Engineering Mathematics : Differential Calculus & Vector Calculus

By Vishnu Pratap Singh|Updated : March 3rd, 2022

Complete coverage of syllabus is a very important aspect for any competitive examination but before that important subject and their concept must be covered thoroughly. In this article, we will cover Engineering Mathematics : Differential Calculus & Vector Calculus                                                                                                                                                                                                                                            

Limits

Let us consider a function f(x) defined in an interval l. if we see the behavior of f(x) become closer and closer to a number l as x  a then l is said to be limit of f(x)at x=a.

Left-Hand Limit

Let  function f(x) is said to approach l as x →a from left if for an arbitrary positive small number ε ,a small positive number δ (depends on ε) such that 

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Right Hand Limit

Let  function f(x) is said to approach l as x → a from right if for an arbitrary positive small number ε ,a small positive number δ (depends on ε) such that

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L- Hospital Rule:

When 

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must not be zero, where f(n) and g(n) are nth derivative of f(x) and g(x).

L- Hospital Rule for the form (∞ – ∞, 0 × ∞):

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L-Hospital Rule for the form (0°, 1, ∞°):

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CONTINUITY

A function y = f(x) is said to be continuous if the graph of the function is a continuous curve. On the other hand, if a curve is broken at some point say x = a, we say that the function is not continuous or discontinuous.

Definition:

A function f(x) is said to be continuous at x = a if and only if the following three conditions are satisfied:

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Properties of continuous functions:

(i) A function which is continuous in a closed interval is also bounded in that interval.

(ii) A continuous function which has opposite signs at two points vanishes at least once between these points and vanishing point is called root of the function.

(iii) A continuous function f(x) in the closed interval [a, b] assumes at least once every value between f(a) and f(b), it being assumed that

 f(a) ≠ f(b).

 

DIFFERENTIABILITY

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Note.

A Necessary condition for the Existence of a Finite Derivative

Continuity is necessary but not Sufficient for the existence of finite derivatives.

 

Fundamental Theorem:

Rolle’s Theorem:

If

(i)   f(x) is continuous is the closed interval [a, b],

(ii)  f’(x) exists for every value of x in the open interval (a, b) and

(iii) f (a) = f(b), then there is at least one value c of x in (a, b) such that f’ (c) = 0.

Lagrange’s Mean-Value Theorem:

If

 (i) f(x) is continuous in the closed interval [a, b], and

(ii) f’(x) exists in the open interval (a, b),

 then there is at least there is at one value c of x (a, b),

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Cauchy’s Mean-value theorem:

If (i) f(x) and g(x) be continuous in [a, b]

(ii) f’(x) and g’(x) exist in (a, b) and

(iii) g’(x) ≠ 0 for any value of x in (a, b),

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limit

 

Basic Differentiation Formulas.

Suppose f  and g are differentiable functions, c is any real number, then

s

d

Vectors

  • The Scalars are quantities that only have a magnitude like mass,  field strength. Many times it is often useful to have a quantity that has not only a magnitude but also a direction; such a quantity is called a vector. Examples of quantities represented by vectors include velocity, acceleration, and virtually any type of force (frictional, gravitational, electric, magnetic, etc.)
  • The magnitude (or length) of a vector v with initial point (x_1,y_1,z_1) and terminal point (x_2,y_2,z_2) is

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    Vectors obey the natural intuitive laws of addition and scalar multiplication:

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    The figures below illustrate the operations of addition and scalar multiplication in the two-dimensional case.

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  •  

    Addition of vectors Scalar Multiplication

Dot Product and Cross product:

here dot product of two vectors a=<a_1,a_2,a_3> and b=<b_1,b_2,b_3> is given by

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An equivalent definition of the dot product is

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The cross product of two vectors a=<a_1,a_2,a_3> and b=<b_1,b_2,b_3> is given by

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Functions of Two or More Variables:

1. Partial Derivatives

Differentiating a function of more than one variable is more complicated than differentiating a function of one variable. For a function of several variables, the rate of change of the function depends on direction!. Consider the function

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  • Example

    For the function

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    find the partial derivatives of f with respect to x and y and compute the rates of change of the function in the x and y directions at the point (-1,2). Initially we will not specify the values of x and y when we take the derivatives; we will just remember which one we are going to hold constant while taking the derivative. First, hold y fixed and find the partial derivative of f with respect to x:

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    Second, hold x fixed and find the partial derivative of f with respect to y:

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    Now, plug in the values x=-1 and y=2 into the equations. We obtain f_x(-1,2)=10 and f_y(-1,2)=28.

2. The Gradient and Directional Derivative:

The gradient of a function w=f(x,y,z) is the vector function:

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For a function of two variables z=f(x,y), the gradient is the two-dimensional vector <f_x(x,y),f_y(x,y)>. This definition generalizes in a natural way to functions of more than three variables.

Examples

For the function z=f(x,y)=4x2+y2. The gradient is

                                 grad f=<8x, 2y>

3. Divergence and Curl of Vector Fields:

Divergence of a Vector Field

The divergence of a vector field F=<P(x,y,z),Q(x,y,z),R(x,y,z)>, denoted by div F, is the scalar function defined by the dot product

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Here is an example. Let

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The divergence is given by:

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Curl of a Vector Field

The curl of a vector field F=<P(x,y,z),Q(x,y,z),R(x,y,z)>, denoted curlF, is the vector field defined by the cross product

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 An alternative notation is


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The above formula for the curl is difficult to remember. An alternative formula for the curl is

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 Det means the determinant of the 3x3 matrix. Recall that the determinant consists of a bunch of terms which are products of terms from each row. The product of the terms on the diagonal is

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As you can see, this term is part of the x-component of the curl.

ExampleF=<xyz,ysin z, ycos x>.

curl F = <cos x - ycos z, xy + ysin z, -xz>.

4. Line Integrals:

Green's Theorem:

Green's Theorem states thatdisplaymath42

Here it is assumed that P and Q have continuous partial derivatives on an open region containing R.

5. Surface Integrals:

Stokes' Theorem: Stokes' Theorem relates line integrals of vector fields to surface integrals of vector fields.

Stokes' Theorem statesdisplaymath57

In general C is the boundary of S and is assumed to be piecewise smooth. For the above equality to hold the direction of the normal vector n and the direction in which C is traversed must be consistent. Suppose that n points in some direction and consider a person walking on the curve C with their head pointing in the same direction as n. For consistency C must be traversed in such a way so that the surface is always on the left.

The Divergence Theorem 

The Divergence Theorem relates relates volume integrals to surface integrals of vector fields. Let R be a region in xyz space with surface S. Let n denote the unit normal vector to S pointing in the outward direction. Let F(x,y,z)=<P(x,y,z),Q(x,y,z),R(x,y,z)> be a vector field whose components P, Q, and R have continuous partial derivatives. The Divergence Theorem states:

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Here div F is the divergence of F. There are various technical restrictions on the region R and the surface S; see the references for the details. The theorem is valid for regions bounded by ellipsoids, spheres, and rectangular boxes.

6. Volume Integrals:

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GREEN’S THEOREM

If R is a closed region in the x-y plane bounded by a single closed curve C and if M (x, y) and N (x, y) are a continuous function of x and y having continuous derivative in R then

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STROKES THEOREM

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 GAUSS DIVERGENCE THEOREM

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