A Comparison Between Recent Experimental Results and Existing Correlations for Microfin Tubes for Refrigerant and Nanolubricants Mixtures Two Phase Flow Boiling

Driven by higher energy efficiency targets, there is critical need for major heat transfer enhancements in heat exchangers. Nanolubricants, that is, nanoparticles dispersed in the non-volatile component of a mixture, have the potential to increase the heat transfer coefficient by 20% or more for two-phase flow boiling with small or no penalization on the two-phase flow pressure drop. The present work builds upon these intriguing yet unexplained findings, which were documented in the experiments of the present study for one type of nanolubricant, but for which a theory still does not exist. This paper presents a comparison between existing models in the literature and recent new experimental data for two phase flow boiling in a microfin tube of refrigerant R410A and nanolubricants mixtures. Alumina Oxide (g-Al2O3) based nanolubricants with 40 nominal particle diameter of approximately spherical shape were investigated. The nanoparticles concentration in the lubricant varied from 10 to about 20 in mass percentage, and the lubricant concentration varied from 0 up to 3% in mass percentage. The models available in the open domain literature were not able to capture the effects of the nanoparticles on the two-phase flow heat transfer coefficient. The augmented thermal conductivity of the lubricant due to the addition of highly conductive nanoparticles was not the main mechanism responsible for the heat transfer enhancements. The discrepancy between the simulation results and the experimental data was postulated to be due to non-Newtonian behaviors due to the presence of nanoparticles and surfactants. The flow development of the liquid phase of the mixture and the localized thickening and thinning of the liquid film thickness around the inner walls of the tube can alter the film local convective thermal resistance.

Thermodynamic and Heat Transfer Properties of Al2O3 Nanolubricants

In vapor compression cycles, a small portion of the oil circulates with the refrigerant throughout the system components, while most of the oil stays in the compressors. In heat exchangers, the lubricant in excess penalizes the heat transfer and increases the pressure losses: both effects are highly undesired but yet unavoidable. Nanoparticles dispersed in the excess lubricant are expected to provide enhancements in heat transfer. While solubility and miscibility of refrigerants in polyolesters (POE) lubricant are well established knowledge there is a lack of information regarding if and how nanoparticles dispersed in the lubricant affect these two properties. This paper presents experimental data of solubility and miscibility of three types of Al2O3 nanolubricants with refrigerant R410A. The nanoparticles were dispersed in POE lubricant by using different surfactants and dispersion methods and the nanolubricants showed lower refrigerant R410A solubility than that of POE oil. High viscosity suspensions are expected to stabilize the nanoparticles and avoid clustering. This aspect was verified in the present paper for the Al2O3 nanolubricants and long term stability and the degree of agglomeration, when present, were measured. The data identified optimum combinations of surfactants to achieve stable and uniform nanolubricant dispersions for several months. Surfactants affected slightly the thermal conductivity, specific heat, viscosity, and solubility properties of the nanolubricants. The specific heats of the nanolubricants were lower than that of POE oil at temperatures from 0 to 20°C while they were similar at 40°C. Thermal conductivity ranged from 1.1 times higher at 5°C to 1.4 times higher at 40°C than that of POE lubricant. The viscosity was about 2.6 times higher at 5°C while it was similar to that of POE lubricant at 40°C. The thermal and transport properties data for three nanolubricants provided in this paper advance the basic understanding of nanoparticles interaction with R410A refrigerant and POE lubricant mixtures

