The SUMO toolbox: a tool for automatic regression modeling and active learning

Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alternative. Due to the computational cost of these high fidelity simulations, surrogate models are often employed as a drop-in replacement for the original simulator, in order to reduce evaluation times. In this context, neural networks, kernel methods, and other modeling techniques have become indispensable. Surrogate models have proven to be very useful for tasks such as optimization, design space exploration, visualization, prototyping and sensitivity analysis. We present a fully automated machine learning tool for generating accurate surrogate models, using active learning techniques to minimize the number of simulations and to maximize efficiency

Development and validation of a finite deformation fibre kinking model for crushing of composites

A mesoscale model for fibre kinking onset and growth in a three-dimensional framework is developed and validated against experimental results obtained in-house as well as from the literature. The model formulation is based on fibre kinking theory i.e. the initially misaligned fibres rotate due to compressive loading and nonlinear shear behaviour. Furthermore, the physically-based response is computed in a novel and efficient way using finite deformation theory. The model validation starts by correlating the numerical results against compression tests of specimens with a known misalignment. The results show good agreement of stiffness and strength for two specimens with low and high misalignment. Fibre kinking growth is validated by simulating the crushing of a flat coupon with the fibres oriented to the load direction. The numerical results show very good agreement with experiments in terms of crash morphology and load response

Parametric optimization of bio-inspired engineered sandwich core

The present study aims to design an efficient honeycomb cell structure for enhanced energy absorption. Elytra and bamboo bio-inspired parts were compared using a multi-criteria decision-making methodology (COPRAS) and finite element analysis (through Abaqus/CAE) to select the optimal candidate geometry for the study. A circular elytra-inspired geometry featuring four reinforcing cylinders was selected, demonstrating an increase in Specific Energy Absorption (SEA) of over 68% compared to a baseline geometry of the same mass. Structure optimization, aided by a genetic algorithm (NSGA-II), significantly improved crashworthiness parameters, presenting optimized values for design variables, This resulted in an increase in SEA by up to 94% and a 34% improvement in Crushing Force Efficiency (CFE) compared to a baseline geometry. The robust correlation between the algorithm and Finite Element Method (FEM) results highlights its usefulness for initial design, reducing computational demands. The research selects a circular elytra-inspired geometry featuring four reinforcing cylinders and showcasing the potential of multi-objective optimization algorithm in conjunction with FEM analysis in creating high-performance, lightweight structures for passive safety in aeronautics

Unsupervised Representation Learning for Diverse Deformable Shape Collections

We introduce a novel learning-based method for encoding and manipulating 3D surface meshes. Our method is specifically designed to create an interpretable embedding space for deformable shape collections. Unlike previous 3D mesh autoencoders that require meshes to be in a 1-to-1 correspondence, our approach is trained on diverse meshes in an unsupervised manner. Central to our method is a spectral pooling technique that establishes a universal latent space, breaking free from traditional constraints of mesh connectivity and shape categories. The entire process consists of two stages. In the first stage, we employ the functional map paradigm to extract point-to-point (p2p) maps between a collection of shapes in an unsupervised manner. These p2p maps are then utilized to construct a common latent space, which ensures straightforward interpretation and independence from mesh connectivity and shape category. Through extensive experiments, we demonstrate that our method achieves excellent reconstructions and produces more realistic and smoother interpolations than baseline approaches.Comment: Accepted at International Conference on 3D Vision 202

Strategies of Domain Decomposition to Partition Mesh-Based Applications onto Computational Grids

In this paper, we evaluate strategies of domain decomposition in Grid environment to solve mesh-basedapplications. We compare the balanced distribution strategy with unbalanced distribution strategies. While the former is acommon strategy in homogenous computing environment (e.g. parallel computers), it presents some problems due tocommunication latency in Grid environments. Unbalanced decomposition strategies consist of assigning less workload toprocessors responsible for sending updates outside the host. The results obtained in Grid environments show that unbalanceddistributions strategies improve the expected execution time of mesh-based applications by up to 53%. However, this is not truewhen the number of processors devoted to communication exceeds the number of processors devoted to calculation in thehost. To solve this problem we propose a new unbalanced distribution strategy that improves the expected execution time up to43%. We analyze the influence of the communication patterns on execution times using the Dimemas simulator.Peer ReviewedPostprint (published version

Crash Certification by Analysis - Are We There Yet?

This paper addresses the issue of crash certification by analysis. This broad topic encompasses many ancillary issues including model validation procedures, uncertainty in test data and analysis models, probabilistic techniques for test-analysis correlation, verification of the mathematical formulation, and establishment of appropriate qualification requirements. This paper will focus on certification requirements for crashworthiness of military helicopters; capabilities of the current analysis codes used for crash modeling and simulation, including some examples of simulations from the literature to illustrate the current approach to model validation; and future directions needed to achieve "crash certification by analysis.

Value proposition development of early stage computational fluid dynamics analysis in automotive product development

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2009.Includes bibliographical references (p. 63-64).Concurrent engineering initiatives and the closely related principle of front-loading development processes - identifying and solving problems early rather than waiting for traditional development and test processes to uncover them - have been shown to be highly effective in improving product development performance. This often means shifting to new experimentation technologies that can be used much earlier in the development process than traditional technologies, delivering performance assessments much faster. Thus problems within new design ideas are exposed much sooner, allowing for cost-effective problem solving techniques without having to rewind significant parts of the development process. Front-loading accelerates innovation by permitting new ideas to be tested and refined faster than traditional techniques, allowing them to be incorporated into products without the risks often associated with the use of unproven ideas. Traditional methods might still be needed for fine-tuning a design, but new rapid-feedback technologies have demonstrated their value when used within their limitations. Front-loading has gained acceptance in many vehicle product development organizations, but one field in which it has not yet been introduced for early-stage design assessments and problem solving is air flow analysis. The earliest stages of design for a new vehicle focus largely on the shape and character of the vehicle's surfaces, which in turn have a significant influence on many aspects of the vehicle's performance.(cont.) Thus the introduction of new experimentation technologies like Computational Fluid Dynamics (CFD) requires a great deal more consideration due to their impact on these critical early stages of product development, but the value of these methods and changes can be demonstrated. The resulting changes required in the development organization to support these methods - including preservation of important creative processes and a pragmatic view of the complexities of process change - are found to be complex but approachable given suitable motivation, realistic mindset and a holistic view.by Charles Alexander.S.M

Virtual Test like future tool in the Mechanical Test

This thesis intends to study the viability of using virtual testing in daily work develop by the aerospace industry. In order to do that a phase of research has been done where some technical papers of studies realized by diferent companies are presented to see the results obtained and the benefts. Then the program MSC.ADAMS was the one chosen for this thesis so a frst approach to this software is shown. To put into practice all the new concepts and skills about ADAMS, a piece has been selected to perform a simulation. The piece is the Gravel Defector of the NLG of the aircraft C295. This mechanism is going to be frst simplifed and then several modifcations are going to be performed to improve the simulation as much as possible. Finally, a physical test has been carried out in order to have real results to which the ones of the virtual simulation can be compared.Ingeniería Aeroespacia