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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Interaction of Water with Carbon Fiber Reinforced Polymer Laminates under Dynamic Loading Conditions

With the aim of simulating the real conditions during the navigation, in this framework, impact tests were performed by loading CFRP laminates immersed in the water from the front side. The specimens, made by vinyl ester matrix because of the lower moisture absorption compared to the other epoxy systems, are in the presence of the water to accurately simulate the incidental impact on the hull of the vessel, immersed or partially immersed, during the navigation. It can be caused by the ice in the Arctic Ocean, by a projectile due to a terroristic attack or by debris from an explosion. The results are compared with the ones obtained by loading the laminates in pure traditional impact tests in the air. The influence of the clamping device has been tested too. The exciting results obtained on carbon fiber laminates in the air (pure traditional impact tests), water-backed (with water on the back side) and waterfront (water only on the front side) conditions at room temperature are here reported and compared. They highlighted the critical role played by the complex fluid–structure interaction.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Modeling hydrodynamic interactions in soft materials with multiparticle collision dynamics

Multiparticle collision dynamics (MPCD) is a flexible and robust mesoscale computational technique for simulating solvent-mediated hydrodynamic interactions in soft materials. Here, we provide a critical overview of the MPCD method and summarize its current strengths and limitations. The capabilities of the method are highlighted by reviewing its recent applications to simulate diverse phenomena, ranging from the flow of complex fluids and thermo-osmotic transport to bacterial swimming and active particle self-assembly. We also discuss outstanding challenges and emerging methodological developments that are expected to greatly expand the applicability of MPCD to other systems of technological importance.

catalysis (heterogeneous)↗
DOE OSTI2022

Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithms

Machine learning methods have been extensively used to study the dynamics of complex fluid flows. One such algorithm, known as adaptive neural fuzzy inference system (ANFIS), can generate data-driven predictions for flow fields, but has not been applied to natural geophysical flows in large-scale rivers. Herein, we demonstrate the potential of ANFIS to produce three-dimensional (3D) realizations of the instantaneous flood flow field in several large-scale, virtual meandering rivers. The 3D dynamics of flood flow in large-scale rivers were obtained using large-eddy simulation (LES). The LES results, i.e., the 3D velocity components, were employed to train the learnable coefficients of an ANFIS. Further, the trained ANFIS, along with a few time-steps of LES results (precursor data) were then used to produce 3D realizations of flood flow fields in large-scale rivers with geometries other than the one the ANFIS was trained with. We also used the trained ANFIS to generate 3D realizations of river flow at a discharge other than that the ANFIS was trained with. The flow field results obtained from ANFIS were validated using separate LES runs to assess the accuracy of the 3D instantaneous realizations of the machine learning algorithm. An error analysis was conducted to quantify the discrepancies among the ANFIS and LES results for various flood flow predictions in large-scale rivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

On the numerical treatment of dissipative particle dynamics and related systems

We review and compare numerical methods that simultaneously control temperature while preserving the momentum, a family of particle simulation methods commonly used for the modelling of complex fluids and polymers. The class of methods considered includes dissipative particle dynamics (DPD) as well as extended stochastic-dynamics models incorporating a generalized pairwise thermostat scheme in which stochastic forces are eliminated and the coefficient of dissipation is treated as an additional auxiliary variable subject to a feedback (kinetic energy) control mechanism. In the latter case, we consider the addition of a coupling of the auxiliary variable, as in the Nosé–Hoover–Langevin (NHL) method, with stochastic dynamics to ensure ergodicity, and find that the convergence of ensemble averages is substantially improved. To this end, splitting methods are developed and studied in terms of their thermodynamic accuracy, two-point correlation functions, and convergence. In terms of computational efficiency as measured by the ratio of thermodynamic accuracy to CPU time, we report significant advantages in simulation for the pairwise NHL method compared to popular alternative schemes (up to an 80% improvement), without degradation of convergence rate. The momentum-conserving thermostat technique described here provides a consistent hydrodynamic model in the low-friction regime, but it will also be of use in both equilibrium and nonequilibrium molecular simulation applications owing to its efficiency and simple numerical implementation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Multiscale graph neural network autoencoders for interpretable scientific machine learning

