Search NASA⌕ Search

SEARCH · Search NASA

Results for “Prediction Chaos”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Performance of wave function and Green's function methods for non-equilibrium many-body dynamics

Theoretical descriptions of the non-equilibrium dynamics of quantum many-body systems essentially employ either (i) explicit treatments, relying on the truncation of the expansion of the many-body wave function, (ii) compressed representations of the many-body wave function, or (iii) evolution of an effective (downfolded) representation through Green's functions. In this work, we select representative cases of each of the methods and address how these complementary approaches capture the dynamics driven by intense field perturbations to non-equilibrium states. Under strong driving, the systems are characterized by strong entanglement of the single-particle density matrix and natural populations approaching those of a strongly interacting equilibrium system. We generate a representative set of results that are numerically exact and form a basis for a critical comparison of the distinct families of methods. We demonstrate that the compressed formulation based on similarity-transformed Hamiltonians (coupled-cluster approach) is practically exact in weak fields and, hence, weakly or moderately correlated systems. Coupled cluster, however, struggles for strong driving fields, under which the system exhibits strongly correlated behavior, as measured by the von Neumann entropy of the single-particle density matrix. The dynamics predicted by Green's functions in the (widely popular) G W approximation are less accurate, but improve significantly upon the mean-field results in the strongly driven regime. Published by the American Physical Society 2025

Reeves, Cian C. (ORCID:0009000642581845)↗
DOE OSTI2020

Using Residential and Office Building Archetypes for Energy Efficiency Building Solutions in an Urban Scale: A China Case Study

Building energy consumption accounts for 36% of the overall energy end use worldwide and is growing rapidly as developing countries continue to urbanize. Understanding the energy use at urban scale will lay the foundation for identification of energy efficiency opportunities to be deployed at speed. China has almost half of global new constructions and plays an important role in building suitability. However, an open source national building energy consumption database is not available in China. To provide data support for building energy consumptions, this paper used a simulation method to develop an urban building energy consumption database for a pilot city in Wuhan, China. First, residential, small, and large office building archetype energy models were created in EnergyPlus to represent typical building energy consumption in Wuhan. The baseline reference model simulation results were further validated using survey data from the literature. Second, stochastic simulations were conducted to consider different design parameters and occupants’ energy usage intensity scenarios, such as thermal properties of the building envelope, lighting power density, equipment power density, HVAC (heating, ventilation and air conditioning) schedule, etc. A building energy consumption database was generated for typical building archetypes. Third, data-driven regression analysis was conducted to support quick building energy consumption prediction using key high- level building information inputs. Finally, a web-based urban energy platform and an interface were developed to support further third-party application development. The research is expected to provide fast energy efficiency building design solutions for urban planning, new constructions as well as building retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

The Tribomechadynamics Research Challenge: Confronting blind predictions for the linear and nonlinear dynamics of a thin-walled jointed structure with measurement results

The present article summarizes the submissions to the Tribomechadynamics Research Challenge announced in 2021. The task was a blind prediction of the vibration behavior of a system comprising a thin plate clamped on two sides via bolted joints. Both geometric and frictional contact nonlinearities are expected to be relevant. Provided were the CAD models and technical drawings of all parts as well as assembly instructions. The main objective was to predict the frequency and damping ratio of the lowest-frequency mode as function of the amplitude. Many different prediction approaches were pursued, ranging from well-known methods to very recently developed ones. After the submission deadline, the system has been fabricated and tested. The aim of this article is to evaluate the current state of the art in modeling and vibration prediction, and to provide directions for future methodological advancements.

