Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models

Accurate determination of fuel properties of complex mixtures over a wide range of pressure and temperature conditions is essential to utilizing alternative fuels. The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. Here, Gaussian Process (GP) and probabilistic generative models are adopted. GP is a popular non-parametric Bayesian approach to build surrogate models mainly due to its capacity to handle the aleatory and epistemic uncertainties. Generative models have shown the ability of deep neural networks employed with the same intent. In this work, ML analysis is focused on two particular properties, the fuel density and diffusion, but it can also be extended to other physicochemical properties. This study explores the versatility of the ML models to handle multi-fidelity data. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.The research leading to these results had received funding from the Brazilian National Agency of Petroleum, Natural Gas and Biofuels (ANP) through Programa de Recursos Humanos (PRH) under the PRH 8 - Mechanical Engineering for the Efficient Use of Biofuels, grant agreement numbers F0A5.EDDE.B5C0.3BCB and 2B61.4F5C.A83B.A713.Peer ReviewedPostprint (published version

An Experimental Assessment of Transverse Adaptive Fir Filters as Applied to Vibrating Structures Identification

The present work is aimed at assessing the performance of adaptive Finite Impulse Response (FIR) filters on the identification of vibrating structures. Four adaptive algorithms were used: Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS), Transform-Domain Least Mean Squares (TD – LMS) and Set-Membership Binormalized Data-Reusing LMS Algorithm (SM – BNDRLMS). The capability of these filters to perform the identification of vibrating structures is shown on real experiments. The first experiment consists of an aluminum cantilever beam containing piezoelectric sensors and actuators and the second one is a steel pinned-pinned beam instrumented with accelerometers and an electromechanical shaker

Nonlinear dynamics and control of multibody systems

National audienc

Computational simulation of hydraulic fracturing nonlinear dynamics using Gaussian processes surrogates

High-Fidelity physics based computational models enables the design and optimization of complex engineered processes. Moreover, important and strategic decisions might be taken relying on those computational models predictions. Therefore, there is a need for improving their robustness and reliability. Therefore, understanding the impacts on the predictions due to unavoidable input and model structures uncertainties, often referred to as Uncertainty Quantification (UQ), has become a major issue. A key aspect in this context is the demand of a significant computational effort involving many-queries of a computer code. That might be lessen by the use of reduced order models or any form of surrogates. Here, we employ Gaussian Processes (GPs) as a surrogate (often referred to as emulators) for a computer code devoted to Hydraulic Fracturing simulation.Non UBCUnreviewedThis collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.Facult

A STAGGERED SCHEME WITH ADAPTIVE TIME STEP CONTROL FOR FLUID-STRUCTURE INTERACTION

The coupling between a rigid body and an incompressible fluid is investigated. Within the framework of ALE, we use a residual-based variational multiscale (RBVMS) formulation to solve the incompressible Navier Stokes equations. Mesh updating is accomplished by a parallel edge-based solution of a non-homogeneous scalar diffusion problem in each spatial coordinate. This work is in the continuation of previous results presented in Miras et al. (2015). We use here a staggered type of coupling with a prediction/correction approach for the forces applied on the rigid body. A time stepping by a Proportional-Integral-Derivative controller based on the CFL number is also presented. The coupling approach is tested on different cases coming from the literature and in the area of Vortex Induced Vibrations (VIV), allowing to evaluate the performance of the method in term of accuracy and robustness. We give particular attention to the parameters used to compute the force/moment prediction