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DOE OSTI2014

Variable cascade dynamics and intermittency in the solar wind at 1 AU

In recent studies by ourselves and others of third-moment expressions for the rate of energy cascade in the solar wind, it has been shown that relatively large volumes of data are needed to produce convergent averages. These averages are in good agreement with independently obtained estimates for the average heating rate for a solar wind plasma under those conditions. The unanswered question has been whether the convergence issue is the result of intermittent dynamics or simple measurement uncertainties. In other words, is the difficulty in obtaining a single result that characterizes many similarly prepared samples due to in situ dynamics that create physically real variations or simple statistics? There have been publications showing evidence of intermittent dynamics in the solar wind. Here we show that the third-moment expressions and the computed energy cascade for relatively small samples of data comparable to the correlation length are generally well-formed estimates of the local dynamics. This leads us to conclude that intermittency and not simple measurement uncertainties are responsible for the slow convergence to expected heating rates. We partially characterize the scale size of the intermittency to be comparable to or smaller than the correlation length of the turbulence and we attempt to rephrase the discussion of third moments in terms of intermittent dynamics.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Regimes of evaporation and mixing behaviors of nanodroplets at transcritical conditions

The objective of this paper is to examine the fundamental mechanisms responsible for the transition between subcritical evaporation and supercritical dense-fluid-mixing in the absence of convection effects, specifically focusing on the liquid–vapor interfacial dynamics. To isolate the dynamics of this transition process, we characterize the different physical behaviors exhibited by an -dodecane nanoscale droplet placed in different nitrogen ambient conditions across the fuel’s critical point. We employ a continuum-based interface-resolving diffuse-interface method to explore the underlying phase-exchange mechanisms that bring about such distinct dynamics. Following the comparison against molecular dynamics simulations and experiments of evaporating droplets and experimental data for vapor–liquid equilibria, a parametric study at various ambient conditions and droplet sizing is performed to identify four regimes of evaporation/mixing behaviors: sub- and supercritical droplet evaporation, and sub- and supercritical dense-fluid-mixing. It is shown that the distinction in the phase-exchange mechanisms in these four regimes are brought about by the different thermodynamic phases the droplet center can exhibit during the evaporation/mixing process: subcritical liquid, supercritical liquid-like, subcritical gaseous, and supercritical gas-like, respectively. It is shown that the subcritical dense-fluid-mixing behavior is a direct result of nanoconfinement of the liquid–vapor interfacial structure and thus is not present for large droplet sizes. Finally, the present study also shows that the supercritical phase-exchange dynamics can follow two different pathways: supercritical droplet-like evaporation and supercritical dense-fluid-mixing. Furthermore, promoting the early transition to supercritical dense-fluid-mixing can significantly expedite the phase-exchange process through the disintegration of the liquid-like droplet core.

42 ENGINEERING↗

DOE OSTI2023

Dynamically Formed Active Sites on Liquid Boron Oxide for Selective Oxidative Dehydrogenation of Propane

Boron-based catalysts have been shown to be both active and selective for driving the oxidative dehydrogenation of propane (ODHP) without the use of precious metals. This reaction occurs at temperatures that melt the oxide catalyst which challenges our ability to identify the liquid structures of the boron oxide phase under reaction conditions, hindering the understanding of its active sites and reaction mechanism. By combining ab initio molecular dynamics simulation, in-situ Raman characterization, and microkinetic modeling, we propose that the di-coordinated boron sites (BO2) in liquid boron oxide are the active species for O2 activation under reaction conditions. The formed peroxy-like species (>B-O-O-B<) can be viewed as a moderate oxidant for ODHP. The dynamical >B-O* dangling bond originated from >B-O-O-B< site as well as the liquid B2O3 structure itself, plays a critical role in the abstraction of H atoms from propane (C3H7 radical formation). Microkinetic modeling reveals C3H7 radical formation to be the main rate controlling step (~75% degree of rate control) with the dehydration of boron hydroxyls (B-OHs) to recover the di-coordinated boron active sites controlling the remainder of the rate (~25% degree of rate control). Moreover, the activation barriers are found to strongly depend upon the surface B-OH concentration. These findings provide significant insights into the active site and reaction mechanisms on boron-based catalysts for ODHP and underlie the importance of understanding the liquid nature of the catalyst to account for the catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2012

