TAUMAT LLC — National Science Foundation SBIR Phase I: EN

Description

The broader impact/commercial potential of this Small Business Innovation Research Phase I project aims to establish a new approach to high-performance central processing unit (CPU) thermal management that focuses on the development and application of innovative solid-solid thermal energy storage (TES) materials and hardware.Increasingly, steady-state cooling solutions are unable to keep up with the required operating frequencies and resulting thermal loads of temperature-sensitive computing and electronic components.As a result, these components are throttled down to reduce heating.This results in the desired temperature reduction but inevitably leads to clock speed and performance reductions as well. The proposed project aims to challenge this existing tradeoff and produce CPU heat sinks that can maintain 3X computational performance ‘sprints’ with no added weight/volume nor electrical energy expenditure, in a scalable and easily deployable, drop-in form factor. Fueled by a global demand for high-performance computing, internet-of-things, and handheld electronics, the market for high-performance CPU coolers is rising with a market size of about $2.04 billion and a compound annual growth rate of 3.73-4.64% over the next decade.The target solid-solid TES heatsink is transferable to battery fast charging, system-on-chip devices, and the power electronic market._x000D_ _x000D_ The intellectual merit of this project resides in newly-identified thermal energy storage materials to shift the paradigm in CPU cooler design away from simply maximizing steady-state heat dissipation towards an optimized approach that combines high steady-state dissipation with high-capacity thermal storage.This Phase I project has three primary research objectives: i) develop analytical and numerical topology optimization approaches to identify ideal thermal energy storage material properties and composite heat transfer/capacity structures for CPU applications: ii) leverage data-driven shape memory alloy discovery using an artificial intelligence framework to identify and ultimately arc-melt new thermal energy storage materials that exhibit high-latent heat, high-conductivity, low hysteresis, and/or the ideal combination of material properties based on CPU requirements: and iii) design, fabricate, and test prototypes for model validation and concept demonstration. These technical efforts, combined with risk reduction and mitigation steps, and techno-economic and manufacturing analysis will enable leap-ahead improvements in an ever-expanding array of high-power, thermally limited applications._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.