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DOE OSTI2023

Molecular Dynamics Investigation of Nanoscale Hydrophobicity of Polymer Surfaces: What Makes Water Wet?

The wettability of a polymer surface–related to its hydrophobicity or tendency to repel water–can be crucial for determining its utility, such as for a coating or a purification membrane. While wettability is commonly associated with the macroscopic measurement of a contact angle between surface, water, and air, the molecular physics that underlie these macroscopic observations are not fully known, and anticipating relative behavior of different polymers is challenging. To address this gap in molecular-level understanding, we use molecular dynamics simulations to investigate and contrast interactions of water with six chemically distinct polymers: polytetrafluoroethylene, polyethylene, polyvinyl chloride, poly(methyl methacrylate), Nylon 66, and polyvinyl alcohol. We show that several prospective quantitative metrics for hydrophobicity agree well with experimental contact angles. Moreover, the behavior of water in proximity to these polymer surfaces can be distinguished with analysis of interfacial water dynamics, extent of hydrogen bonding, and molecular orientation–even when macroscopic measures of hydrophobicity are similar. The predominant factor dictating wettability is found to be the extent of hydrogen bonding between polymer and water, but the precise manifestation of hydrogen bonding and its impact on surface-water structure varies. In the absence of hydrogen bonding, other molecular interactions and polymer mechanics control hydrophobic ordering. Furthermore, these results provide new insights into how polymer chemistry specifically impacts water-polymer interactions and translates to surface hydrophobicity. Such factors may facilitate the design or processing of polymer surfaces to achieve targeted wetting behavior, and presented analyses can be useful in studying the interfacial physics of other systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO 2 ) dataset generated from a "melt-quench" ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO 2 with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Mechanism of monovalent and divalent ion mobility in Nafion membrane: An atomistic simulation study

Polymer cation exchange membranes (CEMs) are widely used in water treatment processes. The fundamental factors that control the separation and selectivity of cations with different valences in CEMs are not fully understood. In this study, we use atomistic molecular dynamics simulations to investigate the underlying molecular mechanisms that control the mobility of cations with different valences in Nafion membranes. Our results indicate structural differences in binding of monovalent and divalent cations, which gives rise to differences in ion diffusion in Nafion. Monovalent cations are shown to be “territorially” bound, i.e., bound while partially hydrated, to the fixed charge groups whereas divalent cations are shown to be “site” bound, i.e., bound while fully dehydrated, to the charge groups on the polymer. This difference in binding structure gives rise to differences in transport characteristics of cations in Nafion.

Chemistry↗

DOE OSTI2026

Lessons Learned from Ecosystem-Scale Experimental Field Studies (Workshop Report)

Efforts to understand and predict ecosystem responses to environmental change require long-term, large-scale, spatially representative experiments and observations that capture natural variability, test predictive models, and generate transferable knowledge. Such studies are indispensable for unraveling the complexities of terrestrial ecosystems and their responses to disturbances and evolving environmental conditions, while generating the data necessary for developing mechanistic models and predictive tools that inform decision-making processes. Having a rich history of designing and executing large-scale ecosystem experiments, the U.S. Department of Energy’s Environmental System Science program convened a workshop in January 2025 that brought together leaders in the field to distill critical lessons from decades of experience in large-scale experiments. The workshop aimed to (1) provide an ecosystem experiment primer for best practices, thus ensuring a high scientific return on investment for funding agencies, and (2) offer a robust framework for the design and management of future research initiatives. This report synthesizes insights and experiences from workshop participants and is structured to capture the entire research life cycle, from goal setting and design to operations, adaptive management, team dynamics, collaborations, and the often overlooked aspect of decommissioning. By synthesizing decision-making and lessons learned across diverse research approaches, the report aims to provide a template of essential factors to consider when designing successful long-term, large-scale ecosystem experiments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Microphysics of liquid water in sub-10 nm ultrafine aerosol particles

