Lightning Threat Forecast Simulation Using the Schrodinger- Electrostatic Algorithm

Many models have been propounded for forecasting lightning. Though majority of the model had shown accuracy, the response time in detecting natural phenomenon is quite low. In this model, we used the mathematical experimentation of the micro scale plasmas to develop the macro scale atmospheric plasma which we believe is a major influence of lightning.The Schrödinger-electrostatic algorithm was propounded to further increase both the accuracy and alacrity of detecting natural phenomena. According to our theoretical experimentation, the air density plays a major role in lightning forecast.Our guess was verified using the Davis Weather Station to track the air density both in the upper and lower atmosphere.The air density in the upper atmosphere showed prospect as a vital factor for lightning forecast

Manufacturability and Analysis of Topologically Optimized Continuous Fiber Reinforced Composites

Researchers are unlocking the potential of Continuous Fiber Reinforced Composites for producing components with greater strength-to-weight ratios than state of the art metal alloys and unidirectional composites. The key is the emerging technology of topology optimization and advances in additive manufacturing. Topology optimization can fine tune component geometry and fiber placement all while satisfying stress constraints. However, the technology cannot yet robustly guarantee manufacturability. For this reason, substantial post-processing of an optimized design consisting of manual fiber replacement and subsequent Finite Element Analysis (FEA) is still required. To automate this post-processing in two dimensions, two (2) algorithms were developed. The first one is aimed at filling the space of a topologically optimized component with fibers of prescribed thickness. The objective is to produce flawless fiber paths, meaning no self-intersections, no tight turns, and no overlapping between fibers. It does so by leveraging concepts from elementary geometry and the Signed Distance Function of a topologically optimized domain. The manufacturable fiber paths are represented using Non-Uniform Rational Basis Splines, which can be readily conveyed to a 3D-printer as The second algorithm then calls a meshing routine to spatially discretize the topologically optimized domain. It takes input from the first algorithm to automatically create and append, orientations and material flags to the spatial elements produced by the meshing routine. Finally, it generates output that is then input to FEA software. The software is written in the C-programming language using the PETSc library. A load case is validated against MSC NASTRAN

Modelo de interconexión y entrenamiento redes neuronales artificiales, basado en recompensa y castigo empleando técnicas de vida artificial

A diferencia de las redes neuronales artificiales, los modelos biológicos desarrollan su arquitectura de manera espontánea usando reglas de interconexión y aprendizaje determinados en principio por mecanismos genéticos pero que se desarrollan de acuerdo a la influencia del medio ambiente. A lo largo de este trabajo se aplica un mecanismo de señalización basado en el modelo de reacción-difusión de Gierer-Meinhardt para construir la morfología de una red inicialmente no conectada. Durante el proceso de morfogénesis al mismo tiempo que se genera la morfología de interconexión, la red modifica los pesos sinápticos empleando información de error originada en la salida y propagada hacia adelante a través de las conexiones sinápticas. A partir de la experimentación se observa una clara influencia de los mecanismos de señalización molecular sobre las reglas de propagación local, acortando considerablemente el tiempo de interconexión neuronal. El aprendizaje mediante una regla local simple ocurre durante el proceso de morfogénesis siempre que las entradas logren hacer sinapsis directa o indirectamente con la salida.Abstract. Unlike artificial neural networks, biological models develop spontaneous architecture using certain rules of interconnection and learning defined initially by genetic mechanisms but strongly affected by the influence of the environment. Throughout this paper, a signaling mechanism based on the reaction-diffusion model of Gierer-Meinhardt generates the morphology of an initially unconnected network. While morphogenesis occurs, the network modifies its synaptic weights using error information caused in the output and propagated back through synaptic connections. From experimentation a clear influence of molecular signaling mechanisms on the rules of local spread, shortening considerably the observed time of neuronal interconnection. Learning through a simple local rule occurs during morphogenesis only if there are direct or indirect sinapses among inputs and the output.Maestrí