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Transonic flow past the complex cavity-sub-cavity configurations
Authors:
A. Kuniyil,
H. Bansal,
J. J. Patel,
R. Kumar,
R. Sriram,
G. Kanagaraj,
Niranjan S. Ghaisas,
H. Ogawa,
S. K. Karthick
Abstract:
The study investigates the physics of unsteady flow in complex cavity geometries operating in the transonic regime. A two-dimensional Detached Eddy Simulation (DES) approach is used for the preliminary analysis. The cavity configuration examined in this work arises from the integration of a scramjet engine with a launch vehicle. In this integrated geometry, the isolator section serves as a deep su…
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The study investigates the physics of unsteady flow in complex cavity geometries operating in the transonic regime. A two-dimensional Detached Eddy Simulation (DES) approach is used for the preliminary analysis. The cavity configuration examined in this work arises from the integration of a scramjet engine with a launch vehicle. In this integrated geometry, the isolator section serves as a deep sub-cavity, while the Single Expansion Ramp Nozzle (SERN) constitutes the primary cavity. The combined arrangement therefore constitutes a complex cavity-sub-cavity system, which is referred to as such throughout the paper. The qualitative analysis revealed a feedback loop within the complex cavity-sub-cavity system, leading to high-pressure oscillations across the geometry. A monotonic increase in pressure loading is observed with increasing Mach number. Varying the cavity topology demonstrated that modifications to the primary cavity geometry strongly alter shear-layer dynamics and significantly affect the pressure distribution within the cavity-sub-cavity system. To mitigate adverse pressure oscillations, passive control strategies, including trailing-edge wall chamfering and a ventilated (slotted) sub-cavity, are investigated. Among the configurations studied, the slotted sub-cavity case exhibits the most pronounced suppression of pressure loads, particularly on the sub-cavity end wall. Spectral Proper Orthogonal Decomposition (SPOD) analysis also revealed the restructuring of dominant coherent modes in response to topological variations and to the implementation of passive control, providing insight into the underlying governing mechanism.
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Submitted 7 April, 2026;
originally announced April 2026.
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Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogates
Authors:
Yoeri Poels,
Koen Minartz,
Harshit Bansal,
Vlado Menkovski
Abstract:
Simulation is a powerful tool to better understand physical systems, but generally requires computationally expensive numerical methods. Downstream applications of such simulations can become computationally infeasible if they require many forward solves, for example in the case of inverse design with many degrees of freedom. In this work, we investigate and extend neural PDE solvers as a tool to…
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Simulation is a powerful tool to better understand physical systems, but generally requires computationally expensive numerical methods. Downstream applications of such simulations can become computationally infeasible if they require many forward solves, for example in the case of inverse design with many degrees of freedom. In this work, we investigate and extend neural PDE solvers as a tool to aid in scaling simulations for two-phase flow problems, and simulations of oil expulsion from a pore specifically. We extend existing numerical methods for this problem to a more complex setting involving varying geometries of the domain to generate a challenging dataset. Further, we investigate three prominent neural PDE solver methods, namely the UNet, DRN, and U-FNO, and extend them for characteristics of the oil-expulsion problem: (1) spatial conditioning on the geometry; (2) periodicity in the boundary; (3) approximate mass conservation. We scale all methods and benchmark their speed-accuracy trade-off, evaluate qualitative properties, and perform an ablation study. We find that the investigated methods can accurately model the droplet dynamics with up to three orders of magnitude speed-up, that our extensions improve performance over the baselines, and that the introduced varying geometries constitute a significantly more challenging setting over the previously considered oil expulsion problem.
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Submitted 16 July, 2024; v1 submitted 27 May, 2024;
originally announced May 2024.
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Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
Authors:
Tung Nguyen,
Rohan Shah,
Hritik Bansal,
Troy Arcomano,
Romit Maulik,
Veerabhadra Kotamarthi,
Ian Foster,
Sandeep Madireddy,
Aditya Grover
Abstract:
Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it…
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Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it difficult to understand what truly contributes to their success. Here we introduce Stormer, a simple transformer model that achieves state-of-the-art performance on weather forecasting with minimal changes to the standard transformer backbone. We identify the key components of Stormer through careful empirical analyses, including weather-specific embedding, randomized dynamics forecast, and pressure-weighted loss. At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals. During inference, this allows us to produce multiple forecasts for a target lead time and combine them to obtain better forecast accuracy. On WeatherBench 2, Stormer performs competitively at short to medium-range forecasts and outperforms current methods beyond 7 days, while requiring orders-of-magnitude less training data and compute. Additionally, we demonstrate Stormer's favorable scaling properties, showing consistent improvements in forecast accuracy with increases in model size and training tokens. Code and checkpoints are available at https://github.com/tung-nd/stormer.
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Submitted 22 October, 2024; v1 submitted 6 December, 2023;
originally announced December 2023.