The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes. This is accomplished here with the development of a novel graph neural network (GNN) autoencoding architecture with demonstrations on complex fluid flow applications. To address the first goal of interpretability, the GNN autoencoder achieves reduction in the number nodes in the encoding stage through an adaptive graph reduction procedure. Further, this reduction procedure essentially amounts to flowfieldconditioned node sampling and sensor identification, and produces interpretable latent graph representations tailored to the flowfield reconstruction task in the form of so-called masked fields. These masked fields allow the user to (a) visualize where in physical space a given latent graph is active, and (b) interpret the time-evolution of the latent graph connectivity in accordance with the time-evolution of unsteady flow features (e.g. recirculation zones, shear layers) in the domain. To address the goal of unstructured mesh compatibility, the autoencoding architecture utilizes a series of multi-scale message passing (MMP) layers, each of which models information exchange among node neighborhoods at various lengthscales. The MMP layer, which augments standard single-scale message passing with learnable coarsening operations, allows the decoder to more efficiently reconstruct the flowfield from the identified regions in the masked fields. Analysis of latent graphs produced by the autoencoder for various model settings are conducted using unstructured snapshot data sourced from large-eddy simulations in a backward-facing step (BFS) flow configuration with an OpenFOAM-based flow solver at high Reynolds numbers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Combined selection of the dynamic model and modeling error in nonlinear aeroelastic systems using Bayesian Inference

Here, we report a Bayesian framework for concurrent selection of physics-based models and (modeling) error models. We investigate the use of colored noise to capture the mismatch between the predictions of calibrated models and observational data that cannot be explained by measurement error alone within the context of Bayesian estimation for stochastic ordinary differential equations. Proposed models are characterized by the average data-fit, a measure of how well a model fits the measurements, and the model complexity measured using the Kullback–Leibler divergence. The use of a more complex error models increases the average data-fit but also increases the complexity of the combined model, possibly over-fitting the data. Bayesian model selection is used to find the optimal physical model as well as the optimal error model. The optimal model is defined using the evidence, where the average data-fit is balanced by the complexity of the model. The effect of colored noise process is illustrated using a nonlinear aeroelastic oscillator representing a rigid NACA0012 airfoil undergoing limit cycle oscillations due to complex fluid–structure interactions. Several quasi-steady and unsteady aerodynamic models are proposed with colored noise or white noise for the model error. The use of colored noise improves the predictive capabilities of simpler models.

42 ENGINEERING↗
DOE OSTI2023

A reduced-order model of concentration polarization in reverse osmosis systems with feed spacers

Feed spacers in reverse osmosis systems generate complex fluid flows that limit computational fluid dynamics (CFD) simulations to small length and time scales. That limits our ability to simulate mineral scaling and other membrane fouling phenomena, which occur over longer length and time scales. Thus motivated, we develop a reduced model that replaces the CFD simulation of the velocity field with an analytical model that mimics spacers. This focuses the remaining numerical effort on simulating the advection–diffusion equation governing solute transport. We motivate and validate the model with CFD simulations and bench-scale experiments of spacer filaments in three different arrangements, including cases of unsteady vortex shedding. We show that the model produces a roughly 10,000-fold speedup compared to CFD, and accurately reproduces CFD predictions of not only the average and maximum concentrations, but also the local concentration distribution along the membrane. Here, we also demonstrate the model for simulating a feed channel with a length-to-height ratio of 200. The model provides a simple testbed for exploratory studies of multispecies transport, precipitation, and membrane fouling phenomena for which simulating spacers is often prohibitive.

