engineeringkit: Computational Engineering in Python

engineeringkit makes the models behind 12 engineering domains computable, inspectable and visualizable. Choose a domain to find its foundational ideas, runnable examples and full API in one place. NumPy and SciPy provide mature numerical solvers; EngineeringKit exposes engineering equations, SI conventions and intermediate results for learning and teaching.

Each domain at a glance

• engineeringkit.aerodynamics – Airfoil and wing baselines, drag polars and compressible flow.

  

• engineeringkit.communications – Modulation and pulse shaping, noise, capacity, detection, PCM, source and channel coding, fading and link budgets.

  

• engineeringkit.control_systems – Classical stability, compensator design, PID with anti-windup, nonlinear loops, state feedback, LQR and estimation.

  

• engineeringkit.electrical_engineering – DC and AC circuits, transients, electronics, machines and power systems.

  

• engineeringkit.fluid_engineering – Hydrostatics, flow measurement, momentum, pipe friction, drag, similarity, open channels, pumps and networks.

  

• engineeringkit.heat_transfer – Conduction, convection, radiation, fins and heat exchangers.

  

• engineeringkit.materials – Crystals and diffusion, elastic constants, hardness, yield criteria, composites and laminates, fatigue and notches, fracture, creep and dislocation plasticity.

  

• engineeringkit.mechanical_engineering – Rigid-body motion, Lagrange’s equations, machine elements, mechanisms, cams and multi-degree-of-freedom vibration.

  

• engineeringkit.reliability – Lifetimes and life data, accelerated testing, networks, structural reliability, FMEA, fault trees and maintenance.

  

• engineeringkit.signal_processing – Sampling and rate conversion, the FFT and DCT, spectra and AR models, IIR and FIR design, adaptive filters.

  

• engineeringkit.structural_engineering – Members, sections, beams on any supports, columns, stress and strain at a point, pressure vessels, energy methods, trusses, frames, cables and arches.

  

• engineeringkit.thermodynamics – Ideal and real gases, mixtures, the two laws, steady-flow devices, engine and refrigeration cycles, exergy, steam properties, psychrometrics and combustion.

  

Start here

Installation and quickstart explains installation and basic use, and the quickstart notebook runs in Colab or in JupyterLite. Four tutorials follow one problem across several domains: A beam from load to fatigue life, A feedback loop in the time and frequency domains, A power plant from cycle to cooling tower and A radio link from antenna to bit errors. Each domain also has a set of exercises with worked solutions. The domain guides describe units, sign conventions and model assumptions; Numerical validation policy records the benchmark policy, and Scope and remaining limitations identifies remaining scope limits. Version 0.2.0 includes all twelve domains and enforces 100% production statement coverage. That metric does not prove scientific correctness or model applicability.

from engineeringkit.structural_engineering import SimplySupportedBeam
beam = SimplySupportedBeam(5, 200e9, 8e-6)
result = beam.solve_point_load(10000)
print(result.summary())
fig, axes = result.plot()

Related toolkits

Sibling Python toolkits for learning and teaching, built the same way:

• blockchainkit – cryptography, consensus and blockchain systems

• chemistrykit – computational chemistry

• financialkit – quantitative finance

• mathematicskit – computational mathematics

• physicskit – computational physics

• tbkit – tight-binding models, band topology and quantum transport