Abstract. Ultrafine aerosol particles with sizes smaller than 50 nm have been shown in recent studies to serve as a large source of cloud condensation nuclei that can promote additional cloud droplet formation under supersaturation conditions. Knowledge of the microphysics of liquid water in these droplets remains limited, particularly in the sub-10 nm particle size range, due to experimental and theoretical challenges associated with the complexity of aerosol components and the small length scales of interest (e.g., difficulty of precisely sampling the liquid–air interface, questionable validity of mean-field theoretical representations). Here, we carried out molecular dynamics (MD) simulations of aerosol particles with diameters between 1 and 10 nm and characterized atomistic-level structure and water dynamics in well-mixed and phase-separated systems with different particle sizes, NaCl salinities, and pimelic acid (PML) organic surface loadings as a function of distance from the time-averaged Gibbs dividing interface or instantaneous water–air interface. We define a sphericity factor (ϕ) that can shed light on the phase-mixing state of nanodroplets, and we reveal an unexpected dependence of mixing state on droplet size. Our results also evidence an ion concentration enhancement in ultrafine aerosols, which should modulate salt nucleation kinetics in sub-10 nm droplets, and provide detailed characterization of the influence of droplet size on surface tension and on water self-diffusivity near the interface. Analysis of water evaporation free energy and water activity demonstrates the validity of the Kelvin equation and Köhler theory at droplet sizes larger than 4 nm under moderate salinities and organic loadings and the need for further extension to account for ion concentration enhancement in sub-10 nm aerosols, droplet-size-dependent phase separation effects, and a sharp decrease in the cohesiveness of liquid water in sub-4 nm droplets. Finally, we show that an idealized fractional surface coating factor (fs) can be used to categorize and reconcile water accommodation coefficients (α*) observed in MD simulations and experimental results in the presence of organic coatings, and we resolve the droplet size dependence of α*.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1971

A predictive model of human performance.

An attempt is made to develop a model describing the overall responses of humans to exercise and environmental stresses for prediction of exhaustion vs an individual's physical characteristics. The principal components of the model are a steady state description of circulation and a dynamic description of thermal regulation. The circulatory portion of the system accepts changes in work load and oxygen pressure, while the thermal portion is influenced by external factors of ambient temperature, humidity and air movement, affecting skin blood flow. The operation of the model is discussed and its structural details are given.

Walters, R. F.↗

NASA NTRS1989

Aeroelastic modeling for the FIT (Functional Integration Technology) team F/A-18 simulation

As part of Langley Research Center's commitment to developing multidisciplinary integration methods to improve aerospace systems, the Functional Integration Technology (FIT) team was established to perform dynamics integration research using an existing aircraft configuration, the F/A-18. An essential part of this effort has been the development of a comprehensive simulation modeling capability that includes structural, control, and propulsion dynamics as well as steady and unsteady aerodynamics. The structural and unsteady aerodynamics contributions come from an aeroelastic mode. Some details of the aeroelastic modeling done for the Functional Integration Technology (FIT) team research are presented. Particular attention is given to work done in the area of correction factors to unsteady aerodynamics data.

Zeiler, Thomas A.↗

DOE OSTI2025

On a Critical Acceleration Scale of Dark Matter in ΛCDM and Dynamical Dark Energy

