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The onset of thermoacoustic instabilities in lean-premixed gas-turbine combustors is a crucial problem leading to degradation in engine and emissions performance and shortened component life. The main aim of this study is to propose a…

Data Analysis, Statistics and Probability · Physics 2007-05-23 G. Gianni , G. Mariotti , E. Paganini , S. Sello

Hydrogen combustion systems operated under fuel-lean conditions offer great potential for low emissions. However, these operating conditions are also susceptible to intrinsic thermodiffusive combustion instabilities. Even though technical…

Fluid Dynamics · Physics 2025-07-14 Max Schneider , Hendrik Nicolai , Vinzenz Schuh , Matthias Steinhausen , Christian Hasse

In reacting flow systems, thermoacoustic instability characterized by high amplitude pressure fluctuations, is driven by a positive coupling between the unsteady heat release rate and the acoustic field of the combustor. When the underlying…

Fluid Dynamics · Physics 2022-02-16 Jayesh Dhadphale , Vishnu R. Unni , Abhishek Saha , R. I. Sujith

The Automatic Dependent Surveillance Broadcast protocol is one of the latest compulsory advances in air surveillance. While it supports the tracking of the ever-growing number of aircraft in the air, it also introduces cybersecurity issues…

Machine Learning · Computer Science 2022-03-23 Antoine Chevrot , Alexandre Vernotte , Bruno Legeard

This article presents a modeling framework for a class of multiphase chemical systems based on non-equilibrium thermodynamics. Compartmental modeling is used to establish the dynamic properties of liquid-vapor systems operating far from…

Statistical Mechanics · Physics 2019-02-15 Aaron Romo-Hernandez , Nicolas Hudon , B. Erik Ydstie , Denis Dochain

Annular chambers, consisting of multiple flame nozzles, are frequently used in many industrial processes, for example, rocket engines and gas turbines. In the study, we proposed a novel approach to the problem of annular combustion with…

Fluid Dynamics · Physics 2025-03-04 Tao Yang , Yuan Ma , Peng Zhang

First-principles Markov Chain Monte Carlo sampling is used to investigate uncertainty quantification and uncertainty propagation in parameters describing hydrogen kinetics. Specifically, we sample the posterior distribution of thirty-one…

Numerical Analysis · Mathematics 2018-03-12 John Bell , Marcus Day , Jonathan Goodman , Ray Grout , Matthias Morzfeld

Recent advancements in the integration of artificial intelligence (AI) and machine learning (ML) with physical sciences have led to significant progress in addressing complex phenomena governed by nonlinear partial differential equations…

Machine Learning · Computer Science 2024-05-14 Rixin Yu , Erdzan Hodzic , Karl-Johan Nogenmyr

Preferential diffusion plays a critical role in the evolution of lean premixed hydrogen flames, influencing flame surface corrugation and overall flame behavior. Simulating such flames with tabulated chemistry (TC) methods remains…

This work presents a numerical study of a diffusion flame in a reacting, two-dimensional, turbulent, viscous, multi-component, compressible mixing layer subject to a large favorable streamwise pressure gradient. The boundary-layer equations…

Fluid Dynamics · Physics 2026-04-01 Sylvain L. Walsh , Lei Zhan , Carsten Mehring , Feng Liu , William A. Sirignano

Supersonic combustion plays a vital role in various applications, including scramjets, dual-mode ramjets, and pulse detonation engines. However, the flame characteristics can vary significantly, depending on the application. To model…

Fluid Dynamics · Physics 2023-06-21 Xu Zhu , Jian An , Nana Wang , Jian Zhang , Zhuyin Ren

We introduce a deep learning method to simulate the motion of particles trapped in a chaotic recirculating flame. The Lagrangian trajectories of particles, captured using a high-speed camera and subsequently reconstructed in 3-dimensional…

Machine Learning · Statistics 2018-12-13 Pai Liu , Jingwei Gan , Rajan K. Chakrabarty

We present a phenomenological reduced-order model to capture the transition to thermoacoustic instability in turbulent combustors. The model is based on the framework of synchronization and considers the acoustic field and the unsteady heat…

Adaptation and Self-Organizing Systems · Physics 2022-06-14 Yue Weng , Vishnu R. Unni , R. I. Sujith , Abhishek Saha

Combustion is the primary process in gas turbine engines, where there is a need for efficient air-fuel mixing to enhance performance. High-shear swirl injectors are commonly used to improve fuel atomization and mixing, which are key factors…

Machine Learning · Computer Science 2024-09-25 PK Archhith , SK Thirumalaikumaran , Balasundaram Mohan , Saptharshi Basu

Wind turbine reliability is critical to the growing renewable energy sector, where early fault detection significantly reduces downtime and maintenance costs. This paper introduces a novel ensemble-based deep learning framework for…

Machine Learning · Computer Science 2025-10-20 Rekha R Nair , Tina Babu , Alavikunhu Panthakkan , Balamurugan Balusamy , Wathiq Mansoor

In this experimental study on a laboratory-scale turbulent annular combustor with sixteen swirl-stabilized burners, we study the flame-flame and flame-acoustic interactions during different dynamical states associated with the longitudinal…

Fluid Dynamics · Physics 2021-01-11 Amitesh Roy , Samarjeet Singh , Asalatha Nair , Swetaprovo Chaudhuri , R I Sujith

Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are most salient. While recent work from our group and…

Many safety-critical scientific and engineering systems evolve according to differential-algebraic equations (DAEs), where dynamical behavior is constrained by physical laws and admissibility conditions. In practice, these systems operate…

Machine Learning · Computer Science 2026-04-14 Minxing Zheng , Zewei Deng , Liyan Xie , Shixiang Zhu

We introduce an anomaly detection method for multivariate time series data with the aim of identifying critical periods and features influencing extreme climate events like snowmelt in the Arctic. This method leverages the Variational…

Machine Learning · Computer Science 2024-07-16 Tolulope Ale , Nicole-Jeanne Schlegel , Vandana P. Janeja

Emission from the interstellar medium can be a significant contaminant of measurements of the intensity and polarization of the cosmic microwave background (CMB). For planning CMB observations, and for optimizing foreground-cleaning…

Cosmology and Nongalactic Astrophysics · Physics 2021-04-21 Ben Thorne , Lloyd Knox , Karthik Prabhu