151 citations · 537 across the 31 of their papers we have counts for
31 papers
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data
Wai Tong Chung, Bassem Akoush, Pushan Sharma +9
Analysis of compressible turbulent flows is essential for applications related to propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 TB networ…
A high-order diffused-interface approach for two-phase compressible flow simulations using a Discontinuous Galerkin framework
Niccolò Tonicello, Matthias Ihme
A diffused-interface approach based on the Allen-Cahn phase field equation is developed within a high-order Discontinuous Galerkin framework. The interface capturing technique is b…
Accelerating large-eddy simulations of clouds with Tensor Processing Units
Sheide Chammas, Qing Wang, Tapio Schneider +3
Clouds, especially low clouds, are crucial for regulating Earth's energy balance and mediating the response of the climate system to changes in greenhouse gas concentrations. Despi…
Reaction Nanoscopy of Ion Emission from Sub-wavelength Propanediol Droplets
Philipp Rosenberger, Ritika Dagar, Wenbin Zhang +5
Droplets provide unique opportunities for the investigation of laser-induced surface chemistry. Chemical reactions on the surface of charged droplets are ubiquitous in nature and c…
A High-resolution Large-eddy Simulation Framework for Wildland Fire Predictions using TensorFlow
Qing Wang, Matthias Ihme, Rod R. Linn +6
As the impact of wildfires has become increasingly more severe over the last decades, there is continued pressure for improvements in our ability to predict wildland fire behavior…
Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior
John Burge, Matthew R. Bonanni, R. Lily Hu +1
The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer…
The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning
Wai Tong Chung, Ki Sung Jung, Jacqueline H. Chen +1
In general, large datasets enable deep learning models to perform with good accuracy and generalizability. However, massive high-fidelity simulation datasets (from molecular chemis…
Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data
Fantine Huot, R. Lily Hu, Nita Goyal +3
Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present `Next Day Wildfire Spread,' a curated, large-scale, multivariate data…
A TensorFlow Simulation Framework for Scientific Computing of Fluid Flows on Tensor Processing Units
Qing Wang, Matthias Ihme, Yi-Fan Chen +1
A computational fluid dynamics (CFD) simulation framework for fluid-flow prediction is developed on the Tensor Processing Unit (TPU) platform. The TPU architecture is featured with…
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion
Wai Tong Chung, Aashwin Ananda Mishra, Matthias Ihme
Many practical combustion systems such as those in rockets, gas turbines, and internal combustion engines operate under high pressures that surpass the thermodynamic critical limit…
Heat transfer augmentation by recombination reactions in turbulent reacting boundary layers at elevated pressures
Nikolaos Perakis, Oskar Haidn, Matthias Ihme
A study of a reacting boundary layer flow with heat transfer at conditions typical for configurations at elevated pressures has been performed using a set of direct numerical simul…
Requirements Towards Predictive Simulations of Turbulent Reacting Flows
Matthias Ihme
Significant progress has been made on the model development for simulating turbulent reacting flows. As a consequence, we are currently in a position where key-physical aspects of…
A General Drag Coefficient for Flow over a Sphere
Narendra Singh, Michael Kroells, Chenxi Li +4
A generalized physics-based expression for the drag coefficient of spherical particles moving in a fluid is derived. The proposed correlation incorporates essential rarefied physic…
Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics
John Burge, Matthew Bonanni, Matthias Ihme +1
As the climate changes, the severity of wildland fires is expected to worsen. Models that accurately capture fire propagation dynamics greatly help efforts for understanding, respo…
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Fantine Huot, R. Lily Hu, Matthias Ihme +6
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly…
Data-assisted combustion simulations with dynamic submodel assignment using random forests
Wai Tong Chung, Aashwin Ananda Mishra, Nikolaos Perakis +1
In this investigation, we outline a data-assisted approach that employs random forest classifiers for local and dynamic combustion submodel assignment in turbulent-combustion simul…
Transcritical Mixing and Auto-Ignition of n-dodecane Liquid Fuel using a Diffused Interface Method
Luis Bravo, Peter C. Ma, Matthias Ihme
High-fidelity simulations of transcritical spray mixing and auto ignition in a combustion chamber are performed at high pressure and temperature conditions using a recently develop…
Computational Modeling and Analysis of Diesel-fuel Injection and Autoignition at Transcritical Conditions
Matthias Ihme, Peter C. Ma, Luis Bravo
The need for improved engine efficiencies has motivated the development of high-pressure combustion systems, in which operating conditions achieve and exceed critical conditions. A…
Lyapunov exponent and Wasserstein metric as validation tools for assessing short-time dynamics and quantitative model evaluation of large-eddy simulation
Hao Wu, Peter C. Ma, Yu Lv +1
In this work, methods for the evaluation of LES-quality and LES-accuracy are presented, which include the Lyapunov exponent for the analysis of short-time predictability of LES-cal…
Numerical analysis on mixing processes for transcritical real-fluid simulations
Peter C. Ma, Hao Wu, Daniel T. Banuti +1
The accurate and robust simulation of transcritical real-fluid flows is crucial for many engineering applications. Diffused interface methods are frequently employed and several nu…
Direct numerical simulations of turbulent channel flow under transcritical conditions
Peter C. Ma, Xiang I. A. Yang, Matthias Ihme
Turbulent flows under transcritical conditions are present in regenerative cooling systems of rocker engines and extraction processes in chemical engineering. The turbulent flows a…
Application of Pareto-efficient combustion modeling framework to large eddy simulations of turbulent reacting flows
Hao Wu, Peter C. Ma, Thomas Jaravel +1
In the application of the combustion models based on low-dimensional manifolds (for instance flamelet models) to large-eddy simulation (LES) of reactive turbulent flows, the modeli…
Efficient time stepping for reactive turbulent simulations with stiff chemistry
Hao Wu, Peter C. Ma, Matthias Ihme
A combination of a steady-state preserving operator splitting method and a semi-implicit integration scheme is proposed for efficient time stepping in simulations of unsteady react…
Compositional inhomogeneities as a source of indirect combustion noise
Luca Magri, Jeff O'Brien, Matthias Ihme
The generation of indirect combustion noise by compositional inhomogeneities is examined theoretically. For this, the compact nozzle theory of~\cite{MARBLE_CANDEL_JSV1977} is exten…