Saikumar Yeratapally

Saikumar Yeratapally

Research Engineer I

Tel: (757) 864-1476


Saikumar Yeratapally


  • Ph.D., Purdue University, 2015
  • M.S., Carnegie Mellon University, 2010
  • BE. (Hons), Birla Institute of Technology and Science, Goa, 2009

Work Experience

  • Research Engineer I, National Institute of Aerospace, 2017-present
  • Post-Doctoral Research Scholar, National Institute of Aerospace, 2016-2017

Research Areas/Expertise

  • Computational Materials
  • Microscale fatigue crack growth in polycrystalline metals and alloys
  • Crystal Plasticity
  • Additive Manufacturing
  • Materials Informatics
  • Uncertainty Quantification

Current Research


Modeling deformation of polycrystalline materials using Crystal Plasticity

The complex microstructure of polycrystalline materials governs the heterogeneous deformation of polycrystalline metals and alloys. It is important to take into consideration both the morphological and crystallographical attributes of microstructure while computationally solving for the heterogeneous stress and strain state within the microstructure. The research focusses on generating statistical instantiations of 3D microstructure volume that represent the actual material as closely as possible. Further, the constitutive relations of polycrystalline material deformation are modeled using rate-dependent crystal plasticity framework.

Investigation of fatigue crack initiation and small crack growth in polycrystalline alloys

It is cumbersome to understand what features drive fatigue crack initiation using experimental and microscopy techniques alone. They need to be complemented with high-fidelity computational tools to understand why fatigue cracks prefer to initiate at specific locations within the microstructure. Additionally, once the fatigue crack initiates, its growth rate is controlled by the surrounding microstructure. Since a significant portion (~80%) of the total fatigue life of a component is spent in the microscale regime, it is important to develop microscale crack growth algorithms that link microstructure variability to scatter in fatigue life of the component. The focus of this research is to effectively use crystal plasticity framework to develop efficient and well-calibrated microscale fatigue crack growth algorithms that predict fatigue scatter.

Effect of Defects on fatigue performance of additively manufactured polycrystalline alloys

Powder-based AM processes are associated with the formation of multiple types of process-specific defects (or pores). The performance of the component is directly dependent on the type of pores, their density, their proximity to the free surface, in addition to the complex crystallographic and morphological attributes of the microstructure of the material. In order to fully characterize the effect of defects on fatigue performance, it is imperative to model the defects within the microstructure volume and quantitatively solve for strain localization based on various attributes. The research work uses high-fidelity crystal plasticity finite element framework to study the mechanistic effect of defects.

Establishing Process-Structure-Property linkages in additively manufactured materials

The advent of additive manufacturing (AM) has opened the doors for a multi-level “bottom-up” design of materials by delivering the ability to tailor location-specific and properties by controlling process parameters. From an engineering systems-design perspective, a “top-down” approach is preferred for designing materials for functionality. The “top-down” approach is essentially an inverse problem that needs to be solved to obtain a sub-space of process parameters that would lead to creation of materials that meet desired performance requirements. Using high-fidelity process-modeling and property-predictive tools is a sub-optimal way to solve inverse problems due to the inherent non-linear, path-dependent behavior of material response. This demands the development of reduced-order models that help establish Process-Structure-Propertylinkages which in turn enables the design of materials for functionality. The current study proposes development of such a reduced-order model by taking leverage of data science techniques which are used to extract knowledge from high-fidelity simulation tools.



Newman, J.A., Tayon, W.A., Ruggles, T.J., Yeratapally, S.R., Brice, C.A., Hochhalter, J.D., Baughman, J.M., Claytor, H.D., “Characterization of Titanium Alloys Produced by Electron Beam Directed Energy Deposition”, NASA/TM-2018-220111.

Yeratapally, S.R., Hochhalter, J.D., Ruggles, T.J., Sangid, M.D., Investigation of fatigue crack incubation and growth in cast MAR-M247 subjected to low cycle fatigue at room temperature”, International Journal of Fracture 2017; 208:79-96.

Yeratapally, S.R., Glavicic, M.G., Argyrakis, C., Sangid, M.D., “Bayesian uncertainty quantification and propagation for validation of a microstructure sensitive model for prediction of fatigue crack initiation,” Reliability Engineering & System Safety 2017; 164: 110-123.

Yeratapally, S.R. Glavicic, M.G., Hardy, M., Sangid, M.D., “Microstructure based fatigue life prediction framework for polycrystalline nickel-base superalloys with emphasis on the role played by twin boundaries in crack initiation”, Acta Materialia 2016; 107:152-167.   

Azeredo, B.P., Yeratapally, S.R., Kacher, J., Ferreira, P.M., Sangid, M.D., “An experimental and computational study of size-dependent contact-angle of dewetted metal nanodroplets below its melting temperature”, Applied physics letters 2016; 109, 213101

Sangid, M.D., Yeratapally, S.R., Rovinelli, A., “Validation of microstructure-based material modeling“, AIAA, National Harbor, Maryland, January 13 – 17, 2014