State-of-the-Art for the use of Phase-Change Materials in Tanks Coupled with Heat Pumps

With the goal of increasing heat storage in the same accumulation volumes, phase-change materials are considered. There are different substances with different phase-change temperatures that can be used for storing heating or cooling implemented in heat pump systems for applications of space heating and cooling, ventilation or domestic hot water production. Reducing the size of the buffer tanks used with heat pumps, avoiding the oversizing of heat pumps or detaching thermal energy production and consumption are among the benefits that could result from the combination of heat pumps and latent heat thermal energy storage. In addition, this form of thermal energy storage allows enhancing the use of renewable energy sources as heat sources for heat pump systems. Most previous review works focus mainly on the different materials available that can be used as phase-change materials. Conversely, this review encloses, classifies and describes the results of different works found in the literature that studied individual solutions to enhance the performance of systems combining heat pumps and latent heat thermal energy storage.acceptedVersio

Improving generation and evaluation of long image sequences for embryo development prediction

Generating synthetic time series data, such as videos, presents a formidable challenge as complexity increases when it is necessary to maintain a specific distribution of shown stages. One such case is embryonic development, where prediction and categorization are crucial for anticipating future outcomes. To address this challenge, we propose a Siamese architecture based on diffusion models to generate predictive long-duration embryonic development videos and an evaluation method to select the most realistic video in a non-supervised manner. We validated this model using standard metrics, such as Fréchet inception distance (FID), Fréchet video distance (FVD), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mean squared error (MSE). The proposed model generates videos of up to 197 frames with a size of 128×128, considering real input images. Regarding the quality of the videos, all results showed improvements over the default model (FID = 129.18, FVD = 802.46, SSIM = 0.39, PSNR = 28.63, and MSE = 97.46). On the coherence of the stages, a global stage mean squared error of 9.00 was achieved versus the results of 13.31 and 59.3 for the default methods. The proposed technique produces more accurate videos and successfully removes cases that display sudden movements or changes.Xunta de Galicia | Ref. ED481A 2021/286Agencia Estatal de Investigación | Ref. PID2020-113673RB-I00Xunta de Galicia | Ref. ED431C 2022/03-GR

Thermophysical properties of functionalized graphene nanoplatelet dispersions for improving efficiency in a wind turbine cooling system

A new generation of heat transfer fluids, nanofluids, can play a major role in the development of today’s renewable energies. In the particular case of wind turbines, an undesirable overheating of electrical and mechanical components can provoke a noticeable reduction of overall efficiency due to the temperature is a limiting factor to the electricity generation or even very expensive repair cost because of an unexpected crash of generators, or others turbine components. Dispersions of multiple-layer graphene nanostructures with high thermal conductivity in conventional working fluids are a promising type of new heat transfer fluids due to the excellent performance of nanoadditives in heat transference. Hence, determining the thermophysical properties of these nanomaterials under different conditions is the first step and key issue for analysing and optimizing the dispersions. Although water-based graphene nanoplatelet nanofluids have been investigated and some correlations can be found in the literature, scarce studies were conducted using other industrial working fluids as base fluids. The purpose of this study is to carry out a thorough thermophysical characterization of different loaded samples of functionalized graphene nanoplatelet dispersions in an industrial heat transfer fluid, Havoline XLC Pre-mixed 50/50. Four different nanofluids at mass concentrations (0.25, 0.50, 0.75 and 1.0) wt.% of functionalized graphene nanoplatelets powder were produced. In order to obtain improved long-term stabilities, sodium dodecyl benzene sulphonate was added to the samples at a mass concentration of 0.125 % in relation to the base fluid without appreciable variations in the pH value. Stability was assessed through zeta potential and dynamic light scattering measurements. Tests for determining thermal conductivity were conducted with a transient hot wire technique in a wide temperature range. In addition, densities, dynamic viscosities and specific heat capacities of the samples were experimentally determined at different temperatures in order to carry out further studies such as experimental convective heat transfer coefficients and pressure drops. Increases in thermal conductivity up to 7.3 % were found with not very high viscosity rises.Papers presented at the 13th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Portoroz, Slovenia on 17-19 July 2017 .International centre for heat and mass transfer.American society of thermal and fluids engineers

