Frost Porosity Measurement Using Capacitive Sensor

Frosting is a dynamic process because of the changes in the frost-air interface temperature as the frost layer grows. The frost properties, such as frost density and frost porosity, are highly dependent on the frosting conditions and vary with time even under a constant environmental and operational conditions. Precise detection of frost properties is important for understanding frosting mechanisms and predicting frost growth, and it is also important for defrost control in many applications. So far there have been very few research reports on dynamic frost porosity measurement, and most work reports an average measurement approach, which is undertaken by measuring the mass and volume within certain frost accumulating period to estimate the averaged frost properties. Those approaches ignore the temporal variation of frost properties with an assumption that the frost buildup at a constant porosity, at least within a certain time period. As the result, there is a distinct deviation between different frost models, because as a very important input of models, most empirical frost porosity correlations were based on different time intervals of measurement. Frost, as a mixture of ice crystal and air, could have its properties estimated based on the percentage of each component. In this work, a capacitive sensor is developed to detect the capacitance variation as frost growing, which together with the dielectric constant of ice and air, could be used to determine the temporal porosity according to the Maxwell-Garnett (MG) theory. An interdigital electrode designed in this work is fabricated using photolithography technique (shown in Figure 1), together with the PCB connector and a commercial digital converter (FDC 2214) can sense the capacitance reading with a 0.0001 pF resolution. 3-D printed Polyvinyl-chloride porous structure with controlled porosity filled with/without gelatin of different concentration (shown in Figure 2) has been used to valid the sensor’s responding function. Frost porosity was measured under different conditions with known sensor function and the empirical correlation of frost porosity is provided in this work and compared with existing work. This work presents a new method to dynamically detect the frost porosity as frost growing, and it is a big contribution to the mass-based defrost strategy development and frost growth modeling

Frost Growth Detection Using Capacitive Sensor

Frost buildup on surfaces could be an undesired situation in many applications. In refrigeration and heat pump system, typically, frost grows on the fin surface of the heat exchanger due to different environmental/operational conditions. On one hand, it can block the air flow and increase air-side pressure drop; on the other hand, can increase the thermal resistance and deteriorate heat transfer performance. As a result, frost buildup can significantly reduce the system’s COP. Therefore, most systems encountered frost buildup run the defrost cycle. The frost growth process is affected by many factors, such as environmental conditions (air humidity, temperature, flow rate), operational conditions (working fluids, saturated temperature), heat exchangers (structures, fin type and fin surface wettability) et. al.. All those factors are coupled together, which makes frost growth a very complex dynamic process with variable spatial distribution of its characteristic parameters. It is very important to dynamically detect frost growth for both effective defrost control and precise frost modelling. In this work, a capacitive sensor for frost detection has been developed, which consists of three parts as shown in Figure 1(a): 1) commercial capacitive to digital converter (FDC2214 from Texas Instruments and the resolution of the reading is 0.0001pF), 2) PCB connector and 3) fabricated electrodes. The fabricated copper electrode is attached to the PCB connector, which is mounted to the capacitive to digital converter and connected to the computer by a USB interface. Capacitance variation can be measured when the target properties changes. The interdigital electrodes has a high sensitivity and were fabricated by lithophotography, using copper laminates/ deposited copper thin layer as shown in Figure 1(b) The sensitivity can be affected by metallization ratios, width and thickness of the insulation layer, which are also explored in this work. The frost grows on a cold plate which is placed in the wind tunnel with a controlled air temperature, humidity and flow rate. The electrode of the capacitive sensor is located beside the side wall of the cold plate, as shown in Figure 1(c). The frost growth process can be detected and reflected by the capacitance variation of the sensor, as shown in Figure 2, the capacitance variation can reflect different stage of the frost growth period, starting from condensation to mature growth. Images are also captured by a CCD camera to calibrate the signal. This work demonstrates the dynamic frost growth detection at the first time and could play a significant role to understanding frost growth mechanism and defrost control strategy

Arthroscopic reconstruction of shoulder's labrum with extensive tears

AbstractBackgroundWith the continuous development of arthroscopic techniques, the majority of superior labrum anterior-posterior (SLAP) lesions can be treated with minimally invasive endoscopic repair. The aim of this study was to determine the efficacy of arthroscopic capsulolabral reconstruction of SLAP lesions with extensive tears.MethodsEighteen patients with SLAP lesions with extensive tears (median age, 27.50 years) were included in this study. Twelve patients had type-V SLAP lesions, 4 patients had type-VIII SLAP lesions, and 2 patients had deeply located SLAP lesions. The average duration of follow-up was 15.83 months (range, 11–22 months). Outcome measures included shoulder range of motion (ROM), American Shoulder and Elbow Surgeons (ASES) and Constant-Murley scores, and visual analogue scale (VAS) pain score.ResultsAfter arthroscopic surgery, shoulder forward flexion, shoulder external rotation, and external rotation in 90° of abduction were significantly greater than before surgery (169.5° vs. 165.5°, P = 0.001), (90° vs. 63.5°, P < 0.001), and (90° vs. 81.5°, P = 0.004), respectively. Median ASES and Constant-Murley scores after surgery were both 94 as compared to 77.0 and 77.5, respectively, before surgery (both, P < 0.001). The median VAS score decreased to 1.5 after surgery as compared to 6 before surgery (P < 0.001).ConclusionsArthroscopic repair of SLAP lesions with extensive tears can achieve good outcomes