Abstract Universal acceleration a 0 emerges in various empirical laws, yet its fundamental nature remains unclear. Using Illustris and Virgo N -body simulations, we focus on the velocity and acceleration fluctuations in collisionless dark matter involving long-range gravity. For comparison, in the kinetic theory of gases, molecules undergo random elastic collisions involving short-range interactions, where only velocity fluctuations are relevant. Hierarchical structure formation proceeds through the merging of smaller halos to form larger halos, which facilitates a continuous energy cascade from small to large halos at a constant rate ε u ≈ −10 −7 m 2 s −3 . Velocity fluctuations involve a critical velocity u c ∝ (1 + z ) −3/4 . Acceleration fluctuations involve a critical acceleration a c ∝ (1 + z ) 3/4 . Two critical quantities are related by the rate of energy cascade ε u ≈ − a c u c /[2(3 π ) 2 ], where factor 3 π is from the angle of incidence during merging. With critical velocity u c on the order of 300 km s −1 at z = 0, the critical acceleration is determined to be a c 0 ≡ a c ( z = 0) ≈ 10 −10 m s −2 , suggesting a c might explain the universal acceleration a 0 ≈ 10 −10 m s −2 in the empirical Tully–Fisher relation or modified Newtonian dynamics. The redshift evolution a c ∝ (1 + z ) 3/4 is in good agreement with Magneticum and EAGLE simulations and in reasonable agreement with limited observations. This suggests a larger a 0 at a higher redshift such that galaxies of fixed mass rotate faster at a higher redshift. Note that for dark energy (DE) density ρ DE 0 ≈ a c 0 2 / G = 1 0 − 10 J m −3 , we postulate an entropic origin of the DE from acceleration fluctuations of dark matter, analogous to the gas pressure from velocity fluctuations. This leads to a dynamical DE coupled to the structure evolution involving a relatively constant DE density followed by a slow weakening phase, suggesting possible deviations from the standard ΛCDM paradigm.

N-body simulations↗

DOE OSTI2024

Disentangling plant- and environment-mediated drivers of active rhizosphere bacterial community dynamics during short-term drought

Abstract Mitigating the effects of climate stress on crops is important for global food security. The microbiome associated with plant roots, the rhizobiome, can harbor beneficial microbes that alleviate stress, but the factors influencing their recruitment are unclear. We conducted a greenhouse experiment using field soil with a legacy of growing switchgrass and common bean to investigate the impact of short-term drought severity on the recruitment of active bacterial rhizobiome members. We applied 16S rRNA and 16S rRNA gene sequencing for both crops and metabolite profiling for switchgrass. We included planted and unplanted conditions to distinguish environment- versus plant-mediated rhizobiome drivers. Differences in community structure were observed between crops and between drought and watered and planted and unplanted treatments within crops. Despite crop-specific communities, drought rhizobiome dynamics were similar across the two crops. The presence of a plant more strongly explained the rhizobiome variation in bean (17%) than in switchgrass (3%), with a small effect of plant mediation during drought observed only for the bean rhizobiome. The switchgrass rhizobiome was stable despite changes in rhizosphere metabolite profiles between planted and unplanted treatments. We conclude that rhizobiome responses to short-term drought are crop-specific, with possible decoupling of plant exudation from rhizobiome responses.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2017

Tortuosity Computations of Porous Materials using the Direct Simulation Monte Carlo

Low-density carbon fiber preforms, used as thermal protection systems (TPS) materials for planetary entry systems, have permeable, highly porous microstructures consisting of interlaced fibers. Internal gas transport in TPS is important in modeling the penetration of hot boundary-layer gases and the in-depth transport of pyrolysis and ablation products. The gas effective diffusion coefficient of a porous material must be known before the gas transport can be modeled in material response solvers; however, there are very little available data for rigid fibrous insulators used in heritage TPS.The tortuosity factor, which reflects the efficiency of the percolation paths, can be computed from the effective diffusion coefficient of a gas inside a porous material and is based on the micro-structure of the material. It is well known, that the tortuosity factor is a strong function of the Knudsen number. Due to the small characteristic scales of porous media used in TPS applications (typical pore size of the order of 50 micron), the transport of gases can occur in the rarefied and transitional regimes, at Knudsen numbers above 1. A proper way to model the gas dynamics at these conditions consists in solving the Boltzmann equation using particle-based methods that account for movement and collisions of atoms and molecules.In this work we adopt, for the first time, the Direct Simulation Monte Carlo (DSMC) method to compute the tortuosity factor of fibrous media in the rarefied regime. To enable realistic simulations of the actual transport of gases in the porous medium, digitized computational grids are obtained from X-ray micro-tomography imaging of real TPS materials. The SPARTA DSMC solver is used for simulations. Effective diffusion coefficients and tortuosity factors are obtained by computing the mean-square displacement of diffusing particles.We first apply the method to compute the tortuosity factors as a function of the Knudsen number for computationally designed materials such as random cylindrical fibers and packed bed of spheres with prescribed porosity. Results are compared to literature values obtained using random walk methods in the rarefied and transitional regime and a finite-volume method for the continuum regime. We then compute tortuosity factors for a real carbon fiber material with a transverse isotropic structure (FiberForm), quantifying differences between through-thickness and in-plain tortuosities at various Knudsen regimes.