Modeling of Lubricant Effects in a Microchannel Type Condenser

In HVAC and refrigeration systems, a small portion of the oil circulates with the refrigerant flow through the cycle components, while most of the oil stays in the compressor. The circulating oil can form a fairly homogeneous mixture with the liquid refrigerant, or it can exist as a separate oil-rich film inside the small tubes and headers of a microchannel heat exchanger; the amount of oil held up is affected by the system conditions. The oil retention in the microchannel type condenser is of particular interest as the amount of oil in excess in this component affects the heat transfer capacity and increases the frictional pressure losses. This paper presents a new physics-based model of the oil retention in microchannel-type condensers. The model calculates the local thermodynamic properties in each section for the refrigerant R-410A and Polyester (POE) oil mixture based on the local oil concentration, pressure, temperature, and mass flux. Then the model, which was experimentally validated, predicts the refrigerant-side heat transfer coefficient and pressure drop. The simulation results indicated that the pressure losses increased by over 20% when the oil mass flow rate fraction increased up to 5 weight percent. The augmented mixture viscosity resulted in high frictional pressure drops and shear stress during the two phase flow condensation. The refrigerant side correlations were validated against literature data for in-tube two-phase flow condensation but further investigation is needed for the single-phase annular type flow in microchannel with refrigerant vapor and oil. At low degree of superheat the heat transfer coefficient of the refrigerant and oil mixture was basically unaffected by the oil mass fraction up to 3 weight percent. When the oil mass fraction was higher than 3 weight percent, then the heat transfer capacity of the condenser decreased. At high degree of superheat, the heat transfer coefficient of the oil and refrigerant mixture was penalized when the Oil Mass Fraction (OMF) was higher than 2 weight percent. Further investigation is needed on the suitability and accuracy of the heat transfer coefficients correlations to be adopted with superheated vapor refrigerant and lubricant film in annular flow at the inlet section of the microchannel type condenser

Bayesian clustering of multiple zero-inflated outcomes

Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on the positive integers. We consider data from multiple count processes. In this context, it is of interest to study the patterns of counts and cluster the subjects accordingly. We introduce a novel Bayesian approach to cluster multiple, possibly related, zero-inflated processes. We propose a joint model for zero-inflated counts, specifying a hurdle model for each process with a shifted Negative Binomial sampling distribution. Conditionally on the model parameters, the different processes are assumed independent, leading to a substantial reduction in the number of parameters as compared with traditional multivariate approaches. The subject-specific probabilities of zero-inflation and the parameters of the sampling distribution are flexibly modelled via an enriched finite mixture with random number of components. This induces a two-level clustering of the subjects based on the zero/non-zero patterns (outer clustering) and on the sampling distribution (inner clustering). Posterior inference is performed through tailored Markov chain Monte Carlo schemes. We demonstrate the proposed approach on an application involving the use of the messaging service WhatsApp. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'

Tough Tables: Carefully Evaluating Entity Linking for Tabular Data

Table annotation is a key task to improve querying the Web and support the Knowledge Graph population from legacy sources (tables). Last year, the SemTab challenge was introduced to unify different efforts to evaluate table annotation algorithms by providing a common interface and several general-purpose datasets as a ground truth. The SemTab dataset is useful to have a general understanding of how these algorithms work, and the organizers of the challenge included some artificial noise to the data to make the annotation trickier. However, it is hard to analyze specific aspects in an automatic way. For example, the ambiguity of names at the entity-level can largely affect the quality of the annotation. In this paper, we propose a novel dataset to complement the datasets proposed by SemTab. The dataset consists of a set of high-quality manually-curated tables with non-obviously linkable cells, i.e., where values are ambiguous names, typos, and misspelled entity names not appearing in the current version of the SemTab dataset. These challenges are particularly relevant for the ingestion of structured legacy sources into existing knowledge graphs. Evaluations run on this dataset show that ambiguity is a key problem for entity linking algorithms and encourage a promising direction for future work in the field

Bayesian nonparametric modelling of multiple graphs with an application to ethnic metabolic differences

We propose a novel approach to the estimation of multiple Gaussian graphical models (GGMs) to analyse patterns of association among a set of metabolites, under different conditions. Our motivating application is the SABRE (Southall And Brent REvisited) study, a triethnic cohort study conducted in the United Kingdom. Through joint modelling of pattern of association corresponding to different ethnic groups, we are able to identify potential ethnic differences in metabolite levels and associations, with the aim of gaining a better understanding of different risk of cardiometabolic disorders across ethnicities. We model the relationship between a set of metabolites and a set of covariates through a sparse seemingly unrelated regressions model and we use GGMs to represent the conditional dependence structure among metabolites. We specify a dependent generalised Dirichlet process prior on the edge inclusion probabilities to borrow strength across groups and we adopt the horseshoe prior to identify important biomarkers. Inference is performed via Markov chain Monte Carlo

STILTool: a Semantic Table Interpretation evaLuation Tool

This paper describes STILTool, an open-source tool for the automatic evaluation of the quality of semantic annotations computed by semantic table interpretation approaches. STILTool provides a graphical interface allowing users to analyse the correctness of the annotations of tabular data. The tool also provides a set of statistics in order to identify the most common error patterns