42 ENGINEERING↗
DOE OSTI2020

Modeling strategy for progressive failure prediction in lithium-ion batteries under mechanical abuse

The prediction for the internal failure of lithium-ion batteries (LIBs) under external mechanical abuse loading remains a challenge for safe design. This paper systematically studies the modeling approach for progressive failure simulation and short-circuit prediction. Hemispherical indentation experiments are conducted on a battery specimen containing 5 representative sandwich (RS) layers to examine the deformation and progressive failure behavior of battery components. Three different models, viz, a high-fidelity detailed model, an intermediate homogenized model and a fully homogenized model, are developed and employed to simulate the progressive failure in a multi-layer battery specimen. The simulation results obtained using the three models all correlate well with experimental phenomena, capturing the overall stress-strain response and ultimate failure of the battery structure. The numerical results can help us understand the failure behavior and the mechanism for mechanical abuse-induced short circuit in LIBs. The feasibility of the three models for use in progressive failure prediction is compared by analyzing the capability of the models in predicting the critical failure events, the computational efficiency of the models, and the feasibility to implement coupled mechanical-electrochemical-thermal simulations. The results of this work provide useful insights on practical choices for the modeling strategy and safety design of LIBs under mechanical abuse conditions.

25 ENERGY STORAGE↗
DOE OSTI2023

The predictive power of phylogeny on growth rates in soil bacterial communities

Abstract Predicting ecosystem function is critical to assess and mitigate the impacts of climate change. Quantitative predictions of microbially mediated ecosystem processes are typically uninformed by microbial biodiversity. Yet new tools allow the measurement of taxon-specific traits within natural microbial communities. There is mounting evidence of a phylogenetic signal in these traits, which may support prediction and microbiome management frameworks. We investigated phylogeny-based trait prediction using bacterial growth rates from soil communities in Arctic, boreal, temperate, and tropical ecosystems. Here we show that phylogeny predicts growth rates of soil bacteria, explaining an average of 31%, and up to 58%, of the variation within ecosystems. Despite limited overlap in community composition across these ecosystems, shared nodes in the phylogeny enabled ancestral trait reconstruction and cross-ecosystem predictions. Phylogenetic relationships could explain up to 38% (averaging 14%) of the variation in growth rates across the highly disparate ecosystems studied. Our results suggest that shared evolutionary history contributes to similarity in the relative growth rates of related bacteria in the wild, allowing phylogeny-based predictions to explain a substantial amount of the variation in taxon-specific functional traits, within and across ecosystems.

Walkup, Jeth (ORCID:0000000279082963)↗
DOE OSTI2022

Manifold Learning-Based Polynomial Chaos Expansions for High-Dimensional Surrogate Models

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantities of interest of the computational or analytical model. For this purpose, we employ Grassmannian diffusion maps, a two-step nonlinear dimension reduction technique which allows us to reduce the dimensionality of the data and identify meaningful geometric descriptions in a parsimonious and inexpensive manner. Polynomial chaos expansion is then used to construct a mapping between the stochastic input parameters and the diffusion coordinates of the reduced space. An adaptive clustering technique is proposed to identify an optimal number of clusters of points in the latent space. The similarity of points allows us to construct a number of geometric harmonic emulators which are finally utilized as a set of inexpensive pretrained models to perform an inverse map of realizations of latent features to the ambient space and thus perform accurate out-of-sample predictions. Thus, the proposed method acts as an encoder-decoder system which is able to automatically handle very high-dimensional data while simultaneously operating successfully in the small-data regime. The method is demonstrated on two benchmark problems and on a system of advection-diffusion-reaction equations which model a first-order chemical reaction between two species. In all test cases, the proposed method is able to achieve highly accurate approximations which ultimately lead to the significant acceleration of UQ tasks.

42 ENGINEERING↗
DOE OSTI2021

Atomic-scale homogeneous plastic flow beyond near-theoretical yield stress in a metallic glass

Abstract The onset of yielding and the related atomic-scale plastic flow behavior of bulk metallic glasses at room temperature have not been fully understood due to the difficulty in performing the atomic-scale plastic deformation experiments needed to gain direct insight into the underlying fundamental deformation mechanisms. Here we overcome these limitations by combining a unique sample preparation method with atomic force microscopy-based indentation, which allows study of the yield stress, onset of yielding, and atomic-scale plastic flow of a platinum-based bulk metallic glass in volumes containing as little as approximately 1000 atoms. Yield stresses markedly higher than in conventional nanoindentation testing were observed, surpassing predictions from current models that relate yield stress to tested volumes; subsequent flow was then established to be homogeneous without exhibiting collective shear localization or loading rate dependence. Overall, variations in glass properties due to fluctuations of free volume are found to be much smaller than previously suggested.

