Search NASA⌕ Search

SEARCH · Search NASA

Results for “fluid chemistry”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Prebiotic Processing induced by Comet and Meteor Impact

In their study of organic synthesis from impact shocks using the laser-induced-plasma (LIP) technique, McKay and Borucki(l) found that organic synthesis preferentially occurred in a reducing gas mixture rich in methane, and not in a mixture rich in carbon dioxide. This result means chemical models based on the thermodynamical equilibrium approach do not apply to shock chemistry. In this study, we employ the technique of reacting flow, i.e., chemical kinetics in a fluid flow, to simulate the chemistry occurring in LIP and in the wake region from comet or meteor impact. Three different air compositions have been used: (1) 1/3 CO2 and 2/3 H2, (2) pure CH4, and (3) 1/4 CH4, 1/4 CO2, and 1/2 H2O. The stoichiometric ratio of gas mixtures (1) and (3) are kept the same. For (1) we obtain equal mole fractions of CO and H2O as the major products and for (2) C2H2 is the major product. In both cases our results are in agreement with Ref. (1). For (3) we find an interesting case where the nature of chemicals produced to be critically dependent on the flowfield temperature. At the higher temperature part of the wake region, CO and H2O are the dominant products, whereas in the cooler region C2H2 is the dominant product. Further studies of these reactions, as well as for the gas mixture including N2, are being pursued.

Dateo, Christopher E.↗
DOE OSTI2023

A sui generis whipping-instability-based self-sequencing multi-monodisperse 2D spray from an anisotropic microfluidic liquid jet device

Well-defined aerosols pave the way for versatile basic and applied research. Here, we demonstrate a unique whipping instability that generates from a high-aspect-ratio microfluidic device resulting in a unique steady-state gas-focused whipping jet (WJ) without any need for electrification. This WJ device emanates a multi-monodisperse whipping spray jet with a two-dimensional (2D) profile. We demonstrate this phenomenon based on various fluidic parameters theoretically and experimentally. The 2D WJ’s unique behavior is derived using analytical fluid dynamics to explain jet diameter, whipping regime, and spreading angle. The phenomenon is further characterized experimentally by measuring the angle with respect to the flow rate, the distances between droplets, the droplet shapes, and the reproducibility of these parameters. We also explain the precise fabrication of such inexpensive devices. Lastly, we highlight these devices’ potential use as sample environments in versatile applications ranging from cryoelectron microscopy over mass spectrometry to drug formulation and structural studies at X-ray free-electron lasers.

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

Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores

Predicting the diffusion coefficient of fluids under nanoconfinement is important for many applications including the extraction of shale gas from kerogen and product turnover in porous catalysts. Due to the large number of important variables, including pore shape and size, fluid temperature and density, and the fluid–wall interaction strength, simulating diffusion coefficients using molecular dynamics (MD) in a systematic study could prove to be prohibitively expensive. Here, we use machine learning models trained on a subset of MD data to predict the self-diffusion coefficients of Lennard-Jones fluids in pores. Our MD data set contains 2280 simulations of ideal slit pore, cylindrical pore, and hexagonal pore geometries. We use the forward feature selection method to determine the most useful features (i.e., descriptors) for developing an artificial neutral network (ANN) model with an emphasis on easily acquired features. Our model shows good predictive ability with a coefficient of determination (i.e., R 2 ) of ~0.99 and a mean squared error of ~2.9 × 10 –5 . Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Probing Pore Size and Connectivity in Porous Silicas Using 13C MAS NMR Spectroscopy of Supercritical Methane

