Y.X. / Dynamics Lab

Chassis Intelligence,
Made Visible.

Vehicle dynamics, K&C reasoning, and AI evidence systems.

View Work Read Methods

Identity

One Lab,
Three Operating Modes.

Y.X. / Dynamics Lab operates across the full stack, from physical dynamics to verifiable evidence to motion intelligence.

01

Vehicle
Dynamics

Measure, model, and validate the physics that shape vehicle behavior.

Pitch / Roll / Wheel Travel
02

AI Evidence
Systems

Turn raw material into verifiable context with traceable evidence pipelines.

Context + Validator
03

Motion
Visualization

Render high-fidelity motion scenes for analysis, review, and communication.

Three.js Motion Scenes
Map the system
Y.X. / Dynamics Lab 3D Demo Motion Viewer

Motion Scenes
You Can Read.

A public explanatory model, not engineering simulation.

No tire model / no mass model / no real vehicle data.
Anonymous exposed chassis visualization
FL Travel-12 mm
Pitch+2.6 deg
RR Travel+27 mm
Wheel Travel Avg+24 mm
Scene
Select
Open the interaction
01

PDF

Source Ingestion

Files 24
02

Structure

Layout & Segmentation

Blocks 12,631
03

Clean Index

Normalize & Index

827 -> 375
04

Retrieve

Hybrid Search (Local)

Top1 94.29%
05

Context Pack

Assemble Traceable Context

Avg 1317.7
06

Validator

Consistency & Quality Checks

8 / 8 Pass
Pipeline status Ready 827 -> 375 slices Top1 94.29% 8 validator checks

Knowledge System / Evidence Factory

Evidence
Before
Answer.

A local workflow for turning technical material into traceable AI context.

Inspect the chain

K&C / KC Relation Layer

Relations Are
Not All Equal.

Direct evidence, inferred structure, and review-only candidates stay visually separated.

See Relation Logic
56Concepts 89Relation Candidates 60Evidence Links 34Eval Pass
Suspension
Kinematics [12]
Steering
Feel [10]
Kingpin
Geometry [9]
Vehicle
Response [8]
K&C
Metrics [6]
Tire Lateral
Dynamics [11]
Relation Type Legend A Direct Evidence B Inferred Structure C Review Only
No raw K&C curves. No project data.

Y.X. / Dynamics Lab

Real Work,
Public Boundaries.

Methods can be shown; sensitive projects stay abstracted.

No supplier names No test location No raw data
Read the anonymized method
CaseA-27Anonymized
Vehicle ClassProgram Phase
Evaluation WindowData Source
72%Confidence
SteeringHandlingRideRoll BrakingTireK&CValidation Gap
SteeringSignal Strength Medium
RideSignal Strength High
RollSignal Strength Low
Subjective Signal→Possible Engineering Cause→Evidence Needed
What this suggestsWhat would confirm
Evidence TypeSim / Test / FieldGap PriorityMedium

AI Content Lab / Engineering Media Pipeline

Engineering
Ideas Need
Production
Systems.

Voice, visuals, briefs, and reusable skills treated as repeatable tooling.

4TTS Paths 12Item Brief Frame { }Source of Truth JSON
Explore Content Workflow
01TTS Comparison

A Neutral

B Clear

C Warm

D Crisp

02Suspension Micro-Lesson
03Industry Brief JSON 
{
  "version": "1.0",
  "topic": "suspension",
  "audience": "engineers"
}
04Fact + Impact Draft

Fact Camber becomes more negative as suspension compresses.

Impact Helps tire contact patch align in cornering.

05Archive Package

assets/

lesson-video.mp4

brief.json

manifest.json

Build With Evidence.
Move With Clarity.

Semi-anonymous engineering portfolio for vehicle dynamics, K&C reasoning, and AI tooling.

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