Direct simulation of thermally and mechanically coupled particle-laden flow

This work describes a unique technique to simulate continuously and directly coupled fluid flow and moving particles including both mechanical and thermal interactions between the flow, particles, and flow paths. The particles/flow paths are discretized within a computational fluid dynamics flow domain so that the local flow and temperature field conditions surrounding each particle or other solid body are known along with the local temperature distribution within the particle and other solids. Contact conduction between solid bodies including contact resistance, conjugate heat transfer at the fluid–solid interfaces, and even radiation exchanges between solid surfaces and between solid surfaces and the fluid are incorporated in the thermal interactions and a soft collision model simulates the solid body mechanical contact. The ability to capture these local flow and thermal effects removes reliance on correlations for fluid forces and for heat transfer coefficients/exchange and removes restrictions on the flow regime and particle size and volume fraction considered. Larger particle sizes and higher particle concentration conditions can be studied with local effects captured. The method was tested for a range of particle thermal and mechanical properties, driving pressures, and for limited radiation parameters. The results reveal important information about the basic thermal and flow phenomena that cannot be obtained in standard modeling methods and demonstrate the utility of the modeling method. The technique can be applied to examine phenomena dependent on local thermal conditions such as chemical reactions, material property variation, agglomerate formation, and phase change. The methods can also be used as a basis for machine learning algorithm development for flows with large particle counts so that more detailed phenomena can be considered compared to those provided by standard techniques with reduced computational costs compared to those with fully resolved particles in the flow.

Measurement Uncertainty and Representation of Tensile Mechanical Properties in Metals

The International Organization for Standardization Technical Committee for Metallic Materials—Tensile Testing stated in 2011 that temperature and strain rate variations would induce a change in the results of tensile tests, termed as the measurement uncertainty of tensile mechanical properties in metals. The uncertainty means that the tensile testing results of a specimen at a temperature and strain rate are not the original mechanical properties possessed prior to the testing. Hence, since the time of Galileo the results of tensile testing have been incorrectly interpreted as the original mechanical properties of specimens, thereby forming a paradox. At the turn of the 21st century, the micro-theory of metallic elastic deformation was proposed, identifying that a change in microstructure at atomic level could occur during elastic deformation, leading to a change in the concentration of solute (impurity) at grain boundaries/around dislocations. The micro-theory has been used to explain the mechanism of the measurement uncertainty. Different tensile temperatures and strain rates correspond to different durations of elastic deformation during tensile testing, different concentrations of solute at grain boundaries/dislocations, and thus different mechanical properties. On this basis, a new technology system of tensile testing is suggested, i.e., a “mechanical property–tensile strain rate” curve at a given test temperature can be used to evaluate the original mechanical property. The higher the strain rate is, the closer the property on the curve is to the original property. Therefore, to determine the original mechanical property of the tested metal, a sufficiently high strain rate is required. The curve can also characterize the property variation of the tested metal in service with the service time. In addition, the property characterized by a point on the curve can represent the property of the tested metal when processing-deformed with the corresponding strain rate. As an example of the application of the new technology system, the property of high-entropy alloys is represented with a curve. The results show that the new technology system could change the conceptual framework and testing technology system of metallic mechanics.

Seismic facies discrimination incorporating relative rock physics

Seismic facies discrimination is usually performed based on a rock-physics-driven quantitative interpretation approach. The accuracy of the study of rock physics largely impacts the reservoir and fluid recognition. However, the study is commonly conducted with absolute well logs without removing the trend effect. Such an approach may introduce inappropriate low-frequency information and bias further analysis of seismic data (crossplotting, facies probability density function generation, and projection angle determination). By contrast, relative rock physics with the trend decomposed reflects the rock-property variation of the overburden and underlying formation. The relative portions are more consistent with the seismic reflectivity, providing an alternative tool to facies interpretation through a seismic inversion scheme. A workflow for seismic facies discrimination has been investigated that incorporates relative rock physics, long short-term memory-based nonlinear seismic inversion, and Bayesian classification. This workflow is employed in a case study from Songliao Basin in northeast China, through which the results of relative and absolute approaches in key steps are analyzed and compared. The consistency of facies, determined through relative and absolute methods with petrophysical interpretation, is calculated. The relative analysis exhibits improved agreement with petrophysical interpretation in overall facies and reservoir sand discrimination of the blind wells. This indicates the potential to minimize the trend bias by integrating relative rock physics in quantitative interpretation.

ENERGY-BASED MULTIFUNCTIONAL EFFICIENCY METRIC FOR MULTIFUNCTIONAL COMPOSITE ANODES IN STRUCTURAL BATTERIES

A multifunctional efficiency metric is developed using mean-field micromechanics solutions to quantify the multifunctionality of the multifunctional composite anodes. Multifunctional efficiency metrics evaluate the volume and/or mass savings or performance increase when structural and functional materials are replaced by multifunctional materials [1]. The proposed methodology compares the total energy associated with different functionalities, such as elastic strain energy and electric charge energy of the multifunctional materials with the total energy of the single function structural and functional material. To achieve volume and mass savings, the energy of different functionalities is set to be the same between the multifunctional and traditional single- functional materials, and, at the same time, the volume and/or mass of the multifunctional composite needs to be smaller than that of the combination of single- functional materials. The volumes and/or mass savings can be expressed using the properties of multifunctional and traditional single-functional materials. In this work, structural anodes made from silicon nanoparticles, reduced graphene oxide, and aramid nanofibers are used as an example to calculate the mass savings compared to a traditional anode with structural support. The existing multifunctionality metrics are based on the rule of mixtures method, which is adequate for certain geometries and loading conditions, such as in-plane directions for laminate composites. However, if multifunctional composite materials involve multiple phases, material property variation during the charging process, and complex geometries or orientations of the structural and functional phases, a more comprehensive method is required to accurately capture the multifunctional efficiency. The multifunctional efficiency varies significantly during the charging and discharging process. This new metric can provide both upper and lower bounds of multifunctional efficiency. This new multifunctional efficiency metric will help optimize the selection and arrangement of different phases in the multifunctional and quantify the optimization results.