Measuring the pore size and pore-size distributions and exploring the fluid-exchange dynamics between different types of pores in porous materials remains a significant experimental challenge but is critical to understanding catalysis, chromatography, nutrient cycling, and a whole range of geochemical phenomena, including shale gas and tight gas extraction. Here, we present the results of 1D 13 C NMR and 2D exchange spectroscopy (EXSY) NMR investigations of a porous silica using supercritical methane (scCH 4 ) as a direct probe of pore size and fluid exchange between pore types. The results show that the 13 C chemical shift of scCH 4 adsorbed in nanometer-scale silica pores becomes more negative with increasing pore diameter, in agreement with trends reported for gas hydrates, zeolites, MOFs, and clays and other microporous (<10 nm pores) geochemical materials. These 13 C chemical shifts follow a natural log–linear trend with pore size in the vacuum-dried porous silicas studied here, allowing one to predict pore size based on the 13 C chemical shift in dry silica nanopores. The EXSY and 1D 13 C NMR results both show that CH 4 exchanges between pore and bulk fluid environments over rate scales from 0.01 to 1 kHz in vacuum-dried samples and that exchange dynamics must be considered when interpreting 13 C NMR of pore-adsorbed CH 4 . Introducing H 2 O to the system causes the 13 C chemical shift to become more negative with increasing H 2 O content as a result of H 2 O preferentially filling small pores, forcing CH 4 to occupy larger pores that are better connected to the bulk environment. Likewise, the 1D 13 C data show a decreased distribution of exchange rates between pore-adsorbed and bulk CH 4 in the presence of H 2 O.

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

Local Rearrangement in Adsorption Layers of Nanoconfined Ethane

Fluids under nanoconfinement control many environmental and engineering processes. In particular, nanoconfined ethane commonly occurs in hydrocarbon production and gas separation applications. While previous research has measured the pore critical properties and self-diffusivity of nanoconfined ethane, the relationship between molecular organization and phase behavior in nanoporous media persists as a knowledge gap. Through experimental adsorption measurements and molecular dynamics simulations, we report the impact of pore wall proximity, pore size, and temperature on the structure of nanoconfined ethane. Finally, we offer evidence for an energetically favorable rearrangement of ethane molecules in the adsorption layer that may contribute to more accurate gas-in-place estimates and more efficient design of gas separation membranes.

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

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

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

A simulation-based parametric study of CLOU chemical looping reactor performance

Chemical looping with oxygen uncoupling (CLOU) is a variant on chemical looping combustion in which the oxygen carrier releases gaseous O 2 in the fuel reactor, making it well-suited for solid fuels, since the released gaseous O 2 readily reacts with solid char. This work presents several computational fluid dynamic (CFD) simulations of copper-based CLOU in a dual fluidized bed system, each with different operating conditions. The modeling predicted that coal particle sizes as large as 1000 μm did not significantly affect performance. Increased oxygen carrier copper loading resulted in an excess of gaseous oxygen in the product gas stream. Decreasing the oxygen carrier bed mass as well as reducing the air reactor fluidizing velocity did not supply enough oxygen to the fuel reactor to complete combustion of the coal. This generated a failure state in which the temperature continued to decrease in the fuel reactor from the lack of combustion, which in turn reduced the O 2 equilibrium partial pressure, further lowering the amount of combustion possible. Sufficient O 2 can be maintained in the fuel reactor by ensuring a high enough air reactor velocity and a large enough supply of oxygen carrier inventory to handle the chosen coal feed rate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Investigation of countercurrent flow profile and liquid holdup in random packed column with local CFD data

Liquid holdup and mass transfer area are critical parameters used in packed column design and CO 2 capture efficiency prediction. Compared to experimental methods, 3D computational fluid dynamics (CFD) can provide high-resolution data for studying countercurrent flow hydrodynamics in a packed column. In this paper, a framework was established for modeling the liquid–gas countercurrent flow in a random packed column with pall rings. Besides the column-averaged information, the radial pall ring distribution, velocity, and liquid holdup profiles are obtained in the simulations. The radial two-phase velocity and liquid holdup profiles show significant fluctuations caused by the non-uniform pall ring distribution. The entrance effect and wall influence are also observed in the packed column. By using local countercurrent flow information, the packing specific area $a_p$ and liquid velocity $u_L$ range are significantly expanded for liquid holdup hL prediction. This enables us to develop the liquid holdup correlation with one packing size and six CFD runs only. The proposed power law of the liquid holdup correlation $h_L~u_L^{0.44}$ indicates the random packed column falls in a viscous to turbulent transition regime with a Reynolds Number range of [6.7–40.2]. The derived liquid holdup correlation is in good agreement with existing correlations that are developed using the column-averaged experimental data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Numerical simulation for the design of induction heating based radio frequency reactor for ethylene production

