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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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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Kinetic model development and Bayesian uncertainty quantification for the complete reduction of Fe-based oxygen carriers with CH 4 , CO, and H 2 for chemical looping combustion

In this work, three kinetic models are developed and calibrated for the complete multi-step reduction of an Fe-based oxygen carrier (OC) particle with CH 4 , CO, and H 2 , using data from thermogravimetric analysis. The complete reduction rate profiles exhibit complex dynamics whose trajectory is significantly different depending on the reducing gas. A Bayesian model building and parameter estimation framework is applied for simultaneous parameter and model structure uncertainty quantification. The final models show excellent agreement between model predictions and calibration data, as well as new data not used for calibration (for the reduction of the OC with CH 4 ). Parameter uncertainty is quantified by determining joint posterior distribution, and model structure uncertainty is addressed by incorporating Gaussian process stochastic functions (represented by Bayesian smoothing splines) into the kinetic models. The final kinetic models with discrepancy functions are readily employable in equation-oriented simulation and optimization platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities

Here we propose a high dimensional Bayesian inference framework for learning heterogeneous dynamics of a COVID-19 model, with a specific application to the dynamics and severity of COVID-19 inside and outside long-term care (LTC) facilities. We develop a heterogeneous compartmental model that accounts for the heterogeneity of the time-varying spread and severity of COVID-19 inside and outside LTC facilities, which is characterized by time-dependent stochastic processes and time-independent parameters in ~ 1500 dimensions after discretization. To infer these parameters, we use reported data on the number of confirmed, hospitalized, and deceased cases with suitable post-processing in both a deterministic inversion approach with appropriate regularization as a first step, followed by Bayesian inversion with proper prior distributions. To address the curse of dimensionality and the ill-posedness of the high-dimensional inference problem, we propose use of a dimension-independent projected Stein variational gradient descent method, and demonstrate the intrinsic low-dimensionality of the inverse problem. We present inference results with quantified uncertainties for both New Jersey and Texas, which experienced different epidemic phases and patterns. Moreover, we also present forecasting and validation results based on the empirical posterior samples of our inference for the future trajectory of COVID-19.

42 ENGINEERING↗
DOE OSTI2024

Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations

Abstract Large‐scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub‐surface transport of particles since direct observation of real world underground fracture networks is generally infeasible. While these simulators have successfully been used in several engineering applications, estimates of output quantities of interest (QoI) — such as breakthrough time of particles reaching the edge of the system — suffer from two distinct types of uncertainty. A run of a DFN simulator requires several parameters to be set that dictate the placement and size of fractures, the density of fractures, and the overall permeability of the system; uncertainty on the proper parameters will lead to uncertainty in the QoI, called epistemic uncertainty. Furthermore, since these input settings to DFN simulators control the stochastic processes which place fractures and govern flow, understanding how this randomness affects the QoI requires several runs of the simulator at distinct random seeds. The uncertainty in the QoI attributed to different realizations (i.e., different seeds) of the same random process (i.e., identical input parameters) leads to a second type of uncertainty, called aleatoric uncertainty. In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty. Beyond the specific takeaways on which input variables influence uncertainty in the QoI the most, a major contribution of this paper is the introduction of a statistically rigorous workflow for characterizing the uncertainty in DFN flow simulations that exhibit heteroskedasticity.

