We present a sparse sensing framework based on Dynamic Mode Decomposition (DMD) to identify flow regimes and bifurcations in large-scale thermo-fluid systems. Motivated by real-time control of thermal-fluid flows buildings equipment, we apply this method Direct Numerical Simulation (DNS) data set 2D laterally heated cavity. The resulting solutions can be divided into several regimes, ranging from steady chaotic flow. DMD modes eigenvalues capture the main temporal spatial scales dynamics...

Abstract Building reduced-order models (ROMs) is essential for efficient forecasting and control of complex dynamical systems. Recently, autoencoder-based methods building such have gained significant traction, but their demand data limits use when the scarce expensive. We propose aiding a model’s training with knowledge physics using collocation-based physics-informed loss term. Our innovation builds on ideas from classical collocation numerical analysis to embed known equation into...

This paper develops a data-driven method for control of partial differential equations (PDE) based on deep reinforcement learning (RL) techniques. We design Deep Fitted Q-Iteration (DFQI) algorithm that works directly with high-dimensional representation the state PDE, thus allowing us to avoid model order reduction step common in conventional PDE approaches. apply DFQI problem flow time-varying 2D convection-diffusion as simplified heating, ventilating, air conditioning (HVAC) room. also...

There is considerable interest in the automotive industry computer aided engineering tools that support rapid development of quality products. We describe some design and implementation issues for such a tool, namely, hardware-in-the-loop (HIL) simulator, context powertrain control system software development. This HIL used to verify production controller module (PCM) performance. Hence, driveline dynamics are simulated on hardware PCM "hardware" loop. requirements from users' developer's...

A novel method for reduced-order modeling of turbulent flows is discussed in the context fully Rayleigh-B\'enard convection. The can be used to control mean profiles, discern spectral properties flows, and improve data-driven techniques.

In this paper, we propose a motion planner for quadrotors in windy environments. We extend well-known convex polynomial optimization (CPO) method to incorporate known stochastic input uncertainties. particular, focus on quadrotor unmanned aerial vehicle (UAV), and new objective direct minimization of the squared ${{{\mathcal{L}}}_2}$-norm UAV thrust, $\left\| f \right\|_{{{{\mathcal{L}}}_2}}^2$. show that first two moments \right\|_{{{{\mathcal{L}}}_2}}^2$ are variables CPO problem, can be...

In addition to its public health crisis, COVID-19 pandemic has led the shutdown and closure of workplaces with an estimated total cost more than $16 trillion. Given long hours average person spends in buildings indoor environments, this research article proposes data-driven control strategies design optimal airflow minimize exposure occupants viral pathogens built environments. A general framework is put forward for designing velocity field proximal policy optimization, a reinforcement...

This paper focuses on an application of dynamic mode decomposition (DMD) identification methods and robust estimation theory to thermo-fluid systems modelled by the Boussinesq equations. First, we use Dynamic Mode Decomposition with control (DMDc) construct a reduced order linear model for Due inherent uncertainties in real applications, propose estimators that minimize H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> norm from...

This paper formulates a class of partial differential equation (PDE) control problems as reinforcement learning (RL) problem. We design an RL-based algorithm that directly works with the state PDE, infinite dimensional vector, thus allowing us to avoid model order reduction, commonly used in conventional PDE controller approaches. apply method problem flow for time-varying 2D convection-diffusion simplified heating, ventilating, air conditioning (HVAC) room.

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Recent work has shown that reinforcement learning (RL) is a promising approach to control dynamical systems described by partial differential equations (PDE). This paper shows how use RL tackle more general PDE problems have continuous high-dimensional action spaces with spatial relationship among dimensions. In particular, we propose the concept of descriptors, which encode regularities spatially-extended dimensions and enable agent PDEs. We provide theoretical evidence suggesting this can...

Coupled simulation of building energy systems (BES) and computation fluid dynamics (CFD) often focuses on the integration air handlers with indoor environment, does not incorporate vapor compression into analysis, yielding inaccurate prediction consumption.This paper presents a coupled to explore pull-down performance room conditioning system.The dynamic models air-conditioner are constructed in Modelica, whereas environment is simulated OpenFOAM.Dynamic characteristics will be compared...

Abstract Naïve estimation of horizontal wind velocity over complex terrain using measurements from a single wind-LiDAR introduces bias due to the assumption uniform in any plane. While Computational Fluid Dynamics (CFD)-based methods have been proposed for removal, there are several issues exist implantation. For instance, upstream atmospheric boundary layer thickness or direction unknown. Conventional CFD-based corrections use trial and error estimate bias. Such approaches not only become...

Single-pixel imaging is an efficient image acquisition process where light from a target scene passed through spatial modulator and then projected onto single photodiode with high temporal rate. The reconstruction achieved using computational methods that leverage prior assumptions on the structure. In this paper, we propose to model structure of dynamic spatio-temporal reduced-order learned training data examples. Specifically, by combining single-pixel implemented as neural ordinary...