Computational fluid dynamics↗
DOE OSTI2021

Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks

Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. Furthermore, the results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Temperature-Dependent Speciation of Ni(II) in Molten Chloride, Bromide, and Iodide Salts

Understanding the fundamental speciation and local structure of metal cations─such as nickel, which is present as a corrosion product─in molten salt media is essential for assessing their impact on the thermal and physical properties of salts used for advanced nuclear energy applications. In this study, we employed an integrated approach of combining X-ray absorption spectroscopy (XAS), UV–vis spectroscopy, and ab initio molecular dynamics (AIMD) to investigate the halide-dependent speciation of divalent nickel ions, Ni(II), in LiCl–KCl, LiBr–KBr, and LiI–KI eutectic salt mixtures from room temperature to 600 °C. Our findings show that both halide type and temperature significantly influence the local coordination of nickel in these molten salt systems. A notable aspect of Ni(II) speciation in LiI–KI salts is the formation of a polyiodide species. In conclusion, our findings provide critical insights into metal ion speciation in molten salt systems, enhancing our understanding of the factors that govern the physicochemical properties of these complex fluids.

36 MATERIALS SCIENCE↗
DOE OSTI2021

XPCS Microrheology and Rheology of Sterically Stabilized Nanoparticle Dispersions in Aprotic Solvents

In this study, X-ray photon correlation spectroscopy (XPCS) microrheology and conventional bulk rheology were performed on silica nanoparticle dispersions associated with battery electrolyte applications to probe the properties of these specific complex materials and to explore the utility of XPCS microrheology in characterizing nanoparticle dispersions. Sterically stabilized shear-thickening electrolytes were synthesized by grafting poly(methyl methacrylate) chains onto silica nanoparticles. Coated silica dispersions containing 5–30 wt % nanoparticles dispersed in propylene carbonate were studied. In general, both XPCS microrheology and conventional rheology showed that coated silica dispersions were more viscous at higher concentrations, as expected. The complex viscosity of coated silica dispersions showed shear-thinning behavior over the frequency range probed by XPCS measurements. However, measurements using conventional mechanical rheometry yielded a shear viscosity with weak shear-thickening behavior for dispersions with the highest concentration of 30% particles. Our results indicate that there is a critical concentration needed for shear-thickening behavior, as well as appropriate particle size and surface polymer chain length, for this class of nanoparticle-based electrolytes. The results of this study can provide insights for comparing XPCS microrheology and bulk rheology for related complex fluids and whether XPCS microrheology can capture expected macroscopic rheological properties by probing small-scale particle dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Colloidal Nanoparticle Interaction Transition during Solvent Evaporation Investigated by in-Situ Small-Angle X-ray Scattering

We performed in-situ scanning small-angle X-ray scattering (SAXS) experiments in order to probe the,drying of a single suspended droplet of silica colloids. It has been demonstrated that the formation of a nanoparticle shell during drying can be confirmed just by measuring the temporal evolution of the spatial transmission profile across the drying droplet. We observed that the shrinkage of the droplet stops once the shell is formed. The temporal dependence Of the shell thickness and droplet radius has been estimated by quautitative analysis of the functionality of the transmission profiles. It is' revealed that the position of the correlation peak originating from interactions between silica nanoparticles evolves linearly during the initial stage of drying and exhibits sigmoidal growth behavior in later stages. The interaction between colloidal particles, in different drying stages has been investigated We provide,experimental confirmation of the transition from repulsive interaction to a capillary driven short-range,attraction,during shell formation. Finally, the present work demonstrates that in situ scanning SAXS on,a suspended droplet is an invaluable technique for monitoring the dynamic self organization of colloids as it probes the drying of complex,fluids without the interference of a substrate.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Emulator of PR‐DNS: Accelerating Dynamical Fields With Neural Operators in Particle‐Resolved Direct Numerical Simulation