Inflow Characterization and Aerodynamics Measurements on a SWT-2.3-101 Wind Turbine

Post processing techniques for aerodynamic data acquired from a Siemens SWT-2.3101 turbine have been developed and applied in this paper. The turbine is installed at the National Wind Technology Center (NWTC) as part of Cooperative Research and Development Agreement between Siemens Wind Power and the National Renewable Energy Laboratory (NREL) under U.S. Department of Energy (DOE) sponsorship. The results indicate that the use of these corrections is essential for accurate analysis of the data. An example of local inflow angles, velocities, and inflow velocity over the rotor plane derived from measurements from a 5-hole probe is also presented. Finally, the pressure measurements are used to characterize unsteady phenomenon, namely, rotational augmentation and dynamic stall on an inboard station. The results show that the rotational augmentation can considerably increase the attached flow regime compared to the 2D Computational Fluid Dynamics (CFD) results. The dynamic stall event was seen to significantly delay the stall. Furthermore, the non-dimensionalized vortex convection derived from the dynamic stall event was found to agree well with results from others studies.

17 WIND ENERGY↗

DOE OSTI2026

Ionic-content-driven restructuring of spirobisindane ionene networks: implications for mechanics, self-healing, and gas transport

Polymers of intrinsic microporosity (PIMs) offer exceptional gas permeability but remain brittle and susceptible to physical aging, limiting their durability in separation applications. Here, we introduce a reconfigurable microporous polymer network that uniquely integrates permanent PIM microporosity with autonomous, intrinsic self-healing driven by imidazolium-based ionic motifs. Spirobisindane units generate the intrinsic free-volume architecture, while an imidazolium-containing polyamide ionene supplies dynamic ionic and hydrogen-bonding interactions that reorganize under mild activation. Incorporation of imidazolium-based ionic liquids further tunes cohesion, mobility, and densification, enabling the network to relax, re-associate, and retain microporosity without structural collapse. Through a comprehensive multiscale approach combining spectroscopy, scattering, thermal and mechanical characterization with all-atom molecular dynamics and density functional theory calculations, we elucidate how ionic content, as a single control parameter that reshapes free-volume distributions, modulates local coordination environments, and governs relaxation and healing kinetics. At intermediate ionic loadings, the networks achieve rapid, repeatable self-healing while maintaining CO$_2$ selectivity, demonstrating an optimal balance between segmental mobility and structural integrity. By establishing how hierarchical ionic interactions couple structure, dynamics, and transport in microporous ionene networks, this work provides generalizable design rules for adaptive soft-matter systems that require simultaneous mechanical resilience, reconfigurability, and selective gas transport.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Non-intrusive reduced-order modeling for dynamical systems with spatially localized features

This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains that exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers’ model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of 1% with an online speedup factor of approximately 8$\times$ compared to the numerical simulation.

42 ENGINEERING↗

DOE OSTI2019

Generation of the configurational ensemble of an intrinsically disordered protein from unbiased molecular dynamics simulation

Significance A major challenge in biology is characterizing the structural flexibility of intrinsically disordered proteins (IDPs). Ensemble-averaged experimental data do not provide the underlying protein structures. Here, we performed independently small-angle neutron and X-ray scattering experiments and unbiased molecular dynamics simulations to probe the solution structure of an IDP. We report that enhancing the sampling of the simulations can generate an ensemble of IDP structures in quantitative agreement with scattering and NMR, without the need for biasing the simulation or reweighting the results. The demonstration of established simulation technology that produces accurate physical models of flexible biosystems may pave the way to relating conformational flexibility to biological function.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗

DOE OSTI2016

Characterization of the High-Speed-Stage Bearing Skidding of Wind Turbine Gearboxes Induced by Dynamic Electricity Grid Events: Preprint