Experimental Study of Condensation Heat Transfer of R134a on Oil-infusion Surfaces

Dropwise condensation, since first recognized in 1930, has stimulated interest because its heat transfer coefficient (HTC) is much higher than film condensation. For some applications, not only a higher heat transfer performance is desired, but also the retention of the fluids on the surface can be a big issue. For example, the refrigerant retention in some enhanced tube can block the contact of the vapor-solid interface and increase the thermal resistance; it also can increase the charge of refrigerant because certain amount of refrigerant could not go through the system cycle. Many efforts were dedicated to modifying the surface and promote dropwise condensation, and most research focus on the condensation of water vapor. It is very challenging to promote dropwise condensation for working fluids with a lower surface tension than water, such as refrigerant. Research have been conducted on dropwise condensation for low surface tension fluids using oil-infusion surface, which is promoted by the contact of drop to the liquid-vapor interface instead of solid-vapor interface. However, the effectiveness and efficiency of the oil-infusion surface is still a critical challenge, and the heat transfer mechanism of dropwise condensation with such liquid-liquid interface stays unclear. In this work, condensation of R134a on oil-immerged surfaces is investigated. Heat transfer coefficient is measured, and formation of the condensate is observed using a high speed camera. Two cavity surfaces of different porous scale are examined, of which, one is nanoscale pores and another is microscale pores Mineral oil of low miscibility to R134a is soaked to be saturated in the cavity prior to the experiment. All experiments were conducted under saturated condition of ambient temperature (around 22 °C) in a pressure chamber. The subcool level of the condensation is 10 °C. Images of the local condensation formation is analyzed and heat transfer coefficient is also compared for different surfaces. The duration of the oil-infusion surface is also tested for both surfaces

Electron slingshot acceleration in relativistic preturbulent shocks explored via emitted photon polarization

Transient electron dynamics near the interface of counterstreaming plasmas at the onset of a relativistic collisionless shock (RCS) is investigated using particle-in-cell simulations. We identify a slingshot-like injection process induced by the drifting electric field sustained by the flowing focus of backwards-moving electrons, which is distinct from the well-known stochastic acceleration. The flowing focus signifies the plasma kinetic transition from a preturbulent laminar motion to a chaotic turbulence. We find a characteristic correlation between the electron dynamics in the slingshot acceleration and the photon emission features. In particular, the integrated radiation from the RCS exhibits a counterintuitive non-monotonic dependence of the photon polarization degree on the photon energy, which originates from a polarization degradation of relatively high-energy photons emitted by the slingshot-injected electrons. Our results demonstrate the potential of photon polarization as an essential information source in exploring intricate transient dynamics in RCSs with relevance for earth-based plasma and astrophysical scenarios.Comment: 8 pages, 5 figure

Toward attention-based learning to predict the risk of brain degeneration with multimodal medical data

IntroductionBrain degeneration is commonly caused by some chronic diseases, such as Alzheimer’s disease (AD) and diabetes mellitus (DM). The risk prediction of brain degeneration aims to forecast the situation of disease progression of patients in the near future based on their historical health records. It is beneficial for patients to make an accurate clinical diagnosis and early prevention of disease. Current risk predictions of brain degeneration mainly rely on single-modality medical data, such as Electronic Health Records (EHR) or magnetic resonance imaging (MRI). However, only leveraging EHR or MRI data for the pertinent and accurate prediction is insufficient because of single-modality information (e.g., pixel or volume information of image data or clinical context information of non-image data).MethodsSeveral deep learning-based methods have used multimodal data to predict the risks of specified diseases. However, most of them simply integrate different modalities in an early, intermediate, or late fusion structure and do not care about the intra-modal and intermodal dependencies. A lack of these dependencies would lead to sub-optimal prediction performance. Thus, we propose an encoder-decoder framework for better risk prediction of brain degeneration by using MRI and EHR. An encoder module is one of the key components and mainly focuses on feature extraction of input data. Specifically, we introduce an encoder module, which integrates intra-modal and inter-modal dependencies with the spatial-temporal attention and cross-attention mechanism. The corresponding decoder module is another key component and mainly parses the features from the encoder. In the decoder module, a disease-oriented module is used to extract the most relevant disease representation features. We take advantage of a multi-head attention module followed by a fully connected layer to produce the predicted results.ResultsAs different types of AD and DM influence the nature and severity of brain degeneration, we evaluate the proposed method for three-class prediction of AD and three-class prediction of DM. Our results show that the proposed method with integrated MRI and EHR data achieves an accuracy of 0.859 and 0.899 for the risk prediction of AD and DM, respectively.DiscussionThe prediction performance is significantly better than the benchmarks, including MRI-only, EHR-only, and state-of-the-art multimodal fusion methods