Tortuosity↗

DOE OSTI2025

Effective field theory factorization for diffraction

We derive a factorization formula for coherent and incoherent ep diffraction using the soft collinear effective theory, utilizing multiple power expansion parameters to handle different kinematic regions. This goes beyond the known hard-collinear diffractive factorization to address the small-x Regge dynamics and Pomeron exchange from first principles. The effective field theory analysis also uncovers and factorizes an important irreducible incoherent background generated by color-nonsinglet exchange, dubbed “quasi-diffraction”, for which we calculate the associated Sudakov suppression. For unpolarized scattering we show that there are four diffractive structure functions at leading power, and point out the importance of studying ${F}_{3,4}^D$ through asymmetries, in addition to ${F}_{2,L}^D$. For the quasi-diffractive background, we make model independent predictions for ratios of the corresponding structure functions in a perturbative kinematic region. Our analysis also makes predictions for six leading-power spin-dependent structure functions. Finally, we provide connections to diffractive parton distributions, and assess the Ingelman-Schlein model. Our work lays a path for further QCD-based studies of diffraction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Effects of Fe atoms on hardening of a nickel matrix: Nanoindentation experiments and atom-scale numerical modeling

Significant hardening effect due to Fe concentrations in Ni-based alloys with face-centered-cubic structure has been studied by using a combined experimental and atomistic-based computational approach via nanoindentation tests. The obtained experimental load–displacement data for the [0 0 1] crystal orientation reached a qualitative good agreement with molecular dynamics simulations results, leading to strong evidence that the main strengthening factors are associated to sluggish dislocation diffusion, reduced defect sizes and the nucleation of tetrahedral stacking faults. Here, interstitial type prismatic dislocation loops mainly formed by $\frac{1}{6}$$\langle$1 1 2$\rangle$ Shockley dislocations are nucleated during the loading process, where their interaction leads to the formation of pyramidal shaped stacking fault which are mainly created by $\frac{1}{3}$$\langle$1 0 0$\rangle$ Hirth dislocations lines. Observing both types of defects coexisting in the same plastic deformation zone by both approaches. Reported mechanical data, measured experimentally and interpreted numerically, are also in accordance with microstructural SEM and TEM investigations.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Machine learning prediction on the fractional free volume of polymer membranes

Fractional free volume (FFV) characterizes the microstructural level features of polymers and affects their properties including thermal, mechanical, and separation performance. Experimental measurements and theoretical analyses have been used to quantify the FFV of polymers, but challenges remain because of their limitations. Experimental measurements are laborious and based on semi empirical equations, while Bondi’s group contribution theory involves ambiguities like the determination of van der Waals volume and the choice of factor values in the theoretical equation. To efficiently evaluate the FFV of polymers, this study utilizes high-throughput molecular dynamics (MD) simulations to build a large dataset regarding polymer’s FFV. Based on this large dataset, we further build machine learning (ML) models to establish the composition-structure relation. Inspired by group contribution theory which correlates polymer’s functional groups to FFV, our ML models correlate polymer’s substructures or physico-chemical indexes to FFV. Here, our study first benchmarks the MD simulation protocol to obtain reliable FFV of polymers and then carries out high-throughput MD simulations for more than 6,500 homopolymers and 1,400 polyamides. Such a large and diverse dataset makes the well-trained ML models more generalizable, compared with the group contribution theory. The efficiency of a feed forward neural network model is further demonstrated by applying it to a hypothetical polyimide dataset of more than 8 million chemical structures. The predicted FFVs of hypothetical polyimides are further validated by MD simulations. The obtained FFVs of the 8 million polymers, plus their previously reported gas separation performances, demonstrate the promising capability of ML virtual screening for the discovery of polymer membranes with exceptional permeability/selectivity.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Mass enhancement in 3 d and s - p perovskites from symmetry breaking

