Chapter 4: Problem 17
Show \(\mathbf{u} \times(\mathbf{v} \times \mathbf{w})=(\mathbf{u} \cdot \mathbf{w}) \mathbf{v}-(\mathbf{u} \times \mathbf{v}) \mathbf{w} .\)
Short Answer
Expert verified
The vector identity is proven by applying the Bac-Cab rule.
Step by step solution
01
Understand the Exercise
The exercise requires proving a vector identity: \( \mathbf{u} \times (\mathbf{v} \times \mathbf{w}) = (\mathbf{u} \cdot \mathbf{w}) \mathbf{v} - (\mathbf{u} \cdot \mathbf{v}) \mathbf{w} \). This is a known vector triple product identity that simplifies a cross product of a vector with another cross product.
02
Recall Vector Triple Product Identity
The identity we need, \( \mathbf{u} \times (\mathbf{v} \times \mathbf{w}) = (\mathbf{u} \cdot \mathbf{w}) \mathbf{v} - (\mathbf{u} \cdot \mathbf{v}) \mathbf{w} \), is a standard result. To prove this, we can use the Bac-Cab rule, another name for this identity.
03
Consider the Left-Hand Side (LHS)
The LHS is \( \mathbf{u} \times (\mathbf{v} \times \mathbf{w}) \). We start by considering the distribution of the \( \mathbf{u} \) vector across the \( \mathbf{v} \times \mathbf{w} \) component.
04
Apply Bac-Cab Rule
According to the Bac-Cab rule, for any three vectors \( \mathbf{a}, \mathbf{b}, \mathbf{c} \), we have: \( \mathbf{a} \times (\mathbf{b} \times \mathbf{c}) = (\mathbf{a} \cdot \mathbf{c}) \mathbf{b} - (\mathbf{a} \cdot \mathbf{b}) \mathbf{c} \). Applying this rule to our vectors, we get \( \mathbf{u} \times (\mathbf{v} \times \mathbf{w}) = (\mathbf{u} \cdot \mathbf{w}) \mathbf{v} - (\mathbf{u} \cdot \mathbf{v}) \mathbf{w} \).
05
Match Both Sides
The expression derived from the rule, \( (\mathbf{u} \cdot \mathbf{w}) \mathbf{v} - (\mathbf{u} \cdot \mathbf{v}) \mathbf{w} \), matches the given expression on the right-hand side of the initial identity. Thus, the identity is proven.
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Key Concepts
These are the key concepts you need to understand to accurately answer the question.
Vector Identities
In vector calculus, vector identities are crucial tools that help simplify complex expressions. They provide standard results and formulas that can be used to derive new ideas or solve problems. The identity \[ \mathbf{u} \times (\mathbf{v} \times \mathbf{w}) = (\mathbf{u} \cdot \mathbf{w}) \mathbf{v} - (\mathbf{u} \cdot \mathbf{v}) \mathbf{w} \]is a classic example known as the vector triple product identity. This particular identity allows us to simplify calculations involving nested cross products, where a vector is crossed with the result of another cross product.
- "Bac-Cab Rule" is another name for this identity and is akin to a special distributive property.
- To use this, just remember the pattern: cross-dots produce vectors multiplied by the original scalars.
Cross Product
The cross product, denoted by \( \mathbf{u} \times \mathbf{v} \), is a binary operation on two vectors in three-dimensional space. Unlike the dot product, the result of a cross product is a vector. This vector is perpendicular to the plane containing the initial vectors, \( \mathbf{u} \) and \( \mathbf{v} \).
- The magnitude of the cross product is equal to the area of the parallelogram that the vectors span.
- Mathematically, if \( \mathbf{u} = \langle u_1, u_2, u_3 \rangle \) and \( \mathbf{v} = \langle v_1, v_2, v_3 \rangle \), then \( \mathbf{u} \times \mathbf{v} = (u_2v_3 - u_3v_2, u_3v_1 - u_1v_3, u_1v_2 - u_2v_1) \).
- Anti-commutative: \(\mathbf{u} \times \mathbf{v} = - (\mathbf{v} \times \mathbf{u}) \).
- Distributive over addition: \(\mathbf{u} \times (\mathbf{v} + \mathbf{w}) = \mathbf{u} \times \mathbf{v} + \mathbf{u} \times \mathbf{w} \).
Dot Product
The dot product, also known as the scalar product, is a measure of how parallel two vectors are. When calculating the dot product \( \mathbf{u} \cdot \mathbf{v} \), the result is a scalar rather than a vector. This makes dot products crucial for simplifying expressions and finding orthogonal projections.
- If \( \mathbf{u} = \langle u_1, u_2, u_3 \rangle \) and \( \mathbf{v} = \langle v_1, v_2, v_3 \rangle \), then \( \mathbf{u} \cdot \mathbf{v} = u_1v_1 + u_2v_2 + u_3v_3 \).
- The dot product is commutative, meaning \( \mathbf{u} \cdot \mathbf{v} = \mathbf{v} \cdot \mathbf{u} \).
- Distributes over vector addition: \( \mathbf{u} \cdot (\mathbf{v} + \mathbf{w}) = \mathbf{u} \cdot \mathbf{v} + \mathbf{u} \cdot \mathbf{w} \).
- The cosine of the angle \( \theta \) between vectors can be found using \( \cos\theta = \frac{\mathbf{u} \cdot \mathbf{v}}{\|\mathbf{u}\| \|\mathbf{v}\|} \).