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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Computational fluid dynamics (CFD) enables modeling, analysis, and visualization of phenomena that would usually be impossible or extremely expensive to measure experimentally.
Abstract Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by approximating mappings between infinite-dimensional Banach spaces, yet their performance degrades with system size and complexity. We propose an approach for learning neural operators in latent spaces, facilitating real-time predictions for highly nonlinear and multiscale systems on high-dimensional domains. Our method utilizes the deep operator network architecture on a low-dimensional latent space to efficiently approximate underlying operators. Demonstrations on material fracture, fluid flow prediction, and climate modeling highlight superior prediction accuracy and computational efficiency compared to existing methods. Notably, our approach enables approximating large-scale atmospheric flows with millions of degrees, enhancing weather and climate forecasts. Here we show that the proposed approach enables real-time predictions that can facilitate decision-making for a wide range of applications in science and engineering.
Localized dynamics of RAS, including regions distal to the nucleotide-binding site, is of high interest for elucidating the mechanisms by which RAS proteins interact with effectors and regulators and for designing inhibitors. Among several oncogenic mutants, methyl relaxation dispersion experiments reveal highly synchronized conformational dynamics in the active (GMPPNP-bound) KRAS G13D , which suggests an exchange between two conformational states in solution. Methyl and 31 P NMR spectra of active KRAS G13D in solution confirm a two-state ensemble interconverting on the millisecond timescale, with a major Pγ atom peak corresponding to the dominant State 1 conformation and a secondary peak indicating an intermediate state different from the known State 2 conformation recognized by RAS effectors. High-resolution crystal structures of active KRAS G13D and KRAS G13D -RAF1 RBD complex provide snapshots of the State 1 and 2 conformations, respectively. We use residual dipolar couplings to solve and cross-validate the structure of the intermediate state of active KRAS G13D , showing a conformation distinct from those of States 1 and 2 outside the known flexible switch regions. The dynamic coupling between the conformational exchange in the effector lobe and the breathing motion in the allosteric lobe is further validated by a secondary mutation in the allosteric lobe, which affects the conformational population equilibrium.
We characterize the dynamical state of many-body bosonic and fermionic many-body models with intersite Gaussian couplings, on-site non-Gaussian interactions, and local dissipation comprising incoherent particle loss, particle gain, and dephasing. We first establish that, for fermionic systems, if the dephasing noise is larger than the non-Gaussian interactions, irrespective of the Gaussian coupling strength, the system state is a convex combination of Gaussian states at all times. Furthermore, for bosonic systems, we show that if the particle loss and particle gain rates are larger than the Gaussian intersite couplings, the system remains in a separable state at all times. Building on this characterization, we establish that at noise rates above a threshold, there exists a classical algorithm that can efficiently sample from the system state of both the fermionic and bosonic models. Finally, we show that, unlike fermionic systems, bosonic systems can evolve into states that are not convex Gaussian even when the dissipation is much higher than the on-site non-Gaussianity. Similarly, unlike bosonic systems, fermionic systems can generate entanglement even with noise rates much larger than the intersite couplings.
Abstract In this article, we introduce a decentralized digital twin (DDT) modeling framework and its potential applications in computational science and engineering. The DDT methodology is based on the idea of federated learning, a subfield of machine learning that promotes knowledge exchange without disclosing actual data. Clients can learn an aggregated model cooperatively using this method while maintaining complete client-specific training data. We use a variety of dynamical systems, which are frequently used as prototypes for simulating complex transport processes in spatiotemporal systems, to show the viability of the DDT framework. Our findings suggest that constructing highly accurate decentralized digital twins in complex nonlinear spatiotemporal systems may be made possible by federated machine learning.
Imposing a boundary condition on a structure can significantly alter its dynamic properties. However, sometimes the specifics of the new boundary conditions are not known. When the effects of a boundary condition are uncertain or there is not enough information, engineers need to excite the complex structure to obtain these modified properties. In order to experimentally obtain the new properties, engineers need multiple experiments and many outputs for interpolation in order to sufficiently represent the entire structure. In this study, the researchers attached a stinger to a cantilever beam, acting as a new transverse restraint of unknown properties. This paper presents a conversion expression that predicts the dynamic behavior of any point in the system with the new boundary condition. This expression relies only on one impact hammer experiment with one output and the model of the stinger-free cantilever beam, referred to as the simple structure. Researchers estimated the Transfer Function (TRF) of the beam and compared it with an experimentally measured TRF to validate the method. The mean absolute error of the estimated TRF compared to the experimental TRF is 1.99 dB. This demonstrates the use of the proposed method for estimating unmeasured TRFs in a system with an uncertain boundary condition using a single input, single output (SISO) test and a model of the simple structure.