Temporal development GAN (TD-GAN): crafting more accurate image sequences of biological development

In this study, we propose a novel Temporal Development Generative Adversarial Network (TD-GAN) for the generation and analysis of videos, with a particular focus on biological and medical applications. Inspired by Progressive Growing GAN (PG-GAN) and Temporal GAN (T-GAN), our approach employs multiple discriminators to analyze generated videos at different resolutions and approaches. A new Temporal Discriminator (TD) that evaluates the developmental coherence of video content is introduced, ensuring that the generated image sequences follow a realistic order of stages. The proposed TD-GAN is evaluated on three datasets: Mold, Yeast, and Embryo, each with unique characteristics. Multiple evaluation metrics are used to comprehensively assess the generated videos, including the Fréchet Inception Distance (FID), Frechet Video Distance (FVD), class accuracy, order accuracy, and Mean Squared Error (MSE). Results indicate that TD-GAN significantly improves FVD scores, demonstrating its effectiveness in generating more coherent videos. It achieves competitive FID scores, particularly when selecting the appropriate number of classes for each dataset and resolution. Additionally, TD-GAN enhances class accuracy, order accuracy, and reduces MSE compared to the default model, demonstrating its ability to generate more realistic and coherent video sequences. Furthermore, our analysis of stage distribution in the generated videos shows that TD-GAN produces videos that closely match the real datasets, offering promising potential for generating and analyzing videos in different domains, including biology and medicine

Flow behaviour of glycolated water suspensions of functionalized graphene nanoplatelets

The heat transfer performance of the conventional fluids used in heat exchange processes improves by dispersing nanoparticles with high thermal conductivity, as many researches have shown in the last decades. The heat transfer capability of a fluid depends on several physical properties among which the rheological behavior is very relevant, as we have previously pointed out. In this study, different samples of nanofluids have been analyzed by using a DHR-2 rotational rheometer of TA Instruments with concentric cylinder geometry in the temperature range from (278.15 to 323.15) K. The used base fluids were two different binary mixtures of propylene glycol and water at (10:90)% and (30:70)% mass ratios. Two different mass concentrations (viz. 0.25 and 0.5 wt.%) of graphene nanoplatelets functionalized with sulfonic acid (graphenit- HW6) were dispersed in these two base fluids. Firstly, with the goal of checking and calibrating the operation of the rheometer, the viscosity-shear stress curves for pure propylene glycol, Krytox GPL102 oil, and the two base fluids were experimentally determined. A detailed comparative study with those well-known data over the entire range of temperature was stabilized obtaining deviations in viscosity less than 3.5%. Then, the flow curves of the different nanofluid samples were studied at different temperatures to characterize their flow behavior.Papers presented to the 12th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Costa de Sol, Spain on 11-13 July 2016

Phase-based regional oxygen metabolism (PROM) using MRI

Venous oxygen saturation (Yv) in cerebral veins and the cerebral metabolic rate of oxygen (CMRO2) are important indicators for brain function and disease. Although MRI has been used for global measurements of these parameters, currently there is no recognized technique to quantify regional Yv and CMRO2 using noninvasive imaging. This article proposes a technique to quantify CMRO2 from independent MRI estimates of Yv and cerebral blood flow. The approach uses standard gradient-echo and arterial spin labeling acquisitions to make these measurements. Using MR susceptometry on gradient-echo phase images, Yv was quantified for candidate vein segments in gray matter that approximate a long cylinder parallel to the main magnetic field. Local cerebral blood flow for the identified vessel was determined from a corresponding region in the arterial spin labeling perfusion map. Fick's principle of arteriovenous difference was then used to quantify CMRO2 locally around each vessel. Application of this method in young, healthy subjects provided gray matter averages of 59.6% ± 2.3% for Yv, 51.7 ± 6.4 mL/100 g/min for cerebral blood flow, and 158 ± 18 μmol/100 g/min for CMRO2 (mean ± SD, n = 12), which is consistent with values previously reported by positron emission tomography and MRI. Magn Reson Med, 2012.National Institutes of Health (U.S.) (NIH grant T90-DA022759)National Institutes of Health (U.S.) (NIH grant T32-GM07753)Siemens Aktiengesellschaft (Siemens-MIT Alliance)National Institutes of Health (U.S.) (NIH grant R01- EB007942