In some d-electron oxides, the measured effective mass m* exptl has long been known to be significantly larger than the model effective mass m* model deduced from mean-field band theory, i.e., m* exptl =βm* model , where β>1 is the “mass-enhancement” or “mass-renormalization” factor. Previous applications of density functional theory (DFT), based on a symmetry-restricted structure with the smallest number of possible magnetic, orbital, and structural degrees of freedom, missed such mass enhancement. This fact has been taken as evidence of strong electronic correlation, often described via the symmetry-restricted dynamic mean-field approach of the many-body theory, being the exclusive enabling physics. This paper uses instead a static density functional approach that does not restrict positional or spin degrees of freedom (symmetry-broken structures). This approach analyzes the contributions of different symmetry-broken modalities to mass enhancement for a few nominally highly correlated d-electron perovskites as well as the nominally uncorrelated, closed-shell s-p bonding perovskites. It shows that the energy-lowering symmetry-broken spin effects (e.g., nonzero local moment in the paramagnetic phase) and structural effects (e.g., atomic displacement) as described in mean-field DFT already manifest mass enhancement for both electrons and holes in a range of d-electron perovskites SrVO 3 , SrTiO 3 , BaTiO 3 , and LaMnO 3 , as well as p-electron perovskites CsPbI 3 and SrBiO 3 , including both metals (SrVO 3 ) and insulators (the rest). This is revealed only when enlarged unit cells of the same parent global symmetry, which are large enough to allow for symmetry-breaking distortions and concomitant variations in spin order, are explored for their ability to lower the total energy. Positional symmetry breaking that leads to mass enhancement includes octahedral rotation in halide perovskites such as CsPbI 3 , Jahn-Teller-like Q${+}\atop{2}$ distortion in LaMnO 3 , and bond disproportionation in SrBiO 3 , while magnetic symmetry breaking resulting in mass enhancement includes the formation of a distribution of local moments in SrVO3 that averages to zero in the paramagnetic phase. Not all symmetry breaking leads to significant mass enhancement, e.g., the rather small octahedral rotations in the nearly perfectly cubic SrTiO 3 cause negligible mass enhancement, as do the paraelectric displacements in BaTiO 3 . In principle, by ergodicity, the two descriptions, i.e., the symmetry-restricted dynamic approach with a single time-fluctuating magnetic moment and the symmetry-broken mean-field approach with a static distribution of spatially fluctuated local moments, are not mutually exclusive but are a choice of representation and consequently, a choice of computational efficiency. In approximate implementations, the symmetry-broken mean-field approach appears to remove much of what was strong correlation in dynamically correlated symmetry-restricted solutions, leaving smaller (“weak”) residual correlation with respect to the exact solution.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Quantum Monte Carlo Calculations of Magnetic Form Factors in Light Nuclei

Here, we present Quantum Monte Carlo calculations of magnetic form factors in A = 6-10 nuclei, based on Norfolk two- and three-nucleon interactions, and associated one- and two-body electromagnetic currents. Agreement with the available experimental data for 6 Li, 7 Li, 9 Be and 10 B up to values of momentum transfer q ~ 3 fm -1 is achieved when two-nucleon currents are accounted for. We present a set of predictions for the magnetic form factors of 7 Be, 8 Li, 9 Li, and 9 C. In these systems, two body currents account for ~ 40-60% of the total magnetic strength. Measurements in any of these radioactive systems would provide valuable insights on the nuclear magnetic structure emerging from the underlying many-nucleon dynamics. A particularly interesting case is that of 7 Be, as it would enable investigations of the magnetic structure of mirror nuclei

