MARYLAND ENERGY AND SENSOR TECHNOLOGIES, LLC — Department of Defense SBIR Phase I: N231-068
MARYLAND ENERGY AND SENSOR TECHNOLOGIES, LLC — SBIR Phase I award from Department of Defense.
- Amount
- $142,213
- Agency
- Department of Defense · Navy
- Program / Phase
- SBIR · Phase I
- Topic
- N231-068
- Solicitation
- 23.1
- NAICS
- —
- Place of performance
- MD
- Period
- 2023-07-17 → 2024-01-16
Description
Navy seeks a passive thermal management solution that can maintain the cryogenic temperature (30 K) for a 100 W superconducting system for 2 hours when the use of active cryocoolers is limited. Cu-based shape memory alloys (SMA) have the potential to meet Navy’s requirements. An SMA undergoes diffusion-less solid-state phase transformation, accompanied by a latent heat as a result of the first order structural change. Upon heating up from its low temperature phase, a SMA will absorb heat before it completes the solid-state transformation. A SMA capable of absorbing 1.4 J/g heat at 30 K will meet Navy’s need. It can be machined into linear plates and installed into cryogenic refrigeration systems for high-temperature superconductor equipment. However, at cryogenic temperatures (30-77 K), most SMAs stop “working”: they lose all their ability to transform because the small thermal energy fluctuation at cryogenic temperature is not enough to overcome the energy barrier between the solid phases. Cu-Al-Zn, Cu-Al-Mn, and Cu-Al-Ni are three exceptions. Although none of their presently known latent heat values can meet Navy’s targets, they are not too far off from the requirement. Here, we propose a concerted machine learning guided materials development effort to search around the known Cu-Al-based SMAs with the goal of discovering novel compositions of Cu-SMAs with latent heat 1.4 J/g at 30-77 K. Our machine learning algorithm is based on Bayesian active learning, which has proven to be effective in dramatically reducing the overall number of necessary experiments. Combinatorial arc melting methods will be used to prepare the samples with compositions prescribed by the algorithm. Physical Properties Measurement System will be used to characterize thermal properties of the prepared samples. We expect 10 iterations will be more than enough to converge on the final compositions which satisfy the requirement. Candidate alloys will be studied for manufacturability and feasibility of cryogenic applications. A 5 kg plate will be fabricated and tested as the final deliverable of the project. ???????