Evolved Structures Guide for GSFC Applications
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Evolved Structures Generative Design↗
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Ab Initio Electronic Structure Calculations of CNN for CN Excitation Studies
Titan’s atmosphere is composed mostly of N 2 with a small amount of CH 4 , and so, shock layers around craft entering Titan’s atmosphere will contain a variety of molecules formed from H, C, and N atoms, including the cyanogen radical CN. Sensitivity analysis has shown that the radiative heat flux predicted by computational fluid dynamics (CFD) simulations of Titan entry has up to 14% uncertainty due to the rate coefficients for collisional (de)excitation reactions that control the population of CN in its first and second excited states. The red and violet emission bands from CN’s first and second excited states, respectively, are known to be large sources of radiative heat flux on capsules entering Titan’s atmosphere.[2, 3] So, the simulated population of CN in its first and second excited states is very important, but currently has some inherent uncertainty coming from the data for the rate coefficients for reaction 1. The goal of the present project is to provide improved rate coefficient data for these reactions from first principles quantum chemistry calculations. This work reports on preliminary electronic structure calculations generated at a large number of triatomic geometries of interest, which show multiple avoided crossings at collinear arrangements. This suggests that collisional (de)excitation of CN by N atoms is likely to proceed through these geometries.
Eric Geistfeld↗
Assessment of Updraft Modeling Bias Using Computational Fluid Dynamics
Golden Eagle (Aquila chrysaetos) habitats may overlap with wind energy development in some regions of the US. Eagles, and similar soaring bird species, are therefore at risk of collision with wind turbines when flying through wind farms. Recently developed behavioral modeling approaches can predict the presence of eagles near turbines within the rotor-swept layer but require reliable prediction of atmospheric flowfield conditions. In particular, the vertical component of the wind speed dictates a soaring bird's ability to maintain or gain altitude, since they rely on updrafts to subsidize their flight. In this work, we investigate the atmospheric conditions around a wind farm in complex terrain and compare methods for atmospheric characterization. We use computational fluid dynamics (specifically, large-eddy simulations, or LES) to simulate the atmospheric boundary layer over a region encompassing multiple wind farms with high temporal and spatial resolution (seconds and 10's of meters, respectively). We compare traditional non-simulation-based methods of determining the orographic updraft potential based on wind direction, terrain slope and aspect, with the flowfields from LES that include both orographic updrafts alone and combined thermal and orographic updrafts. Preliminary analysis suggests that although the model captures the horizontal pattern of vertical updrafts, their magnitude can be improved with information about the surface heat flux, which is usually correlated with time of the day. Within our study region, we found that the low-fidelity model may over- or underestimate updraft potential by up to 400% at 80 m AGL, depending on local orographic features. This can result in an inaccurate representation of eagle presence and, consequently, risk. Another important finding is that flowfield time-averaging can hide important details about the flight environment, including how thermally generated flow structures within the atmospheric boundary layer (e.g., convective rolls and/or cells) may be important drivers of eagle flight.
atmospheric turbulence↗
Evaluating the Use of Foundational Chemical Language Models in Multimodal Graph Fusion
Rapid and accurate prediction of the physicochemical properties of molecules given their structures remains a key challenge in cheminformatics. Machine learning approaches offer high-throughput options, but the optimality of inductive biases and data representations are up for debate. For example, BERT-based masked language models (MLMs) can be trained in a self-supervised way on hundreds of millions to billions of readily available SMILES strings. Another option is graph neural networks (GNNs), which can operate directly on molecular structures. Yet, generating accurate molecular geometry is computationally expensive, leading to a relative scarcity in data compared to SMILES strings. It is attractive to combine these two paradigms by pre-training an LM on a large corpus of SMILES strings and embedding these representation into a geometric graph neural network. Despite the promise of such an approach, and contrary to previous studies, we find mixed results with the combination of the LMs and GNNs on several molecule datasets. In particular, we found evidence for improvement on the FreeSolv and QM7 benchmarks, but degraded performance on the ESOL, LIPO and QM9 datasets compared to a GNN baseline.