MobyDeep: A lightweight CNN architecture to configure models for text classification

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGNowadays, trends in deep learning for text classification are addressed to create complex models to deal with huge datasets. Deeper models are usually based on cutting edge neural network architectures, achieving good results in general but demanding better hardware than shallow ones. In this work, a new Convolutional Neural Network (CNN) architecture (MobyDeep) for text classification tasks is proposed. Designed as a configurable tool, resultant models (MobyNets) are able to manage big corpora sizes under low computational costs. To achieve those milestones, the architecture was conceived to produce lightweight models, having their internal layers based on a new proposed convolutional block. That block was designed and customized by adapting ideas from image to text processing, helping to squeezing model sizes and to reduce computational costs. The architecture was also designed as a residual network, covering complex functions by extending models up to 28 layers. Moreover, middle layers were optimized by residual connections, helping to remove fully connected layers on top and resulting in Fully CNN. Corpus were chosen from the recent literature, aiming to define real scenarios when comparing configured MobyDeep models with other state-of the-art works. Thus, three models were configured in 8, 16 and 28 layers respectively, offering competitive accuracy results

Systematic method for morphological reconstruction of the semicircular canals using a fully automatic skeletonization process

We present a novel method to characterize the morphology of semicircular canals of the inner ear. Previous experimental works have a common nexus, the human-operator subjectivity. Although these methods are mostly automatic, they rely on a human decision to determine some particular anatomical positions. We implement a systematic analysis where there is no human subjectivity. Our approach is based on a specific magnetic resonance study done in a group of 20 volunteers. From the raw data, the proposed method defines the centerline of all three semicircular canals through a skeletonization process and computes the angle of the functional pair and other geometrical parameters. This approach allows us to assess the inter-operator effect on other methods. From our results, we conclude that, although an average geometry can be defined, the inner ear anatomy cannot be reduced to a single geometry as seen in previous experimental works. We observed a relevant variability of the geometrical parameters in our cohort of volunteers that hinders this usual simplification

Multiparametric renal magnetic resonance imaging: A reproducibility study in renal allografts with stable function

Monitoring renal allograft function after transplantation is key for the early detection of allograft impairment, which in turn can contribute to preventing the loss of the allograft. Multiparametric renal MRI (mpMRI) is a promising noninvasive technique to assess and characterize renal physiopathology; however, few studies have employed mpMRI in renal allografts with stable function (maintained function over a long time period). The purposes of the current study were to evaluate the reproducibility of mpMRI in transplant patients and to characterize normal values of the measured parameters, and to estimate the labeling efficiency of Pseudo-Continuous Arterial Spin Labeling (PCASL) in the infrarenal aorta using numerical simulations considering experimental measurements of aortic blood flow profiles. The subjects were 20 transplant patients with stable kidney function, maintained over 1 year. The MRI protocol consisted of PCASL, intravoxel incoherent motion, and T1 inversion recovery. Phase contrast was used to measure aortic blood flow. Renal blood flow (RBF), diffusion coefficient (D), pseudo-diffusion coefficient (D*), flowing fraction ( f ), and T1 maps were calculated and mean values were measured in the cortex and medulla. The labeling efficiency of PCASL was estimated from simulation of Bloch equations. Reproducibility was assessed with the within-subject coefficient of variation, intraclass correlation coefficient, and Bland-Altman analysis. Correlations were evaluated using the Pearson correlation coefficient. The significance level was p less than 0.05. Cortical reproducibility was very good for T1, D, and RBF, moderate for f , and low for D*, while medullary reproducibility was good for T1 and D. Significant correlations in the cortex between RBF and f (r = 0.66), RBF and eGFR (r = 0.64), and D* and eGFR (r = -0.57) were found. Normal values of the measured parameters employing the mpMRI protocol in kidney transplant patients with stable function were characterized and the results showed good reproducibility of the techniques