Abstract Particle‐resolved direct numerical simulations (PR‐DNS) play an increasing role in investigating aerosol‐cloud‐turbulence interactions at the most fundamental level of processes. However, the high computational cost associated with high resolution simulations poses considerable challenges for large domain or long duration simulation using PR‐DNS. To address these issues, here we present an emulator of the complex physics‐based PR‐DNS developed by use of the data‐driven Fourier Neural Operator (FNO) method. The effectiveness of the method is showcased by presenting turbulence and temperature fields in a two‐dimensional space. The results demonstrate high accuracy at various resolutions and the emulator is two orders of magnitude cheaper in terms of computational demand compared to the physics‐based PR‐DNS model. Furthermore, the FNO emulator exhibits strong generalization capabilities for different initial conditions and ultra‐high‐resolution without the need to retrain models. These findings highlight the potential of the FNO method as a promising tool to simulate complex fluid dynamics problems with high accuracy, computational efficiency, and generalization capabilities, enhancing our understanding of the aerosol‐cloud‐precipitation system.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Multi-stimuli responsive tetra-PPO 60 -PEO 20 ethylene diamine block copolymer enables pH, temperature, and solvent regulation of Au nanoparticle composite plasmonic response

In this work a gold nanoparticle (NP) hydrogel composite prepared by multi-step cascade synthesis undergoes temperature, pH, and solvent dependent nanoscale structural transformations promoting modulation of the plasmon resonance. The composite is spontaneously prepared from a non-covalent lamellar structured lyotropic mesophase (complex fluid) composed of amphiphiles (DMPC and Triton X-100) that support the reactive constituents, a mixture of hydroxyl and acrylate end-derivatized Tetronic (Poloxamine) polymer and [AuCl4] – . The reaction sequence begins with the reduction of [AuCl4] – by the ethylene diamine linkage and PEO blocks of the Tetronic, yielding Au NPs. The redox reaction generates free radicals initiating crosslinking of the acrylate end-derivatized polymer producing a chemical hydrogel. Optical spectroscopy and electron microscopy confirm Au NP formation. ATR/FT-IR spectroscopy and thermal analysis establish acrylate crosslinking and network formation. X-ray scattering reveals the composite reversibly converts between multilamellar and bicontinuous cubic structures as temperature is increased above 35 °C for composites containing protonated ethylene diamine linkages (un-buffered water or acidic buffer). The reversible changes in nanoarchitecture red shifts and broadens the collective plasmon (from 523 nm to 575 nm) of the composite, an observation attributed to re-organization of confined NPs from 2D sheets (lamellae) to 1D columns (bicontinuous cubic channels). Composites prepared in alkaline buffer do not undergo a structural conversion with temperature, retaining a multilamellar architecture. In addition to pH and thermo-responsivity, the composite swells reversibly in polar solvents, which acts to de-aggregate and aggregate the encapsulated NPs and varies the plasmon resonance by Δ52 nm. This work reports the first combined use of a polymer to achieve both the in situ synthesis of spatially confined plasmonic NPs and their reversible stimuli-triggered re-organization mediated by interconvertible changes in scaffold nanoarchitecture.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine learning predictions of diffusion in bulk and confined ionic liquids using simple descriptors

Ionic liquids have many intriguing properties and widespread applications such as separations and energy storage. However, ionic liquids are complex fluids and predicting their behavior is difficult, particularly in confined environments. We introduce fast and computationally efficient machine learning (ML) models that can predict diffusion coefficients and ionic conductivity of bulk and nanoconfined ionic liquids over a wide temperature range (350–500 K). The ML models are trained on molecular dynamics simulation data for 29 unique ionic liquids as bulk fluids and confined in graphite slit pores. This model is based on simple physical descriptors of the cations and anions such as molecular weight and surface area. Here, we also demonstrate that accurate results can be obtained using only descriptors derived from SMILES (simplified molecular-input line-entry system) codes for the ions with minimal computational effort. This offers a fast and efficient method for estimating diffusion and conductivity of nanoconfined ionic liquids at various temperatures without the need for expensive molecular dynamics simulations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2013

Development of a new type of high pressure calorimetric cell, mechanically agitated and equipped with a dynamic pressure control system: Application to the characterization of gas hydrates

