Interfacial strength development in thermoplastic resins and fiber-reinforced thermoplastic composites

An experimental program to develop test methods to be used to characterize interfacial (autohesive) strength development in polysulfone thermoplastic resin and graphite-polysulfone prepreg during processing is reported. Two test methods were used to examine interfacial strength development in neat resin samples. These included an interfacial tension test and a compact tension (CT) fracture toughness test. The interfacial tensile test proved to be very difficult to perform with a considerable amount of data scatter. Thus, the interfacial test was discarded in favor of the fracture toughness test. Interfacial strength development was observed by measuring the refracture toughness of precracked compact tension specimens that were rehealed at a given temperature and contact time. The measured refracture toughness was correlated with temperature and contact time. Interfacial strength development in graphite-polysulfone unidirectional composites was measured using a double cantilever beam (DCB) interlaminar fracture toughness test. The critical strain energy release rate of refractured composite specimens was measured as a function of healing temperature and contact time

Meta-analysis of viscosity of aqueous deep eutectic solvents and their components

Deep eutectic solvents (DES) formed by quaternary ammonium salts and hydrogen bond donors are a promising green alternative to organic solvents. Their high viscosity at ambient temperatures can limit biocatalytic applications and therefore requires fine-tuning by adjusting water content and temperature. Here, we performed a meta-analysis of the impact of water content and temperature on the viscosities of four deep eutectic solvents (glyceline, reline, N,N-diethylethanol ammonium chloride-glycerol, N,N-diethylethanol ammonium chloride-ethylene glycol), their components (choline chloride, urea, glycerol, ethylene glycol), methanol, and pure water. We analyzed the viscosity data by an automated workflow, using Arrhenius and Vogel-Fulcher-Tammann-Hesse models. The consistency and completeness of experimental data and metadata was used as an essential criterion of data quality. We found that viscosities were reported for different temperature ranges, half the time without specifying a method of desiccation, and in almost half of the reports without specifying experimental errors. We found that the viscosity of the pure components varied widely, but that all aqueous mixtures (except for reline) have similar excess activation energy of viscous flow E-eta(excess)= 3-5 kJ/mol, whereas reline had a negative excess activation energy (E-eta(excess)= - 19 kJ/mol). The data and workflows used are accessible at https://doi.org/10.15490/FAIRDOMHUB.1.STUDY.767.1

Liquid pre-freezing percolation transition to equilibrium crystal-in-liquid mesophase

Pre-freezing anomalies are explained by a percolation transition that delineates the existence of a pure equilibrium liquid state above the temperature of 1st-order freezing to the stable crystal phase. The precursor to percolation transitions are hetero-phase fluctuations that give rise to molecular clusters of an otherwise unstable state in the stable host phase. In-keeping with the Ostwald’s step rule, clusters of a crystalline state, closest in stability to the liquid, are the predominant structures in pre-freezing hetero-phase fluctuations. Evidence from changes in properties that depend upon density and energy fluctuations suggests embryonic nano-crystallites diverge in size and space at a percolation threshold, whence a colloidal-like equilibrium is stabilized by negative surface tension. Below this transition temperature, both crystal and liquid states percolate the phase volume in an equilibrium state of dispersed coexistence. We obtain a preliminary estimate of the prefreezing percolation line for water determined from higher-order discontinuities in Gibbs energy that derivatives the isothermal rigidity [(dp/dρ)T] and isochoric heat capacity [(dU/dT)v] respectively. The percolation temperature varies only slightly with pressure from 51.5°C at 0.1 MPa to around 60°C at 100 MPa. We conjecture that the predominant dispersed crystal structure is a tetrahedral ice, which is the closest of the higher-density ices (II to XV) to liquid water in configurational energy. Inspection of thermodynamic and transport properties of liquid argon also indicate the existence of a similar prefreezing percolation transition at ambient pressures (0.1 MPa) around 90 K, ~6% above the triple point (84 K). These findings account for many anomalous properties of equilibrium and supercooled liquids generally, and also explain Kauzmann’s “paradox” at a “glass” transition.info:eu-repo/semantics/publishedVersio

