T(0) = 800 m, T(40) = 800 − 15×40 = 800 − 600 = 200 m

T(0) = 800 m and T(40) = 200 m: Understanding Linear Decay in Motion

In physics and engineering, understanding motion involves analyzing how variables like distance, velocity, and time interact over specific intervals. One common concept is the linear decrease in a quantity over time — illustrated by a simple but insightful formula:
T(t) = 800 − 15×t, where T represents distance (in meters), and t is time (in seconds).

If we examine this function at two key moments — t = 0 seconds and t = 40 seconds — we uncover meaningful insights about deceleration or consistent removal of distance over time.

Understanding the Context

The Formula Explained

The equation T(t) = 800 − 15×t models a linear decrease in distance:

At t = 0, the initial distance is T(0) = 800 m, meaning the object starts 800 meters from a reference point.
The coefficient −15 represents a constant rate of reduction: the object loses 15 meters each second.
At t = 40 seconds, computing T(40) = 800 − 15×40 = 800 − 600 = 200 m, we find the object has traveled 600 meters and now lies 200 meters away.

T(0) = 800 m, T(40) = 800 − 15×40 = 800 − 600 = 200 m

Key Insights

Calculating Distance and Speed

Let’s break down the timeline:

| Time (s) | Distance (m) — T(t) = 800 − 15t | Distance Traveled (m) | Constant Velocity (m/s) |
|----------|-------------------------------|-----------------------|-------------------------|
| 0 | 800 | — | — |
| 40 | 200 | 600 (800 − 200) | 15 |

The velocity (rate of change of distance) is constant at −15 m/s, indicating uniform deceleration or a controlled reduction in position over time.

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Final Thoughts

Visual Representation: Distance vs. Time Graph

Plotting T(t) against time shows a downward-sloping straight line:

X-axis: time in seconds
Y-axis: distance in meters
Start point (0, 800)
End point (40, 200)

This linear graph visually confirms the constant withdrawal of 15 m per second.

Practical Applications

Such models apply broadly in physics, robotics, and kinematics:

Autonomous drones or vehicles losing range or retreating at a steady speed.
Physical systems discharging energy uniformly (e.g., braking systems).
Simulations where predictable object reduction helps in training or control algorithms.