Hybrid physical-AI based system modeling and simulation approach demonstrated on an automotive fuel cell

This paper presents an approach on how to train a Neural Network model based on a detailed physical Modelica model. The necessary steps to generate training data from simulation will be explained as well as the generation process of a surrogate model. It will be shown, how the surrogate will be re-integrated into the Modelica system model. A benchmark based on accuracy and simulation performance will be performed. The tools used are Modelon Impact, an online modeling and simulation platform, the TensorFlow/Keras toolbox in a Jupyter Notebook which provides a Python-based interface for generating Neural Networks, and the Modelica Neural Network Library that provides functions for constructing Neural Networks within Modelica. The approach is demonstrated on an automotive fuel cell model which is part of an overall vehicle system model. One possible application is to train the neural network via repeated simulations and then to reuse it as an embedded software component for efficiently estimating fuel use and range for various driving cycles and ambient conditions

Corrigendum to Numerical assessment of fan blades screen effect on fan/OGV interaction tonal noise [Journal of Sound and Vibration, 481 September 2020 115428]

[Abstract:] The authors apologise for any inconvenience.The authors acknowledge the financial support from Safran Aircraft Engines. Xesús Nogueira and Luis Ramírez acknowledge the support given by FEDER funds of the European Union, Grants #DPI2015- 68431-R of the Ministerio de Economía y Competitividad and #RTI2018-093366-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of the Spanish Government, and by the Consellería de Cultura, Educación e Ordenación Universitaria of the Xunta de Galicia (program Axudas para potenciación de grupos de investigación do Sistema Universitario de Galicia 2018, grant #ED431C 2018/41)

Axial turbo-expander design for organic Rankine cycle waste-heat recovery with comparative heavy-duty diesel engine drive-cycle performance assessment

Despite the high thermal efficiency achieved by modern heavy-duty diesel engines, over 40% of the energy contained in the fuel is wasted as heat either in the cooling or the exhaust gases. By recovering part of the wasted energy, the overall thermal efficiency of the engine increases and the pollutant emissions are reduced. Organic Rankine cycle (ORC) systems are considered a favourable candidate technology to recover exhaust gas waste heat, because of their simplicity and small backpressure impact on the engine performance and fuel consumption. The recovered energy can be transformed into electricity or directly into mechanical power. In this study, an axial turbine expander for an ORC system was designed and optimized for a heavy-duty diesel engine for which real-world data were available. The impact of the ORC system on the fuel consumption under various operating points was investigated. Compared to an ORC system equipped with a radial turbine expander, the axial design improved fuel consumption by between 2 and 10% at low and high engine speeds. Finally, the benefits of utilising ORC systems for waste heat recovery in heavy-duty trucks is evaluated by performing various drive cycle tests, and it is found that the highest values of fuel consumption were found in the NEDC and the HDUDDS as these cycles generally involve more dynamic driving profiles. However, it was in these cycles that the ORC could recover more energy with an overall fuel consumption reduction of 5 and 4.8%, respectively

A very fast high-order flux reconstruction for Finite Volume schemes for Computational Aeroacoustics

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract:] Given the small wavelengths and wide range of frequencies of the acoustic waves involved in Aeroacoustics problems, the use of very accurate, low-dissipative numerical schemes is the only valid option to accurately capture these phenomena. However, as the order of the scheme increases, the computational time also increases. In this work, we propose a new high-order flux reconstruction in the framework of finite volume (FV) schemes for linear problems. In particular, it is applied to solve the Linearized Euler Equations, which are widely used in the field of Computational Aeroacoustics. This new reconstruction is very efficient and well suited in the context of very high-order FV schemes, where the computation of high-order flux integrals are needed at cell edges/faces. Different benchmark test cases are carried out to analyze the accuracy and the efficiency of the proposed flux reconstruction. The proposed methodology preserves the accuracy while the computational time relatively reduces drastically as the order increases.L. Ramírez and X. Nogueira acknowledge the support provided by the [Grant PID2021-125447OB-I00] funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”, and the funds by [Grant TED2021-129805B-I00] funded by MCIN/AEI/ 10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”. They also acknowledge the funding provided by the Xunta de Galicia (Grant #ED431C 2022/06).Xunta de Galicia; ED431C 2022/0

Improved δ-SPH Scheme With Automatic and Adaptive Numerical Dissipation

[Abstract] In this work we present a δ-Smoothed Particle Hydrodynamics (SPH) scheme for weakly compressible flows with automatic adaptive numerical dissipation. The resulting scheme is a meshless self-adaptive method, in which the introduced artificial dissipation is designed to increase the dissipation in zones where the flow is under-resolved by the numerical scheme, and to decrease it where dissipation is not required. The accuracy and robustness of the proposed methodology is tested by solving several numerical examples. Using the proposed scheme, we are able to recover the theoretical decay of kinetic energy, even where the flow is under-resolved in very coarse particle discretizations. Moreover, compared with the original δ-SPH scheme, the proposed method reduces the number of problem-dependent parameters.This research was funded by the Ministerio de Ciencia, Innovación y Universidades of the Spanish Government, grant number #RTI2018-093366-B-I00, by the Consellería de Educación e Ordenación Universitaria of the Xunta de Galicia (grant number #ED431C 2018/41). Xesús Nogueira has also been funded by the Xunta de Galicia through the program Axudas para a mellora, creación, recoñecemento e estruturación de agrupacións estratéxicas do Sistema Universitario de Galicia (grant number # ED431E 2018/11)Xunta de Galicia; ED431C 2018/41Xunta de Galicia; ED431E 2018/1

Using Clinical Evidence in a national continuing medical education program in Italy.

