Answer:
a) 
, b) 
Explanation:
a) The counterflow heat exchanger is presented in the attachment. Given that cold water is an uncompressible fluid, specific heat does not vary significantly with changes on temperature. Let assume that cold water has the following specific heat:
The effectiveness of the counterflow heat exchanger as a function of the capacity ratio and NTU is:
The capacity ratio is:
Heat exchangers with NTU greater than 3 have enormous heat transfer surfaces and are not justified economically. Let consider that 
. The efectiveness of the heat exchanger is:
The real heat transfer rate is:
The exit temperature of the hot fluid is:
The log mean temperature difference is determined herein:
The heat transfer surface area is:
Length of a single pass counter flow heat exchanger is:
b) Given that tube wall is very thin, inner and outer heat transfer areas are similar and, consequently, the cold side heat transfer coefficient is approximately equal to the hot side heat transfer coefficient.
Answer:
modulus of elasticity for the nonporous material is 340.74 GPa
Explanation:
given data
porosity = 303 GPa
modulus of elasticity = 6.0
solution
we get here modulus of elasticity for the nonporous material Eo that is
E = Eo (1 - 1.9P + 0.9P²) ...............1
put here value and we get Eo
303 = Eo ( 1 - 1.9(0.06) + 0.9(0.06)² )
solve it we get
Eo = 340.74 GPa
Features of Multidimensional scaling(MDS) from scratch is described below.
Explanation:
Multidimensional scaling (MDS) is a way to reduce the dimensionality of data to visualize it. We basically want to project our (likely highly dimensional) data into a lower dimensional space and preserve the distances between points.
If we have some highly complex data that we project into some lower N dimensions, we will assign each point from our data a coordinate in this lower dimensional space, and the idea is that these N dimensional coordinates are ordered based on their ability to capture variance in the data. Since we can only visualize things in 2D, this is why it is common to assess your MDS based on plotting the first and second dimension of the output.
If you look at the output of an MDS algorithm, which will be points in 2D or 3D space, the distances represent similarity. So very close points = very similar, and points farther away from one another = less similar.
Working of MDS
The input to the MDS algorithm is our proximity matrix. There are two kinds of classical MDS that we could use: Classical (metric) MDS is for data that has metric properties, like actual distances from a map or calculated from a vector
.Nonmetric MDS is for more ordinal data (such as human-provided similarity ratings) for which we can say a 1 is more similar than a 2, but there is no defined (metric) distance between the values of 1 and 2.
Uses
Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset. MDS is used to translate "information about the pairwise 'distances' among a set of n objects or individuals" into a configuration of n points mapped into an abstract Cartesian space.
Answer:
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Explanation:
can you translate it into english.....
Answer:
<h2>The Invention of the Internal Combustion Engine (ICE)</h2>
Explanation:
The internal combustion engine is an engine in which ignition and combustion take place in the engine(in one place), the invention of the ICE was an integral part of the industrial revolution, as there was increasing demand for power, and manual labor could not suffice, especially during the mid 19 century.
The ICE made it possible for tasks that demand intensive power consumption to come through to reality, it was as a result of the invention of the ICE that road transportation was made easier for mankind, as the means of transport then was the use of beast of burden, now we have cars, airplanes ship, etc, essentially the invention of ICE reduced the tedious task man would have to engage in for his daily needs