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2012

Paramyxovirus Infection Mimics In Vivo Cellular Dynamics in Three-Demensional Human Bronchio-Epithelial Tissue-Like Assemblies

Respiratory syncytial virus and parainfluenza virus cause severe respiratory disease, especially in infants, children and the elderly. An in vitro model that accurately mimics infection of the human respiratory epithelium (HRE) would facilitate vaccine development greatly. Monolayer cultures traditionally used to study these viruses do not accurately and precisely differentiate the replication efficiencies of wild type and attenuated viruses. Therefore, we engineered novel three-dimensional (3D) tissue-like assemblies (TLAs) of human broncho-epithelial (HBE) cells to produce a more physiologically relevant in vitro model of the HRE. TLAs resemble HRE structurally and by expression of differentiated epithelial cell markers. Most significantly, wild type viruses exhibited a clear growth advantage over attenuated strains in TLAs unlike monolayer cultures. In addition, the TLAs responded to virus infection by secreting pro-inflammatory mediators similar to the respiratory epithelia of infected children. These characteristics make the TLA model a valuable platform technology to develop and evaluate live, attenuated respiratory virus vaccine candidates for human use. Respiratory virus diseases, the most frequent and least preventable of all infectious diseases, range in severity from the common cold to severe bronchiolitis and pneumonia . Two paramyxoviruses, respiratory syncytial virus (RSV) and parainfluenza virus type 3 (PIV3), are responsible for a majority of the most severe respiratory diseases of infants and young children. RSV causes 70% of all bronchiolitis cases and is a major cause of morbidity and mortality worldwide, especially in infants. PIV3 causes 10-15% of bronchiolitis and pneumonia during infancy, second only to RSV, and 40% of croup in infants To date, licensed vaccines are not available to prevent these respiratory diseases. At present, traditional monkey kidney (Vero and LLC-MK2) and human (HEp-2) tissue culture cells and small animal models (mouse, cotton rat, guinea pig, ferret, and hamster) fail to accurately imitate viral replication and human disease states (8). Lacking an authentic model has impeded the development and evaluation of live, attenuated vaccine candidates. Development of a physiologically relevant in vitro tissue culture model that reproduces characteristics of the HRE, the primary target of RSV and PIV3, would aid in predicting clinical attenuation and safety of vaccine candidates. Successful tissue engineering of a 3D human intestinal model using novel NASA technology inspired the development of a tri-culture 3D model for the HRE. Sequential layering of primary mesenchymal cells (comprised of normal human fibroblasts and endothelial cells) followed by BEAS-2B epithelial cells derived from human bronchi and tracheae were recapitulated on Cultisphere and/or cytodex3 microcarriers in cylindrical vessels that rotate horizontally creating an organized epithelial structure. Horizontal rotation randomizes the gravity vector modeling aspects of microgravity. Mesenchymal and epithelial cells grown under these conditions reproduce the structural organization, multi-cellular complexity, and differentiation state of the HRE. The opportunity to study respiratory viruses in a nasal epithelium model is invaluable because the most promising respiratory virus vaccine candidates are live attenuated viruses for intranasal administration. Here we characterize the interactions of respiratory viruses and epithelial cells grown under modeled microgravity in comparison to gravity-ladened monolayers. 3D HBE TLAs and traditional monolayers (2D) are infected at 35 C, the upper temperature of the upper HRE, to simulate in vivo infection conditions. Growth kinetics of wild type (wt) RSV and PIV3 viruses were compared in 2D and 3D cells to that of strains attenuated in humans or rhesus macaques. This novel 3D HBE model also offers an opportunity to study whether the epithelial cell function, especially in host defenses recapitulated by mimicking the structural organization of the HRE. In vivo, airway epithelial cells play a significant and dynamic role in host defense by blocking paracellular permeability and modulating airway function through cellular interactions or tight junctions. As regulators of the innate immune response, epithelial cells constitutively express cytokines, chemokines, and colony stimulating factors including RANTES, IL-8, IL-6, GM-CSF, and G-CSF for proactive host defense. In response to viral infection, epithelial cells induce potent immuno-modulatory and pro-inflammatory cytokines that recruit phagocytic and inflammatory cells to clear the virus and enhance protection. Although disease pathogenesis is classically attributed to the cytopathic effects of the pathogen, severe disease states associated with RSV and PIV3 are attributed to the inflammatory response, especially in infants. RSV is a potent inducer of cytokines and pro-inflammatory mediators in epithelial cells in vivo. A differentiated human epithelial model independent of the complete functional immune system will help elucidate the role of epithelial cells in respiratory disease. We reported here, virus and host cell interactions in 3D HBE TLAs are similar to that in vivo. Because the epithelial cell organization of the TLAs impacts not only the expression of airway epithelial characteristics, but also cellular communication, the TLAs represent a more physiologically relevant model of the HRE than BEAS-2B or other non-tumour monolayer models of respiratory disease. As a result, wild type respiratory viruses have a clear growth advantage over attenuated viruses in TLAs unlike traditional monolayers. In addition, the TLAs respond to wild type virus infection by secreting pro-inflammatory mediators characteristic of infected HRE. TLAs expressing microbial defense mechanisms provide an excellent model to study the interactions of respiratory pathogens with their host and to identify the innate immunity mediators. Therefore, 3D HBE TLAs offer advantages for the study of respiratory viruses and the development of viral vaccine candidates.

