About

Gregory Banyay is an Assistant Research Professor affiliated with the Applied Research Laboratory, the Center for Acoustics and Vibration, and Architectural Engineering Acoustics at Penn State University. His contact email is gab5631@psu.edu. His work is associated with Penn State's Graduate Program in Acoustics, which is recognized as a leading resource for graduate education in acoustics in the United States. The program offers degrees including Master of Engineering in Acoustics, Master of Science in Acoustics, and Doctor of Philosophy in Acoustics, and is part of the College of Engineering at Penn State University.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Physics
• Nuclear engineering
• Algorithm
• Mathematical optimization
• Nuclear physics
• Mathematics
• Statistics

Selected publications

• Mach Number Correction of Rectangular Duct Criticals

  Journal of Pressure Vessel Technology · 2025-01-22 · 1 citations

  articleOpen accessSenior author

  Abstract The theory involved in accounting for the velocity dependence of acoustic cutoff frequencies in rectangular ducts is briefly summarized. This is followed by a comparison with reported duct critical frequencies in a flow test involving isolated cylinders in cross-flow. The agreement with the theory illustrates the importance of addressing the Mach number effects in moderate to high velocity services.

  PublisherOA PDFDOI

• Surrogate modeling terminology for acoustics: verification, validation, and uncertainty quantification

  The Journal of the Acoustical Society of America · 2025-04-01

  articleSenior author

  Surrogate models are emerging as established parts of commercial problem-solving spaces including aeronautics, financial estimating, marketing, automotive, and weather predictions. Being less computationally expensive, these models generally require less expensive hardware, and less time for engineering design. When surrogate models ultimately inform safety and policy decisions prior to monetary decisions, one needs to demonstrate credibility for decision makers. Engineers should be sure that the uncertainty of models are quantified and that the models are verified and validated (a field called VVUQ). To create clarity when discussing VVUQ terminology especially applied to acoustics, we survey the way these terms are currently used and create a glossary for guidance going forward. We use the modeling process applied to a canonical structural dynamics system to demonstrate the application of this vocabulary.

  PublisherDOI

• Parsimonious Uncertainty Quantification for Modeling and Simulation of Flow-Induced Vibrations

  2025-07-20

  article1st authorCorresponding

  Abstract Uncertainties pervade the process of constructing credible computational models, generating simulation results, and interpretation thereof. One must compare simulation results with experiment results, which intend to represent the real world environment (e.g., model validation via correspondence with observations). One must additionally compare the conceptual and mathematical models by which we represent the real world and how those models become encoded on a computer (e.g., model verification via consistency with first principles). Those evaluations of real-world correspondence and phenomenological consistency represent two necessary and distinct approaches to uncertainty quantification. Systems, structures, and components subject to stochastic dynamics, such as flow-induced vibration of piping and pressure vessel systems, necessitate additional and unique approaches to this puzzle. We therefore illustrate an approach to verify, validate, and quantify uncertainty for a structure subject to flow-induced vibrations and discuss how this problem can both leverage existing standards, as well as objectively demonstrate compliance to design objectives.

  PublisherDOI

• Verification and Supplemental Information for the Influence of Tube Frequency Differences on Fluid-Elastic Instability of Tube Arrays In Crossflow

  2025-07-20

  article

  Abstract This paper studies the effect of tube natural frequency differences on fluid-elastic instability (FEI) in tube arrays in fluid crossflow. FEI in heat exchanger tubes, particularly in steam generators, is a significant concern due to its potential to cause tube damage. Design guidelines are essential to mitigate the risk of excessive tube vibration, especially in U-bend steam generator tubes. In the U-bend region, tubes experience high-flow, two-phase transverse conditions, leading to vibrations and accelerated wear, which can result in operational failures. Although extensive research has been conducted on FEI, most of the data documented in the ASME Boiler and Pressure Vessel Code Section III Appendix N pertains to straight-tube arrays in heat exchangers. Experimental data on the onset of fluid elastic instability are 1) surveyed for U-bend and straight tube arrays with asymmetric tube supports that contribute to frequency detuning and 2) compared with the onset of FEI in symmetrically supported straight tubes without frequency detuning. The results show FEI exists in U-bend and straight tube arrays with natural frequency differences but the velocity for the onset of their instability is higher than for comparable straight tube bundles where all tubes have the same natural frequencies. This paper provides supplemental literature data and basis that is relevant to U-bend tube arrays for Appendix N considerations with FEI. This paper discusses the discrepancies observed in U-bend steam generator measurements, focusing on their effects on the FEI constant (K), mass damping, pitch-to-diameter ratio, and critical velocities. We provide a digital dataset to support follow-on experimental or modeling and simulation activities. Based on these additional data points regarding the onset of instability in tube bundles of various array geometries, the damping ratios and the existing mean and lower bound fits will be verified and updated as needed.