Francel, Collin [University of Alabama]↗
Revealing the Structure and Dynamics of Self-Generated Electric and Magnetic Fields Near Plasma Stagnation in Laser-Driven Hohlraums
By coupling newly developed triparticle charged particle radiography with radiography reconstruction algorithms and novel reconstruction postprocessing techniques, the spatial structure and time evolution of self-generated electric and magnetic fields in laser-driven vacuum hohlraums have been quantitatively revealed. Through high-fidelity data from a series of experiments, it is shown that late in the hohlraum evolution (after the end of laser drive) these fields are strongly correlated in both space and time, providing evidence that their evolution is primarily dominated by advection with the plasma flow. At these late times, plasma flow velocities inferred from both gross radiography analysis and field reconstructions (and corroborated with Thomson scattering measurements) indicate that the plasma is approaching stagnation near the hohlraum axis. Finally, these experiments provide not only new physical insight into spontaneously generated hohlraum fields, but also provide important spatially and temporally resolved information for future benchmarking of numerical codes.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Auroral plasma transport processes in the presence of kV potential structures
We have simulated plasma transport processes in the presence of a quasi-two-dimensional current filament, that generated kV potential structure in the auroral region. The simulation consists of a set of one-dimensional flux tube simulations with different imposed time-dependent, field-aligned currents. The model uses the 16 moment system of equations and simultaneously solves coupled continuity and momentum equations and equations describing the transport along the magnetic field lines of parallel and perpendicular thermal energy and heat flows for each species. The lower end of the simulation is at an altitude of 800 km, in the collisional topside ionosphere, while the upper end is at 10 R(sub E) in the magnetosphere. The plasma consists of hot electrons and protons of magnetospheric origin and low-energy electrons, protons, and oxygen ions of ionospheric origin. The dynamical interaction of the individual current filaments with ionospheric and magnetospheric plasma generates a potential structure in the horizontal direction and kilovolt field-aligned potential drops along the field lines. The side-by-side display exhibits the evolution of the implied potential structure in the horizontial direction. In the presence of this potential structure and parallel electric field ionospheric plasma density is depleted and velocity is reduced, while density enhancement and increased velocity is observed in magnetospheric plasma. The ionospheric and magnetospheric electron temperatures increase below 2 R(sub E) due to magnetic mirror force on converging geomagnetic field lines. The primary cross-field motion produced by the horizontal E field (E x B drift) is perpendicular to both of the significant spatial directions and is thus ignorable in this geometry. The effects of other cross-field drift processes are discussed. The simulation thus provides insight into the dynamical evolution of two-dimensional potential structures driven by an imposed finite width, field-aligned current profile.
Ganguli, Supriya B.↗
Mesh Effects on Flow Solutions for a 2-D Multi-Element Airfoil Using Structured Overset Methods
Current best-practices for mesh generation in a structured overset framework are evaluated to determine their effects on the accuracy and convergence of the flow solution on a two-dimensional sectional cut of the High-Lift Common Research Model (HLCRM) wing. Mesh parameters are varied independently and correlated with force and moment coefficients from the flow solutions to identify the trends and sensitivities of the parameter variations, and the characteristics of a best practice mesh. Grid convergence and mesh adaption studies are also performed to determine the minimum mesh quality needed in order for solution-based adaption to reach an accurate flow solution.
T^3↗
Generative Models for Crystalline Materials
Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery.
Metni, Houssam [Karlsruhe Inst. of Technology (KIT↗
Composition and Structure of Fluorescent Graphene Quantum Dots Generated by Enzymatic Degradation of Graphene Oxide
The broad applications of carbon nanomaterials (CNMs) in both materials and life sciences necessitate investigation of their metabolites due to the inevitable contact of CNMs and biological systems. Graphene oxide (GO), along with other types of CNMs, can be enzymatically degraded by myeloperoxidase (MPO), an enzyme released during the innate immune response. However, the enzymatic degradation products are neither well-defined nor well-understood. Some products generated during MPO-catalyzed degradation of GO could emit blue photoluminescence (PL) and were simply dubbed graphene quantum dots (GQDs) without further elucidating their structures. In this work, we use liquid chromatography-mass spectrometry (LC-MS) to isolate and elucidate chemical structures of the MPO-catalyzed degradation products. A general chemical formula screening workflow was developed for the GQDs, which are in the form of polyaromatic hydrocarbons (PAHs), obtained in the degradation products. Structures of the PAHs responsible for the blue PL were further proposed using density functional theory (DFT) calculations. Our results indicated that structures with several conjugated benzene rings are likely to generate the observed PL. This work provides insights into the mechanism of enzymatic degradation and open opportunities for fluorescence imaging of GO in biological systems.
composition and structure screening↗
$\mathrm{IH}$-$\mathrm{GAN}$: A conditional generative model for implicit surface-based inverse design of cellular structures
Variable-density cellular structures can overcome connectivity and manufacturability issues of topologically optimized structures, particularly those represented as discrete density maps. However, the optimization of such cellular structures is challenging due to the multiscale design problem. Past work addressing this problem generally either only optimizes the volume fraction of single-type unit cells but ignoring the effects of unit cell geometry on properties, or considers the geometry–property relation but builds this relation via heuristics. In contrast, we propose a simple yet more principled way to accurately model the property to geometry mapping using a conditional deep generative model, named Inverse Homogenization Generative Adversarial Network (IH-GAN). It learns the conditional distribution of unit cell geometries given properties and can realize the one-to-many mapping from properties to geometries. Here we further reduce the complexity of IH-GAN by using the implicit function parameterization to represent unit cell geometries. Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy (R 2 -scores between target properties and properties of generated unit cells >98%) and 2) improve the optimized structural performance over the conventional variable-density single-type structure. In the minimum compliance example, our IH-GAN generated structure achieves a 79.7% reduction in concentrated stress and an extra 3.03% reduction in displacement. In the target deformation examples, our IH-GAN generated structure reduces the target matching error by 86.4% and 79.6% for two test cases, respectively. We also demonstrated that the connectivity issue for multi-type unit cells can be solved by transition layer blending.