The Value of RFID Technology Enabled Information to Manage Perishables

We address the value of RFID technology enabled information to manage perishables in the context of a supplier that sells a random lifetime product subject to stochastic demand and lost sales. The product's lifetime is largely determined by the time and temperature history in the supply chain. We compare two information cases to a Base case in which the product's time and temperature history is unknown and therefore its shelf life is uncertain. In the first information case, the time and temperature history is known and therefore the remaining shelf life is also known at the time of receipt. The second information case builds on the first case such that the supplier now has visibility up the supply chain to know the remaining shelf life of inventory available for replenishment. We formulate these three different cases as Markov decision processes, introduce well performing heuristics of more practical relevance, and evaluate the value of information through an extensive simulation using representative, real world supply chain parameters.simulation;value of information;RFID;perishable inventory

Atmospheric Mercury Species In Northern Mississippi: Concentrations, Sources, Temporal Patterns, And Soil-Air Exchange

Mercury is a highly toxic element that is found both naturally and as an introduced contaminant in the environment. The majority of Mercury released to the environment is into the atmosphere, where because of its high volatility and long residence time is dispersed globally. In order to better understand the factors controlling the distribution and temporal patterns of atmospheric Mercury species, as well as the sources of airborne Mercury in the mid-south region, concentrations of gaseous elemental mercury (gem), gaseous oxidized mercury (gom), and particulate-bound mercury (pbm), along with meteorological parameters and other ancillary data, were collected for more than a year in Oxford, Mississippi. Mean levels of gem were 1.54 â± 0.32 ng∙m-3 and were lower and more stable in the winter and spring compared with summer and fall. Mean levels for gom and pbm were 3.87 ng∙m-3 and 4.58 ng∙m-3, respectively; levels tended to be highest in the afternoon and lowest in the early morning hours. Precipitation events greatly reduce gom and pbm levels but have little effect on gem. Gom exhibited diurnal patterns characteristic of photochemical oxidation. Atmospheric modeling revealed that higher levels of plume events for airborne hg often occur with air masses from the northern USA. Gaseous mercury exchange between terrestrial surfaces and the atmosphere was also investigated. Mercury fluxes over four landscapes representative of north Mississippi were studied. Mercury emissions were higher during the summer than the winter. The influence of environmental variables, including temperature, solar radiation, humidity, wind speed, soil moisture, and pressure, on mercury fluxes and ambient levels of atmospheric mercury were evaluated. Analytical methods were also developed to measure wet and dry deposition of mercury, and estimates were made for deposition to Enid Lake and the Yocona River watershed. The data was incorporated into a mercury mass-balance model for Enid Lake, which currently has a mercury-based fish consumption advisory. Finally gem concentrations were determined within an academic chemistry building and levels were compared to occupational safety permissible and recommended exposure limits

Numerical modeling of moving carbonaceous particle conversion in hot environments