58 GEOSCIENCES↗
DOE OSTI2021

Soil microbiome predictability increases with spatial and taxonomic scale

Soil microorganisms shape ecosystem function, yet it remains an open question whether we can predict the composition of the soil microbiome in places before observing it. Furthermore, it is unclear whether the predictability of microbial life exhibits taxonomic- and spatial-scale dependence, as it does for macrobiological communities. Here, we leverage multiple large-scale soil microbiome surveys to develop predictive models of bacterial and fungal community composition in soil, then test these models against independent soil microbial community surveys from across the continental United States. We find remark- able scale dependence in community predictability. The predictability of bacterial and fungal communities increases with the spatial scale of observation, and fungal predictability increases with taxonomic scale. These patterns suggest that there is an increasing importance of deterministic versus stochastic processes with scale, consistent with findings in plant and animal communities, suggesting a general scaling relationship across biology. Biogeochemical functional groups and high-level taxonomic groups of microorganisms were equally predictable, indicating that traits and taxonomy are both powerful lenses for understanding soil communities. Here, by focusing on out-of-sample prediction, these findings suggest an emerging generality in our understanding of the soil microbiome, and that this understanding is fundamentally scale dependent

Biogeography↗
DOE OSTI2024

Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning

Agricultural practices affect soil microbes which are critical to soil health and sustainable agriculture. To understand prokaryotic and fungal assembly under agricultural practices, we use machine learning-based methods. We show that fertility source is the most pronounced factor for microbial assembly especially for fungi, and its effect decreases with soil depths. Fertility source also shapes microbial co-occurrence patterns revealed by machine learning, leading to fungi-dominated modules sensitive to fertility down to 30 cm depth. Tillage affects soil microbiomes at 0-20 cm depth, enhancing dispersal and stochastic processes but potentially jeopardizing microbial interactions. Cover crop effects are less pronounced and lack depth-dependent patterns. Machine learning reveals that the impact of agricultural practices on microbial communities is multifaceted and highlights the role of fertility source over the soil depth. Machine learning overcomes the linear limitations of traditional methods and offers enhanced insights into the mechanisms underlying microbial assembly and distributions in agriculture soils.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

How Hall electric fields intrinsically chaotize and heat ions during collisionless magnetic reconnection

The origin of extreme and anomalous ion heating during magnetic reconnection has been a longstanding mystery. It is shown in this paper that, at the core of the process, the stochastic heating mechanism is responsible for this energization. The in-plane Hall electric fields that intrinsically develop during collisionless reconnection not only accelerate ions, but also thermalize them by inducing chaotic motion. The mechanism explains various observed features of ion heating, e.g., its relation to the guide field strength and the ion mass-to-charge ratio.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Deterministic scattering of relativistic electron beams by off-resonant circularly polarized electromagnetic waves

A mechanism is presented whereby relativistic electron beams localized in phase space are deterministically scattered by coherent circularly polarized electromagnetic waves without stochastic processes. It is shown via an exact single-particle analysis that the condition for maximal scattering is an off-resonant condition, contrary to previous kinetic analyses that predict maximal diffusion or interaction at exact resonance or its harmonics. The mechanism, verified by single-particle simulations, enables a fast, nonlinear redistribution of the beam particles. A possible application of this mechanism to runaway electron suppression is presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Efficient computation of N -point correlation functions in D dimensions

We present efficient algorithms for computing the N-point correlation functions (NPCFs) of random fields in arbitrary D-dimensional homogeneous and isotropic spaces. Such statistics appear throughout the physical sciences and provide a natural tool to describe stochastic processes. Typically, algorithms for computing the NPCF components have $\mathscr O$(n N ) complexity (for a dataset containing n particles); their application is thus computationally infeasible unless N is small. By projecting the statistic onto a suitably defined angular basis, we show that the estimators can be written in a separable form, with complexity $\mathscr O$(n 2 ) or $\mathscr O$(n g log n g ) if evaluated using a Fast Fourier Transform on a grid of size n g . Our decomposition is built upon the D-dimensional hyperspherical harmonics; these form a complete basis on the (D – 1) sphere and are intrinsically related to angular momentum operators. Concatenation of (N – 1) such harmonics gives states of definite combined angular momentum, forming a natural separable basis for the NPCF. As N and D grow, the number of basis components quickly becomes large, providing a practical limitation to this (and all other) approaches: However, the dimensionality is greatly reduced in the presence of symmetries; for example, isotropic correlation functions require only states of zero combined angular momentum. We provide a Julia package implementing our estimators and show how they can be applied to a variety of scenarios within cosmology and fluid dynamics. The efficiency of such estimators will allow higher-order correlators to become a standard tool in the analysis of random fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An efficient method to propagate model uncertainty when inverting seismic data for time domain seismic moment tensors