42 ENGINEERING↗
Effect of vortex generators on the power conversion performance and structural dynamic loads of the Mod-2 wind turbine
Applying vortex generators from 20 to 100 percent span of the Mod-2 rotor resulted in a projected increase in annual energy capture of 20 percent and reduced the wind speed at which rated power is reached by nearly 3 m/sec. Application of vortex generators from 20 to 70 percent span, the fixed portion of the Mod-2 rotor, resulted in a projected increase in annual energy capture of about half this. This improved performance came at the cost of a small increase in cyclic blade loads in below rated power conditions. Cyclic blade loads were found to correlate well with the change in wind speed during one rotor revolution.
Sullivan, T. L.↗
Production of Motion Pictures of a Continuously Deforming Structure Using NASTRAN
NASTRAN generation of motion pictures for analysis of elastic structure transient responses under continuous deformation conditions
Reynolds, R. J.↗
Procedure for Efficiently Generating, Checking, and Displaying NASTRAN Input and Output Data for Analysis of Aerospace Vehicle Structures
Auxiliary computer routines for generating, checking, and displaying NASTRAN input and output data for analysis of aerospace vehicle structures
Giles, G. L.↗
Cirrus uncinus generating cells and the evolution of cirriform clouds. I - Aircraft observations of the growth of the ice phase. II The structure and circulations of the cirrus uncinus generating head. III - Numerical computations of the growth of the ice phase
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Heymsfield, A.↗
Modeling iterative reconstruction and displacement field in the large scale structure
The next generation of galaxy surveys like the Dark Energy Spectroscopic Instrument and Euclid will provide datasets orders of magnitude larger than anything available to date. Our ability to model nonlinear effects in late time matter perturbations will be a key to unlock the full potential of these datasets, and the area of initial condition reconstruction is attracting growing attention. Iterative reconstruction developed by Marcel Schmittfull et al. is a technique designed to reconstruct the displacement field from the observed galaxy distribution. The nonlinear displacement field and initial linear density field are highly correlated. Therefore, reconstructing the nonlinear displacement field enables us to extract the primordial cosmological information better than from the late time density field at the level of the two-point statistics. This paper will test to what extent the iterative reconstruction can recover the true displacement field and construct a perturbation theory model for the postreconstructed field. We model the iterative reconstruction process with Lagrangian perturbation theory (LPT) up to third order for dark matter in real space and compare it with N-body simulations. Here we find that the simulated iterative reconstruction does not converge to the nonlinear displacement field, and the discrepancy mainly appears in the shift term, i.e., the term correlated directly with the linear density field. On the contrary, our 3LPT model predicts that the iterative reconstruction should converge to the nonlinear displacement field. We discuss the sources of discrepancy, including numerical noise/artifacts on small scales, and present an ad hoc phenomenological model that improves the agreement.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A mesh regeneration method using quadrilateral and triangular elements for compressible flows
An adaptive remeshing method using both triangular and quadrilateral elements suitable for high-speed viscous flows is presented. For inviscid flows, the method generates completely unstructured meshes. For viscous flows, structured meshes are generated for boundary layers, and unstructured meshes are generated for inviscid flow regions. Examples of inviscid and viscous adaptations for high-speed flows are presented.
Vemaganti, G. R.↗
Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists
The α2a adrenoceptor is a particularly medically relevant subtype of the G protein-couple receptor family. Unfortunately, high-throughput techniques aimed at producing novel drug leads targeted to this receptor have been largely unsuccessful due to the complex pharmacology of adrenergic receptors. As such, cutting-edge in silico ligand- and structure-based assessment and de novo deep learning methods are well-positioned to provide new insights into protein-ligand interactions and potential active compounds. In this work, we (i) collect a dataset of α2a adrenoceptor agonists and provide it as a resource for the drug design community; (ii) use the dataset as a basis to generate candidate active structures via deep learning; and (iii) apply computational ligand- and structure-based analysis techniques to gain new insight into α2a adrenoceptor agonists and assess the quality of the computer-generated compounds. Additionally, we further describe how such assessment techniques can be applied to putative chemical probes with a case study involved proposed medetomidine-based probes.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Computer program performs stiffness matrix structural analysis
Computer program generates the stiffness matrix for a particular type of structure from geometrical data, and performs static and normal mode analyses. It requires the structure to be modeled as a stable framework of uniform, weightless members, and joints at which loads are applied and weights are lumped.
Bamford, R.↗