  PublisherDOI

• Sequential sensor placement for damage detection under frequency-domain dynamics

  Finite Elements in Analysis and Design · 2025-02-11 · 1 citations

  article
  PublisherDOI

• Latent space representation method for structural acoustic assessments

  The Journal of the Acoustical Society of America · 2024-03-01

  article1st authorCorresponding

  When targeting structural acoustic objectives, engineering practitioners face epistemic uncertainties in the selection of optimal geometries and material distributions, particularly during early stages of the design process. Models built for simulating acoustic phenomena generally produce vector-valued output quantities of interest, such as autospectral density and frequency response functions. Given finite compute resources and time we seek computationally parsimonious ways to distill meaningful design information into actionable results from a limited set of model runs, and thus aim to use machine learning to perform model order reduction. Unlike time series data for which recurrent neural networks can learn from prior time steps to inform subsequent steps, frequency-dependent data demands a different machine learning paradigm. We thus evaluate the utility of autoencoders to represent structural acoustic results with a low dimensional latent space to enable such objectives as surrogate modeling for design optimization. We demonstrate the accuracy of autoencoder based methods of constructing a manifold representation for frequency dependent functions of varying modal density and damping, and discuss the predictive capability thereof.

  PublisherDOI

• Latent space representation method for structural acoustic assessments

  Proceedings of meetings on acoustics · 2024-01-01

  article1st authorCorresponding
  PublisherDOI

• Sequential Sensor Placement for Damage Detection Under Frequency-Domain Dynamics

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Mechanics Informed Neutron Noise Monitoring to Perform Remote Condition Assessment for Reactor Vessel Internals

  ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering · 2022-04-29

  article1st authorCorresponding

  Abstract The use of neutron noise analysis in pressurized water reactors to detect and diagnose degradation represents the practice of pro-active structural health monitoring for reactor vessel internals. Recent enhancements to this remote condition monitoring and diagnostic computational framework quantify the sensitivity of the structural dynamics to different degradation scenarios. This methodology leverages benchmarked computational structural mechanics models and machine learning methods to enhance the interpretability of neutron noise measurement results. The novelty of the methodology lies not in the particular technologies and algorithms but our amalgamation into a holistic computational framework for structural health monitoring. Recent experience revealed the successful deployment of this methodology to pro-actively diagnose different degradation scenarios, thus enabling prognostic asset management for reactor structures.

  PublisherDOI

• Predictive capability assessment for physics-guided learning of vortex-induced vibrations

  The Journal of the Acoustical Society of America · 2022-10-01

  article1st authorCorresponding

  We seek here a computationally parsimonious and credible means to simulate the complex phenomena of vortex-induced vibrations (ViV), as one tool to assist in mitigating risk associated with ViV-induced instabilities that can cause non-negligible structural acoustic response. To address current limitations in data-driven modeling, for which credibility assessment proves challenging, or physics-based simulation (i.e., constrained by governing partial differential equations (PDEs)), which often includes prohibitive computational expense, we explore recent state-of-the-art approaches to optimally combine these engineering disciplines via a physics-guided machine learning framework. One can expect that intersecting data-driven modeling with physics-guided simulation offers one means to both maximize the credibility of machine learning based approaches and minimize the computational expense of physics-based modeling approaches.

  PublisherDOI

Frequent coauthors

• John C. Brigham

  University of Pittsburgh

  6 shared

• Scott E. Sidener

  5 shared

• Matthew J. Palamara

  Westinghouse Electric (United States)

  4 shared

• Clarence L. Worrell

  Westinghouse Electric (United States)

  4 shared

• Kavinayan Sivakumar

  3 shared

• Michael M. Zavlanos

  3 shared

• Michael D. Shields

  3 shared

• Wilkins Aquino

  QED Technologies (United States)

  3 shared

Education

• Doctor of Philosophy, Civil & Environmental Engineering

  University of Pittsburgh

  2019

• Master of Science, Mechanical Engineering

  Ohio University

  2006

• Bachelor of Science, Mechanical Engineering

  Ohio University

  2004