Yu, Jiaxin↗
DOE OSTI2022

Pit rim decomposition into multiple quantum dots on surfaces of epitaxial thin films grown on pit-patterned substrates

Here, we report results of dynamical simulations according to an experimentally validated surface morphological evolution model on the formation of regular arrays of quantum dot molecules (QDMs) consisting of 1D arrays of smaller interacting quantum dots (QDs). These QD arrays form along the sides of each pit rim on the surface of a coherently strained thin film epitaxially deposited on a semiconductor substrate, the surface of which consists of a periodic pattern of inverted prismatic pits with rectangular pit openings. We find that this complex QDM pattern results from the decomposition of the pit rim from a “quantum fortress” with four elongated QDs into four 1D arrays of multiple smaller QDs arranged along each side of the pit rim. Systematic parametric analysis indicates that varying the pit opening dimensions and the pit wall inclination directly impacts the number of QDs in the resulting QDM pattern, while varying the pit depth only affects the dimensions of the QDs in the nanostructure pattern. Therefore, the number, arrangement, and sizes of QDs in the resulting pattern of QDMs on the film surface can be engineered precisely by proper tuning of the pit design parameters. Our simulation results are supported by predictions of morphological stability analysis, which explains the pit rim decomposition into multiple QDs as the outcome of a tip-splitting instability and provides a fundamental characterization of the post-instability nanostructure pattern. Our theoretical findings can play a vital role in designing optimal semiconductor surface patterns toward enabling future nanofabrication technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Analysis of multipacting threshold sensitivity to the random distributions of the secondary electron yield parameters

The way multipacting develops, depends strongly on the secondary emission property of the surface material. The knowledge of secondary electron yield is crucial for accurate prediction of the multipacting threshold. Variations in secondary electron yield parameters from experimental measurements create uncertainty, stemming from handling and surface preparation, and these uncertainties significantly affect multipacting threshold predictions. Despite their significance, the previous studies on the multipacting phenomenon did not adequately address the effect of an assumed random distribution of the secondary emission parameters on the multipacting threshold. Therefore, this paper aims to provide a comprehensive statistical study on how the different random distributions of the secondary emission parameters and, as a result, the uncertainty in the secondary electron yield affect multipacting thresholds. We focus on three commonly used distributions, namely uniform, normal, and truncated normal distributions, to define the uncertainty of random inputs. We use the chaos polynomial expansion method to determine how much each of the random parameters contributes to the multipacting threshold uncertainty. Additionally, we calculate Sobol sensitivity indices to evaluate the impact of the individual parameters or groups of parameters on the model outputs and study how different random distributions of these parameters affected the Sobol index results.

physics↗
DOE OSTI2023

Structural inference of networked dynamical systems with universal differential equations

Networked dynamical systems are common throughout science in engineering; e.g., biological networks, reaction networks, power systems, and the like. For many such systems, nonlinearity drives populations of identical (or near-identical) units to exhibit a wide range of nontrivial behaviors, such as the emergence of coherent structures (e.g., waves and patterns) or otherwise notable dynamics (e.g., synchrony and chaos). Here, we seek to infer (i) the intrinsic physics of a base unit of a population, (ii) the underlying graphical structure shared between units, and (iii) the coupling physics of a given networked dynamical system given observations of nodal states. These tasks are formulated around the notion of the Universal Differential Equation, whereby unknown dynamical systems can be approximated with neural networks, mathematical terms known a priori (albeit with unknown parameterizations), or combinations of the two. We demonstrate the value of these inference tasks by investigating not only future state predictions but also the inference of system behavior on varied network topologies. The effectiveness and utility of these methods are shown with their application to canonical networked nonlinear coupled oscillators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A physics-based model of cladding wastage layer formation rate