SUMMARY We present a computationally efficient method to approximately propagate uncertainty when linearly inverting seismic data for point source, time variable moment tensor components. The method is based on the assumption that the data residual, given by the difference between the observed seismic data and the data predicated by a linear inversion, contains the effects of both data and model uncertainty. Our method uses a distribution of data residuals, added directly to the data, in a pseudo-Monte Carlo scheme. Using the assumption that the data residual is a stochastic process, we use the well-known Karhunen–Loève (KL) theorem to construct a distribution of data residuals, where the required basis functions are constructed using Fourier series. The Fourier series are scaled by a product of a random variable and the real-valued spectral amplitudes of the original data residual’s spectrum. Thus, the Fourier series and spectral amplitudes are eigenfunction-eigenvalue pairs used in the KL-based construction of data residual distribution. Using tests with synthetic data, we show that our method compares closely with a Finite Difference Monte Carlo (FDMC) method that we presented previously. More importantly, the method presented here is computationally several orders of magnitude faster than our previous FDMC method, and requires no a priori assumptions of model and/or data uncertainty.

Poppeliers, Christian (ORCID:0000000159526849)↗
DOE OSTI2026

Monitored Fluctuating Hydrodynamics

We introduce a hydrodynamic framework for describing monitored classical stochastic processes. We study the conditional ensembles for these monitored processes—i.e., we compute spacetime correlation functions conditioned on a fixed, typical measurement record. In the presence of global symmetries we show that these conditional ensembles can undergo measurement-induced “sharpening” phase transitions as a function of the monitoring rate; moreover, even weak monitoring can give rise to novel critical phases, derived entirely from a classical perspective. We give a simple hydrodynamic derivation of the known “charge-fuzzy phase” for weakly monitored diffusive many-body quantum systems. We show that although the unmonitored symmetric and asymmetric exclusion processes are in different universality classes of transport, the fluctuations in their conditional ensembles flow to the same fixed point with emergent relativistic invariance under monitoring. On the other hand, weakly monitored systems with non-Abelian symmetries enter a novel strongly coupled fixed point with nontrivial dynamical exponent, which we characterize. Our formalism naturally accounts for monitoring general observables, such as currents or density gradients, and allows for a direct calculation of information-theoretic diagnostics of sharpening transitions, including the Shannon entropy of the measurement record.

classical statistical mechanics↗
DOE OSTI2022

Realistic simulations of spin squeezing and cooperative coupling effects in large ensembles of interacting two-level systems

We describe an efficient numerical method for simulating the dynamics of interacting spin ensembles in the presence of dephasing and decay. The method builds on the discrete truncated Wigner approximation for isolated systems, which combines the mean-field dynamics of a spin ensemble with a Monte Carlo sampling of discrete initial spin values to account for quantum correlations. Here we show how this approach can be generalized for dissipative spin systems by replacing the deterministic mean-field evolution by a stochastic process, which describes the decay of coherences and populations while preserving the length of each spin. We demonstrate the application of this technique for simulating nonclassical spin-squeezing effects or the dynamics and steady states of cavity QED models with 10 5 interacting two-level systems. This opens up the possibility to perform accurate real-scale simulations of a diverse range of experiments in quantum optics or with solid-state spin ensembles under realistic laboratory conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Reaction dynamics and in-medium nucleon-nucleon cross section with 12 C+ 1 H at 95 MeV/nucleon

