scholarly journals Rotational diffusion and rotational correlations in frictional amorphous disk packings under shear

Soft Matter ◽  
2021 ◽  
Author(s):  
Dong Wang ◽  
Nima Nejadsadeghi ◽  
Yan Li ◽  
Shashi Shekhar ◽  
Anil Misra ◽  
...  
Keyword(s):  
Rotational Diffusion ◽  
Packing Fraction ◽  
System Pressure ◽  
Diffusive Dynamics ◽  
Disk Packings

Particles in a packing will rotate when the packing is deformed. We find that rotations display diffusive dynamics set by particle friction and packing fraction. Rotations are spatially anticorrelated and directly indicative of the system pressure.

scholarly journals Contact network changes in ordered and disordered disk packings

Soft Matter ◽  
2020 ◽  
Vol 16 (41) ◽  
pp. 9443-9455 ◽  
Author(s):  
Philip J. Tuckman ◽  
Kyle VanderWerf ◽  
Ye Yuan ◽  
Shiyun Zhang ◽  
Jerry Zhang ◽  
...  
Keyword(s):  
Packing Fraction ◽  
Contact Network ◽  
Contact Networks ◽  
Disk Packings

There are two ways to transition between different contact networks, point and jump changes, as shown in a packing fraction-strain landscape.

A SYSTEM FOR PROTECTING AN OIL DIFFUSION PUMP AGAINST FAILURE OF THE MECHANICAL FORE PUMP, COOLING WATER, OR ELECTRICITY

1970 ◽  
Vol 28 ◽  
pp. 344-345
Author(s):  
W. C. Bigelow ◽  
F. B. Drogosz ◽  
S. Nitschke
Keyword(s):  
High Vacuum ◽  
Cooling Water ◽  
Effective Protection ◽  
Pump Line ◽  
Diffusion Pump ◽  
System Pressure ◽  
Control Relay ◽  
Vacuum Systems ◽  
Vacuum Gage ◽  
Pressure Switch

High vacuum systems with oil diffusion pumps usually have a pressure switch to protect against Insufficient cooling water; however, If left unattended for long periods of time, failure of the mechanical fore pump can occur with equally serious results. The device shown schematically in Fig. 1 has been found to give effective protection against both these failures, yet it is inexpensive and relatively simple to build and operate.With this system, pressure in the fore pump line is measured by thermocouple vacuum gage TVG (CVC G.TC-004) whose output is monitored by meter relay MRy (Weston 1092 Sensitrol) which is set to close if the pressure rises above about 0.2 torr. This energizes control relay CRy (Potter & Brumfield KA5Y 120VAC SPDT) cutting off power to solenoid-operated fore line valve Vf (Cenco 94280-4 Norm. Closed) which closes to prevent further leakage of air into the diffusion pump

Compressor Issues for Hydrogen Production and Transmission Through a Long Distance Pipeline Network

2008 ◽  
Vol 59 (4) ◽  
Author(s):  
Fred Starr ◽  
Calin-Cristian Cormos ◽  
Evangelos Tzimas ◽  
Stathis Peteves
Keyword(s):  
Pressure Ratio ◽  
High Strength Steels ◽  
High Strength ◽  
Energy System ◽  
Pipeline System ◽  
Hydrogen Energy ◽  
Long Distance ◽  
System Pressure ◽  
Production Of Hydrogen ◽  
Plant Exit

A hydrogen energy system will require the production of hydrogen from coal-based gasification plants and its transmission through long distance pipelines at 70 � 100 bar. To overcome some problems of current gasifiers, which are limited in pressure capability, two options are explored, in-plant compression of the syngas and compression of the hydrogen at the plant exit. It is shown that whereas in-plant compression using centrifugal machines is practical, this is not a solution when compressing hydrogen at the plant exit. This is because of the low molecular weight of the hydrogen. It is also shown that if centrifugal compressors are to be used in a pipeline system, pressure drops will need to be restricted as even an advanced two-stage centrifugal compressor will be limited to a pressure ratio of 1.2. High strength steels are suitable for the in-plant compressor, but aluminium alloy will be required for a hydrogen pipeline compressor.

Application of neural networks in predictions of brake wear particulate matter emission

2021 ◽  
pp. 095440702110363
Author(s):  
Saša Vasiljević ◽  
Jasna Glišović ◽  
Nadica Stojanović ◽  
Ivan Grujić
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particulate Matter ◽  
Traffic Control ◽  
World Health ◽  
Vehicle Speed ◽  
System Pressure ◽  
The Neural Network ◽  
Vehicle Systems ◽  
Brake Wear

According to the World Health Organization, air pollution with PM10 and PM2.5 (PM-particulate matter) is a significant problem that can have serious consequences for human health. Vehicles, as one of the main sources of PM10 and PM2.5 emissions, pollute the air and the environment both by creating particles by burning fuel in the engine, and by wearing of various elements in some vehicle systems. In this paper, the authors conducted the prediction of the formation of PM10 and PM2.5 particles generated by the wear of the braking system using a neural network (Artificial Neural Networks (ANN)). In this case, the neural network model was created based on the generated particles that were measured experimentally, while the validity of the created neural network was checked by means of a comparative analysis of the experimentally measured amount of particles and the prediction results. The experimental results were obtained by testing on an inertial braking dynamometer, where braking was performed in several modes, that is under different braking parameters (simulated vehicle speed, brake system pressure, temperature, braking time, braking torque). During braking, the concentration of PM10 and PM2.5 particles was measured simultaneously. The total of 196 measurements were performed and these data were used for training, validation, and verification of the neural network. When it comes to simulation, a comparison of two types of neural networks was performed with one output and with two outputs. For each type, network training was conducted using three different algorithms of backpropagation methods. For each neural network, a comparison of the obtained experimental and simulation results was performed. More accurate prediction results were obtained by the single-output neural network for both particulate sizes, while the smallest error was found in the case of a trained neural network using the Levenberg-Marquardt backward propagation algorithm. The aim of creating such a prediction model is to prove that by using neural networks it is possible to predict the emission of particles generated by brake wear, which can be further used for modern traffic systems such as traffic control. In addition, this wear algorithm could be applied on other vehicle systems, such as a clutch or tires.

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