Argyrodites are a compositionally diverse family of materials that exhibit remarkable ion transport properties. While the average crystal structures of argyrodites have been extensively studied, ion transport in these materials is governed by a confluence of dynamic processes spanning the cation, anion, and polyanionic sublattices. This Perspective synthesizes recent advances in understanding the role of dynamics in structural behavior and ion transport properties. We examine the compositional and structural motifs that govern order−disorder transitions within the argyrodite family and further explore how ion hopping is facilitated by lattice dynamics, from long-range phonons to local rotational dynamics of polyanionic species. Through the lens of dynamics spanning multiple time and length scales, we establish guiding principles that govern transport phenomena and highlight avenues of future study for the argyrodite family of ion conductors.
Viscosity modifying agents are one of the most critical components of hydraulic fracturing fluids, ensuring the efficient transport and deposition of proppant into fissures.
Conclusions: certain data-driven models do implicitly what physics simulations do explicitly; theoretical underpinnings in Koopman theory, delay-coordinate embeddings, and Mori-Zwanzig formalism; best approach for given system with finite data an open question; physics-informed machine learning (PIML) emerging framework for combining explicit and implicit modeling.
Complex Network (CN) is a graph-theory based depiction of relation shared by various elements of a complex-dynamical system such as the atmosphere. Here we applied the concept of CN to understand the directionality and topological structure of summer extreme precipitation events (SEPEs) over the conterminous United States (CONUS). The SEPEs are calculated based on the 95th percentile daily rainfall at 0.5ox0.5o spatial resolution for CONUS to investigate the multi-dimensional characteristics of precipitation extremes. The derived CN coefficients (e.g., betweenness centrality, clustering coefficient, orientation, and network divergence) reveal important structural and dynamical information about the topology of the SEPEs and improve understanding of the dominant meteorological patterns. The initiation and propagation of SEPEs from the source-zones to the sink-zones are identified. The SEPEs are influenced by topography, dominant wind patterns, and moisture sources in terms of their topological structure and spatial dynamics.
Dynamics are intricately linked with activity and selectivity when it comes to catalysis, as noted for instance in the enzymatic principles of induced fit and allostery, and yet the range of motions heterogeneous catalytic sites are able to undergo is poorly understood. Solid-state nuclear magnetic resonance (NMR) spectroscopy is perhaps the only tool capable of probing the rapid conformational dynamics found in heterogeneous catalysts but has historically been restricted by its low sensitivity, limiting the detail with which structures can be resolved. In this work, we apply solid-state NMR and dynamic nuclear polarization, in combination with density functional theory modeling, to reveal the high-resolution structure and motional freedom of a scandium supported complex in three dimensions. The results are contrasted with the study of the analogous homoleptic complex in the crystalline state, highlighting the impacts that surface structure may have on the dynamics of supported complexes.
This work proposes an extension of neural ordinary differential equations (NODEs) by introducing an additional set of ODE input parameters to NODEs. This extension allows NODEs to learn multiple dynamics specified by the input parameter instances. Our extension is inspired by the concept of parameterized ordinary differential equations, which are widely investigated in computational science and engineering contexts, where characteristics of the governing equations vary over the input parameters. We apply the proposed parameterized NODEs (PNODEs) for learning latent dynamics of complex dynamical processes that arise in computational physics, which is an essential component for enabling rapid numerical simulations for time-critical physics applications. For this, we propose an encoder-decoder-type framework, which models latent dynamics as PNODEs. We demonstrate the effectiveness of PNODEs with important benchmark problems from computational physics.
