude and maneuver targeting modeling were refined using the experiences and data accumulated during and following the commissioning phase. The Navigation Team at NASA Goddard Space Flight Center’s Flight Dynamics Facility responded to these challenges and implemented methods for trajectory optimization and improving maneuver efficiency.

Flight Dynamics↗

James Webb Space Telescope Navigation Optimization Challenges

This paper details the orbit determination, solar radiation pressure (SRP) modeling, and station-keeping maneuver planning for the NASA James Webb Space Telescope during the routine science phase of the mission. The complexities of SRP modeling driven by the vehicle’s large area, attitude profile, and attitude constraints for maneuver execution entail unique challenges for spaceflight navigation. The techniques utilized to combine predictive attitude and maneuver targeting modeling were refined using the experiences and data accumulated during and following the commissioning phase. The Navigation Team at NASA Goddard Space Flight Center’s Flight Dynamics Facility responded to these challenges and implemented methods for trajectory optimization and improving maneuver efficiency.

Flight Dynamics↗

Orbit tomography of energetic particle distribution functions

Both fast ions and runaway electrons are described by distribution functions, the understanding of which are of critical importance for the success of future fusion devices such as ITER. Typically, energetic particle diagnostics are only sensitive to a limited subsection of the energetic particle phase-space which is often insufficient for model validation. However, previous publications show that multiple measurements of a single spatially localized volume can be used to reconstruct a distribution function of the energetic particle velocity-space by using the diagnostics' velocity-space weight functions, i.e. velocity-space Tomography. In this work we use the recently formulated orbit weight functions to remove the restriction of spatially localized measurements and present orbit tomography, which is used to reconstruct the 3D phase-space distribution of all energetic particle orbits in the plasma. Through a transformation of the orbit distribution, the full energetic particle distribution function can be determined in the standard {energy, pitch, r, z}-space. We benchmark the technique by reconstructing the fast-ion distribution function of an MHD-quiescent DIII-D discharge using synthetic and experimental FIDA measurements. We also use the method to study the redistribution of fast ions during a sawtooth crash at ASDEX upgrade using FIDA measurements. Lastly, a comparison between the orbit tomography and velocity-space tomography is shown.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Heat transfer phase change paint tests of 0.0175-scale models (nos. 21-0 and 46-0) of the Rockwell International space shuttle orbiter in the AEDC tunnel B hypersonic wind tunnel (test OH25A)

Tests were conducted in a hypersonic wind tunnel using various truncated space shuttle orbiter configurations in an attempt to establish the optimum model size for other tests examining body shock-wing leading edge interference effects. The tests were conducted at Mach number 8 using the phase change paint technique. A test description, tabulated data, and tracings of isotherms made from photographs taken during the test are presented.

Dye, W. H.↗

AeroCom Phase III Multi-Model Evaluation of the Aerosol Life Cycle and Optical Properties Using Ground and Space-Based Remote Sensing as Well as Surface In Situ Observations