Bearing behavior is an important factor for wind turbine drivetrain reliability. Extreme loads and dynamic excitations pose challenges to the bearing design and therefore its performance. Excessive skidding of the bearing rollers should be avoided because it can cause scuffing failures. Excitations coming from wind and the electricity grid can subject the drivetrain to fluctuating torque and nontorque loads. Wind-induced excitations have been investigated predominantly in literature. However, modern wind turbines are subjected more and more to grid-induced loads because of stricter electricity grid regulations. For example, during fault-ride-through events, turbines are required to stay connected for a longer period of time during the grid failure. This work investigates the influence of electrically induced excitations on the skidding behaviour of the tapered roller bearings on the high-speed stage of a wind turbine gearbox. This skidding behaviour during dynamic events is described as a potential bearing failure initiator by many researchers; however, only limited full-scale dynamic testing is documented. Therefore, a dedicated gridloss-type event is defined in the paper and conducted in a dynamometer test on a full-scale wind turbine nacelle. During the event, a complete electricity grid failure is simulated while the turbine is at rated speed and predefined torque levels. Particular focus is on the characterization of the high-speed shaft tapered roller bearing slip behavior. Strain-gauge bridges in grooves along the circumference of the outer ring are used to characterize the bearing load zone in detail. It is shown that during the torque reversals of the transient event, roller slip can be induced. This indicates the potential of the applied load case to go beyond the preload of the tapered roller bearing. Furthermore, the relation between the applied torque and skidding level is studied.

17 WIND ENERGY↗

DOE OSTI2016

Characterization of the High-Speed-Stage Bearing Skidding of Wind Turbine Gearboxes Induced by Dynamic Electricity Grid Events

17 WIND ENERGY↗

DOE OSTI2022

The root-zone soil moisture spectrum in a mediterranean ecosystem

Storage of water within soil pores of the root zone introduce memory effects in the dynamics of soil moisture that are considerably longer than the integral timescale of many atmospheric processes. Thus, hydro-climatic states can be “sustained” through land-surface heat and water vapor fluxes primarily because they can “feed off” on this long-term soil moisture memory. Root-zone soil moisture memory is only but one feature characterizing the spectrum of soil moisture dynamics, which is analyzed here using a combination of long-term measurements and models. In particular, the spectrum of root-zone soil moisture content in a Mediterranean ecosystem is examined using 14-years of half-hourly measurements. A distinguishing hydro-climatic feature in such ecosystems is that sources (mainly rainfall) and sinks (mainly evapotranspiration) of soil moisture are roughly out of phase with each other. For over 4 decades of time scales and 7 decades of energy, the canonical shape of the measured soil moisture spectrum is shown to be approximately Lorentzian determined by the soil moisture variance and its memory but with two exceptions: the occurrences of a peak at diurnal-to-daily time scales and a weaker peak at near annual time scales. Model calculations and spectral analysis demonstrate that diurnal and seasonal variations in hydroclimate forcing responsible for variability in evapotranspiration had minor impact on the normalized shape of the soil moisture spectrum. However, their impact was captured by adjustments in the temporal variance. These findings indicate that precipitation and not evapotranspiration variability dominates the multi-scaling properties of soil moisture variability consistent with prior climate model simulations. Furthermore, the soil moisture memory inferred by the annual peak of soil moisture (340 d) is consistent with climate model simulations, while the memory evaluated from the loss function of a linearized mass balance approach leads to a smaller value (50 d), highlighting the effect of weak non-stationarity on soil moisture variability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Chaotic state to self-organized critical state transition of serrated flow dynamics during brittle-to-ductile transition in metallic glass

We study serrated flow dynamics during brittle-to-ductile transition induced by tuning the sample aspect ratio in a Zr-based metallic glass. The statistical analysis reveals that the serrated flow dynamics transforms from a chaotic state characterized by Gaussian-distribution serrations corresponding to stick-slip motion of randomly generated and uncorrelated single shear band and brittle behavior, into a self-organized critical state featured by intermittent scale-free distribution of shear avalanches corresponding to a collective motion of multiple shear bands and ductile behavior. The correlation found between serrated flow dynamics and plastic deformation might shed light on the plastic deformation dynamic and mechanism in metallic glasses.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

