Engineering 102 with fpv kpis

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

FPV KPIs

#cd ./poc/fpv-kpis

After talking about energy/thermodynamics, batteries and aerospace/propulsion/aerodynamics, it was time to land some KPIs for my 5inch dron:

KPI / Metric	Category	What It Measures / Tells You	Your Value / Status	Benchmark / Expected Value
All-Up Weight (AUW)	Mass	Total flight mass (drone + battery)	550 g (0.55 kg)	500 – 650 g (typical 5" quad)
Hover Specific Thrust	Aerodynamics	Lift generated per watt of electrical hover power	4.23 g/W	3.5 – 5.0 g/W (5" tri-blade)
Disk Loading	Aerodynamics	Mass per swept rotor area; predicts floatiness vs wind resistance	1.08 g/cm² (10.85 kg/m²)	0.9 – 1.3 g/cm²
Peak Power Loading	Propulsion	Peak electrical power pushed through the rotor disk	1.97 W/cm² (19.7 kW/m²)	1.5 – 2.5 W/cm²
Specific Excess Power ($SEP$)	Dynamics	Power headroom per kg for vertical acceleration & dive pullouts	1,582 W/kg (1.58 kW/kg)	1,200 – 2,000 W/kg (freestyle)
Dynamic Range Factor	Agility	Ratio of max punch-out power to hover power ($P_{\text{max}} / P_{\text{hover}}$)	7.69 : 1	6:1 – 9:1 (freestyle/race)
Total Pack Resistance ($R_{\text{pack}}$)	Battery	Internal DC resistance of all 4 cells combined	20 mΩ (5 mΩ / cell)	12 – 25 mΩ (healthy 4S pack)
Peak Battery Voltage Sag	Battery	Instantaneous voltage drop at full punch ($71.4\text{ A} \times 0.02\ \Omega$)	1.43 V	1.2 – 1.8 V (healthy)
Peak Thermal Loss ($I^2R$)	Efficiency	Electrical power burned internally as heat inside pack cells	~102 W (~10.2% of punch)	8 – 15% of peak power
Hover Thermal Loss ($I^2R$)	Efficiency	Battery heating loss during static hover	~1.5 W (~1.1% of hover)	< 2% (negligible)
Ideal Induced Hover Power ($P_i$)	Aerodynamics	Theoretical minimum power required to hover under Momentum Theory	35.6 W	—
Hover Figure of Merit ($FoM$)	Aerodynamics	Aerodynamic rotor efficiency relative to theoretical limit ($P_i / P_{\text{hover}}$)	27.4%	25% – 35% (mini-rotor systems)
Hover Motor RPM	Propulsion	Rotor speed needed to generate 550 g of static thrust	Missing (need Bidirectional DShot log)	11,000 – 12,200 RPM
Hover Throttle %	Control	Throttle stick position required to maintain steady altitude	Missing (read in Blackbox log)	28% – 34%
In-Flight Dynamic Resistance	Battery	True total loop IR (cells + XT60 + wires) via Blackbox $\Delta V / I$	Missing (calculate from log punch)	22 – 26 mΩ
Wh Turnaround Efficiency	Power System	In-flight energy consumed vs wall energy delivered to recharge	Missing (compare log Wh to charger Wh)	85% – 90%
Specific Energy per Distance	Range / Cruise	Energy consumed per kilometer traveled in forward flight	Missing (requires GPS speed)	2.2 – 3.0 Wh/km (140–190 mAh/km)
Best Cruise Speed	Flight Envelope	Forward ground speed yielding minimum Wh/km	Missing (requires GPS speed)	55 – 70 km/h (15–19 m/s)
Safe Out-and-Back Radius	Navigation	Maximum operational return distance using 80% usable pack Wh	Missing (derived from GPS Wh/km)	~2.2 – 2.6 km (for 1300–1500mAh)

My 5inch dron has

CAD x Design

Design for manufacturing was an interesting book.

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

FEM x FEA

Yes—the local FEA workflow is operational on this PC: got it done here, after the fpv stand 3d printed

FreeCAD → BRep reconstruction
Gmsh tetrahedral meshing
CalculiX solving
JSON summaries and VTU visualization output
Coarse/fine screening cases

Run it with:

cd /home/jalcocert/Desktop/poc/blender/dron-standing-fea ./run-fea.sh cases/frame-screen.json ./run-fea.sh cases/frame-screen-fine.json

• We built and tested a fully local FEA pipeline:

Verified all eight printable STLs are watertight.
Installed project-local Gmsh and CalculiX—no system/admin installation needed.
Used FreeCAD to reconstruct the STL as a faceted BRep.
Created automatic tetrahedral meshes with quality reporting.
Applied a placeholder 19.62 N load—equivalent to 2 kg—to the isolated stand frame.
Solved coarse and fine meshes and exported viewable VTU results.

Results:

Coarse: 3.08 mm displacement, 27.65 MPa peak stress.
Fine: 5.96 mm displacement, 41.35 MPa peak stress.

Because the values changed substantially, the model is not converged. The software workflow works, but this particular test is only a pipeline demonstration—not validation of the complete assembled stand.

alt text

Generative Design

You can get started with Calculix x Beso

And you have them both in FreeCAD!

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

Prep ULM x PPL

After watching some motivating videos about ppl who can already fly

I decided its time to get LLMs to help me with the prep of the ULM exam

And I got a PWA ready: https://jalcocertech.com/ulm-ppl

#git init
claude --dangerously-skip-permissions -p """i want to prepare for the ULM and PPL flight exams and have thought to make a simple web app that sources qna from a markdown
with the options and will help me prepare for the exam, if i fail, it should show an explanation in the end of the reasoning""" #yolo
#.\serve.ps1

Verified on the built dist/ (served locally, headless Edge):

/ulm-ppl/ renders: 103 questions, 9 banks, 3 generators, Ready to start, no load error
200 on /ulm-ppl/sw.js, manifest.webmanifest, questions/manifest.json, assets/icon-192.png
_headers and _redirects present at dist/ root where Cloudflare reads them
Site root unaffected — 200, title JAlcocer|Tech Made Simple

Not verifiable locally: the _headers/_redirects rules themselves — the Python test server ignores them; only Cloudflare applies them. Check after deploy that jalcocertech.com/ulm-ppl (no slash) 301s to the slash form.

Remaining on your side: commit and push, and confirm Rocket Loader is off on the zone.

C:\Users\j–e-\Desktop\ulm-ppl untouched — still the source of truth. When you edit questions there, re-copy questions/ into public/ulm-ppl/questions/. Want me to add a small sync script so that’s one command?

http://localhost:8080/ oh I mean jalcocertech.com/ulm-ppl

Conclusions

FAQ

Fund your R&D - Sell and BILL

https://serverless-invoices.pages.dev/invoices?lang=en

https://jsonformatter.org/

Selfhosted Information flow