ALT-AI Mechanical

Smarter Machines, Better Futures.

This division focuses on harnessing AI to enhance the performance, efficiency, and durability of mechanical components and physical subsystems. From turbine blades to propeller dynamics, we use AI to extract actionable insights from data, reduce prototyping cycles, and push mechanical parts to new thresholds of performance.

Platform

We bring together AI practitioners, mechanical engineers, and simulation experts to reimagine the design and performance optimization of physical parts. Our scope includes fluid mechanics, mechanical dynamics, and structural integrity, and intentionally excludes systems modeling, control theory, or logistical optimization.

We work at the interface of:

Physics-Informed AI for design and failure prediction
Topology Optimization of components for aerospace, automotive, marine, and energy
Digital Twin Simulations of real-world stressors

Core Competencies

AI-Assisted Design for Manufacturability (DfM)
Shape and Surface Optimization using generative algorithms
Stress and Fatigue Prediction via hybrid models (AI + Finite Element Analysis)
Data-Driven Fluid Dynamics for propeller, nozzle, and duct designs
Rapid Prototyping Recommendations based on thermal and mechanical limits

Areas of collaboration

Key Applications

Energy Systems: Redesign heat exchangers, turbines, and transmission parts for higher throughput and durability

Aerodynamics: Optimize winglets, diffusers, and chassis airflow using neural networks trained on CFD data

Hydrodynamics: Improve propeller and hull shapes with deep learning applied to drag and wake analysis

Rotating Machinery: Enhance thermal tolerance and energy efficiency of engines, turbines, and pumps

Additive Manufacturing: Use AI to refine printable topologies for strength, vibration control, and thermal expansion

Advanced Topics

1. Topology Optimization
We apply evolutionary algorithms and gradient-based methods to generate lightweight structures with maximal load-bearing capacity—suitable for aerospace, automotive, and robotic limbs.

2. Physics-Informed Neural Networks (PINNs)
Used to embed boundary conditions and physical laws directly into the training of models that simulate material deformation, thermomechanical behavior, or fluid dynamics.

3. Data-Driven CFD Acceleration
By training AI models on simulation datasets, we dramatically reduce the time and computational cost of running fluid dynamics studies—making design iteration 10x faster.

4. Failure Mode Prediction
We use AI to detect and predict failure-prone geometries by integrating empirical fatigue data and stress-strain simulations across varying operational loads.

5. Multiphysical Coupling Analysis
We combine AI with co-simulation (e.g., thermal-mechanical-electromagnetic) to optimize components such as magnetic actuators, e-motors, and combustion chamber parts.

6. Biologically Inspired Design
We use generative models to replicate natural forms — like bird wings, fish fins, or beehive patterns — that offer optimal flow dynamics, lightweight support, or vibration dampening in real-world mechanical environments.

Projects Underway

Blade shape optimization for low-noise drones
Lightweight lattice infills for 3D printed mechanical arms
AI-accelerated CFD design for agricultural water pumps
Erosion-resistant turbine component design using generative mode