Clay Minerals

Clay minerals are important components of the environment and are involved or implicated in processes such as the uptake of pollutants and the release of nutrients and as potential platforms for a number of chemical reactions. Owing to their small particle sizes (typically, on the order of microns or smaller) and mixing with a variety of other minerals and soil components, advanced characterization methods are needed to study their structures, dynamics, and reactivities. In this article, we describe the use of solid-state NMR methods to characterize the structures and chemistries of clay minerals. Early one-pulse magic-angle spinning (MAS) NMR studies of 27Al and 29Si have now been enhanced and extended with new studies utilizing advanced methodologies (such as Multiple Quantum MAS) as well as studies of less-sensitive nuclei. In additional work, the issue of reactivity of clay minerals has been addressed, including studies of reactive surface area in the environment. Utilizations of NMR-sensitive nuclides within the clay minerals themselves, and in molecules that react with specific sites on the clay mineral surfaces, have aided in understanding the reactivity of these complex aluminosilicate systems.

Mueller, Karl T.↗

DOE OSTI2012

About an Optimal Visiting Problem

In this paper we are concerned with the optimal control problem consisting in minimizing the time for reaching (visiting) a fixed number of target sets, in particular more than one target. Such a problem is of course reminiscent of the famous 'Traveling Salesman Problem' and brings all its computational difficulties. Our aim is to apply the dynamic programming technique in order to characterize the value function of the problem as the unique viscosity solution of a suitable Hamilton-Jacobi equation. We introduce some 'external' variables, one per target, which keep in memory whether the corresponding target is already visited or not, and we transform the visiting problem in a suitable Mayer problem. This fact allows us to overcome the lacking of the Dynamic Programming Principle for the originary problem. The external variables evolve with a hysteresis law and the Hamilton-Jacobi equation turns out to be discontinuous.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Dynamic Event Tree advancements and control logic improvements

The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification, data mining analysis and optimization studies. RAVEN is currently equipped with three different sampling categories: Forward samplers (Monte Carlo, Latin Hyper Cube, Stratified, Grid Sampler, Factorials, etc.), Adaptive Samplers (Limit Surface search, Adaptive Polynomial Chaos, etc.) and Dynamic Event Tree (DET) samplers (Deterministic and Adaptive Dynamic Event Trees). The main subject of this document is to report the activities that have been done in order to: start the migration of the RAVEN/RELAP-7 control logic system into MOOSE, and develop advanced dynamic sampling capabilities based on the Dynamic Event Tree approach. In order to provide to all MOOSE-based applications a control logic capability, in this Fiscal Year an initial migration activity has been initiated, moving the control logic system, designed for RELAP-7 by the RAVEN team, into the MOOSE framework. In this document, a brief explanation of what has been done is going to be reported. The second and most important subject of this report is about the development of a Dynamic Event Tree (DET) sampler named “Hybrid Dynamic Event Tree” (HDET) and its Adaptive variant “Adaptive Hybrid Dynamic Event Tree” (AHDET). As other authors have already reported, among the different types of uncertainties, it is possible to discern two principle types: aleatory and epistemic uncertainties. The classical Dynamic Event Tree is in charge of treating the first class (aleatory) uncertainties; the dependence of the probabilistic risk assessment and analysis on the epistemic uncertainties are treated by an initial Monte Carlo sampling (MCDET). From each Monte Carlo sample, a DET analysis is run (in total, N trees). The Monte Carlo employs a pre-sampling of the input space characterized by epistemic uncertainties. The consequent Dynamic Event Tree performs the exploration of the aleatory space. In the RAVEN code, a more general approach has been developed, not limiting the exploration of the epistemic space through a Monte Carlo method but using all the forward sampling strategies RAVEN currently employs. The user can combine a Latin Hyper Cube, Grid, Stratified and Monte Carlo sampling in order to explore the epistemic space, without any limitation. From this pre-sampling, the Dynamic Event Tree sampler starts its aleatory space exploration. As reported by the authors, the Dynamic Event Tree is a good fit to develop a goal-oriented sampling strategy. The DET is used to drive a Limit Surface search. The methodology that has been developed by the authors last year, performs a Limit Surface search in the aleatory space only. This report documents how this approach has been extended in order to consider the epistemic space interacting with the Hybrid Dynamic Event Tree methodology.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2025

