Quantifying Fluid Shear Stress in a Rocking Culture Dish

Fluid shear stress (FSS) is an important stimulus for cell functions. Compared with the well established parallel-plate and cone-and-plate systems, a rocking “see-saw” system offers some advantages such as easy operation, low cost, and high throughput. However, the FSS spatiotemporal pattern in the system has not been quantified. In the present study, we developed a lubrication-based model to analyze the FSS distributions in a rocking rectangular culture dish. We identified an important parameter (the critical flip angle) that dictates the overall FSS behaviors and suggested the right conditions to achieving temporally oscillating and spatially relatively uniform FSS. If the maximal rocking angle is kept smaller than the critical flip angle, which is defined as the angle when the fluid free surface intersects the outer edge of the dish bottom, the dish bottom remains covered with a thin layer of culture medium. The spatial variations of the peak FSS within the central 84% and 50% dish bottom are limited to 41% and 17%, respectively. The magnitude of FSS was found to be proportional to the fluid viscosity and the maximal rocking angle, and inversely proportional to the square of the fluid depth-to-length ratio and the rocking period. For a commercial rectangular dish (length of 37.6 mm) filled with ∼2 mL culture medium, the FSS at the center of the dish bottom is expected to be on the order of 0.9 dyn/cm2 when the dish is rocked +5° at 1 cycle/s. Our analysis suggests that a rocking “see-saw” system, if controlled well, can be used as an alternative method to provide low-magnitude, dynamic FSS to cultured cells

Forecasting of Two-Phase Flow Patterns in Upward Inclined Pipes via Deep Learning

Conventionally, the boundaries of gas-liquid flow regime transition are extremely sensitive to the inclination of flow channels. However, traditional two-dimensional flow regime maps have difficulties to reflect this fact as it can only accommodate two independent variables, which are often the gas and liquid superficial velocities. Few investigators have been able to propose a single model with accessible inputs under the considerations of the whole range of upward inclined angels. In this paper, we developed a novel approach by applying a typical machine learning (ML) method, artificial neural network (ANN), to predict flow pattern along upward inclined pipe (0 ~ 90°) using easily accessible parameters as inputs, namely, superficial velocities of individual phase and inclination angles. TensorFlow, a new generation and popular open-source foundation for ML programming, was used for building the ANN model, which was trained and tested by experimental data (1952 data points) that were reported in the literature. The predicting results show that ANN identifications have a satisfying agreement with experimental observations. The predicting accuracies of stratified smooth, stratified wavy, annular, intermittent, bubble flow are all above 90%, with the only exception of dispersed bubble flow (73%). In addition, the validation of the model was extended by comparing the ANN’s performance with well-established two-phase transition boundary models among different flow regimes. Comparing against conventional methods based on either correlation or flow regime map, the developed ANN model is expected to be a more efficient tool in flow pattern prediction. Furthermore, the impact of inclination angles on final ANN outputs was evaluated quantitatively. Results showed, given flow conditions fixed, variations of inclination angles have a significant influence on gas- liquid flow patterns in channels of conventional sizes

フダンソウ (Beta vulgaris L.subsp.cicla) の塩・アルカリ障害及び耐性に関する生理学的研究

内容の要約広島大学(Hiroshima University)博士(農学)Doctor of Agriculturedoctora

Modeling and Performance Evaluation of Multistage Serial Manufacturing Systems with Rework Loops and Product Polymorphism

This paper studies multistage serial manufacturing systems with the integrated consideration of machine failures, process defects, multiple rework loops, etc. In particular, multiple rework loops and product polymorphism lead to a more complex conversion of internal material flows, and therefore it's difficult to model and analyse such manufacturing systems. A modular modeling method based on Generalized Stochastic Petri Nets (GSPN) is presented to characterize the material flows, it is capable of representing the processing differences resulting from product polymorphism comparing with traditional Markov model or Queuing network model. By analysing the model, the processing ratio of each workstation is inferred. Using 2M1B (two-machine and one-buffer) Markov cell model as the building blocks, which is obtained based on the GSPN models for their isomorphism, an overlapping decomposition method is then developed for evaluating the performance of the multistage serial systems with rework loops. Numerical experiments and a case study of a powertrain assembly line illustrate the efficiency of the proposed method

A Heuristic Approach to Solve an Industrial Scalability Problem

In recent years, the rapid change of market demand is increasing the need for scalability as a key characteristic of manufacturing systems. Scalability allows production capacity to be rapidly and cost-effectively reconfigured in different situation with different requirements and constraints. Our industrial partners are facing quarterly scalability problems involving a multi-unit and multi-product manufacturing system. In this paper, an original approach is presented to solve this kind of problems. Starting from the original manufacturing system configuration and process plan, a set of practical principles are introduced to seek for the feasible configurations; a GA is designed to search in the global solution space. A balancing objective function is defined and used to rank the proposed configurations. A real case study with 4-unit / 4-product situation demonstrates both the validity and efficiency of the proposed approach