The Classic Models
: growth is proportional to both the population and the remaining room. The rate is a downward parabola in , fastest exactly at half capacity , and every positive solution flows to the carrying capacity .
Why this form: a population far below capacity grows like , as if resources were unlimited; the factor is the fraction of room still available, which throttles growth to as reaches and makes it negative above . The rate is a parabola with zeros at and , so its vertex is midway, at , where the rate is . With and the fastest growth is per unit time, at .
Cooling: the excess over ambient decays exponentially — if the excess halves in 10 minutes it halves again in the next 10. Mixing: ; the equilibrium is where inflow balances outflow, always equal to incoming concentration times tank volume.
Why the excess: Newton's law says heat flows at a rate proportional to the temperature difference, . The difference satisfies , so it — not — decays exponentially, and equal times multiply it by equal factors. For a tank, bookkeeping gives the equation: salt enters at (concentration in)(flow in) and leaves at (current concentration )(flow out). With equal flows, the equilibrium is where the tank's concentration matches the incoming one, so .
One step of size : — follow the tangent line briefly, then re-aim. On from with : estimate vs. the true ; Euler undershoots convex solutions, and halving halves the error.
Why it undershoots here: the solution bends upward, so its tangent line at lies below it, and one step along the tangent ends below the curve: against , an error of about . Two steps of re-aim halfway: , error about . The error has not quite halved; it approaches exact halving as shrinks, because Euler's error is proportional to only for small (see the proof below).
Newton cooling = drug elimination = RC discharge = mixing = depreciation: all about the right variable. Recognizing the archetype is the modeling skill — once you see "rate proportional to amount (or excess)," you already know the solution, the half-life, and the long-run behavior.
Coffee at in a room cools to in minutes. When does it reach ?
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Work with the excess over the room, : it starts at and satisfies , so . At the excess is : , so per minute. The target is an excess of : gives minutes. Sanity check: each minutes multiplies the excess by , so after minutes — just above , and minutes finishes the job.
Building a Model from Rates
A first-order model is a sentence about rates: . Write each rate with units, find the equilibrium where they balance, and look for the archetype: whenever the rate is proportional to the distance from an equilibrium , the model is for , with solution .
Why units first: every term must have units of amount per time (grams per minute, degrees per minute); a term that does not is a modelling error, caught before any calculus. Why the equilibrium: it is the one number the model announces without solving, and the exponential archetype then says exactly how the gap to it closes — by the factor per unit time, with time constant .
A thermometer reading is taken from a fridge into a room. After minutes it reads . When does it read ?
Model the excess (negative here, since the thermometer is colder than the room): , , and .
Step 1 — the excess went from to in minutes: it halves every minutes, so and . No need to compute — the halving time carries the same information ( per minute).
Step 2 — is an excess of : , so and minutes.
Step 3 — check by halvings: takes minutes, and needs a bit under half of another -minute period.
Step 4 — the last degree takes longest: going from to took minutes, but to takes more than more, because the driving difference keeps shrinking.
A tank holds L of water with g of salt dissolved. Brine with g/L enters at L/min; the mixed solution leaves at L/min. How much salt is in the tank when it holds L?
The volume grows by L/min: . Salt enters at g/min and leaves at g/min, so
Step 1 — the equation is linear: . Its integrating factor is .
Step 2 — multiply through: , so .
Step 3 — the start: gives , so .
Step 4 — the tank holds L at : g, a concentration of g/L.
Step 5 — read the formula: is the salt the tank would hold at the incoming concentration g/L; the second term is the initial deficit, fading as . Treating as constant here (using for the outflow concentration) would be wrong — see the sidenote.
A skydiver's velocity obeys (gravity minus a drag proportional to speed), . Find the terminal velocity and the time to reach of it.
Equilibrium first: when m/s — the speed at which drag balances gravity.
Step 1 — the excess over equilibrium, , satisfies , the same archetype as cooling. So and .
Step 2 — of terminal: , so and s.
Step 3 — read the time constant s: each s closes of the remaining gap, and each s closes .
Step 4 — the model says terminal velocity is approached, never reached; a real skydiver is within a fraction of a percent after about s ().
