Magnetization Plateau of Classical Ising Model on Shastry-Sutherland Lattice

We study the magnetization for the classical antiferromagnetic Ising model on the Shastry-Sutherland lattice using the tensor renormalization group approach. With this method, one can probe large spin systems with little finite-size effect. For a range of temperature and coupling constant, a single magnetization plateau at one third of the saturation value is found. We investigate the dependence of the plateau width on temperature and on the strength of magnetic frustration. Furthermore, the spin configuration of the plateau state at zero temperature is determined.Comment: 6 pages, 8 figure

Experimental Evaluation of Local Air-side Heat Transfer Coefficient on Single Fins

Quantification of local air-side heat transfer coefficient (HTC) on fin-and-tube heat exchanger fins is a challenging task, especially when the fin geometry is complex. A newly developed experimental method can obtain two-dimensional HTC distribution on fin surfaces with a high resolution (8.9 μm). In this research, a thin (20 μm) yellow coating is sprayed on the surface of the fins. The coated fins were exposed in the wind tunnel which has the tracer gas (50 ppmv ammonia) well mixed in the airflow. During the experiment, the color of coated surface changes from yellow to blue. The rate of local mass transfer is correlated with the rate of local color change. Then, by applying the analogy between heat and mass transfer, local HTCs on the fin surfaces are quantified. The method accuracy has been validated on fundamental shapes such as flat plates, and cylinders. Two-dimensional HTC distributions on a four-row wavy fin and a four-row slit fin have been obtained by employing this new method. As there is no local measurement on these geometries exists, the averaged HTCs on the fin surfaces have been critically compared to the averaged HTCs from entire heat exchanger measurements from the literature. This method has provided an accurate, robust, and inexpensive tool to evaluate local air-side HTCs for real-scale heat exchangers with complex fin geometry and multiple tube rows

Application of a Color Change Coating to Evaluate Flow Velocity Distribution and Wall Shear Stress of Fan Blade

A mass transfer method has been employed to characterize tangential flow velocity around fan blades. In the current research, two commercial air-conditioning fan blade samples have been investigated. The blade surfaces were covered with a 10 μm yellow coating by a spray method. The fan and a camera are driven by a motor and shaft system that can synchronize the rotational speed. Therefore, a relative stay still image of the fan blade during rotation can be obtained. Subsequently, a small amount (50 ppmv) of ammonia has been injected into an environmental chamber that has the fan installed and rotating. The coating material on the blade surfaces absorbs the ammonia from the airflow and responds with a color change from yellow to blue. At the same time, the color change was recorded by the camera. The surface color change can be quantified by image processing which represents the local mass transfer. According to the fluid mechanics and analogy between mass transfer, heat transfer, and Colburn’s relation for turbulent flow, local tangential flow velocity distribution at the external of the boundary layer on the blade surface can be quantified. Thereafter, friction factor and wall shear stress can be calculated accordingly. Comparing to other experimental and computational methods, this new experimental method provides a robust way to evaluate turbulent flow velocity around the rotational fan blades. The results show this method is promising to be employed to evaluate and optimize fan systems to improve efficiency and reduce noise

Novel Visualization Method to Quantify Local Air-side Heat Transfer Coefficient

Low air-side heat transfer coefficients (HTC) are often the bottleneck of heat exchanger performance. However, the measurement of local air-side heat transfer coefficients for the entire heat transfer surface is not a trivial task. Current experimental methods often only work on scaled-up samples which typically cannot deliver continuous HTC distributions across the surface of interest. Moreover, some methods require very precise and costly equipment such as lasers. Therefore, a novel visualization method is designed to obtain local air-side HTC distributions of an entire heat exchanger with a relatively simple experimental facility. The method relies on measuring mass transfer and applying the analogy between heat and mass transfer to determine heat transfer. The process involves a thin acidic coating on the heat transfer surface which is exposed in a wind tunnel to a suitable trace gas, in this case, a low concentration ammonia-air mixture. The coating will absorb the ammonia and gradually change color from yellow to green to blue, depending on the exposure time and local ammonia concentration. By observing the color difference, the mass transfer could be acquired and local HTC is calculated subsequently. In order to develop this method, different metal samples such as aluminum, copper and stainless steel were coated and tested. The thickness and evenness of coatings are measured by a stylus profiler. The results show that the coating characteristic will not have a significant impact on airflow and the boundary layer conditions. So, the conditions of applying the analogy between mass and heat transfer are fulfilled. Preliminary mass transfer experiments show continuous color change on a flat plate which is proportional to mass transfer of ammonia from the free stream flow to the coated surface. Thus, this new method is very promising to acquire local HTC with a visualization approach on different geometries

How is Gaze Influenced by Image Transformations? Dataset and Model

Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time consuming and expensive. Most of current studies on human attention and saliency modeling have used high quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial network (dubbed GazeGAN). A modified UNet is proposed as the generator of the GazeGAN, which combines classic skip connections with a novel center-surround connection (CSC), in order to leverage multi level features. We also propose a histogram loss based on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in terms of luminance distribution. Extensive experiments and comparisons over 3 datasets indicate that GazeGAN achieves the best performance in terms of popular saliency evaluation metrics, and is more robust to various perturbations. Our code and data are available at: https://github.com/CZHQuality/Sal-CFS-GAN