Digital Twin Lab



Pilot Projects

1. Digital Twin for Smart Farming

Pilot Project: Digital Twins for In-season Precision Crop Management

Our team will use Cotton as a target crop for demonstrating the feasibility of the proposed DT for smart farming. Cotton is particularly suitable because its undetermined, perennial growth habits present a challenge to producers in managing the balance between vegetative and reproductive growth, leading to crop maturity, yield, and fiber quality. However, the concepts outlined here can be readily transferred to other cotton-producing states and adapted to other crops such as soybean, corn, wheat, sorghum, tomatoes, potatoes, spinach, forages, and others.

2. Digital Twin for Geo-Engineering

TAMIDS-DTL Pilot Project: Digital Twin for Enhancing Geo-Systems (Drilling and Production) in the Context of Clean Energy Extraction

We will build a DT for the drilling process. In this case,  a central contribution of the proposed task is the development of a multifaceted framework that combines simulation, data assimilation, automated control, and optimization for coupled nonlinear drilling processes into the DT framework of a drilling rig. . We will therefore build on our previous work, especially combining expertise in modeling hydraulic fracturing and drilling processes to equip our controls and trajectory tracking strategies with estimation algorithms to determine (1) the state of the drill-bit and (2) design optimal ROP, RPM, and plasma discharge to create efficient microcracks into the rock formation. We will be connecting a physical drilling rig (miniaturized drilling rig) developed in previous works with a digital platform simulating a drilling operation. 

Funded Projects

1. NIST PSIAP – Digital Twin

Funded Project – A Digital-Twin-Enabled Testbed for Public Safety Communication Technologies

The project aims to build a digital-twin-enabled testbed with state-of-the-art user interface/user experience technologies and advanced simulation models to provide a photorealistic virtual reality environment for first responders and emergency managers to engage, experience, and explore the latest sensing and communication technologies.


Funded Project – Smart Communities, Smart Responders (SCSR) – An AI for IoT Information (AI3) Prize Competition

The Smart Communities, Smart Responders – AI for IoT Information (AI3) Prize Competition calls participants to utilize data from multiple IoT devices to deliver an AI system to help first responders leverage the data coming from IoT devices, smart buildings, and other public data streams. Texas A&M University, Texas A&M Engineering Extension Service, and US Ignite will run this new program with $1.2 million in funding provided by NIST’s Public Safety Innovation Accelerator Program – Artificial Intelligence for IoT Information (PSAIP – AI3) cooperative agreement.