Publications by Jiannong Fang | EPFL Graph Search

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In the last few decades, laboratory experiments and direct numerical simulations of turbulent boundary layers, performed at low to moderate Reynolds numbers, have found very-large-scale motions (VLSMs) in the logarithmic and outer regions. The size of VLSMs was found to be 10-20 times as large as the boundary-layer thickness. Recently, few studies based on field experiments examined the presence of VLSMs in neutral atmospheric boundary-layer flows, which are invariably at very high Reynolds numbers. Very la

It is necessary to take into account the micro discretization of natural rock when studying its macroscopic failure behavior. This requirement has resulted in renewed and increased interest in the discrete or framework/lattice numerical modeling techniques ...

Stably stratified turbulent flows over an unbounded, smooth, planar sloping surface at high Grashof numbers are examined using direct numerical simulations ( DNS). Four sloping angles ( alpha = 15 degrees; 30 degrees; 60 degrees and 90 degrees) and three G ...

Large-eddy simulation is used to investigate very-large-scale motions (VLSMs) in the neutrally stratified atmospheric boundary layer at a very high friction Reynolds number, . The vertical height of the computational domain is m, which corresponds to the t ...

Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer vision and ...

Thermally driven slope flows are ubiquitous in nature and play a major role in regulating local microclimates in valleys, glaciers and ice-sheets. They control in large part surface momentum, heat and moisture fluxes, and their effects must therefore be accounted for in weather prediction and climate models. Due to the interplay between shear and buoyancy in generating and destroying turbulence, the thermal and hydrodynamic boundary layers that characterizes slope flows are very shallow, with turbulent moti

Probabilistic forecasting of regional surface solar irradiance provides critical technical support for the reliable operation and grid integration of large-scale and distributed photovoltaic power systems. However, stochastic fluctuations driven by cloud dynamics substantially complicate the characterization of solar irradiance uncertainty at the regional scale, thereby posing significant challenges to probabilistic regional forecasting. To address this, we propose WIP-Flow, a novel Weakly Informative Prior

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

Many mountainous regions with high wind energy potential are characterized by multi-scale variabilities of vegetation in both spatial and time dimensions, which strongly affect the spatial distribution of wind resource and its time evolution. To this end, ...

Accurate modeling of complex terrain, especially steep terrain, in the simulation of wind fields remains a challenge. It is well known that the terrain-following coordinate transformation method (TFCT) generally used in atmospheric flow simulations is rest ...

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real ...

A MLS-based lattice spring model is presented for numerical modeling of elasticity of materials. In the model, shear springs between particles are introduced in addition to normal springs. However, the unknowns contain only particle displacements but no pa ...

Advanced artificial intelligence (AI) models typically achieve high accuracy in wind power forecasting, but their internal mechanisms lack interpretability, which undermines user confidence in forecast value and strategy execution. To this end, this paper ...

We here perform an intercomparison of TFCT and IBM for large-eddy simulation of a turbulent wind field over a three-dimensional hill, for which wind-tunnel measurements are available for validation. The experimental dataset contains both vertical and horizontal profiles of mean velocities and variances, which allows an in-depth comparison of the numerical models. For the TFCT, a new algorithm for solving the pressure Poisson equation in the transformed coordinate system is proposed and first validated for a

Most of neural networks require massive data for model training, which limits their forecast accuracy for short-term solar power forecasting in data-scarce scenarios. To address this limitation, this paper aims to apply the large language models (LLMs), specifically Chronos model, to short-term solar power forecasting in data-scarce scenarios. In particular, a framework is introduced to convert continuous time-series data in solar power forecasting into the discrete data format required by LLMs. Once pre-tr

It is known that, when the immersed boundary method (IBM) is implemented within spectral-like methods, the Gibbs oscillation seriously deteriorates the calculation of derivatives near the body surface. In this paper, a radial basis function (RBF) based smo ...

This paper presents the OpenMP parallel implementation of the Distinct Lattice Spring Model (DLSM). The motivation of this study is to reduce the computing time and increase the computational capacity of the DLSM code. Firstly, the basic theory of DLSM is introduced. As an explicit method, the DLSM is very suitable for parallelization implementation. Only a few code changes are needed for the OpenMP implementation. Then; the detail parallel design of the OpenMP implementation is presented. Finally, numerica

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

Jiannong Fang

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Large-eddy simulation of very-large-scale motions in atmospheric boundary-layer flows

A coupled distinct lattice spring model for rock failure under dynamic loads

Direct numberical simulation of turbulent slope flows up to Grash of number Gr=2.1 x 10(11)

Large-Eddy Simulation of Very-Large-Scale Motions in the Neutrally Stratified Atmospheric Boundary Layer

TimeGPT in load forecasting: A large time series model perspective

The vertical structure of eddy diffusivity in pure slope flows over smooth surfaces

Weakly Informative Priors Flow Matching for Probabilistic Regional Solar Irradiance Forecasting

Improving the accuracy and interpretability of neural networks for wind power forecasting

Shifts in wind energy potential following land-use driven vegetation dynamics in complex terrain

Intercomparison of Terrain-Following Coordinate Transformation and Immersed Boundary Methods for Large-Eddy Simulation of Wind Fields over Complex Terrain

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

A MLS-based lattice spring model for simulating elasticity of materials

On the high frequency oscillatory tube flow of healthy human blood

Can we trust explainable artificial intelligence in wind power forecasting?

Intercomparison of Terrain-Following Coordinate Transformation and Immersed Boundary Methods for Large-Eddy Simulation of Wind Fields over Complex Terrain

Short-Term Solar Power Forecasting with Large Language Model

Towards oscillation-free implementation of the immersed boundary method with spectral-like methods

OpenMP parallelization of the three-dimensional distinct lattice spring model

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Improved SPH methods for simulating free surface flows of viscous fluids