Breakout Session

  14:30 - 14:50

Wartsila Italia S.p.A | Luciano Perinel
EnginSoft | Gianluca Parma
Large bore engine lubrication system: oil flow and pressure analysis by Moving Particle Simulation

Lubrication is a crucial aspect when talking about efficiency and durability of any moving machine parts and, in particular, for large internal combustion engines. Wärtsilä and EnginSoft built a Moving Particle Simulation model (MPS) to analyse the big-end-bearings oil feeding system of an 18-cylinders engine configuration. MPS is a mesh-less method to solve the Navier-Stokes equations which discretize fluid as a gathering of particles. Modelling and simulation of the engine using Finite Volume CFD techniques would be unfeasible due to the motion of the engine parts, which would make the management of the mesh motion not practicable. By simulating the flow through the channels, the Moving Particle Simulation model allowed calculating the transient pressure behaviour in crucial areas of the bearing. Comparison of two bearing designs highlighted differences in terms of pressure stability, peaks and low values that could potentially lead to cavitation issues

Curriculum L. Perinel   |   Curriculum G. Parma

  14:50 - 15:10

Alfa Laval | Nicoletta Spazzadeschi
Alfa Laval | Danish Taufiq
Validation of a Methanol fuel supply system model of a 2-stroke dual-fuel marine diesel engine

Methanol is recognized as one of the strategic fuels to achieve the ambitious decarbonization goals set by the International Maritime Organization for the next decades. As a leader company in the marine sector, Alfa Laval has experience in designing and supplying methanol fuel-supply systems for marine diesel engines. In the present study, Alfa Laval together with Enginsoft shows the results of the simulation of a methanol fuel supply system currently running on a methanol carrier. Scope of the study is to develop a validated CFD 1-D model, able to reproduce the existing set of data collected from a real plant, both in steady-state and transient conditions, as well as its interaction with upstream and downstream parts of the overall fuel line, from tank to engine. The validated model will allow Alfa Laval to simulate the behavior of the plant under different conditions and to give remote support to customers, setting up the basis for a new digital approach to product development.

Curriculum N. Spazzadeschi   |   Curriculum D. Taufiq

  15:10 -15:30

Cubit | Michele Farnesi
An Innovative Optimization Procedure for Supercavitating Wing Sections

The challenging design of a high-performance speed craft, which aims to set the new sailing speed record, requires an hydrodynamics optimization procedure with an innovative approach to obtain the desired performances facing with computational costs and component feasibility. A 2D section is optimized in supercavitating regimes in compliance to structural and manufacturer technological requirements of the section-based wing. The section’s hydrodynamics features are evaluated with a CFD tool; then, thanks to the knowledge built during this project, it is possible to fulfill the lack of know how about supercavitating wings behavior to obtain the section-based 3D wing’s overall performances prediction. The feasibility of the wing is evaluated with a FEM analysis using a custom force imposing method based on CFD outputs. This procedure led to a well-defined and fast design space exploration obtaining a wing Drag reduction of more than 25% respect a reference solution with an imposed target lift.

Curriculum

  15:30 - 15:50

University of Modena and Reggio Emilia | Andrea Fornaciari
Upshifting Next Generation Suspension Design

This study presents the work undertaken by Dana Incorporated to develop a new independent suspension axle for an off-highway vehicle (OHV). This multi-disciplinary simulation activity combines improvements to the kinematic and dynamic performance of the suspension while also examining the constraints of the mechanical design and the hydraulic system, as well as the cost of the suspension. The primary goal of this study was to assess the capabilities of an automated optimization process developed using design optimization software “modeFRONTIER” which integrated several tools including Creo®, MSC Adams®, and Simcenter Amesim®. This project also served to assist Dana in successfully integrating this methodology into future workflow process enhancing independent suspension axles.

Curriculum

15:50 – 16:20 BREAK

  16:20 - 16:40

FCMS | Werner Pohl
Environmentally friendly travel bus design of the future

This paper focusses on innovative methods to reduce the CO2-emission of travel busses by new ways to realize weight reduction. A combination of shape variants for load carrying bus sub-structures with a mix of usual mild steel and high-strength stainless steel, making use of a geometry-driven design, fast running surrogate models, comprehensive design optimization, and an automated refeed of optimized surrogate models into detailed correct geometry models for validation analyses unveals weight potentials, which cannot be discovered by traditional CAE methodology. Technically, the key is a full associativity between CAD and CAE. Commercially, the cost aspects are made part of the design process. Both material and welding cost have been considered. Loadings are stress, fatigue and rollover based on internationally accepted layout criteria for travel busses

Curriculum

  16:40 - 17:00

RICARDO | Pietro Del Negro
Fatigue Analysis on a Motorcycle Frame

Fatigue Analysis on a Motorcycle Frame using FEMFAT. The Fatigue analysis was performed on the structure itself and on the welded seams. In the presentation will be shown the comparison between numerical results and phisical fatigue tests results performed on the motorcycle.

Curriculum

  17:00 - 17:20

Monolith AI | Isabel Ashworth
Predicting 3D fields of transient CFD simulation data using deep learning to accelerate the development of Internal Combustion Engines

As in many engineering industries, development timelines for Internal Combustion Engines (ICE) are too strict to allow for full exploration of design permutations through large volumes of simulations and physical tests. To address this challenge, this study demonstrates how the performance of new ICE designs simulated under new conditions can be predicted using machine learning. Using a historic dataset of transient CFD data, deep learning models prove to be able to accurately predict the transient evolution of scalar performance metrics, but also entire 3D flow fields of simulation data. With such a simulation-machine learning approach, a complete understanding of the correlation between design variables and simulation outcomes is established. This allows finding performance-optimised designs for target simulation metrics in near-real-time. This study was developed by Monolith AI in partnership with Siemens Digital Industries Software.

Curriculum