Within the framework of the AeroCom (Aerosol Comparisons between Observations and Models) initiative, the state-of-the-art modelling of aerosol optical properties is assessed from 14 global models participating in the phase III control experiment (AP3). The models are similar to CMIP6/AerChemMIP Earth System Models (ESMs) and provide a robust multi-model ensemble. Inter-model spread of aerosol species lifetimes and emissions appears to be similar to that of mass extinction coefficients (MECs), suggesting that aerosol optical depth (AOD) uncertainties are associated with a broad spectrum of parameterised aerosol processes. Total AOD is approximately the same as in AeroCom phase I (AP1) simulations. However, we find a 50 % decrease in the optical depth (OD) of black carbon (BC), attributable to a combination of decreased emissions and lifetimes. Relative contributions from sea salt (SS) and dust (DU) have shifted from being approximately equal in AP1 to SS contributing about 2∕3 of the natural AOD in AP3. This shift is linked with a decrease in DU mass burden, a lower DU MEC, and a slight decrease in DU lifetime, suggesting coarser DU particle sizes in AP3 compared to AP1. Relative to observations, the AP3 ensemble median and most of the participating models underestimate all aerosol optical properties investigated, that is, total AOD as well as fine and coarse AOD (AODf, AODc), Ångström exponent (AE), dry surface scattering (SCdry), and absorption (ACdry) coefficients. Compared to AERONET, the models underestimate total AOD by ca. 21 % ± 20 % (as inferred from the ensemble median and interquartile range). Against satellite data, the ensemble AOD biases range from −37 % (MODIS-Terra) to −16 % (MERGED-FMI, a multi-satellite AOD product), which we explain by differences between individual satellites and AERONET measurements themselves. Correlation coefficients (R) between model and observation AOD records are generally high (R>0.75), suggesting that the models are capable of capturing spatio-temporal variations in AOD. We find a much larger underestimate in coarse AODc (∼ −45 % ± 25 %) than in fine AODf (∼ −15 % ± 25 %) with slightly increased inter-model spread compared to total AOD. These results indicate problems in the modelling of DU and SS. The AODc bias is likely due to missing DU over continental land masses (particularly over the United States, SE Asia, and S. America), while marine AERONET sites and the AATSR SU satellite data suggest more moderate oceanic biases in AODc. Column AEs are underestimated by about 10 % ± 16 %. For situations in which measurements show AE > 2, models underestimate AERONET AE by ca. 35 %. In contrast, all models (but one) exhibit large overestimates in AE when coarse aerosol dominates (bias ca. +140 % if observed AE < 0.5). Simulated AE does not span the observed AE variability. These results indicate that models overestimate particle size (or underestimate the fine-mode fraction) for fine-dominated aerosol and underestimate size (or overestimate the fine-mode fraction) for coarse-dominated aerosol. This must have implications for lifetime, water uptake, scattering enhancement, and the aerosol radiative effect, which we can not quantify at this moment. Comparison against Global Atmosphere Watch (GAW) in situ data results in mean bias and inter-model variations of −35 % ± 25 % and −20 % ± 18 % for SCdry and ACdry, respectively. The larger underestimate of SCdry than ACdry suggests the models will simulate an aerosol single scattering albedo that is too low. The larger underestimate of SCdry than ambient air AOD is consistent with recent findings that models overestimate scattering enhancement due to hygroscopic growth. The broadly consistent negative bias in AOD and surface scattering suggests an underestimate of aerosol radiative effects in current global aerosol models. Considerable inter-model diversity in the simulated optical properties is often found in regions that are, unfortunately, not or only sparsely covered by ground-based observations. This includes, for instance, the Sahara, Amazonia, central Australia, and the South Pacific. This highlights the need for a better site coverage in the observations, which would enable us to better assess the models, but also the performance of satellite products in these regions. Using fine-mode AOD as a proxy for present-day aerosol forcing estimates, our results suggest that models underestimate aerosol forcing by ca. −15 %, however, with a considerably large interquartile range, suggesting a spread between −35 % and +10 %.

space-based remote sensing↗

Probabilistic risk assessment of the Space Shuttle. Phase 3: A study of the potential of losing the vehicle during nominal operation. Volume 2: Integrated loss of vehicle model

The application of the probabilistic risk assessment methodology to a Space Shuttle environment, particularly to the potential of losing the Shuttle during nominal operation is addressed. The different related concerns are identified and combined to determine overall program risks. A fault tree model is used to allocate system probabilities to the subsystem level. The loss of the vehicle due to failure to contain energetic gas and debris, to maintain proper propulsion and configuration is analyzed, along with the loss due to Orbiter, external tank failure, and landing failure or error.

Fragola, Joseph R.↗

Data-driven prediction of α IIb β 3 integrin activation paths using manifold learning and deep generative modeling

The integrin heterodimer is a transmembrane protein critical for driving cellular process and is a therapeutic target in the treatment of multiple diseases linked to its malfunction. Activation of integrin involves conformational transitions between bent and extended states. Some of the conformations that are intermediate between bent and extended states of the heterodimer have been experimentally characterized, but the full activation pathways remain unresolved both experimentally due to their transient nature and computationally due to the challenges in simulating rare barrier crossing events in these large molecular systems. An understanding of the activation pathways can provide new fundamental understanding of the biophysical processes associated with the dynamic interconversions between bent and extended states and unveil new putative therapeutic targets. In this work, we apply nonlinear manifold learning to coarse-grained molecular dynamics simulations of bent, extended, and two intermediate states of αI I b β3 integrin to learn a low-dimensional embedding of the configurational phase space. We then train deep generative models to learn an inverse mapping between the low-dimensional embedding and high-dimensional molecular space and use these models to interpolate the molecular configurations constituting the activation pathways between the experimentally characterized states. Furthermore, this work furnishes plausible predictions of integrin activation pathways and reports a generic and transferable multi-scale technique to predict transition pathways for biomolecular systems.

97 MATHEMATICS AND COMPUTING↗

Application of System Operational Effectiveness Methodology to Space Launch Vehicle Development and Operations

The Department of Defense (DoD) defined System Operational Effectiveness (SOE) model provides an exceptional framework for an affordable approach to the development and operation of space launch vehicles and their supporting infrastructure. The SOE model provides a focal point from which to direct and measure technical effectiveness and process efficiencies of space launch vehicles. The application of the SOE model to a space launch vehicle's development and operation effort leads to very specific approaches and measures that require consideration during the design phase. This paper provides a mapping of the SOE model to the development of space launch vehicles for human exploration by addressing the SOE model key points of measurement including System Performance, System Availability, Technical Effectiveness, Process Efficiency, System Effectiveness, Life Cycle Cost, and Affordable Operational Effectiveness. In addition, the application of the SOE model to the launch vehicle development process is defined providing the unique aspects of space launch vehicle production and operations in lieu of the traditional broader SOE context that examines large quantities of fielded systems. The tailoring and application of the SOE model to space launch vehicles provides some key insights into the operational design drivers, capability phasing, and operational support systems.

