Engineering 101

Blogs

Engineering 101

July 22, 2026

TL;DR

What if I’d had to study engineering again?

Intro

Those years were crazy: calculus with the Taylor Polynomials , phase diagrams

algebra…

Now, some of my colleagues are married.

How have I ended up writing about heat transfer and electrical engineering?

Who knows :)

Modelling 101

Before jumping to details…

Be aware of what you will be building on top of.

Mech

git clone /ThreeBodies
cd ./ThreeBodies/ThePoincareLab

make deploy #https://the-poincare-lab.pages.dev/

2D is a thing

But 3D has its unique magic.

jalcocert.github.io/JAlcocerT/ai-scripts-and-animated-data/#the-3-body-problem

$$\ddot{\vec{r}}_1 = -G m_2 \frac{\vec{r}_1 - \vec{r}_2}{|\vec{r}_1 - \vec{r}_2|^3} - G m_3 \frac{\vec{r}_1 - \vec{r}_3}{|\vec{r}_1 - \vec{r}_3|^3}$$

https://jalcocert.github.io/JAlcocerT/about-constrained-mechanism/#equations-of-motion-what-they-are-and-how-to-solve-them

1st order: $\frac{dx}{dt} = f(x, t)$ (velocity defined by position)

2nd order: $\frac{d^2x}{dt^2} = f(x, \dot{x}, t)$ (acceleration defined by position and velocity)

For mechanical systems, Newton’s 2nd law gives us 2nd-order differential equations: $$m\ddot{x} = F(x, \dot{x}, t)$$

The Lagrangian approach gives us these same equations, but derived automatically from energy rather than force: $$\frac{d}{dt}\left(\frac{\partial L}{\partial \dot{q}}\right) - \frac{\partial L}{\partial q} = 0$$

Kinetic Energy T: Always comes from the center of mass velocity of each body, plus rotational motion $$T = \sum_i \left(\frac{1}{2}m_i v_{cm,i}^2 + \frac{1}{2}I_i \omega_i^2\right)$$
Potential Energy V: Comes from the center of mass height in gravity field, plus any other potential fields $$V = \sum_i m_i g h_{cm,i} + V_{springs}$$

https://www.youtube.com/shorts/UjD94SGqIdM

https://www.youtube.com/shorts/IiuDQhFgmq4

MBSD

Luckily Ive had a come back to this topic:

Tire Modelling has some interesting assumptions

At least with the honesty ~~unlike other disciplines~~ to call it magic formula

MBSD

Source Code of a MBSD framework in Python

git clone https://github.com/JAlcocerT/mbsd

Fluids

https://jalcocert.github.io/JAlcocerT/fluids/

Aerodynamics

https://jalcocert.github.io/JAlcocerT/aerospace-101/

There are really cool tinkerers out there:

https://www.youtube.com/@OpenSourceFPV

https://www.youtube.com/@rctestflight

https://www.youtube.com/@NicholasRehm/videos https://www.youtube.com/watch?v=gZQEOjyjwhc

https://www.youtube.com/@thinkflight https://www.youtube.com/watch?v=VCgpRQXFEaU

https://www.youtube.com/@RcLifeOn https://www.youtube.com/watch?v=zP1nS3sIu2U

https://www.youtube.com/@NicholasRehm/videos who created https://github.com/nickrehm/dRehmFlight

Came here for engines

For the PISTON engines (ICE) i mean.

Aint no replacement for displacement some say

alt text

https://www.youtube.com/watch?v=bWvv8Y4qhOA

https://www.youtube.com/watch?v=XctfCw4fKUg

https://www.youtube.com/watch?v=ZvdsGlxg2Bs

https://www.youtube.com/watch?v=1HqXs301_K8

https://www.youtube.com/watch?v=CJNhoSXvxYs

Termodynamics

Heat Transfer

Solar

Electro Magn

Signals and Telemetry

After mastering electro magnetism, you can derive interesting fenomena like Friis.

https://jalcocert.github.io/JAlcocerT/selfhosted-connectivity/

LoRa and ELRS are a thing

This is a great video around: RF, Modulation, noise and Lora constrasting with WIFI connectivity!

Converging vs Non-Converging 📌

ExpressLRS receivers utilize LoRa technology to achieve long-range communication, even in challenging environments like forests.

By employing unique signal modulation techniques, these receivers can decode signals below the noise floor, allowing for impressive range capabilities compared to traditional systems.

Key points:

ExpressLRS receivers can maintain connections over distances of up to 100 kilometers, even with obstacles like forests.
Traditional Wi-Fi struggles with range due to higher frequency signals being easily blocked, while lower frequencies like 2.4 GHz used by ExpressLRS have longer wavelengths that penetrate obstacles better.
Digital modulation techniques like FSK and QAM allow for higher data rates, but LoRa focuses on maximizing range by decoding signals below the noise floor.
LoRa uses a “chirp” signal and Fast Fourier Transform (FFT) to extract frequency content, enabling it to filter out noise effectively.
The video discusses four different ExpressLRS receivers, highlighting one that violates typical RF design rules by using a via for the antenna connection.
The importance of impedance matching in RF design is emphasized, explaining how reflections can impact signal quality.