Reaction dynamics and effective in-medium nucleon-nucleon (NN) cross section are studied, using the antisymmetrized molecular dynamics (AMD) model with one of the simplest heavy ion collisions, 12 C+ ⁢1 H. The roles of different stochastic processes are studied, which are installed in the original AMD. One is the diffusion process as a part of the quantum fluctuation originated from the Fermi motion during the time evolution of the wave packets in the mean field (AMD/D). The second is a coalescence treatment for light clusters with A ≤ 8 (AMD/D-COALS). The third is the collision process, installed in AMD with different form of the in-medium NN cross sections. These different processes are examined, using the experimental angular distributions and energy spectra of light charged particles and intermediate mass fragments. The effective in-medium NN cross sections are evaluated from the simulated AMD events and compared with the experimentally extracted values. A good agreement is obtained between the AMD values from the 12 C + 1 H reaction at 95 MeV/nucleon and the experimental results for nuclear matter.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Unified, Geometric Framework for Nonequilibrium Protocol Optimization

Controlling thermodynamic cycles to minimize the dissipated heat is a long-standing goal in thermodynamics, and more recently, a central challenge in stochastic thermodynamics for nanoscale systems. Here, we introduce a theoretical and computational framework for optimizing nonequilibrium control protocols that can transform a system between two distributions in a minimally dissipative fashion. These protocols optimally transport a system along paths through the space of probability distributions that minimize the dissipative cost of a transformation. Furthermore, we show that the thermodynamic metric—determined via a linear response approach—can be directly derived from the same objective function that is optimized in the optimal transport problem, thus providing a unified perspective on thermodynamic geometries. As a result, we investigate this unified geometric framework in two model systems and observe that our procedure for optimizing control protocols is robust beyond linear response.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Full Counting Statistics of Charge in Chaotic Many-Body Quantum Systems

We investigate the full counting statistics of charge transport in U(1)-symmetric random unitary circuits. We consider an initial mixed state prepared with a chemical potential imbalance between the left and right halves of the system and study the fluctuations of the charge transferred across the central bond in typical circuits. Using an effective replica statistical mechanics model and a mapping onto an emergent classical stochastic process valid at large on-site Hilbert space dimension, we show that charge transfer fluctuations approach those of the symmetric exclusion process at long times, with subleading t –1/2 quantum corrections. Here, we discuss our results in the context of fluctuating hydrodynamics and macroscopic fluctuation theory of classical nonequilibrium systems and check our predictions against direct matrix-product state calculations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Coherent Transfer of Lattice Entropy via Extreme Nonlinear Phononics in Metal Halide Perovskites

Entropy transfer in metal halide perovskites, characterized by significant lattice anharmonicity and low stiffness, underlies the remarkable properties observed in their optoelectronic applications, ranging from solar cells to lasers. The conventional view of this transfer involves stochastic processes occurring within a thermal bath of phonons, where the lattice arrangement and energy flow from higher- to lower-frequency modes. Here, we unveil a comprehensive chronological sequence detailing a conceptually distinct coherent transfer of entropy in a prototypical perovskite CH 3 NH 3 Pbl 3 . The terahertz periodic modulation imposes vibrational coherence into electronic states, leading to the emergence of mixed (vibronic) quantum beat between approximately 3 and 0.3 THz. We highlight a well-structured bidirectional time-frequency transfer of these diverse phonon modes, each developing at different times and transitioning from high to low frequencies from 3 to 0.3 THz, before reversing direction and ascending to around 0.8 THz. First-principles molecular dynamics simulations disentangle a complex web of coherent-phononic coupling pathways and identify the salient roles of the initial modes in shaping entropy evolution at later stages. Capitalizing on coherent entropy transfer and dynamic anharmonicity presents a compelling opportunity to exceed the fundamental thermodynamic (Shockley-Queisser) limit of photoconversion efficiency and to pioneer novel optoelectronic functionalities. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Data-driven Minimum Entropy Control for Stochastic Nonlinear Systems using the Cumulant-Generating Function

