Crazy Guy what do uh mean ?
Answer:
In ferrous metal iron present but on the other hand in the non ferrous material iron does not present.That is why there is a different heat treatment process for ferrous and nonferrous materials.
Ferrous materials contains iron is the main constitute.Like steel ,cast iron ,wrought iron .Steel and cast iron are the alloy element of iron ans carbon.Wrought iron is the purest from of iron.
Heat treatment process for ferrous materials :
1.Normalizing
2.Annealing
3.Quenching
4.Surface hardening
Heat treatment process for non ferrous materials :
Mostly annealing process is used for non ferrous materials.After annealing non ferrous will become soft.
When two metal plates are joined then they form a bimetallic structure.The bimetallic structure is used to find the relationship of thermal temperature and the mechanical displacement.
The use of bimetallic structure -In clock ,thermometers ,engines.
Answer:
R= 1.25
Explanation:
As given the local heat transfer,
But we know as well that,
Replacing the values
Reynolds number is define as,
Where V is the velocity of the fluid and \upsilon is the Kinematic viscosity
Then replacing we have
<em>*Note that A is just a 'summary' of all of that constat there.</em>
<em>That is 
</em>
Therefore at x=L the local convection heat transfer coefficient is
Definen that we need to find the average convection heat transfer coefficient in the entire plate lenght, so
The ratio of the average heat transfer coefficient over the entire plate to the local convection heat transfer coefficient is
Answer:
a)- True
Explanation:
If two statements are inconsistent with each other it means that they are not telling the same, if they are not telling the same it means that only one of them COULD be true, but there is a third option where the two statements are wrong and non statement is telling the true...so:
If we have two statements inconsistent with each other, AT LEAST one of the statements is false.
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.
