Towards Vehicle-Level Simulator Aided Failure Mode, Effect, and Diagnostic Analysis of Automotive Power Electronics Items

The increasing demand for Electronic Control Units able to perform safety-relevant tasks leads the automotive industry to find novel verification methodologies, capable to decrease the time-to-market and, at the same time, to improve the quality of the assessment. The ISO26262:2018 automotive functional safety standard requires to follow a strict development process, compliant with its “safety lifecycle”. It includes all the phases of the item life, from the concept to the decommissioning. The phase that places most difficulties about its objectivity and repeatability is the hardware/software integration verification since, usually, the software is in charge to mitigate the effects of some possible hardware failures. This paper proposes a novel technique, based on a simulation-based approach, to aid the designers during the Failure Mode, Effect, and Diagnostic Analysis (FMEDA). We consider a power electronics module, to be embedded into electric vehicles powertrains, as a challenging practical example. We performed some tests on it, considering a rear traction car with two independent electric motors, one per each wheel. This system, to allow the vehicle to curve, has to act like a differential gear. Hence, it has a strong safety impact on the driveability of the car. All the involved components have been simulated propagating their behaviours up to the entire vehicle. Due the strong coupling between item failures and vehicle dynamics, a structured way based on coupling fault injection with vehicle dynamic simulation is desirable

Pyroclasts of the first phases of the explosive-effusive PCCVC volcanic eruption: physicochemical analysis

The morphology, texture, grain size and other physicochemical characteristics of pyroclastic material from the first phases of the Puyehue-Cordon Caulle volcanic complex (PCCVC) eruption, (Southern Andes, Chile), can be associated to the model recently reported for the magma storage and its ascent conditions. The eruption started June 4th 2011, and the studied volcanic material corresponds to that collected in Argentine territory at different distances from the source, between 4 and 12 June 2011. The explosive-effusive volcanic process of the first days occurred with the simultaneous emplacement of lava flows and the venting of pyroclastic material, ejecting two well differentiated types of particles. The more abundant was constituted by rhyolitic and light color pumice fragments, characterized by a typical vesicular texture, easy fragmentation and absence of occluded crystalline phases. Particles found in minor proportion were dark color, different in shape and texture and rich in Fe and Ti. They seemed to be more effective for the interaction with emitted gases in the upper part of the column, for this reason, they appeared partially covered by condensation products. The ascent conditions of the magma affected its rheological behavior through variations in the degassing, viscosity and fragmentation. On the other hand, distance to the source, depositional time, volcanic evolution and environmental conditions are factors that affect the chemical composition of collected ash. So, the SiO2/FeO ratio not only increases with the distance but also with the deposition time and volcanic activity. The work was done with the aid of several techniques such as a laser-sediment analyzer, X-ray diffraction (XRD), chemical analysis (bulk and surface), SEM microscopy and Raman “microprobe” spectroscopy. On the other hand, the physicochemical behavior of the pyroclastic material allows us to suggest eventual applications

Moving from phenomenological to predictive modelling: Progress and pitfalls of modelling brain stimulation in-silico

Brain stimulation is an increasingly popular neuromodulatory tool used in both clinical and research settings; however, the effects of brain stimulation, particularly those of non-invasive stimulation, are variable. This variability can be partially explained by an incomplete mechanistic understanding, coupled with a combinatorial explosion of possible stimulation parameters. Computational models constitute a useful tool to explore the vast sea of stimulation parameters and characterise their effects on brain activity. Yet the utility of modelling stimulation in-silico relies on its biophysical relevance, which needs to account for the dynamics of large and diverse neural populations and how underlying networks shape those collective dynamics. The large number of parameters to consider when constructing a model is no less than those needed to consider when planning empirical studies. This piece is centred on the application of phenomenological and biophysical models in non-invasive brain stimulation. We first introduce common forms of brain stimulation and computational models, and provide typical construction choices made when building phenomenological and biophysical models. Through the lens of four case studies, we provide an account of the questions these models can address, commonalities, and limitations across studies. We conclude by proposing future directions to fully realise the potential of computational models of brain stimulation for the design of personalized, efficient, and effective stimulation strategies

Stimulating Multiple-Demand Cortex Enhances Vocabulary Learning

It is well established that networks within multiple-demand cortex (MDC) become active when diverse skills and behaviors are being learnt. However, their causal role in learning remains to be established. In the present study, we first performed functional magnetic resonance imaging on healthy female and male human participants to confirm that MDC was most active in the initial stages of learning a novel vocabulary, consisting of pronounceable nonwords (pseudowords), each associated with a picture of a real object. We then examined, in healthy female and male human participants, whether repetitive transcranial magnetic stimulation of a frontal midline node of the cingulo-opercular MDC affected learning rates specifically during the initial stages of learning. We report that stimulation of this node, but not a control brain region, substantially improved both accuracy and response times during the earliest stage of learning pseudoword– object associations. This stimulation had no effect on the processing of established vocabulary, tested by the accuracy and response times when participants decided whether a real word was accurately paired with a picture of an object. These results provide evidence that noninvasive stimulation to MDC nodes can enhance learning rates, thereby demonstrating their causal role in the learning process. We propose that this causal role makes MDC candidate target for exper- imental therapeutics; for example, in stroke patients with aphasia attempting to reacquire a vocabulary

Optical response of strongly absorbing inhomogeneous materials: Application to paper degradation

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