Influence of Actuator Movement Rate During Acquisition of Inlet Characterization Data

This report considers using a process to acquire performance data for supersonic mixed-compression inlets that is faster and less costly than the previous process. In the previous process for acquiring data during wind tunnel experiments, actuators for backpressuring an inlet moved in discrete increments, paused for dynamics to dampen, measured data for 5 s, recorded the data, and then reduced the data to graphic plots of pressure recovery versus mass capture ratio. This discrete data acquisition procedure was methodical, reliable, and accurate. However, acquiring data to characterize the inlet consumed significant wind-on time. Wind tunnel operation was costly when considering maintenance, energy, and human resources (operators and research team); therefore, the price for each data point was high. This report presents a quicker method to obtain all measurements, one which can also be used in blowdown wind tunnels. This approach, identified as the dynamic inlet characteristic data acquisition procedure, uses in situ dynamic high-speed pressure sensors to measure pressure signals. The backpressuring actuator moves continuously, and measurements from high-speed pressure sensors are recorded as the actuator progresses to and past the incremental points. Despite its advantages, the approach raised the following questions: (1) Will the new process be as accurate as the previous process? and (2) Are there any hidden costs? These concerns are addressed in this report. This report reviews a study that was undertaken to compare wind tunnel experiment data taken using the previous discrete data acquisition procedure with data recorded using the dynamic inlet characterization data acquisition procedure. In this report, the wind tunnel experiment and the study objectives are described. The processes to reduce the data for analysis are explained, and a discussion of the results is given. Finally, conclusions from this study that may guide further work in this area are presented.

Inlet Characterization Curves↗

DOE OSTI2010

Reinforcement Mechanism Of Polyurethane-Urea/Clay Nanocomposites Probed By Positron Annihilation Lifetime Spectroscopy And Dynamic Mechanical Analysis

A basis for quantitative analysis of the reinforcement mechanism of polyurethane-urea/clay nanocomposites using two characterization methods, positron annihilation life time spectroscopy (PALS) and dynamic mechanical analysis (DMA) is provided. DMA was used to measure the constrained volume fraction of amorphous soft segments induced by nanoclay and the storage modulus of the nanocomposites. The interfacial interactions in the nanocomposites were investigated by PALS. The modulus enhancement of the organoclay nanocomposites was found to have a good correlation with the volume fraction of the constrained region and the interfacial interactions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2022

Characterization of Acoustic Emissions From Analogue Rocks Using Sparse Regression‐DMDc

Abstract Moisture loss in rock is known to generate acoustic emissions (AE). Phenomena that result in AE during drying are related to the movement of fluids through the pores and induced‐cracks that arise from differential mineral shrinkage, especially in clay‐bearing rock. AE from the movement of fluids occurs from the reconfiguration of fluid interfaces during drying, while AE from mineral shrinkage involves the debonding within or between minerals. Here, analogue rock samples were used to examine the differences in the AE signatures when one or both AE source‐types are present. An unsupervised sparse regression model, Dynamic Mode Decomposition with control, that extends Dynamic Mode Decomposition is used to characterize the AE signals recorded during the drying of porous analogue rock samples fabricated with ordinary Portland cement, with and without clay. This method can effectively and accurately reconstruct acoustic signals emitted from samples that only experience moisture loss without cracking. However, the method struggles to reconstruct signals from samples with intricate crack networks that formed during drying because AE generating mechanisms can emit contemporaneously, and the resulting waves propagate through drying‐induced cracks that can lead to multiple internal reflections. Thus, the differential reconstruction accuracy of time series generated by different underlying physical processes provides a robust filter for reducing large data catalogs. In general, both dynamics and sparse initiating events are learned directly from data and this method exposes a data hierarchy based on the complexity of the intrinsic dynamics.

58 GEOSCIENCES↗