Many renewable energy conversion processes rely on the movement of protons as well as electrons through either electrocatalysis or photoexcitation. The simulation of such processes requires a quantum mechanical description of coupled nuclear-electronic dynamics in a solvent or heterogeneous chemical environment. The overall objective of this project is the development of theoretical and computational capabilities for simulating nuclear-electronic quantum dynamics in complex environments and the creation of high-performance, open-source software. This multiscale framework will enable simulations of the real-time dynamics of nonequilibrium excited state proton-coupled electron transfer, quantum decoherence, vibronic energy transfer, and ultrafast radiolysis, as well as their associated time-resolved multidimensional spectroscopies. An important outcome of this project will be a sustainable, reusable, and interoperable open-source software ecosystem. This software will be designed for emerging exascale and future national leadership computers. Another key outcome will be a multiscale quantum dynamics method and software enabling simulations of nonequilibrium nuclear-electronic quantum dynamics in complex environments.
Dynamic bonds are a powerful approach to tailor the mechanical properties of elastomers and introduce shape-memory, self-healing, and recyclability. Among the library of dynamic crosslinks, electrostatic interactions among oppositely charged ions have been shown to enable tough and resilient elastomers and hydrogels. In this work, we investigate the mechanical properties of ionically crosslinked ethyl acrylate-based elastomers assembled from oppositely charged copolymers. Using both infrared and Raman spectroscopy, we confirm that ionic interactions are established among polymer chains. We find that the glass transition temperature of the complex is in between the two individual copolymers, while the complex demonstrates higher stiffness and more recovery, indicating that ionic bonds can strengthen and enhance recovery of these elastomers. We compare cycles to increasing strain levels at different strain rates, and hypothesize that at fast strain rates ionic bonds dynamically break and reform while entanglements do not have time to slip, and at slow strain rates ionic interactions are disrupted and these entanglements slip significantly. Further, we show that a higher ionic to neutral monomer ratio can increase the stiffness, but its effect on recovery is minimal. Lastly, taking advantage of the versatility of acrylates, ethyl acrylate is replaced with the more hydrophilic 2-hydroxyethyl acrylate, and the latter is shown to exhibit better recovery and self-healing at a cost of stiffness and strength. The design principles uncovered for these easy-to-manufacture polyelectrolyte complex-inspired bulk materials can be broadly applied to tailor elastomer stiffness, strength, inelastic recovery, and self-healing for various applications.
Dynamic simulation modeling is used at Y-12 to evaluate and forecast nuclear material inventories and production capacities to ensure that future supply can meet mission demand. Model outputs are analyzed by numerous Y-12 organizations and programs and coordinated with NNSA’s Office of Secondary Stage Production Modernization. Data-driven decisions for both short-term and long-term strategic planning for Y-12 mission execution are informed by the model. Dynamic simulation modeling capabilities for Y-12 nuclear material production continue to be expanded and refined.
Introduction: Physical and non-physical processes that occur in nature may influence biological processes, such as dissemination of infectious diseases. However, such processes may be hard to detect when they are complex systems. Because complexity is a dynamic and non-linear interaction among numerous elements and structural levels in which specific effects are not necessarily linked to any one specific element, cause-effect connections are rarely or poorly observed. Methods: To test this hypothesis, the complex and dynamic properties of geo-biological data were explored with high-resolution epidemiological data collected in the 2001 Uruguayan foot-and-mouth disease (FMD) epizootic that mainly affected cattle. County-level data on cases, farm density, road density, river density, and the ratio of road (or river) length/county perimeter were analyzed with an open-ended procedure that identified geographical clustering in the first 11 epidemic weeks. Two questions were asked: (i) do geo-referenced epidemiologic data display complex properties? and (ii) can such properties facilitate or prevent disease dissemination? Results: Emergent patterns were detected when complex data structures were analyzed, which were not observed when variables were assessed individually. Complex properties–including data circularity–were demonstrated. The emergent patterns helped identify 11 counties as ‘disseminators’ or ‘facilitators’ (F) and 264 counties as ‘barriers’ (B) of epidemic spread. In the early epidemic phase, F and B counties differed in terms of road density and FMD case density. Focusing on non-biological, geographical data, a second analysis indicated that complex relationships may identify B-like counties even before epidemics occur. Discussion: Geographical barriers and/or promoters of disease dispersal may precede the introduction of emerging pathogens. If corroborated, the analysis of geo-referenced complexity may support anticipatory epidemiological policies.