In this report, lower length scale simulations to inform an engineering-scale model of fuel-cladding chemical interaction (FCCI) conducted under the auspices of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in FY22 are described. An overall strategy for the implementation of the BISON model is described, and the past and current lower length scale work on FCCI formation is put into the context of this overall strategy. Density functional theory and kinetic Monte Carlo simulations are used to determine the diffusion coefficient of neodymium in iron. Density functional theory calculations are also used to parameterize a parabolic approximation for the dependence of free energy on composition for the intermetallic compound Fe 17 Nd 2 . A phase-field model of the Fe-Nd system was developed that includes the random solid solution body-centered cubic phase and the intermetallic Fe 17 Nd 2 . The model was used to simulate growth of the intermetallic layer in a diffusion couple and the results were compared to experiment. The model’s prediction of the parabolic growth constant is within 40 % of the experimental value. The model is also used to simulate the formation of a grain boundary attack layer that is observed in experiment. The simulations show enhanced Nd concentration along the grain boundaries, but do not clearly show formation of a Fe 17 Nd 2 layer

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Homologous mutations in human β, embryonic, and perinatal muscle myosins have divergent effects on molecular power generation

Mutations at a highly conserved homologous residue in three closely related muscle myosins cause three distinct diseases involving muscle defects: R671C in β-cardiac myosin causes hypertrophic cardiomyopathy, R672C and R672H in embryonic skeletal myosin cause Freeman–Sheldon syndrome, and R674Q in perinatal skeletal myosin causes trismus-pseudocamptodactyly syndrome. It is not known whether their effects at the molecular level are similar to one another or correlate with disease phenotype and severity. To this end, we investigated the effects of the homologous mutations on key factors of molecular power production using recombinantly expressed human β, embryonic, and perinatal myosin subfragment-1. We found large effects in the developmental myosins but minimal effects in β myosin, and magnitude of changes correlated partially with clinical severity. The mutations in the developmental myosins dramatically decreased the step size and load-sensitive actin-detachment rate of single molecules measured by optical tweezers, in addition to decreasing overall enzymatic (ATPase) cycle rate. In contrast, the only measured effect of R671C in β myosin was a larger step size. Our measurements of step size and bound times predicted velocities consistent with those measured in an in vitro motility assay. Finally, molecular dynamics simulations predicted that the arginine to cysteine mutation in embryonic, but not β, myosin may reduce pre-powerstroke lever arm priming and ADP pocket opening, providing a possible structural mechanism consistent with the experimental observations. This paper presents direct comparisons of homologous mutations in several different myosin isoforms, whose divergent functional effects are a testament to myosin’s highly allosteric nature.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Electric-Field-Induced Spatially Dynamic Heterogeneity of Solvent Motion and Cation Transference in Electrolytes

While electric fields primarily result in migration of charged species in electrolytic solutions, the solutions are dynamically heterogeneous. Solvent molecules within the solvation shells of the cation will be dragged by the field while free solvent molecules will not. Here we combine electrophoretic NMR measurements of ion and solvent velocities under applied electric fields with molecular dynamics simulations to interrogate different solvation motifs in a model liquid electrolyte. Measured values of the cation transference number $(t^0_+)$ agree quantitatively with simulation-based predictions over a range of electrolyte concentrations. Solvent-cation interactions strongly influence the concentration-dependent behavior of $t^0_+$. We identify a critical concentration at which most of the solvent molecules lie within solvation shells of the cations. The dynamic heterogeneity of solvent molecules is minimized at this concentration where $t^0_+$ is approximately equal to zero.