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A naturally anti-diffusive compressible two phases Kapila model with boundedness preservation coupled to a high order finite volume solver

This paper presents a two phases flow model combined with a high order finite volume solver on unstructured mesh. The solver is highly conservative and preserves the sharpness of the interface without any reconstruction. Special care has been taken for boundedness preservation, as a high order scheme does not guaranty the boundedness of the volume fraction. The efficiency of the method is demonstrated with two numerical experiments: the simple advection test and the interaction between the shock and a bubble. Although experiments have been carried out with fine mesh, it is also demonstrated that the method allows satisfactory results to be obtained with coarse mesh

Performance assessment of a standard radial turbine as turbo expander for an adapted solar concentration ORC

Organic Rankine cycles are one of the available solutions for converting low grade heat source into electrical power. However the development of plants tends to be very expansive due to the specific design of the expander. Usually, the input parameters for designing an ORC plant are the temperature and power of the heat and cold sources. They lead to the selection of a working fluid, pressures and temperatures. The expander is then designed based on the required operating parameters. Using standard turbine easily available on the market and with well known performances would allow to reduce the development and manufacturing cost. However, the ORC would have to be adapted to make the expander work in its best conditions. For a solar concentrated heat source, the temperature and power can be adapted by adjusting the concentration factor and the total area of the collector. In this paper, a given gas turbine is considered to be used as the expander of the ORC. Knowing the turbine's performances with air, the optimal operating parameters (pressure, temperature, flow rate and rotational speed) of the ORC with different fluids are sought based on similitude rules. The adaptation aims to maintain the same density evolution, inlet speed triangle and inlet Mach number with the working fluid as with air. The performance maps of the turbine are then computed with CFD simulations and showed a maximum isentropic efficiency close to the one with air, about 78%

Axial Turbo-Expander Design for Organic Rankine Cycle Waste-Heat Recovery With Comparative Heavy-Duty Diesel Engine Drive-Cycle Performance Assessment

Data Availability Statement: The datasets presented in this article are not readily available because of commercial reasons. Requests to access the datasets should be directed to Brunel University [email protected] the high thermal efficiency achieved by modern heavy-duty diesel engines, over 40% of the energy contained in the fuel is wasted as heat either in the cooling or the exhaust gases. By recovering part of the wasted energy, the overall thermal efficiency of the engine increases and the pollutant emissions are reduced. Organic Rankine cycle (ORC) systems are considered a favourable candidate technology to recover exhaust gas waste heat, because of their simplicity and small backpressure impact on the engine performance and fuel consumption. The recovered energy can be transformed into electricity or directly into mechanical power. In this study, an axial turbine expander for an ORC system was designed and optimized for a heavy-duty diesel engine for which real-world data were available. The impact of the ORC system on the fuel consumption under various operating points was investigated. Compared to an ORC system equipped with a radial turbine expander, the axial design improved fuel consumption by between 2 and 10% at low and high engine speeds. Finally, the benefits of utilising ORC systems for waste heat recovery in heavy-duty trucks is evaluated by performing various drive cycle tests, and it is found that the highest values of fuel consumption were found in the NEDC and the HDUDDS as these cycles generally involve more dynamic driving profiles. However, it was in these cycles that the ORC could recover more energy with an overall fuel consumption reduction of 5 and 4.8%, respectively

Theoretical and experimental study of mechanical losses in automotive turbochargers

The aim of the present work is to show an approximation, through an experimental an theoretical study, to quantify the mechanical losses in a turbocharging system. These are linked to the dynamics in the turbo shaft bearings, both axial and radial. Theoretical and experimental methodologies are presented in order to develop a mechanical losses model. The experimental work consists on a measurement campaign in quasi-adiabatic operating conditions, while in the theoretical part, a mathematical model is developed taking into account the radial and axial bearings. The model uses some assumptions in order to solve the Navier-Stokes equations, leading to a simplified model which includes viscosity and the Reynolds number of the oil film formed on the bearings. The proposed model has shown a good agreement with the experimental dataThe authors of this paper wish to thank M.A. Ortiz and V. Ucedo for their invaluable work during the experimental setup and campaign, F.J. Arnau, Ph.D, for its excellent job maintaining and expanding Open WAMs code base and M.A. Reyes-Belmonte for all his hard and rigorous work extrapolating turbine maps and preparing and launching the simulations. This work has been partially supported by the Spanish Ministerio de Ciencia e Innovacion through grant number DPI2010-20891-C02-02.Serrano Cruz, JR.; Olmeda González, PC.; Tiseira Izaguirre, AO.; García-Cuevas González, LM.; Lefebvre, A. (2013). Theoretical and experimental study of mechanical losses in automotive turbochargers. Energy. 55:888-898. https://doi.org/10.1016/j.energy.2013.04.042S8888985