Deatly, Anne M.↗

NASA NTRSDate not supplied

Development of Buffet Functions using Frequency-Dependent Coherence Factors

Launch vehicle transonic buffet environments produce large fluctuating pressures on the surface of the vehicle. Transonic buffet occurs as a launch vehicle passes through the transonic regime, typically Mach 0.8 to 1.2, where phenomena such as flow separation and shock dynamics produce large fluctuating pressures. The unsteady aerodynamic forces produced by these phenomena can excite both global and local vehicle structural response. The consequences of incorporating poorly characterized buffet forces into the design process can range from inefficient structures that can reduce the vehicle capabilities to undersized structures that, in the worst case scenario, may result in structural failure (Refs. 1, 2, 3). It is thus imperative to appropriately characterize the transonic buffet environment.

buffet↗

NASA NTRSDate not supplied

Multidisciplinary Dynamic Testing Challenges in Validating the NASA Artemis Architecture

NASA is in the midst of bold and exciting next steps in human exploration and spaceflight. The designs of the new Space Launch System (SLS), the Orion spacecraft and the Exploration Ground Systems (EGS) for vehicle processing and launch are essentially complete and there has been significant progress in manufacturing and assembly of specific hardware for the Artemis I and Artemis II missions. Equally as important, the program level and integrated system level testing and analyses are also well underway to support integrated verification, validation, and certificate of flight readiness (CoFR) for the first Artemis mission. Testing and analysis are key to addressing technical challenges that the Artemis missions offer. Building block approaches are required that provide the right balance between component, system, and/or element level testing that satisfies verification and validation objectives and where, uncertainties are quantified and minimized. Artemis I is a system of systems that requires a fusion of test and analysis that adeptly characterizes critical interfaces between major program elements. NASA is implementing new in-situ testing that fuse traditional aerospace structures with civil structures, such as the Integrated Modal Test for the Artemis I vehicle where the Mobile Launcher and Crawler Transporter serve as a support structure whose dynamics couple with that of the Artemis I vehicle. This new paradigm requires a closer inspection of structural behavior of the Crawler Transporter and the Mobile Launcher as they now serve multiple purposes. This requires a paradigm shift to look beyond experimental modal techniques and incorporates operational modal analysis techniques to validate dynamic models from data collected during rollout to the launch pad. A further complicating factor is the Crawler Transporter generated ground forces have numerous harmonics making extracting dynamic responses of the Artemis I, Mobile Launcher, and Crawler Transporter coupled system challenging. This discussion explores all these challenges with and attempts to understand how we best build confidence in systems and system-of-systems performance capabilities and margins and understand uncertainties.

Joel W Sills↗