Watson, Michael D.↗

Assessment of external contamination for Space Station scientific payloads

This paper presents the analysis of the nature, the source, and the effect of contamination encountered by payloads external to the Space Station spacecraft. The contaminant modeling study determined the key contaminant sources for the Space Station payloads, together with the location, the duration, the major constituents of the contaminant, and the possibility of contaminant control. The next phase of the contamination modeling study investigates the compatibility of external Space Station payloads while on the transverse boom and the dual keel option. Other predictions include depositions on payload surfaces facing along +X, +Y, and +Z directions. An additional analysis will utilize the above data to predict the spectral brightness of the contaminants along payload lines-of-sight, making it possible for payload users to determine the impact of the contaminant background relative to the natural background.

Primeaux, Gary R.↗

Measurements of W + W - + ≥ 1 jet production cross-sections in pp collisions at $ \sqrt{s} $ = 13 TeV with the ATLAS detector

Fiducial and differential cross-section measurements of W + W - production in association with at least one hadronic jet are presented. These measurements are sensitive to the properties of electroweak-boson self-interactions and provide a test of perturbative quantum chromodynamics and the electroweak theory. The analysis is performed using proton-proton collision data collected at $ \sqrt{s} $ = 13 TeV with the ATLAS experiment, corresponding to an integrated luminosity of 139 fb -1 . Events are selected with exactly one oppositely charged electron-muon pair and at least one hadronic jet with a transverse momentum of p T > 30 GeV and a pseudorapidity of |η| < 4.5. After subtracting the background contributions and correcting for detector effects, the jet-inclusive W + W - + ≥ 1 jet fiducial cross-section and W + W - + jets differential cross-sections with respect to several kinematic variables are measured. These measurements include leptonic quantities, such as the lepton transverse momenta and the transverse mass of the W + W - system, as well as jet-related observables such as the leading jet transverse momentum and the jet multiplicity. Limits on anomalous triple-gauge-boson couplings are obtained in a phase space where interference between the Standard Model amplitude and the anomalous amplitude is enhanced.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Evolution of highly multimodal Rayleigh–Taylor instabilities

Rayleigh–Taylor (RT) instabilities are important fluid instabilities that arise in inertial confinement fusion (ICF) capsule implosions, and many other contexts. Multi-mode coupling is observed in experiments and plays a substantial role in material mix from RT instabilities. In this work, we study the evolution of highly multimodal perturbations (power law distribution) that approximate those found at manufactured material interfaces. We use simulations of over 2000 different perturbations in the LANL code xRAGE to identify distinct phases in the processes of bubble growth and bubble merger which can be visualized in a 2D phase portrait with clear regimes of mode growth and decay. Our results show that the dynamic evolution of the instability strongly depends on the mode of the perturbations and mode interactions. The merger process accelerates bubble growth. A non-Markovian region and a transition of the instability from: (1) initial exponential growth to (2) linear growth and to (3) quadratic growth and asymptotic behavior, are clearly captured in the phase space. We have developed a quantitative model of bubble growth that reproduces the dynamic behavior of ensembles of perturbations. Implications for ICF capsules designed for robustness against instabilities are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. Here, we highlight the application and suitability of different representations, from expert-guided “engineered” descriptors to automatically “learned” features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data. The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used. Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as “hand-crafted” molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure–property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors. Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule’s weakest bond are used in simple physical models of site-selectivity and reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Tools for unbinned unfolding

Machine learning has enabled differential cross section measurements that are not discretized. Going beyond the traditional histogram-based paradigm, these unbinned unfolding methods are rapidly being integrated into experimental workflows. Here, in order to enable widespread adaptation and standardization, we develop methods, benchmarks, and software for unbinned unfolding. For methodology, we demonstrate the utility of boosted decision trees for unfolding with a relatively small number of high-level features. This complements state-of-the-art deep learning models capable of unfolding the full phase space. To benchmark unbinned unfolding methods, we develop an extension of existing dataset to include acceptance effects, a necessary challenge for real measurements. Additionally, we directly compare binned and unbinned methods using discretized inputs for the latter in order to control for the binning itself. Lastly, we have assembled two software packages for the OmniFold unbinned unfolding method that should serve as the starting point for any future analyses using this technique. One package is based on the widely-used RooUnfold framework and the other is a standalone package available through the Python Package Index (PyPI).

47 OTHER INSTRUMENTATION↗