SBIR/STTR Research Award

Cu-Al-X based shape memory alloys with large latent heat for thermal management in 30-80 K

Award identifier 184461c58738fb92741b495f933a8e924a7daccb257730e65462c15f34ec4c77Awarding agency Department of Defense · Navy

Award amount

$142,213

Program and phase

SBIR · Phase I

Award year

2023

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Defense
Branch
Navy
Recipient company
MARYLAND ENERGY AND SENSOR TECHNOLOGIES, LLC
Proposal award date
2023-07-17
Contract end date
2024-01-16
UEI
ZPDLBA6R2CM1
Solicitation number
23.1
Topic code
N231-068

Research abstract

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. ???????

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

3 SBIR/STTR records share this UEI (2012–2023). Reported award amounts total $536,706. Reported amounts are not federal obligations.

Phases: Phase I

Agencies: Department of Defense, National Science Foundation, Department of Energy

Agency intelligence

Department of Defense

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.