Ethylene is a vital petrochemical compound produced in vast amounts yearly by manufacturers that have enough scale to overcome the inherent thermodynamic inefficiencies of the process. In order to address the inefficiencies that prevent smaller scale or intermittent production ethylene, investigation of new production methods are required. In this work, we investigate the use of a radio frequency (RF) based reactor system that generates heat internally as opposed to applying heat externally via steam or direct combustion of fossil fuels. In order to guide the design of an electromagnetic based reactor system, we have created a macroscale model capable of capturing heat generation at the susceptors from a produced electromagnetic field and subsequent chemical reactions. Several susceptor and induction coil designs were investigated to better understand the performance of the system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Multimodal study of the impact of stimulation pH on shale pore structure, with an emphasis on organics behavior in alkaline environments

The tight nature of shale formations calls for hydraulic fracturing techniques able of altering the pore architecture to facilitate the production of hydrocarbon resources. The composition, pH, salinity, density and chemistry of Hydraulic Fracturing Fluids (HFF) vary substantially depending on reservoir characteristics. pH is among the most important stimulation fluid properties, ranging from very acidic, for carbonate reservoirs, to basic, in the case of clay-rich formations. pH regulates what geochemical reactions take place between the stimulation fluid and shale, as well as pore architecture alteration. The dissolution of carbonates and pyrite, and the precipitation of common minerals such as barite, gypsum and iron oxides in acidic environments have been extensively documented. In contrast, alkali stimulation environments and the role of organic components have received less attention. This research provides insight into the role of both minerals and organic components during alkaline stimulation, and the resulting pore architecture alterations. Here, a set of reactive experiments are performed using three different shales, with varying organic matter (OM) content, at different pH (3, 6, 8, 10, 12). The analysis of pore architecture alteration induced by the reaction was performed via nitrogen (N2) gas adsorption and Time-Domain Nuclear Magnetic Resonance (TD-NMR). Inductively Coupled Plasma - Optical Emission Spectrometry (ICP-OES) was used to measure the composition of the stimulation fluid post-reaction. The system with a larger impact on the pore architecture (pH 12) underwent a more extensive analysis through the application of Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and x-ray Diffraction (XRD). Results show that mineral dissolution contributed to the creation of secondary porosity, mainly through dissolution of clays and silicates. We show that the abundance and distribution of organic matter both play a significant role in changes in pore architecture. Most samples showed an increase in inter-organic porosity, likely due to the organic acids within kerogen reacting with the stimulation fluid. Samples with a larger amount and more widespread distribution of organic matter show the most significant alterations. Finally, we also highlight the dissolution process of iron-rich minerals, including framboidal pyrite, as another important source of secondary porosity.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2020

Crossover from Rouse to Reptation Dynamics in Salt-Free Polyelectrolyte Complex Coacervates

Considerable interest in the dynamics and rheology of polyelectrolyte complex coacervates has been motivated by their industrial application as viscosity modifiers. A central question is the extent to which classical Rouse and reptation models can be applied to systems where electrostatic interactions play a critical role on the thermodynamics. By relying on molecular simulations, we present a direct analysis of the crossover from Rouse to reptation dynamics in salt-free complex coacervates as a function of chain length. This crossover shifts to shorter chain lengths as electrostatic interactions become stronger, which corresponds to the formation of denser coacervates. To distinguish the roles of Coulomb interactions and density, here we compare the dynamics of coacervates to those of neutral, semidilute solutions at the same density. Both systems exhibit a universal dynamical behavior in the connectivity-dominated (subdiffusion and normal diffusion) regimes, but the monomer relaxation time in coacervates is much longer and increases with increasing Bjerrum length. This is similar to the cage effect observed in glass-forming polymers, but the local dynamical slowdown is caused here by strong Coulomb attractions (ion pairing) between oppositely charged monomers. Our findings provide a microscopic framework for the quantitative understanding of coacervate dynamics and rheology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1985

The impact of supercomputers on experimentation: A view from a national laboratory

The relative roles of large scale scientific computers and physical experiments in several science and engineering disciplines are discussed. Increasing dependence on computers is shown to be motivated both by the rapid growth in computer speed and memory, which permits accurate numerical simulation of complex physical phenomena, and by the rapid reduction in the cost of performing a calculation, which makes computation an increasingly attractive complement to experimentation. Computer speed and memory requirements are presented for selected areas of such disciplines as fluid dynamics, aerodynamics, aerothermodynamics, chemistry, atmospheric sciences, astronomy, and astrophysics, together with some examples of the complementary nature of computation and experiment. Finally, the impact of the emerging role of computers in the technical disciplines is discussed in terms of both the requirements for experimentation and the attainment of previously inaccessible information on physical processes.

