Design Synthesis of Adaptive Mesoscopic Cellular Structures with Unit Truss Approach and Particle Swarm Optimization Algorithm

Cellular material structures have been engineered at the mesoscopic scale for high performance and multifunctional capabilities. However, the design of adaptive cellular structures - structures with cellular configurations, sizes, and shapes designed for a specific geometric and loading context - has not been sufficiently investigated. In this paper, the authors present a design synthesis method with the use of unit truss approach and particle swarm optimization algorithm to design adaptive cellular structures. A critical review is presented to show the pros and cons of the new design synthesis method and an existing homogenization method. The research extends the application of additive manufacturing in the design of new materials for high performances and benefits its long-term growth.Mechanical Engineerin

Protocol-Guided Teaching: An Experiment in Chinese Basic Education

Protocol-guided teaching, a method of classroom instruction reform started by Donglu Middle School in Nanjing of China has developed into a student-centered teaching model in the context of the deepening reform of Chinese basic education. In this model, the teacher plays an essential guiding role, and the main objective is the development of the student’s autonomous learning ability. The purpose of this article is to increase interest in and conversations about the protocol-guided teaching model among educational professionals by describing the history of the approach, summarizing its features, and highlighting its implementation tactics

Analysis of Mesostructure Unit Cells Comprised of Octet-truss Structures

A unit truss finite element analysis method allowing non-linear deformation is employed to analyze a unit cell comprised of n 3 octet-truss structures for their stiffness and displacement compared to their relative density under loading. Axial, bending, shearing, and torsion effects are included in the analysis for each strut in the octet-truss structure which is then related to the mesostructure level (unit cell). The versatility of additive manufacturing allows for the fabrication of these complex unit cell truss structures which can be used as building blocks for macro-scale geometries. The finite element calculations are compared to experimental results for samples manufactured on a Stereolithography Apparatus (SLA) out of a standard resin.Mechanical Engineerin

Rational approximation solution of the fractional Sharma–Tasso–Olever equation

AbstractIn the paper, we implement relatively new analytical techniques, the variational iteration method, the Adomian decomposition method and the homotopy perturbation method, for obtaining a rational approximation solution of the fractional Sharma–Tasso–Olever equation. The three methods in applied mathematics can be used as alternative methods for obtaining an analytic and approximate solution for different types of differential equations. In these schemes, the solution takes the form of a convergent series with easily computable components. The numerical results demonstrate the significant features, efficiency and reliability of the three approaches

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

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

How daily supervisor abuse and coworker support affect daily work engagement

The purpose of this study was to explore the dynamic and intervention mechanisms of daily abusive experience affecting daily work engagement. Drawing on conservation of resources (COR) theory, we examine the effect of daily abusive supervision on daily work engagement through daily negative emotions from the resource consumption perspective, and the moderation effect of coworker support from the resource provision perspective. Using a daily diary approach and based on a sample of 73 employees for 5 consecutive days in China. The results reveal that daily abusive supervision has a significant negative effect on daily work engagement, daily negative emotions mediate this relationship, and coworker support had a cross-level moderating effect between daily abusive supervision and daily negative emotions. Our study shows ways to boost employees’ daily work engagement and especially ways buffer the negative effect of abused experience on work engagement

Intelligent modeling with physics-informed machine learning for petroleum engineering problems

The advancement in big data and artificial intelligence has enabled a novel exploration mode for the study of petroleum engineering. Unlike theory-based solution methods, the data-driven intelligent approaches demonstrate superior flexibility, computational efficiency and accuracy for dealing with complex multi-scale, and multi-physics problems. However, these intelligent models often disregard physical laws in pursuit of error minimization, which leads to certain uncertainties. Therefore, physics-informed machine learning approaches have been developed based on data, guided by physics, and supported by machine learning models. This study summarizes four embedding mechanisms for introducing physical information into machine learning models, including input databased embedding, model architecture-based embedding, loss function-based embedding, and model optimization-based embedding mechanism. These “data + physics” dualdriven intelligent models not only exhibit higher prediction accuracy while adhering to physic laws, but also accelerate the convergence to improve computational efficiency. This paradigm will facilitate the guide developments in solving petroleum engineering problems toward a more comprehensive and efficient direction.Cited as: Xie, C., Du, S., Wang, J., Lao, J., Song, H. Intelligent modeling with physics-informed machine learning for petroleum engineering problems. Advances in Geo-Energy Research, 2023, 8(2): 71-75. https://doi.org/10.46690/ager.2023.05.0