Here, we present a novel minimum entropy control algorithm for a class of stochastic nonlinear systems subjected to non-Gaussian noises. The entropy control can be considered as an optimization problem for the system randomness attenuation, but the mean value has to be considered separately. To overcome this disadvantage, a new representation of the system stochastic properties was given using the cumulant-generating function based on the moment-generating function, in which the mean value and the entropy was reflected by the shape of the cumulant-generating function. Based on the samples of the system output and control input, a time-variant linear model was identified, and the minimum entropy optimization was transformed to system stabilization. Then, an optimal control strategy was developed to achieve the randomness attenuation, and the boundedness of the controlled system output was analyzed. The effectiveness of the presented control algorithm was demonstrated by a numerical example. In this paper, a data-driven minimum entropy design is presented without pre-knowledge of the system model; entropy optimization is achieved by the system stabilization approach in which the stochastic distribution control and minimum entropy are unified using the same identified structure; and a potential framework is obtained since all the existing system stabilization methods can be adopted to achieve the minimum entropy objective.

42 ENGINEERING↗
DOE OSTI2020

Control of long-distance motion of single molecules on a surface

Spatial control over molecular movement is typically limited because motion at the atomic scale follows stochastic processes. Here, we used scanning tunneling microscopy to bring single molecules into a stable orientation of high translational mobility where they moved along precisely defined tracks. Single dibromoterfluorene molecules moved over large distances of 150 nanometers with extremely high spatial precision of 0.1 angstrom across a silver (111) surface. The electrostatic nature of the effect enabled the selective application of repulsive and attractive forces to send or receive single molecules. The high control allows us to precisely move an individual and specific molecular entity between two separate probes, opening avenues for velocity measurements and thus energy dissipation studies of single molecules in real time during diffusion and collision.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Global Diversity and Biogeography of the Zostera marina Mycobiome

Seagrasses are marine flowering plants that provide critical ecosystem services in coastal environments worldwide. Marine fungi are often overlooked in microbiome and seagrass studies, despite terrestrial fungi having critical functional roles as decomposers, pathogens, or endophytes in global ecosystems. Here, we characterize the distribution of fungi associated with the seagrass Zostera marina, using leaves, roots, and rhizosphere sediment from 16 locations across its full biogeographic range. Using high-throughput sequencing of the ribosomal internal transcribed spacer (ITS) region and 18S rRNA gene, we first measured fungal community composition and diversity. We then tested hypotheses of neutral community assembly theory and the degree to which deviations suggested that amplicon sequence variants (ASVs) were plant selected or dispersal limited. Finally, we identified a core mycobiome and investigated the global distribution of differentially abundant ASVs. We found that the fungal community is significantly different between sites and that the leaf mycobiome follows a weak but significant pattern of distance decay in the Pacific Ocean. Generally, there was evidence for both deterministic and stochastic factors contributing to community assembly of the mycobiome, with most taxa assembling through stochastic processes. The Z. marina core leaf and root mycobiomes were dominated by unclassified Sordariomycetes spp., unclassified Chytridiomycota lineages (including Lobulomycetaceae spp.), unclassified Capnodiales spp., and Saccharomyces sp. It is clear from the many unclassified fungal ASVs and fungal functional guilds that knowledge of marine fungi is still rudimentary. Further studies characterizing seagrass-associated fungi are needed to understand the roles of these microorganisms generally and when associated with seagrasses. Fungi have important functional roles when associated with land plants, yet very little is known about the roles of fungi associated with marine plants, like seagrasses. In this study, we report the results of a global effort to characterize the fungi associated with the seagrass Zostera marina across its full biogeographic range. Although we defined a putative global core fungal community, it is apparent from the many fungal sequences and predicted functional guilds that had no matches to existing databases that general knowledge of seagrass-associated fungi and marine fungi is lacking. This work serves as an important foundational step toward future work investigating the functional ramifications of fungi in the marine ecosystem.

ITS2↗