Thermal Modeling of an Automotive HVAC Unit Using a Coupled POD and Flow Resistance Network Approach

2018 ◽  
Author(s):  
Paul Christ ◽  
Thomas Sattelmayer
Keyword(s):  
Flow Resistance ◽  
Thermal Modeling ◽  
Network Approach ◽  
Resistance Network

scholarly journals Multistage Digital-to-Analogue Chip Based on a Weighted Flow Resistance Network for Soft Actuators

Micromachines ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1016
Author(s):  
Zhou Zhou ◽  
Manman Xu ◽  
Chenlin Zhu ◽  
Gonghan He ◽  
Kunpeng Zhang ◽  
...  
Keyword(s):  
Flow Rate ◽  
Microfluidic Chip ◽  
Flow Resistance ◽  
Design Method ◽  
Control Unit ◽  
Digital Signal ◽  
Technical Problems ◽  
Pressure Signal ◽  
Resistance Network ◽  
Flow Adjustment

A control chip with a multistage flow-rate regulation function based on the correlation between the flow resistance and flow rate has been developed in this article. Compared with the traditional proportional solenoid valve, this kind of flow valve based on microfluidic technology has the characteristics of being light-weight and having no electric drive. It solves such technical problems as how the current digital microfluidic chip can only adjust the flow switch, and the adjustment of the flow rate is difficult. To linearize the output signal, we propose a design method of weighted resistance. The output flow is controlled by a 4-bit binary pressure signal. According to the binary value of the 4-bit pressure signal at the input, the output can achieve 16-stage flow adjustment. Furthermore, we integrate the three-dimensional flow resistance network, multilayer structure microvalve, and parallel fluid network into a single chip by using 3D printing to obtain a modular flow control unit. This structure enables the microflow control signal to be converted from a digital signal to an analogue signal (DA conversion), and is suitable for microflow driving components, such as in microfluidic chip sampling systems and proportional mixing systems. In the future, we expect this device to even be used in the automatic control system of a miniature pneumatic soft actuator.

Flow Resistance Network Analysis in Fan-Cooled High-Density Packaging Electronic Equipment

2015 ◽  
Author(s):  
Takashi Fukue ◽  
Tomoyuki Hatakeyama ◽  
Masaru Ishizuka ◽  
Koichi Hirose ◽  
Kazuma Obata ◽  
...  
Keyword(s):  
Network Analysis ◽  
Flow Resistance ◽  
High Density ◽  
Thermal Design ◽  
Electronic Equipment ◽  
Test Model ◽  
Resistance Network ◽  
Electrical Components ◽  
Cooling Air ◽  
High Density Packaging

This study describes an application of the flow resistance network analysis to thermal design of fan-cooled electronic equipment. Especially, a modeling method of the flow resistance network was investigated. Current electronic equipment becomes smaller and thinner while their functions become more complex. As a result, flow passages for cooling air become complex. In order to simulate the complex airflow in high-density packaging electronic equipment by using the flow resistance network, we tried to develop the flow resistance network by support of the 3D-CFD analysis. A test model which simulates high-density packaging electronic equipment is prepared and the flow resistance network analysis is applied to the prediction of flow rate distribution in the model. Through the investigation, we obtained information and future problems about the development of the flow resistance network in electronic equipment with lots of electrical components.

The Collaborative Network Approach: a model for advancing patient-centric research for Castleman disease and other rare diseases

2019 ◽  
Vol 3 (1) ◽  
pp. 97-105
Author(s):  
Mary Zuccato ◽  
Dustin Shilling ◽  
David C. Fajgenbaum
Keyword(s):  
Rare Diseases ◽  
Treatment Options ◽  
Castleman Disease ◽  
High Impact ◽  
Collaborative Network ◽  
Network Approach ◽  
Grant Applications ◽  
Research Questions ◽  
Novel Strategy ◽  
Patient Centric

Abstract There are ∼7000 rare diseases affecting 30 000 000 individuals in the U.S.A. 95% of these rare diseases do not have a single Food and Drug Administration-approved therapy. Relatively, limited progress has been made to develop new or repurpose existing therapies for these disorders, in part because traditional funding models are not as effective when applied to rare diseases. Due to the suboptimal research infrastructure and treatment options for Castleman disease, the Castleman Disease Collaborative Network (CDCN), founded in 2012, spearheaded a novel strategy for advancing biomedical research, the ‘Collaborative Network Approach’. At its heart, the Collaborative Network Approach leverages and integrates the entire community of stakeholders — patients, physicians and researchers — to identify and prioritize high-impact research questions. It then recruits the most qualified researchers to conduct these studies. In parallel, patients are empowered to fight back by supporting research through fundraising and providing their biospecimens and clinical data. This approach democratizes research, allowing the entire community to identify the most clinically relevant and pressing questions; any idea can be translated into a study rather than limiting research to the ideas proposed by researchers in grant applications. Preliminary results from the CDCN and other organizations that have followed its Collaborative Network Approach suggest that this model is generalizable across rare diseases.

Don’t blame the model: Reconsidering the network approach to psychopathology.

2018 ◽  
Vol 125 (4) ◽  
pp. 606-615 ◽  
Author(s):  
Laura F. Bringmann ◽  
Markus I. Eronen
Keyword(s):  
Network Approach

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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