Answer:
MRR = 1.984
Explanation:
Given that
Depth of cut ,d=0.105 in
Diameter D= 1 in
Speed V= 105 sfpm
feed f= 0.015 ipr
Now the metal removal rate given as
MRR= 12 f V d
d= depth of cut
V= Speed
f=Feed
MRR= Metal removal rate
By putting the values
MRR= 12 f V d
MRR = 12 x 0.015 x 105 x 0.105
MRR = 1.984
Therefore answer is -
1.944
Answer:
Enthalpy of reaction (kJoules/mole)
Heat of formation of products (kJoules/mole)
Heat of reaction of reactants (kJoules/mole)
Explanation:
The general expression for calculating the overall enthalpy of reaction is given as following:
ΔH = ∑ΔH[producst] - ∑Δ[reactants]
Thus, the heat of reaction is given as the difference between the formation of the products and the formation of the reactants. The units are expressed as kJ/mol of reactants or products.
Thus, the three values are fundamental in the determination of the overall energy of the reaction from Hess' Law.
Answer:
Explanation:
The phenomenon can be modelled after the Bernoulli's Principle, in which the sum of heads related to pressure and kinetic energy on ground level is equal to the head related to gravity.
The velocity of water delivered by the fire hose is:
The maximum height is cleared in the Bernoulli's equation:
Answer:
Electromechanical systems or devices are systems or devices that involves the interaction between electrical systems and mechanical systems in which the motion of mechanical parts is converted to electrical energy or made to interact with energy or in which electrical energy is converted to mechanical energy or interacts with a moving mechanical system
Therefore;
Electromechanical systems convert <u>electrical energy</u> input into a <u>mechanical energy</u> output
Explanation:
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.