42 ENGINEERING↗
DOE OSTI2021

MACHINE LEARNING BASED CHEMICAL EXPLOSIVE MODE ANALYSIS (ML-CEMA)

The software consists of a Machine Learning based Chemical Explosive Mode Analysis (ML-CEMA) tool for advanced computational flame diagnostics. CEMA, originally based on eigen-analysis of the local thermochemical system, is capable of identifying reaction fronts and limit phenomena such as auto-ignition and extinction in practical combustion systems, such as internal combustion engines and gas turbine combustors. However, the original CEMA is computationally expensive for large reaction mechanisms that are typically needed to describe fuel chemistry of practical large-hydrocarbon fuels. This novel ML-CEMA tool employs a ML technique to accelerate the eigen-analysis of the basic CEMA approach by orders of magnitude, thus making it suitable for practical fuels. In ML-CEMA,zero-dimensional (0D) reactors and one-dimensional (1D) premixed flames are first used to generate a large number of data points for neural network based ML training. The trained ML model is then used to perform CEMA prediction. This ML-CEMA tool has been demonstrated in canonical 0D and 1D configurations as well as highly-transient three-dimensional spray flames exhibiting multi-mode turbulent combustion, showing promising results. ML-CEMA, as a standalone tool, can be used for computationally-efficient diagnostics of massive datasets generated from both experiments and simulations. For example, based on spatially resolved measurements of a small set of reactive scalars(such as temperature, hydroxyl radical and formaldehyde), ML-CEMA can effectively identify flame fronts and rare events. ML-CEMA also provides a robust online or offline flame feature detection tool. When used for on-the-fly simulations, ML-CEMA further enables zone-adaptive combustion modeling, in which the predicted eigenvalue is used as a robust mode indicator for judicious assignment of locally-valid combustion models. This ML-CEMA based zone-adaptive model can lead to substantial computational cost savings when used for large-scale simulations of multi-mode combustion systems. Third Party Code Web Page to Download Code Web Page Location of Third Party License

Xu, Chao↗
DOE OSTI2025

Delay-resolved spectroscopy in terahertz photonic circuits

Photonic integrated circuits incorporating intersubband transitions are ideal for mid-infrared and terahertz nanophotonics. However, the design of epitaxies has long been inhibited by two factors: the modest predictivity of ab initio theory and the absence of absolute intersubband gain and loss measurements under operating conditions. Existing measurements either yield inaccurate gain profiles or do not accurately assess dependence on frequency, bias, and temperature. Here, we present a delay-resolved absolute-referencing method for accurate gain evaluation without these limitations, addressing a long-standing challenge. By creating a photonic circuit that allows broadband pulses to traverse different lengths of a gain medium, we measure the absolute transmission of intersubband structures. Gain profiles match theoretical predictions at lower temperatures, with gain and dispersion clamping after lasing, and faster-than-expected degradation occurs at higher temperatures. Our approach provides a precise experimental evaluation of temperature-dependent gain performance and gives insight into optimizing temperature performance and frequency comb designs.

Optical spectroscopy↗
DOE OSTI2023

Mechanistic FCCI model for BISON

In this report, multi-scale simulations to develop a mechanistic model of fuel-cladding chemical interaction (FCCI) conducted under the auspices of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in FY23 are described. The model assumes that cladding wastage is dominated by the intermetallic phase (Fe,Cr)$_{17}$Nd$_2$, and calculates production and diffusion of Nd in the fuel, diffusion into the cladding, and formation and growth of the intermetallic wastage layer. Atomistic simulations are used to calculate diffusivity of Nd in the (Fe,Cr) system. Mesoscale simulations, informed by previous atomistic calculations, are used to determine the effective diffusivity of Nd through the fuel, accounting for empty and sodium-filled porosity within the fuel. The BISON model incorporates this data and calculates the wastage layer thickness as a function of time, assuming parabolic growth kinetics based on the best available data. The results of the new mechanistic BISON model are compared to existing assessment cases for the X447 experiment in EBR-II, and show good agreement with experimental data and the existing BISON empirical model. To improve predictions of kinetics for future work, a phase-field model that includes fuel, wastage, and cladding phases is developed. Preliminary testing indicates that the kinetics may differ significantly from the assumed parabolic growth law, suggesting direction for future improvement of the model that will allow it to be applied to other fuel designs with greater confidence.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