Piping input data

Based on the above mentioned, the programme of theoretical and experimental investigation of the main parameters of coal-methanol (or its water solution) mixture pipeline transport should be opened. As the first step of the programme the comparison of power consumption (dependency of hydraulic gradient I on slurry flow velocity V and solid concentration Cs) for the pipeline transport of coal-water mixture and coal-methanol solution mixture was realised. The special laboratory measurements were made to define unknown input data of semi-empirical relationships, i.e. the limit volumetric concentration Cm and the coefficient of mechanical friction of coal in the water or water-methanol solution ka. The resultant comparison of the hydraulic gradient I of the coal-water and coal-methanol solution mixture flow is presented in Figure 2, where density of coal was pc = 1480 kg/m3, diameter of the pipe was D = 0.103 mm, the maximal grain size of coal dmax was less than 0.25 mm, volumetric concentration - C = 20 %. 

Input Data and Computer Output for an Incompressible Fluid in a Pipe Line... 

The computer program PROG34 determines the overall pressure drop for the 6-inch tail pipe having a relieving rate of 27,000 Ib/hr. Table 3-11 illustrates both the input data and computer output. The Mach number at inlet condition is 0.0169, and at the critical condition is 0.8874. The critical pressure is 7.985 psia and the overall pressure drop is 0.213 psi. The compressible fluid flow pattern through the pipe is SUBSONIC. 

The computer program PROG35 calculates the flow regime and the pressure drops for liquid and vapor phases. From the two-phase flow modulus, the overall pressure drop for the 6-inch pipe is calculated. Table 3-12 gives the input data and computer output of the two-phase... 

The computer program PROG36 calculates the Froude numbers for the liquid and vapor phases. In addition, PROG36 will determine whether the pipe is self venting or whether pulsation flow is encountered. Table 3-13 shows the results for the 2-, 4-, and 6-inch (Schedule 40) pipes. Table 3-14 gives a typical input data and computer output for the 2-inch (Schedule 40) pipe. 

The computer program PROG37 evaluates the pressure drop of any given condensate header. The program also determines whether the velocity of the flashed condensate mixture would cause deterioration in the header line. Table 3-15 illustrates the results for the 4-, 6-, and 8-inch headers. Table 3-16 shows a typical input data and computer output for the 4-inch (Schedule 40) pipe. The computed results show that the 4-inch pipe gives the velocity of the flashed condensate mixture to be 7055 ft/ min. This indicates a possible deterioration in the pipe. For the 6- and 8-inch pipes, the velocities are 3109 ft/min, and 1795 ft/min respectively, indicating that the condensate pipe lines will not deteriorate. 

The flow of water required to cool the oil is 23310 Ib/hr. The computer program PROG83 rates the double pipe heat exchanger using either fin tubes or bare tubes. Table 8-23 lists the input data and output of the computer program. The fin efficiency is 43.3 percent and the computed overall heat transfer coefficient is 27.3 Btu/hrft °F. The pressure drops have not been exceeded, and the double pipe exchanger will be satisfactory for the service. 

The computer program PROG85 calculates the heat tracer requirements and heat loss for an insulated pipe line either with tracing or without tracing. Table 8-25 lists the input data and computer output of the... 

The posterior distributions do however not produce a total measure of imcertainty. The assessments are based on a set of assumptions and suppositions and these could hide or camouflage imcertain-ties. Consider for example the use of historical data for stuck-pipe downtime. In the probabilistic analysis we assume that the data are representative for the operations analysed. But the data used could to varying degree reflect the actual operations. There is considerable arbitrariness in the filter adopted to generate the input data of the analysis. The result is that we may experience surprising outcomes relative to what are expected from the probabiUstic analysis. 

Example 3 Liquid Discharge through a Piping System Input Data ... 

ANSPIPE Calculates pipe break probability using the Thomas Model BETA Calculates and draws event trees using word processor and other input BNLDATA Failure rate data... 

Current technolog) allows the use of computer programs and data bases to construct an accurate and detailed flowsheet. This may be a process type diagram or a piping and mechanical/instrument diagram, depending on the input. See Figures 1-9, 1-10, 1-18A and 1-18B. 

In motionless mixers the energy input for mixing is provided by the pressure loss from the mean flow. All manufacturers can provide pressure drop data. These are usually given as a friction factor or as a multiplier for the empty-pipe pressure drop. Values range from 30 to 1000 times the empty-pipe friction factor. 

Heat pipe thermal resistanee (and the heat transfer eoeffieient in the evaporator and eondenser zones) was found using the data of the vapour temperature in the adiabatie zone and the mean temperature in the evaporator and in the eondenser. The heat transfer eoeffieients in the evaporator and eondenser of the flat mHPs depend on two- dimensional hydraulie (pore saturation, eapillary permeability, eapillary pressure) and thermal (temperature distribution along the heat pipe envelope) parameters of deviee. The temperature in the middle of the heated side (heat load input) of the evaporator ean exeeed the symmetrie point temperature on the opposite (non-heated) surfaee of the envelope by nearly 10 °C. 

Whether a pressurized structure will leak or burst when suddenly cracked depends on the material s resistance to rapid crack propagation and its ability to arrest a crack. Thus, the governing material property, namely the stress intensity-crack velocity relationship, is necessary input for designing pressurized pipes and vessels and establishing safe operating conditions. Unfortunately, this material property is not easily measured, and reliable data do not generally exist. 

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