The design and optimization of entrained flow gasifiers is conducted more and more via computational fluid dynamics (CFD). A detailed resolution of single coal particles within such simulations is nowadays not possible due to computational limitations. Therefore the coal particle conversion is often represented by simple 0-D models. For an optimization of such 0-D models a precise understanding of the physical processes at the boundary layer and within the particle is necessary. In real gasifiers the particles experience Reynolds numbers up to 10000. However in the literature the conversion of coal particles is mainly regarded under quiescent conditions. Therefore an analysis of the conversion of single particles is needed. Thereto the computational fluid dynamics can be used. For the detailed analysis of single reacting particles under flow conditions a CFD model is presented. Practice-oriented parameters as well as features of the CFD model result from CFD simulations of a Siemens 200MWentrained flow gasifier. The CFD model is validated against an analytical model as well as two experimental data-sets taken from the literature. In all cases good agreement between the CFD and the analytics/experiments is shown. The numerical model is used to study single moving solid particles under combustion conditions. The analyzed parameters are namely the Reynolds number, the ambient temperature, the particle size, the operating pressure, the particle shape, the coal type and the composition of the gas. It is shown that for a wide range of the analyzed parameter range no complete flame exists around moving particles. This is in contrast to observations made by other authors for particles in quiescent atmospheres. For high operating pressures, low Reynolds numbers, large particle diameters and high ambient temperatures a flame exists in the wake of the particle. The impact of such a flame on the conversion of the particle is low. For high steam concentrations in the gas a flame appears, which interacts with the particle and influences its conversion. Furthermore the impact of the Stefan-flow on the boundary layer of the particle is studied. It is demonstrated that the Stefan-flow can reduce the drag coefficient and the Nusselt number for several orders of magnitude. On basis of the CFD results two new correlations are presented for the drag coefficient and the Nusselt number. The comparison between the correlations and the CFD shows a significant improvement of the new correlations in comparison to archived correlations. The CFD-model is further used to study moving single porous particles under gasifying conditions. Therefore a 2-D axis-symmetric system of non-touching tori as well as a complex 3-D geometry based on the an inverted settlement of monodisperse spheres is utilized. With these geometries the influence of the Reynolds number, the ambient temperature, the porosity, the intrinsic surface and the size of the radiating surface is analyzed. The studies show, that the influence of the flow on the particle conversion is moderate. In particular the impact of the flow on the intrinsic transport and conversion processes is mainly negligible. The size of the radiating surface has a similar impact on the conversion as the flow in the regarded parameter range. On basis of the CFD calculations two 0-D models for the combustion and gasification of moving particles are presented. These models can reproduce the results predicted by the CFD sufficiently for a wide parameter range.:List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .XIII Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XV Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XIX 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 1.1 State of the Art in Carbon Conversion Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.1 Combustion of Solid Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 Gasification of Porous Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2 Classification of the Present Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 1.3 Overview of the Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 2 Basic Theory and Model Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 Geometry and Length Scales of Coal Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9 2.2 Conditions in a Siemens Like 200 MW Entrained Flow Gasifier . . . . . . . . . . . . . . . . . . . . 11 2.2.1 Velocity Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 2.2.2 Temperature Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.3 Particle Volume Fraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19 2.3 Time Scales of the Physical Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.4 Basic Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21 2.5 Conservation Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.6 Gas Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26 2.7 Boundary Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.8 Numerics and Solution Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30 2.9 Mesh and Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31 3 CFD-based Oxidation Modeling of a Non-Porous Carbon Particle . . . . . . . . . . . . . . . . . . . . .37 3.1 Chemical Reaction System for Combustion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37 3.1.1 Heterogeneous Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37 3.1.2 Homogeneous Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40 3.1.3 Comparison of the Semi-Global vs. Reduced Reaction Mechanisms for the Gas Phase . .41 3.2 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43 3.2.1 Validation Against an Analytical Solution of the Two-Film Model . . . . . . . . . . . . . . . . . .43 3.2.2 Validation Against Experiments I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2.3 Validation Against Experiments II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49 3.3 Influence of Ambient Temperature and Reynolds Number . . . . . . . . . . . . . . . . . . . . . . . .51 3.4 Influence of Heterogeneous Kinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.5 Influence of Atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61 3.6 Influence of Operating Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .66 3.7 Influence of Particle Diameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70 3.8 The influence of Particle Shape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.9 Impact of Stefan Flow on the Boundary Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.9.1 Impact of Stefan Flow on the Drag Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83 3.9.2 Impact of Stefan Flow on the Nusselt and Sherwood Number . . . . . . . . . . . . . . . . . . . .85 3.10 Single-Film Sub-Model vs. CFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 3.11 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 4 CFD-based Numerical Modeling of Partial Oxidation of a Porous Carbon Particle . . . . . . . . . .99 4.1 Chemical Reaction System for Gasification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.1.1 Heterogeneous Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .100 4.1.2 Homogeneous Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.2 Two-Dimensional Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.2.1 Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.2.2 Influence of Reynolds Number and Ambient Temperature . . . . . . . . . . . . . . . . . . . . . .109 4.2.3 Influence of Porosity and Internal Surface . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . 120 4.3 Comparative Three-Dimensional Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 4.3.1 Geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .126 4.3.2 Results of the 3-D Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 4.4 Extended Sub-Model for Gasification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .133 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .138 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . .141 5.1 Summary of This Work . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .141 5.2 Recommendations for Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .145 6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 6.1 Appendix I: Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 6.2 Appendix II: Two-Film Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 6.3 Appendix III: Sub-Model for the Combustion of Solid Particles . . . . . . . . . . . . . . . . . . . . 160 6.4 Appendix IV: Sub-Model for the Gasification of Porous Particles . . . . . . . . . . . . . . . . . . . 16