Answer:
the maximum bending stress in the strap is 3.02 ksi
Explanation:
Given the data in the question;
steel strap thickness = 0.125 in
width = 2 in
circular arc radius = 600 in
we know that, standard value of modulus of elasticity of L2 steel is; E = 29 × 10³ ksi;
Now, using simple theory of bending
1/p = M/EI
solve for M
Mp = EI
M = EI / p ----- let this be equation 1
The maximum bending stress in the strap is;
σ = Mc / I -------let this be equation 2
substitute equation 1 into 2
σ = ( EI / p)c / I
σ = ( c/p )E
so we substitute in our values
σ = ( (0.125/2) / 600 )29 × 10³
σ = 0.00010416666 × 29 × 10³
σ = 3.02 ksi
Therefore, the maximum bending stress in the strap is 3.02 ksi
Answer:The Wright brothers invented and flew the first airplane in 1903, recognized as "the first sustained and controlled heavier-than-air powered flight". ... Airplanes had a presence in all the major battles of World War II. The first jet aircraft was the German Heinkel He 178 in 1939.
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.
If a controlled input can transfer (alter) the control system's initial states to some other desired states in a finite amount of time, the control system is said to be controllable.
Using Kalman's test, we can determine whether a control system is controllable. The evolution model for the state variables (time-varying unknowns) and the observation model, which connects the observations to the state variables, make up the state space representation of a dynamical system. The capacity to move a system about in its full configuration space using just specific permitted actions is generally referred to as controllability. The precise definition changes slightly depending on the model type or framework used.
Learn more about control here-
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