Turbine Engine Efficiency improvements by Additive Manufacturing

Navy SBIR 22.1 - Topic N221-074
ONR - Office of Naval Research
Opens: January 12, 2022 - Closes: February 10, 2022 (12:00pm est)

N221-074 TITLE: Turbine Engine Efficiency improvements by Additive Manufacturing

OUSD (R&E) MODERNIZATION PRIORITY: Artificial Intelligence (AI)/Machine Learning (ML);General Warfighting Requirements (GWR)

TECHNOLOGY AREA(S): Air Platforms;Ground / Sea Vehicles;Materials / Processes

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

OBJECTIVE: Link heat transfer and solidification modeling, material databases, innovative cooling design concepts, and Additive Manufacturing (AM) process variables that will enable repair and enhanced efficiency improvements for gas turbine blades and other potential high temperature engine components.

DESCRIPTION: Improving engine performance requires creating new materials and improving design and manufacturing. AM is capable of producing details of complex shapes that cannot be produced by traditional methods. AM could produce nickel-based turbine blades with complex geometries with optimized internal cooling pathways and lattice-structure interiors.

Microstructural control can enable tailored properties. Modeling and simulation tools that analyze thermal flow and solidification, coupled with alloy property databases within a machine learning framework, can link varying AM process variables to arrive at new complex blade designs with optimized, integrated cooling networks and lattice blade structures rather than solid blades to reduce blade weight and improve engine efficiency by increasing operational engine temperatures. Fabricating blades in this manner would avoid current processing steps. With AM processing of these complex architectures, the effects of internal surface roughness will also need to be evaluated.

The AM process should present unique designs not possible with more conventional fabrication processes. The use of AM could lead to more innovative designs capable of more efficiently removing heat for both Navy and commercial applications. The outcome of this technology development effort will be a commercial suite of informatics-derived tools that can be able to reliably analyze and discriminate various sources of materials databases to optimize the capability for developing and new design and fabricating turbines blades with more effective use of the cooling air available to the engine.

PHASE I: Explore the literature to determine the initial AM parameters for a nickel-based superalloy such as Alloy 738 or 718. The focus of Phase I will be to fabricate a generic artifact with a simple network of internal cooling holes, overhangs, and thin/thick sections. The performer can suggest the artifact for evaluation. Suggested size would be an isosceles triangle cross-section about 3-incles (7.5cm) long. The unequal side should be 1-inch (2.5-cm) wide. The company should select an AM process capable of sufficient control and resolution to enable a good understanding of the heat transfer, solidification variables, and other material/process factors which cause defects. Develop conceptual models/algorithms that link alloy chemistry/heat transfer/solidification to the AM process shows geometric and material control while minimizing defects. Consideration will be given to the size of the internal holes and cooling network generated. Analysis of the defects is suggested to be done by non-destructive processes such as optical tomography, in-situ thermographic analysis, ultrasonic monitoring or x-ray tomography. ICME should link to AM process parameters with defect frequency and distribution in the component design, employ and prove feasibility of an approach for a metal AM method. Develop a Phase II plan.

PHASE II: Focus on increasing complexity of the linked AM process and on fatigue critical properties and temperature ranges of interest. In addition to evaluation of microstructure, Phase II should focus on fatigue critical properties at temperature ranges of interest. The AM process should be assessed to fabricate an internal surface roughness that maximizes cooling effectiveness. Further evaluation of effects of defects and control of defects should provide a more in-depth link to ICME-based tools. Residual stress should also be considered within modeling and fabrication tools to reduce residual stresses during fabrication and prevent cracking. The company should work with a turbine engine Original Equipment Manufacturer (OEM). The OEM should provide a range of conditions (cooling channel size, pressure drop tolerance, cooling efficiency).

PHASE III DUAL USE APPLICATIONS: Commercialize the alloys for use in DoD and commercial markets. Engage with the Government and/or public, commercial, company, or professional technical societies that retain materials databases. Interface with a software company that promotes and delivers materials computational programs to explore and develop an integration pathway for the database discriminating program with their software. Transition the material production methodology to a suitable industrial material producer. Transition the ICME code to the commercial entity for potential incorporation of a more comprehensive ICME code.


  1. Carter, L.N.; Attallah, M.M. and Reed, R.C. "Laser Powder Bed Fabrication of Nickel-Base Superalloys: Influence of Parameters; Characterisation, Quantification and Mitigation of Cracking." Superalloys2012, 12th International Symposium on Superalloys, The Minerals, Metals, and Materials Society, pp. 577-586. https://www.tms.org/superalloys/10.7449/2012/Superalloys_2012_577_586.pdf.
  2. Sames, W.J.; List, F.A.; Pannala, S.; Dehoff, R.R. and Babu, S.S. "The metallurgy and processing science of metal additive manufacturing." International Materials Reviews, Vol. 61, Issue 5, 2016, pp.315-360. https://www.tandfonline.com/doi/abs/10.1080/09506608.2015.1116649?journalCode=yimr20.
  3. Parimi, L.L.; Ravi, G.; Clark, D. and Attallah, M.M. "Microstructural and texture development in direct laser fabricated IN718." Materials Characterization Vol. 89, March 2014, pp, 102-111. https://www.sciencedirect.com/science/article/pii/S1044580313003835.
  4. Lippold, J. "Welding Metallurgy and Weldability." John Wiley & Sons, Inc., Hoboken, New Jersey, 2015. https://onlinelibrary.wiley.com/doi/book/10.1002/9781118960332.

KEYWORDS: Additive manufacturing; AM; turbine components; materials databases; machine learning; modeling; solidification; design; heat transfer; AM defects


The Navy Topic above is an "unofficial" copy from the overall DoD 22.1 SBIR BAA. Please see the official DoD Topic website at rt.cto.mil/rtl-small-business-resources/sbir-sttr/ for any updates.

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