For the thermometer, a tempting model is , fitted to : . It predicts and unlimited growth — in a room. The exponential belongs to the difference from the room, which shrinks: , giving . The same slip in the filling tank uses as the outflow concentration; with the volume growing to L that overstates the outflow and gives the wrong answer g instead of g.
A falling object obeys , . Find its terminal velocity and the time at which it reaches half of it.
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Terminal velocity is the equilibrium: gives m/s. The excess satisfies with , so . Half of terminal: , so s — the half-life of the gap, the same number whether the gap is in speed, temperature or salt.
Logistic Growth in Numbers
The solution of , , is
For the curve is an S: it passes at , where the growth rate is largest (), and it is symmetric about that point.
Why has that value: at the formula gives . Why the midpoint is the inflection: , and has exactly at , so the curve switches from bending up to bending down there. Why the symmetry: is a pure exponential, so the curve's shape depends only on .
A population follows with . When does it reach , and what is ?
The solution is with , , and (from ).
Step 1 — half capacity: needs , so . This is the inflection point, where growth is fastest ( per unit time).
Step 2 — .
Step 3 — the S-shape is symmetric about the inflection point: rises from to in time units, and from to in another (solve : ).
Step 4 — a population started above capacity, say , has and decreases toward ; one started at exactly never moves.
A culture is counted at equal intervals: , , . Assuming logistic growth, find the carrying capacity and predict the next count.
The key: for , the reciprocal is — a constant plus a pure exponential. So with (one interval), the gaps shrink by the factor each interval.
Step 1 — the reciprocals are , , . Their successive differences are and , and these also shrink by , so .
Step 2 — find from : multiplying by , , so and .
Step 3 — the rate per interval is , and .
Step 4 — predict: , so . The counts then approach , the next ones being and .
From the counts and — a factor of per interval — an exponential model predicts and then . The logistic fit gives and . Early logistic growth looks exponential because when is small (here ), but the brake tightens with every count. Two data points cannot distinguish the models; the third count, rather than , is what reveals the carrying capacity.
A fish population in a lake follows the logistic model with , per year and . When does it reach ?
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First , so (since ). Reaching needs , so and years. This is the inflection time : from then on the population still grows, but ever more slowly.
Proofs & Why It Matters
Separate and split by partial fractions: . Integrating gives , and solving for : — an S-curve rising to . Every positive start flows to the carrying capacity because .
The law says . Let (the excess); since is constant, — the pure exponential equation, so .
Halving in equal time intervals is exactly the property of exponentials, which is why the "excess halves every 10 minutes" phrasing works.
One Euler step commits error (the first Taylor term the tangent line misses). Reaching a fixed time takes steps, so the accumulated error is on the order of — proportional to . Halve the step, halve the error: Euler is a first-order method.
Going Deeper: Explanations & Worked Problems
For an autonomous equation , enormous insight costs one sketch. Find the zeros of — the equilibria, where the system sits still. Between consecutive zeros, keeps one sign: moves right (up) where , left (down) where .
Mark arrows on a number line and stability is visible: arrows converging on an equilibrium make it stable (perturb, return), diverging make it unstable (perturb, flee). Logistic growth : zeros at and ; between them, so arrows point from toward — the origin is unstable, the carrying capacity stable, and every positive solution S-curves to , all without solving the equation. The phase line is the qualitative theory in miniature: solve only when you need numbers; sketch when you need understanding.
A drug has half-life hours; a dose raises blood concentration by units. When does a single dose drop below unit?
Step 1 — model: with .
Step 2 — pin from the half-life: gives , so .
Step 3 — solve : , so hours.
Step 4 — sanity check in halvings: after three half-lives ( h), and needs about a third more of one half-life — h ✓.
Step 5 — the modeling lesson: the answer depended only on the ratio , so every decade of decay costs the same half-lives, whatever the starting dose.
A L tank starts with pure water. Brine at g/L flows in at L/min; the well-mixed tank drains at L/min. Find the salt .
Step 1 — build the equation from bookkeeping: rate in g/min; rate out (concentration)(flow) ; so .
Step 2 — equilibrium first: at g — which is just (incoming concentration)(volume) , as it must be.
Step 3 — solve: linear with : , so , and gives : .
Step 4 — read the time scale: the "time constant" is min; after min the tank is at g, after an hour g — the exponential approach to equilibrium that governs tanks, cooling coffee, and charging capacitors alike.