Fast Neural Network Predictions from Constrained Aerodynamics Datasets

Incorporating computational fluid dynamics in the design process of jets, spacecraft, or gas turbine engines is often challenged by the required computational resources and simulation time, which depend on the chosen physics-based computational models and grid resolutions. An ongoing problem in the field is how to simulate these systems faster but with sufficient accuracy. While many approaches involve simplified models of the underlying physics, others are model-free and make predictions based only on existing simulation data. We present a novel model-free approach in which we reformulate the simulation problem to effectively increase the size of constrained pre-computed datasets and introduce a novel neural network architecture (called a cluster network) with an inductive bias well-suited to highly nonlinear computational fluid dynamics solutions. Compared to the state-of-the-art in model-based approximations, we show that our approach is nearly as accurate, an order of magnitude faster, and easier to apply. Furthermore, we show that our method outperforms other model-free approaches

Recent advances in parametric nonlinear model order reduction: treatment of shocks, contact and interfaces, structure-preserving hyper reduction, acceleration of multiscale formulations, and application to design optimization

International audienceParametric, projection-based, Model Order Reduction (MOR) is a mathematical tool for constructing a parametric Reduced-Order Model (ROM) by projecting a given parametric High Dimensional Model (HDM) onto a Reduced-Order Basis (ROB). It is rapidly becoming indispensable for a large number of applications including, among others, computational-based design and optimization, multiscale analysis, statistical analysis, uncertainty quantification, and model predictive control. It is also essential for scenarios where real-time simulation responses are desired. During the last two decades, linear, projection-based, parametric MOR has matured and made a major impact in many fields of engineering including electrical engineering, acoustics, and structural acoustics, to name only a few. By comparison, nonlinear, projection-based, parametric MOR remains somehow in its infancy. Nevertheless, giant strides have been recently achieved in many of its theoretical, algorithmic, and offline/online organizational aspects. The main purpose of this lecture is to highlight some of these advances, discuss their mathematical and computer science underpinnings, and report on their impact for an important class of problems in aerodynamics, fluid mechanics, nonlinear solid mechanics and structural dynamics, failure analysis, multiscale analysis, uncertainty quantification, and design optimization. To this effect, nonlinear, projection-based, parametric MOR will be first interpreted as a constrained semidiscretization on a subset of a compact Stiefel manifold, using a low-dimensional basis of global shape functions constructed a posteriori — that is, after some knowledge about the response of the system of interest has been developed. Usually, such a knowledge is gathered using the given parametric HDM and an offline training procedure where the model parameters are sampled with a greedy strategy based on a cost-effective ROM error indicator. Specifically, a set of problems related to the parametric problem of interest are solved at the sampled parameter points using the given HDM, and the computed solution snapshots are compressed to obtain the desired global ROB. Depending on the mathematical type of the governing equations underlying the given HDM, a dual ROB is also constructed and the sought-after nonlinear parametric ROM is constructed by Galerkin (or Petrov-Galerkin) projection of the HDM onto the global ROB (and its dual counterpart)

Jacobian Maps Reveal Under-reported Brain Regions Sensitive to Extreme Binge Ethanol Intoxication in the Rat

Individuals aged 12–20 years drink 11% of all alcohol consumed in the United States with more than 90% consumed in the form of binge drinking. Early onset alcohol use is a strong predictor of future alcohol dependence. The study of the effects of excessive alcohol use on the human brain is hampered by limited information regarding the quantity and frequency of exposure to alcohol. Animal models can control for age at alcohol exposure onset and enable isolation of neural substrates of exposure to different patterns and quantities of ethanol (EtOH). As with humans, a frequently used binge exposure model is thought to produce dependence and affect predominantly corticolimbic brain regions. in vivo neuroimaging enables animals models to be examined longitudinally, allowing for each animal to serve as its own control. Accordingly, we conducted 3 magnetic resonance imaging (MRI) sessions (baseline, binge, recovery) to track structure throughout the brains of wild type Wistar rats to test the hypothesis that binge EtOH exposure affects specific brain regions in addition to corticolimbic circuitry. Voxel-based comparisons of 13 EtOH- vs. 12 water- exposed animals identified significant thalamic shrinkage and lateral ventricular enlargement as occurring with EtOH exposure, but recovering with a week of abstinence. By contrast, pretectal nuclei and superior and inferior colliculi shrank in response to binge EtOH treatment but did not recover with abstinence. These results identify brainstem structures that have been relatively underreported but are relevant for localizing neurocircuitry relevant to the dynamic course of alcoholism