Peterson, V. L.↗
NASA NTRS1985

The impact of supercomputers on experimentation - A view from a National Laboratory

The relative roles of large-scale scientific computers and physical experiments in several science and engineering disciplines are discussed. Increasing dependence on computers is shown to be motivated both by the rapid growth in computer speed and memory, which permits accurate numerical simulation of complex physical phenomena, and by the rapid reduction in the cost of performing a calculation, which makes computation an increasingly attractive complement to experimentation. Computer speed and memory requirements are presented for selected areas of such disciplines as fluid dynamics, aerodynamics, aerothermodynamics, chemistry, atmospheric sciences, astronomy, and astrophysics, together with some examples of the complementary nature of computation and experiment. Finally, the impact of the emerging role of computers in the technical disciplines is discussed in terms of both the requirements for experimentation and the attainment of previously inaccessible information on physical processes.

Peterson, V. L.↗
NASA NTRS1987

Numerical simulation of a supersonic reacting mixing layer

In order to arrive at physical models that can adequately describe supersonic combustion, and develop accurate and efficient numerical techniques for the solution of such models' governing equations, a computer program has been developed for the study of reacting flows which considers the multicomponent diffusion and convection of important chemical species, as well as their finite state reaction and the interaction of the fluid mechanics and the chemistry that occurred. The code employs a hybrid Chebyshev pseudospectral technique for integration of the models' resulting governing equations; the program is here used to study a spatially developing and reacting mixing layer.

Drummond, J. Philip↗
NASA NTRS1988

A two-dimensional numerical simulation of a supersonic, chemically reacting mixing layer

Research has been undertaken to achieve an improved understanding of physical phenomena present when a supersonic flow undergoes chemical reaction. A detailed understanding of supersonic reacting flows is necessary to successfully develop advanced propulsion systems now planned for use late in this century and beyond. In order to explore such flows, a study was begun to create appropriate physical models for describing supersonic combustion, and to develop accurate and efficient numerical techniques for solving the governing equations that result from these models. From this work, two computer programs were written to study reacting flows. Both programs were constructed to consider the multicomponent diffusion and convection of important chemical species, the finite rate reaction of these species, and the resulting interaction of the fluid mechanics and the chemistry. The first program employed a finite difference scheme for integrating the governing equations, whereas the second used a hybrid Chebyshev pseudospectral technique for improved accuracy.

Drummond, J. Philip↗
NASA NTRS1989

Biochemical correlates of neurosensory changes in weightlessness

The possible existence of a relationship between space motion sickness and chemical and biochemical variables measured in body fluids is studied. Clinical chemistry and endocrine measurements from blood and urine samples taken before and after Space Shuttle flights were analyzed along with the occurrence of SMS during flight and provocative testing before flight. Significant positive correlations were observed with serum chloride and significant negative correlations with serum phosphate, serum uric acid, and plasma thyroid stimulating hormone.

Leach, Carolyn S.↗
NASA NTRS1992

Numerical simulations of non-equilibrium shock layers with efficient implicit schemes

Current and future calculations of nonequilibrium shock layers require the use of a very large number of equations, due to a multiplicity of chemical species, excited states, and internal energy modes. The computational cost associated with the use of standard implicit methods becomes prohibitive; it is therefore desirable to examine the potential of several methods and determine if any can be projected to be more efficient and accurate for large systems of equations. Here, the performance of several implicit schemes on several simple practical examples of reacting flows is examined. The Euler equations are solved by three different implicit methods, and two methods of coupling between the fluid dynamics and the chemistry are studied. Several cases of stiffness are considered and both 1D and 2D examples are computed.

Cambier, Jean-Luc↗