A novel prototype of calorimetric cell has been developed allowing experiments under pressure with an in situ agitation system and a dynamic control of the pressure inside the cell. The use of such a system opens a wide range of potential practical applications for determining properties of complex fluids in both pressurized and agitated conditions. The technical details of this prototype and its calibration procedure are described, and an application devoted to the determination of phase equilibrium and phase change enthalpy of gas hydrates is presented. Our results, obtained with a good precision and reproducibility, were found in fairly good agreement with those found in literature, illustrate the various interests to use this novel apparatus.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Density functional theory for the description of spherical non-associating monomers in confined media using the SAFT-VR equation of state and weighted density approximations

As a first step of an ongoing study of thermodynamic properties and adsorption of complex fluids in confined media, we present a new theoretical description for spherical monomers using the Statistical Associating Fluid Theory for potential of Variable Range (SAFT-VR) and a Non-Local Density Functional Theory (NLDFT) with Weighted Density Approximations (WDA). The well-known Modified Fundamental Measure Theory is used to describe the inhomogeneous hard-sphere contribution as a reference for the monomer and two WDA approaches are developed for the dispersive terms from the high-temperature Barker and Henderson perturbation expansion. The first approach extends the dispersive contributions using the scalar and vector weighted densities introduced in the Fundamental Measure Theory (FMT) and the second one uses a coarse-grained (CG) approach with a unique weighted density. To test the accuracy of this new NLDFT/SAFT-VR coupling, the two versions of the theoretical model are compared with Grand Canonical Monte Carlo (GCMC) molecular simulations using the same molecular model. Only the version with the “CG” approach for the dispersive terms provides results in excellent agreement with GCMC calculations in a wide range of conditions while the “FMT” extension version gives a good representation solely at low pressures. Hence, the “CG” version of the theoretical model is used to reproduce methane adsorption isotherms in a Carbon Molecular Sieve and compared with experimental data after a characterization of the material. The whole results show an excellent agreement between modeling and experiments. Thus, through a complete and consistent comparison both with molecular simulations and with experimental data, the NLDFT/SAFT-VR theory has been validated for the description of monomers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Application of one-dimensional stagnation solutions to three-dimensional simulation of compact wire array in absence of radiation

We investigate the stagnation phase of a three-dimensional (3D), magnetohydrodynamic simulation of a compact, tungsten wire-array Z pinch, under the simplifying assumption of negligible radiative loss. In particular, we address the ability of one-dimensional (1D) analytic theory to describe the time evolution of spatially averaged plasma properties from 3D simulation. The complex fluid flows exhibited in the stagnated plasma are beyond the scope of 1D theory and result in centrifugal force as well as enhanced thermal transport. Despite these complications, a 1D homogeneous (i.e., shockless) stagnation solution can capture the increase of on-axis density and pressure during the initial formation of stagnated plasma. Later, when the stagnated plasma expands outward into the imploding plasma, a 1D shock solution describes the decrease of on-axis density and pressure, as well as the growth of the shock accretion region.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Synthesis and characterization of nanorods for magnetic rotational spectroscopy

Magnetic rotational spectroscopy (MRS) with magnetic nanoprobes is a powerful method for in-situ characterization of minute amounts of complex fluids. In MRS, a uniformly rotating magnetic field rotates magnetic micro- or nano-probes in the liquid and one analyzes the features of the probe rotation to extract rheological parameters of liquids. Magnetic properties of nanoprobes must be well characterized and understood to make results reliable and reproducible. Ni and Co nanorods synthesized by electrochemical template synthesis in alumina membranes are discussed in applications to MRS. We employ alternating gradient field magnetometry, X-ray diffraction, and magnetic force microscopy to evaluate and compare properties of these nanorods and study their performance as the MRS probes. It is shown that nickel nanorods do not seem to violate any assumptions of the MRS rigid dipole theory, while cobalt nanorods do.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