A sixth-order finite volume method for the 1D biharmonic operator: application to intramedullary nail simulation

A new very high-order finite volume method to solve problems with harmonic and biharmonic operators for one- dimensional geometries is proposed. The main ingredient is polynomial reconstruction based on local interpolations of mean values providing accurate approximations of the solution up to the sixth-order accuracy. First developed with the harmonic operator, an extension for the biharmonic operator is obtained, which allows designing a very high-order finite volume scheme where the solution is obtained by solving a matrix-free problem. An application in elasticity coupling the two operators is presented. We consider a beam subject to a combination of tensile and bending loads, where the main goal is the stress critical point determination for an intramedullary nail.

Evaluation of the DPMFoam solver

[Extrat] Multiphase flows are relevant in several industrial processes, thus the availability of accurate numerical modeling tools, able to support the design of products and processes, is of much significance. OpenFOAM version 2.3.x comprises a multiphase flow solver able to couple Eulerian and Lagrangian phases using the discrete particles method (DPM), the DPMFoam. In this work the DPMFoam solver is assessed by comparing its predictions with analytical results and experimental and simulated data available in the literature. They are results from Goldschmidt’s [1] and Hoomans’s [2] theses and the analytical Ergun equation. The goal was to define accuracy and performance of DPMFoam in general scientific or commercial applications. Obtained results demonstrate a good agreement with the reference simulation data and is within reasonable deviations from the experimental values. (...)

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

Simulation of OWES with five-Level converter linked to the grid: Harmonic assessment

This paper deals with a computing simulation for an offshore wind energy system taking into account the influence of the marine waves action throughout the floating platform. The wind energy system has a variable-speed turbine equipped with a permanent magnet synchronous generator and a full-power five level converter, injecting energy into the electric grid through a high voltage alternate current link. A reduction on the unbalance of the voltage in the DC-link capacitors of the five-level converter is proposed by a strategic selection of the output voltage vectors. The model for the drive train of the wind energy system is a two mass model, including the dynamics of the floating platform. A case study is presented and the assessment of the quality of the energy injected into the electric grid is discussed.

Numerical simulation of the reflow soldering process

Dissertação de mestrado integrado em Mechanical Engineering

An interactive web-based tool for breast reduction surgery simulation

In this work, we present a 3D web-based interactive tool for numerical modeling and simulation approach to breast reduction surgery simulation, to assist surgeons in planning all aspects related to breast reduction surgery before the actual procedure takes place, thereby avoiding unnecessary risks. In particular, it allows the modeling of the initial breast geometry, the definition of all aspects related to the surgery and the visualization of the post-surgery breast shape in a realistic environment.

Modelling Z→ττ processes in ATLAS with τ-embedded Z→μμ data

This paper describes the concept, technical realisation and validation of a largely data-driven method to model events with Z→ττ decays. In Z→μμ events selected from proton-proton collision data recorded at s√=8 TeV with the ATLAS experiment at the LHC in 2012, the Z decay muons are replaced by τ leptons from simulated Z→ττ decays at the level of reconstructed tracks and calorimeter cells. The τ lepton kinematics are derived from the kinematics of the original muons. Thus, only the well-understood decays of the Z boson and τ leptons as well as the detector response to the τ decay products are obtained from simulation. All other aspects of the event, such as the Z boson and jet kinematics as well as effects from multiple interactions, are given by the actual data. This so-called τ-embedding method is particularly relevant for Higgs boson searches and analyses in ττ final states, where Z→ττ decays constitute a large irreducible background that cannot be obtained directly from data control samples.

An integrated model for simulation of construction phasing of arch concrete dams

Dissertação de mestrado integrado em Civil Engineering

Estimação da função de sobrevivência em dados de eventos recorrentes com censura pela direita

Dissertação de mestrado em Estatística

Single molecule simulation of diffusion and enzyme kinetics

This work presents a molecular-scale agent-based model for the simulation of enzymatic reactions at experimentally measured concentrations. The model incorporates stochasticity and spatial dependence, using diffusing and reacting particles with physical dimensions. We developed strategies to adjust and validate the enzymatic rates and diffusion coefficients to the information required by the computational agents, i.e., collision efficiency, interaction logic between agents, the time scale associated with interactions (e.g., kinetics), and agent velocity. Also, we tested the impact of molecular location (a source of biological noise) in the speed at which the reactions take place. Simulations were conducted for experimental data on the 2-hydroxymuconate tautomerase (EC 5.3.2.6, UniProt ID Q01468) and the Steroid Delta-isomerase (EC 5.3.3.1, UniProt ID P07445). Obtained results demonstrate that our approach is in accordance to existing experimental data and long-term biophysical and biochemical assumptions.