CALCULATORS / DESIGN TRADEOFFS AND OPTIMIZATION

Discrete component sizing calculator

Equal-capacity parallel unit count rounded up; constant per-unit capacity.

optimization-integer-sizing.napkin / editable
✓ checks pass

SCENARIO COMPARISON

Compare the numbers.

This source describes an analytical model. Its quantities and checks are the view.

ScenariocountcapacityNmasskg
41200.8
3900.6

All calculated quantities are listed with the inputs. Switch to Source to inspect the equations.

SCENARIOS / CALCULATED VALUESSAME SOURCE

Change an input to see the consequences.

capacity requirement120.00 N✓ passes

BASELINE INPUTS

CALCULATED

count4
capacity120 N
mass0.8 kg

Edits change the baseline source.

Calculated from your source · not saved
Baseline · Baseline
capacity requirement120.00 N ✓ passes
alternative · alternative
capacity requirement90.00 N ✓ passes

Illustrative first-order example. Checks are editable requirements, not design standards. The broader topic may require additional equations, data, and validation.

Formula and units

This example uses the following equations. Input names and units match the editable source above.

count
count = ceil(required/perUnit)
capacity
capacity = count*perUnit [N]
mass
mass = count*unitMass [kg]

Original worked example

These are the baseline values in the downloadable source. Edits above let you test your own values.

Given

required
100 N
per unit
30 N
unit mass
0.2 kg

Calculated

count
4
capacity
120 N
mass
0.8 kg

Assumptions and limits

Equal-capacity parallel unit count rounded up; constant per-unit capacity.

The requirement checks use the limits written in this example. Passing a check does not establish that the model covers every load, material behavior, or operating condition in a real design.

Download this .napkin example ↓

Take it further with your agent

Napkin keeps the inputs, equations, checks and assumptions together in one study. Your agent sets it up; you change the numbers.

Try with Claude ↗

codex 'Use Napkin (https://napkin.engineering/mcp, or the local Napkin plugin) to build a study: Discrete component sizing. Inputs: candidate values; objective; constraints. Outputs: feasible discrete choices; ranked alternatives. Model scope: Objectives, bounded variables and explicit constraints. Read napkin_capabilities and the current snapshot. Ask me for the values that matter, label assumptions with units, author .napkin source, validate it and apply it with napkin_source using the current revision, then open the study. Add checks for the limits I supply. Keep unsupported analyses explicit.'

Claude needs the Napkin connector; Codex needs the Napkin plugin. Set up your agent ↗

The broader study

The topic brief below describes the broader scope. The worked example above states the particular calculation it implements.

  • Inputs: candidate values, objective, constraints
  • Outputs: feasible discrete choices, ranked alternatives
  • Modes: calculate, compare, sweep
  • Drawing: Tradeoff and Pareto plots

EXECUTABLE WORKED EXAMPLE · FIRST-ORDER MODEL · CHECK THE SCOPE & ASSUMPTIONS

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