Forced Turbulent Convection Along Heated and Cooled Walls with Variable Fluid Properties

The effect of temperature depending material properties on heat and momentum transfer along heated/cooled walls in turbulent pipe flow was investigated using direct numerical simulations (DNS). For the considered thermal wall conditions, always associated with a molecular Prandtl number well over unity Prw = 10, the significantly dampened/enhanced turbulent motion caused by the increase/decrease of the viscosity with distance to the heated/cooled wall, turned out to clearly dominate over the opposite trend of the enthalpy fluctuations. The Nusselt number and, quantitatively less pronounced, the wall friction coefficient are accordingly decreased/increased for the heated/cooled case. A comparison against a well established Nu-correlation unveils the limits of the generally applied approach, which is essentially based on uniform bulk flow conditions and subsequently modified accounting for material property variation, when applied to heated and cooled conditions. An enhanced disparity of the turbulent normal stresses is observed inside the inertial subrange for the heated case, indicating a stronger deviation from isotropic turbulence, which possibly challenges mostly isotropic standard turbulence models.

Challenges With Measuring Supercritical CO2 Compressor Performance When Approaching the Liquid-Vapor Dome

A team led by General Electric Research (GER) and Southwest Research Institute (SwRI) was tasked to design, build and test an advanced 4MW CO2 compressor that would operate near the liquid-vapor dome for Carbon Dioxide (CO2). The US Department of Energy (DoE) Solar Technologies Office (SETO) funded program was targeted towards a Concentrated Solar Power (CSP) plant where optimum power cycle efficiency can be obtained when operated close to the liquid-vapor dome where CO2 is a supercritical fluid (sCO2) as compression power is reduced in the main compressor. However, the CSP cycle and other related supercritical CO2 cycles (fossil, nuclear, waste heat recovery) have considerable compression challenges both mechanically and aerodynamically when operating with a high density fluid that exceeds 70% the density of water. The subject of this paper is highlighting the challenge in determining compressor performance using industry standard measurements. This application is the highest density industrial-scale centrifugal compressor in the world at 720 kg/m3. This paper will investigate the uncertainty when measuring compressor efficiency using ASME PTC-10 instrumentation and the effect of the strong CO2 property variation when operating as a supercritical fluid, near the fluid-vapor dome. Prior work in this area by Wahl will be summarized and compared with the current compressor test program uncertainty. It will be shown that Wahl predicted high uncertainty as well although, the current testing program is even closer to the liquid-vapor dome than the test program under Wahl. The uncertainty analysis has shown that traditional PTC-10 temperature measurements lead to high levels of uncertainty for sCO2 compression near the liquid-vapor dome. The uncertainty is driven by the large changes in thermodynamic properties of sCO2. These property changes are affected by the measured pressure and temperature; however, temperature measurement error is the primary contributor to uncertainty. Because of this, looking at alternate sCO2 property measurements was investigated. Higher quality localized pressure calibration, improving flow measurement accuracy, and measuring density in addition to temperature all significantly improved efficiency uncertainty. The authors confirmed the most significant measurement change is to measure pressure and density through either a densitometer or a Coriolis flow meter which provides a density measurement in conjunction with flow rate accuracy.

Machine Learning and transcritical sprays: A demonstration study of their potential in ECN Spray-A

The present work investigates the application of Machine Learning and Artificial Neural Networks for tackling the complex issue of transcritical sprays, which are relevant to modern compression-ignition engines. Such conditions imply the departure of the classical thermodynamic perspective of ideal gas or incompressible liquid, necessitating the use of costly and elaborate thermodynamic closures to describe property variation and simulation methods. Machine Learning can assist in several ways in speeding up such calculations, either as a compact, trained thermodynamic model that can be coupled to the flow solver, or as a surrogate predictive tool of spray characteristics. In this work, such applications are demonstrated and their performance is assessed against more traditional approaches. Such applications involve the prediction of macroscopic spray characteristics, for example, the spray penetration over time, or the spray distribution in space and time, and predictions of fluid properties for the thermodynamic states encountered in such applications. Macroscopic characteristics can be adequately predicted by relatively simple network structures, involving just a hidden layer of 3–4 neurons, whereas prediction of thermodynamic states requires several layers of 5–20 neurons each. The results of integrating Artificial Neural Networks in transcritical sprays are rather promising; prediction of thermodynamic properties at pressures greater than 1bar has effectively zero error, yielding simulations indistinguishable from standard tabulated approaches with minimal overhead. When used as a regression method for time-histories either of spray characteristics or spray distributions, the results are within experimental uncertainty of similar experiments, not included in the training dataset.