Notable quotes:

“LoRa can decode signals below the noise floor. Even if you cannot see the LoRa signal, the receiver can still decode it.”
“Current flows in circles. The current that flows in the top trace to the antenna, well, an equal and opposite current must flow on the ground plane directly beneath it.”

Which you can complement with:

Circuits

More interesting effects around here.

Specially when you can simulate circuits and see that EMR kickback happen before you order and mount your components.

Thats the beauty of non-toy models: you can bet

Electronics

Chemistry

https://jalcocert.github.io/JAlcocerT/making-soap-at-home/

$$ NaOH(s) \xrightarrow{H_2O} Na^+(aq) + OH^-(aq) + \text{heat} $$

Batteries

What?

This is a rabbit hole…

LiPo Li-ion LiFePo4

Others

Programming

Some say thats not pure engineering.

I dont care at this point

If you are able to make a machine make something repeatable for you - you’ve won

Embedded Systems

IoT is usually a subset or application area of embedded systems, but not all embedded systems are IoT.

A practical distinction:

Embedded systems = computers built into devices to control hardware. IoT = embedded/networked devices that communicate with services, other devices, or the internet.

Examples:

Embedded but not necessarily IoT: flight controller firmware, ESC firmware, washing machine controller, car ECU, microwave controller.
IoT: ESP32 temperature sensor publishing MQTT, smart plug, Zigbee sensor, LoRaWAN tracker, Home Assistant-connected device.
Overlap: an ESP32 running firmware that reads sensors and reports data over WiFi/MQTT is both embedded and IoT.

So for the blog taxonomy, I’d treat Embedded as the broader parent category, with IoT, Drone, FPV, Firmware, MQTT, and Home Automation as more specific angles.

IoT

CAD x Design

Design for manufacturing was an interesting book.

git clone https://github.com/JAlcocerT/3Design

Generative Design

Manufacturing

3D Printing

Which is a way of ~~design for~~ Manufacturing

https://selfh.st/apps/?search=3d

OctoPrint Snappy web interface for your 3D printer 3D Printing Obico Icon Obico Smart 3D printing platform with AI failure detection 3D Printing Manyfold Icon Manyfold Digital asset manager for 3D print files 3D Printing Spoolman Icon Spoolman Inventory tracker for 3D printer filament spools 3D Printing Inventory Management Maker Management Platform Icon Maker Management Platform Digital asset manger for 3D printing

Robotics

iot

https://www.youtube.com/watch?v=Tr_Pq89-BgA https://www.youtube.com/watch?v=wPQf_Pz0APA

Computer Vision

Drones/FPVs

Wanna experience pure Power/Weight?

Get…I mean BUILD a drone

I got:

Meteor 75 pro with up to ~100w with ~50g
Eachine with up to ~400w with
3

There is a lot of OSS around drones: https://github.com/betaflight/betaflight-configurator which is a PWA https://app.betaflight.com/

From the welcome sound to edgeTX

A drone setup is not “one firmware”.

It is usually: EdgeTX -> ExpressLRS -> Betaflight / dRehmFlight -> ESC firmware -> motors/servos

So a bad behavior can come from the radio model, RF link, receiver protocol, UART setup, flight-controller modes, mixer, failsafe, ESC config, or physical build.

That’s a good recurring theme for this series: open source helps because every layer can be inspected, but it also means each layer must be matched deliberately.

Future post ideas in the same drone/firmware lane:

INAV: Betaflight-adjacent, but more navigation/GPS/fixed-wing/long-range focused.
ArduPilot: huge OSS autopilot stack, missions, rovers, planes, copters, boats, simulation.
PX4: professional/research autopilot stack, MAVLink-heavy, SITL/dev workflow.
Bluejay / BLHeli_S / AM32 ESC firmware: the motor-control layer after Betaflight.
MAVLink: protocol post tying ArduPilot/PX4/ground stations together.
QGroundControl / Mission Planner: UI/configuration layer for autopilot stacks.
INAV Configurator: good comparison with Betaflight App.
Multi-Protocol TX Module firmware: relevant to EdgeTX/OpenTX radios and non-ELRS receivers.
OpenHD / RubyFPV: open digital video/telemetry/control stack for FPV.
Cleanflight / Baseflight history: useful “where Betaflight came from” context.

https://www.youtube.com/watch?v=1lfVKcKQ5BI https://www.youtube.com/watch?v=C0KBu2ihp-s&t=19s https://www.youtube.com/watch?v=lvcPUcZ9wAA https://www.youtube.com/watch?v=BoLeQCqns5A