Question

Which of the following do not make sense? (a) \(\mathbf{u} \cdot(\mathbf{v} \times \mathbf{w})\) (b) \(\mathbf{u}+(\mathbf{v} \times \mathbf{w})\) (c) \((\mathbf{a} \cdot \mathbf{b}) \times \mathbf{c}\) (d) \((\mathbf{a} \times \mathbf{b})+k\) (e) \((\mathbf{a} \cdot \mathbf{b})+k\) (f) \((\mathbf{a}+\mathbf{b}) \times(\mathbf{c}+\mathbf{d})\) (g) \((\mathbf{u} \times \mathbf{v}) \times \mathbf{w}\) (h) \((k \mathbf{u}) \times \mathbf{v}\)

Short answer

Options (c) and (d) do not make sense.

Step-by-step solution

Analyze Option (a)

The expression \( \mathbf{u} \cdot (\mathbf{v} \times \mathbf{w}) \) involves the dot product of a vector \( \mathbf{u} \) with the cross product of two vectors \( \mathbf{v} \) and \( \mathbf{w} \). The cross product results in a vector, and the dot product of two vectors is a scalar. This operation is valid and makes sense.

Analyze Option (b)

The expression \( \mathbf{u} + (\mathbf{v} \times \mathbf{w}) \) involves adding a vector \( \mathbf{u} \) and the cross product of two vectors \( \mathbf{v} \) and \( \mathbf{w} \). Both sides of the addition are vectors, so this operation is valid and makes sense.

Analyze Option (c)

The expression \( (\mathbf{a} \cdot \mathbf{b}) \times \mathbf{c} \) involves the cross product of a scalar \( \mathbf{a} \cdot \mathbf{b}) \) with a vector \( \mathbf{c} \). Since the cross product is only defined for two vectors and not a scalar with a vector, this operation does not make sense.

Analyze Option (d)

The expression \( (\mathbf{a} \times \mathbf{b}) + k \) involves adding a vector (result of the cross product) with a scalar \( k \). Since addition is not defined between vectors and scalars, this operation does not make sense.

Analyze Option (e)

The expression \( (\mathbf{a} \cdot \mathbf{b}) + k \) involves adding two scalars since \( \mathbf{a} \cdot \mathbf{b} \) is a scalar (dot product) and \( k \) is a scalar. This operation is valid and makes sense.

Analyze Option (f)

The expression \( (\mathbf{a} + \mathbf{b}) \times (\mathbf{c} + \mathbf{d}) \) involves the cross product of two vectors, since both \( (\mathbf{a} + \mathbf{b}) \) and \( (\mathbf{c} + \mathbf{d}) \) are vectors. This operation is valid and makes sense.

Analyze Option (g)

The expression \( (\mathbf{u} \times \mathbf{v}) \times \mathbf{w} \) involves taking the cross product of the result of a cross product (a vector) with another vector \( \mathbf{w} \). This is valid and makes sense because the cross product of two vectors is a vector, allowing another cross product.

Analyze Option (h)

The expression \( (k \mathbf{u}) \times \mathbf{v} \) involves taking the cross product of two vectors, since \( k \mathbf{u} \) is scalar multiplication of a vector, resulting in another vector. This is valid and makes sense.

Key concepts

Dot Product

In vector calculus, the dot product, also known as the scalar product, involves two vectors and results in a scalar value. The dot product of two vectors \( \mathbf{a} \) and \( \mathbf{b} \) is computed as:\[ \mathbf{a} \cdot \mathbf{b} = |\mathbf{a}| |\mathbf{b}| \cos(\theta) \]where \( \theta \) is the angle between the two vectors. Essentially, it measures how much of one vector goes in the direction of another.
It is important because it provides a way to check if two vectors are orthogonal (perpendicular). When the dot product is zero, the vectors are orthogonal.
Examples of operations involving dot products can be found in various areas such as physics for calculating work done, where force and displacement are vector quantities.

Cross Product

The cross product, or vector product, creates a third vector that is perpendicular to the plane formed by the two original vectors. Given two vectors \( \mathbf{a} \) and \( \mathbf{b} \), the cross product is given by:\[ \mathbf{a} \times \mathbf{b} = |\mathbf{a}| |\mathbf{b}| \sin(\theta) \mathbf{n} \]where \( \mathbf{n} \) is the unit vector perpendicular to the plane containing \( \mathbf{a} \) and \( \mathbf{b} \), and \( \theta \) is the angle between them.
The magnitude of the cross product represents the area of the parallelogram formed by the vectors. This makes it especially useful in geometry and physics when calculating torques or rotational forces.
It's important to remember that the cross product is not commutative (i.e., \( \mathbf{a} \times \mathbf{b} eq \mathbf{b} \times \mathbf{a} \)). In fact, \( \mathbf{b} \times \mathbf{a} = - (\mathbf{a} \times \mathbf{b}) \).

Vector Addition

Vector addition combines two or more vectors into a single resultant vector. This is done by adding corresponding components of the vectors together. Suppose we have vectors \( \mathbf{a} = (a_1, a_2, a_3) \) and \( \mathbf{b} = (b_1, b_2, b_3) \), the sum is:\[ \mathbf{a} + \mathbf{b} = (a_1 + b_1, a_2 + b_2, a_3 + b_3) \]Vector addition follows the commutative (\( \mathbf{a} + \mathbf{b} = \mathbf{b} + \mathbf{a} \)) and associative properties, making calculations straightforward.
Graphic visualization often involves the 'tip-to-tail' method, where the initial point of one vector is placed at the terminal point of the other, creating a triangle or parallelogram that illustrates the resultant vector.
This fundamental operation is used in physics for combining forces, velocities, and other vector quantities.

Scalar Multiplication

Scalar multiplication in vector calculus involves multiplying a vector by a scalar (a real number). This operation scales the vector's magnitude without altering its direction unless the scalar is negative, which reverses the vector's direction.
Consider the vector \( \mathbf{v} = (v_1, v_2, v_3) \) and a scalar \( k \). The product is:\[ k \cdot \mathbf{v} = (k \cdot v_1, k \cdot v_2, k \cdot v_3) \]The result is a vector with a magnitude of \( |k| \times |\mathbf{v}| \). If \( k > 1 \), the vector's length increases, and if \( 0 < k < 1 \), it shortens.
Scalar multiplication is vital for adjusting vector strengths in various fields like physics, where it is often used to describe physical relationships like speed (a scalar) affecting velocity (a vector quantity).
Additionally, it's often a part of larger operations, such as in linear combinations of vectors.