Calidad bacteriológica del agua en playas de Valparaíso y Viña del Mar. III contaminación ambiental

Se determina la contaminación microbiológica del agua de mar. en las playas más importantes de la Bahía de Valparaíso utilizando indicadores bacterianos.Coliformes totales y coliformes fecales se utilizaron como indicadores siguiendo los procedimientos de “Standard Methods”Se realiza una comparación de las técnicas de enumeración entre la fermentación de tubos múltiples y la técnica de filtración por membrana.Los resultados son expresados como media geométricas, para cada una de las estaciones estudiadas.Finalmente, se discuten los resultados obtenidos y la significación que podría tener en relación a la salud pública

Design for producing fiberglass fabric in a lunar environment

The purpose of this project was to design a method of producing a fabric material on the lunar surface from readily available glass fibers. Various methods for forming fabrics were analyzed to determine which methods were appropriate for the lunar conditions. A nonwoven process was determined to be the most suitable process for making a fabric material out of fiberglass under these conditions. Various resins were considered for adhering the fibers. A single thermoplastic resin (AURUM) was found to be the only applicable resin. The end product of the process was determined to be suitable for use as a roadway surfacing material, canopy material, reflective material, or packaging material. A cost analysis of the lunar process versus shipping the end-product from the Earth suggests that the lunar formation is highly feasible. A design for a lunar, nonwoven process was determined and is included

Model Order Reduction for Rotating Electrical Machines

The simulation of electric rotating machines is both computationally expensive and memory intensive. To overcome these costs, model order reduction techniques can be applied. The focus of this contribution is especially on machines that contain non-symmetric components. These are usually introduced during the mass production process and are modeled by small perturbations in the geometry (e.g., eccentricity) or the material parameters. While model order reduction for symmetric machines is clear and does not need special treatment, the non-symmetric setting adds additional challenges. An adaptive strategy based on proper orthogonal decomposition is developed to overcome these difficulties. Equipped with an a posteriori error estimator the obtained solution is certified. Numerical examples are presented to demonstrate the effectiveness of the proposed method

Rat strain differences in brain structure and neurochemistry in response to binge alcohol

RATIONALE: Ventricular enlargement is a robust phenotype of the chronically dependent alcoholic human brain, yet the mechanism of ventriculomegaly is unestablished. Heterogeneous stock Wistar rats administered binge EtOH (3 g/kg intragastrically every 8 h for 4 days to average blood alcohol levels (BALs) of 250 mg/dL) demonstrate profound but reversible ventricular enlargement and changes in brain metabolites (e.g., N-acetylaspartate (NAA) and choline-containing compounds (Cho)). OBJECTIVES: Here, alcohol-preferring (P) and alcohol-nonpreferring (NP) rats systematically bred from heterogeneous stock Wistar rats for differential alcohol drinking behavior were compared with Wistar rats to determine whether genetic divergence and consequent morphological and neurochemical variation affect the brain's response to binge EtOH treatment. METHODS: The three rat lines were dosed equivalently and approached similar BALs. Magnetic resonance imaging and spectroscopy evaluated the effects of binge EtOH on brain. RESULTS: As observed in Wistar rats, P and NP rats showed decreases in NAA. Neither P nor NP rats, however, responded to EtOH intoxication with ventricular expansion or increases in Cho levels as previously noted in Wistar rats. Increases in ventricular volume correlated with increases in Cho in Wistar rats. CONCLUSIONS: The latter finding suggests that ventricular volume expansion is related to adaptive changes in brain cell membranes in response to binge EtOH. That P and NP rats responded differently to EtOH argues for intrinsic differences in their brain cell membrane composition. Further, differential metabolite responses to EtOH administration by rat strain implicate selective genetic variation as underlying heterogeneous effects of chronic alcoholism in the human condition

Movement Interventions for Children with Autism and Developmental Disabilities: An Evidence-Based Practice Project

This review explored the following question: Are the comprehensive treatment models Makoto Therapy, Brain Gym, and Interactive Metronome effective interventions for improving occupational performance including improving executive function, academic performance, and physical coordination in children and adolescents with Autism Spectrum Disorder (ASD)? Because current research on Interactive Metronome, Brain Gym®, and Makoto Therapy fails to address children and adolescents with autism spectrum disorder, presents multiple flaws in research design, and does not measure occupational outcomes such as occupational performance, we recommend that these interventions should not be used as comprehensive treatment models in occupational therapy. We recommend that more occupational-based, methodologically-sound research involving youth with ASD be conducted before implementing these interventions in occupational therapy practice