18 research outputs found
Modulation of homogeneous turbulence seeded with finite size bubbles or particles
The dynamics of homogeneous, isotropic turbulence seeded with finite sized particles or bubbles is investigated in a series of numerical simulations, using the force-coupling method for the particle phase and low wavenumber forcing of the flow to sustain the turbulence. Results are given on the modulation of the turbulence due to massless bubbles, neutrally buoyant particles and inertial particles of specific density 1.4 at volumetric concentrations of 6%. Buoyancy forces due to gravity are excluded to emphasize finite size and inertial effects for the bubbles or particles and their interactions with the turbulence. Besides observing the classical entrapment of bubbles and the expulsion of inertial particles by vortex structures, we analyze the Lagrangian statistics for the velocity and acceleration of the dispersed phase. The turbulent fluctuations are damped at mid-range wavenumbers by the bubbles or particles while the smallscale kinetic energy is significantly enhanced. Unexpectedly, the modulation of turbulence depends only slightly on the dispersion characteristics (bubble entrapment in vortices or inertial sweeping of the solid particles) but is closely related to the stresslet component (finite size effect) of the flow disturbances. The pivoting wavenumber characterizing the transition from damped to enhanced energy content is shown to vary with the size of the bubbles or particles. The spectrum for the energy transfer by the particle phase is examined and the possibility of representing this, at large scales, through an additional effective viscosity is discussed
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring
Inferring the source information of greenhouse gases, such as methane, from
spatially sparse sensor observations is an essential element in mitigating
climate change. While it is well understood that the complex behavior of the
atmospheric dispersion of such pollutants is governed by the
Advection-Diffusion equation, it is difficult to directly apply the governing
equations to identify the source location and magnitude (inverse problem)
because of the spatially sparse and noisy observations, i.e., the pollution
concentration is known only at the sensor locations and sensors sensitivity is
limited. Here, we develop a multi-task learning framework that can provide
high-fidelity reconstruction of the concentration field and identify emission
characteristics of the pollution sources such as their location, emission
strength, etc. from sparse sensor observations. We demonstrate that our
proposed framework is able to achieve accurate reconstruction of the methane
concentrations from sparse sensor measurements as well as precisely pin-point
the location and emission strength of these pollution sources.Comment: 7 pages, 8 figures, 1 tabl
Optimal Sensor Allocation with Multiple Linear Dispersion Processes
This paper considers the optimal sensor allocation for estimating the
emission rates of multiple sources in a two-dimensional spatial domain.
Locations of potential emission sources are known (e.g., factory stacks), and
the number of sources is much greater than the number of sensors that can be
deployed, giving rise to the optimal sensor allocation problem. In particular,
we consider linear dispersion forward models, and the optimal sensor allocation
is formulated as a bilevel optimization problem. The outer problem determines
the optimal sensor locations by minimizing the overall Mean Squared Error of
the estimated emission rates over various wind conditions, while the inner
problem solves an inverse problem that estimates the emission rates. Two
algorithms, including the repeated Sample Average Approximation and the
Stochastic Gradient Descent based bilevel approximation, are investigated in
solving the sensor allocation problem. Convergence analysis is performed to
obtain the performance guarantee, and numerical examples are presented to
illustrate the proposed approach
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process
We present a super-resolution model for an advection-diffusion process with
limited information. While most of the super-resolution models assume
high-resolution (HR) ground-truth data in the training, in many cases such HR
dataset is not readily accessible. Here, we show that a Recurrent Convolutional
Network trained with physics-based regularizations is able to reconstruct the
HR information without having the HR ground-truth data. Moreover, considering
the ill-posed nature of a super-resolution problem, we employ the Recurrent
Wasserstein Autoencoder to model the uncertainty.Comment: 9 pages, 8 figure