Defense & Security
The Synthetik team develops, maintains, and distributes several computational codes for weapon effects analysis working with international defence clients including the U.S. DoD and Dstl (UK), and leverages these technologies to create innovative material technologies for expeditionary protective solutions.
Synthetik’s AI/ML work focuses on multi-modal sensors and multi-dimensional data, including synthetic data generation, machine-assisted annotation, and evaluation of algorithm effectiveness – with application to anomaly detection and security screening.
iBallistix
Computational fluid dynamics (CFD) code for ballistic design
Overview
The U.S. Army seeks to increase the state-of-the-art in interior ballistics modeling and simulation through CFD simulation. With higher fidelity modeling tools readily available, interior ballisticians can more effectively develop charge designs and ignition systems that perform efficiently and reliably. This in turn provides greater confidence in the safety concerns associated with gun launch testing and reduces the number of testing iterations.
01. Objective
Develop a new state-of-the-art multiphase, multidimensional interior ballistics CFD solver for the U.S. Army’s CCDC Armaments Center.
02. Process
Synthetik provides development and technical support to the U.S. DoD for various defense applications including internal ballistics, protective design, and the effect of explosive blast on structures and systems. This work is an extension of this support and will involve the provision of technical guidance in the development of a multiphase, multidimensional interior ballistics solver using the OpenFOAM software as an architecture.
Subterranean seismic survey
Enhancement of seismic surveys using machine learning
Overview
Despite recent advances in seismic imaging and tomography, obtaining an accurate map of geophysical features is still very much an open problem. Synthetik is working with the U.S. Navy to make improvements to specialized geophone devices used to gather seismic data and the software to maximize the spatial resolution of this collected data to better map subterranean features.
01. Objective
Develop a seismic geophysical assessment solution to enable non-destructive subterranean assessments of voids, piles, geological layer composition and structures.
02. Process
Synthetik will enhance tomographic reconstruction of seismic survey data by leveraging neural networks in order to more accurately map subterranean site features. As part of this work, Synthetik will generate a rich dataset of synthetic training examples.
03. Results
The result of the project will be a fast, even real-time, full waveform inversion (FWI) capability. FWI is a technique used (primarily) in geophysics and seismology to obtain high-resolution subsurface models. Synthetik’s step-change in FWI imaging will have wide ranging application in infrastructure projects, UXO clearance, oil and gas, earthquake and environmental imaging and even medical imaging — creating higher resolution images that will greatly improve traditional acoustic sensing technologies, including ultrasound.
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