Approximated models for aerodynamic coefficients estimation in a multidisciplinary design environment

In this paper variable fidelity analyses are investigated. Moreover different kind of approximations to be used in a wide multidisciplinary design environment for aircraft design are built. In order to obtain the surrogate models used in the main design process, a proper framework is built by different design of experiments techniques for process and variables management. Approximated models for the estimation of aerodynamic coefficients are evaluated on design spaces of different dimensions and considering different set of variables (i.e. geometric parameters and flight conditions). They are mainly based on the hybrid combination of Vortex Lattice Method (VLM) models representing the basic low fidelity analysis) and 3D finite volume Computational Fluid Dynamics models (representing the basic high fidelity analysis). Different strategies for the evaluation of the surrogate model are considered and an original methodology for the model construction is here presented

Multidisciplinary Integrated Framework for the Optimal Design of a Jet Aircraft Wing

The preliminary design of a jet aircraft wing, through the use of an integrated multidisciplinary design environment, is presented in this paper. A framework for parametric studies of wing structures has been developed on the basis of a multilevel distributed analysis architecture with a "hybrid strategy" process that is able to perform deterministic optimizations and tradeoff studies simultaneously. The particular feature of the proposed multilevel optimization architecture is that it can use different set of variables, defined expressly for each level, in a multi-level scheme using "low fidelity" and "high fidelity" models, as well as surrogate models. The prototype of the design environment has been developed using both commercial codes and in-house tools and it can be implemented in a geographically distributed and heterogeneous IT contex

Multifidelity modeling for the design of re-entry capsules

The design and optimization of space systems presents many challenges associated with the variety of physical domains involved and their coupling. A practical example is the case of satellites and space vehicles designed to re-enter the atmosphere upon completion of their mission [1]. For these systems, aerodynamics and thermodynamics phenomena are strongly coupled and relate to structural dynamics and vibrations, chemical non equilibrium phenomena that characterize the atmosphere, specific re-entry trajectory, and geometrical shape of the body. Blunt bodies are common geometric configurations used in planetary re-entry (e.g. Apollo Command Module, Mars Viking probe, etc.). These geometries permit to obtain high aerodynamic resistance to decelerate the vehicle from orbital speeds along with contained aerodynamic lift for trajectory control. The large radius-of-curvature of the bodies’ nose allows to reduce the heat flux determined by the high temperature effects behind the shock wave. The design and optimization of these bodies would largely benefit from accurate analyses of the re-entry flow field through high-fidelity representations of the aerodynamic and aerothermodynamic phenomena. However, those high-fidelity representations are usually in the form of computer models for the numerical solutions of PDEs (e.g. Navier-Stokes equations, heat equations, etc.) which require significant computational effort and are commonly excluded from preliminary multidisciplinary design and trade-off analysis. This work addresses the integration of high-fidelity computer-based simulations for the multidisciplinary design of space systems conceived for controlled re-entry in the atmosphere. In particular, we discuss the use of multifidelity methods to obtain efficient aerothermodynamic models of the re-entering vehicles. Multifidelity approaches allow to accelerate the exploration and evaluation of design alternatives through the use of different representations of a physical system/process, each characterized by a different level of fidelity and associated computational expense [2, 3]. By efficiently combining less-expensive information from low-fidelity models with a principled selection of few expensive simulations, multifidelity methods allow to incorporate high-fidelity costly information for multidisciplinary design analysis and optimization [4–7]. This presentation proposes a multifidelity Bayesian optimization framework leveraging surrogate models in the form of gaussian processes, which are progressively updated through acquisition functions based on expected improvement. We introduce a novel formulation of the multifideltiy expected improvement including both data-driven and physics-informed utility functions, specifically implemented for the case of the design optimization of an Orion-like atmospheric re-entry vehicle. The results show that the proposed formulation gives better optimization results (lower minimum) than single fidelity Bayesian optimization based on low-fidelity simulations only. The outcome suggests that the multifidelity expected improvement algorithm effectively enriches the information content with the high-fidelity data. Moreover, the computational cost associated with 100 iterations of our multifidelity strategy is sensitively lower than the computational burden of 6 iterations of a single fidelity framework invoking the high-fidelity model. References [1] Gallais, P., Atmospheric re-entry vehicle mechanics, Springer Science and Business Media, 2007. [2] Peherstorfer, B., Willcox, K., and Gunzburger, M., “Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization,” SIAM Review, Vol. 60, 2018, pp. 550–591. [3] Fernandez-Godino, G., Park, C., Kim, N., and Haftka, R., “Issues in Deciding Whether to Use Multifidelity Surrogates,” AIAA Journal, 2019, p. 16. [4] Mainini, L., and Maggiore, P., “A Multifidelity Approach to Aerodynamic Analysis in an Integrated Design Environment,” AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, AIAA, 2012. [5] Goertz, S., Zimmermann, R., and Han, Z. H., “Variable-fidelity and reduced-order models for aero data for loads predictions,” Computational Flight Testing, 2013, pp. 99–112. [6] Meliani, M., Bartoli, N., Lefebvre, T., Bouhlel, M.A., J., Martins, and Morlier, J., “Multi-fidelity efficient global optimization: Methodology and application to airfoil shape design,” AIAA Aviation 2019 Forum, AIAA, 2019. [7] Beran, P., Bryson, D., Thelen, A., Diez, M., and Serani, A., “Comparison of Multi-Fidelity Approaches for Military Vehicle Design,” AIAA Aviation 2020 Forum, AIAA, 2020

Multifidelity domain-aware learning for the design of re-entry vehicles

The multidisciplinary design optimization (MDO) of re-entry vehicles presents many challenges associated with the plurality of the domains that characterize the design problem and the multi-physics interactions. Aerodynamic and thermodynamic phenomena are strongly coupled and relate to the heat loads that affect the vehicle along the re-entry trajectory, which drive the design of the thermal protection system (TPS). The preliminary design and optimization of re-entry vehicles would benefit from accurate high-fidelity aerothermodynamic analysis, which are usually expensive computational fluid dynamic simulations. We propose an original formulation for multifidelity active learning that considers both the information extracted from data and domain-specific knowledge. Our scheme is developed for the design of re-entry vehicles and is demonstrated for the case of an Orion-like capsule entering the Earth atmosphere. The design process aims to minimize the mass of propellant burned during the entry maneuver, the mass of the TPS, and the temperature experienced by the TPS along the re-entry. The results demonstrate that our multifidelity strategy allows to achieve a sensitive improvement of the design solution with respect to the baseline. In particular, the outcomes of our method are superior to the design obtained through a single-fidelity framework, as a result of the principled selection of a limited number of high-fidelity evaluations

Multifidelity Learning for the Design of Re-Entry Capsules

The design and optimization of re-entry capsules presents many challenges associated with the variety of physical domains involved and their couplings. Examples are capsules for the transfer of astronauts to the international space station and for future Lunar and Martian exploration missions. For these vehicles, aerodynamics and thermodynamics phenomena are strongly coupled and relate to structural dynamics and vibrations, chemical non equilibrium phenomena that characterize the atmosphere, specifi c re-entry trajectory, and geometrical shape of the body. The design and optimization of these capsules would largely benefi t from accurate analyses of the re-entry flow field through high- fidelity representations of the aerothermodynamic phenomena. However, those high- fidelity representations are usually in the form of computer models for the numerical solutions of PDEs (e.g. Navier-Stokes equations, heat equations, etc.) which require signifi cant computational effort and are commonly excluded from preliminary multidisciplinary design and trade-off analysis. This presentation discusses the use of multi fidelity methods to integrate high- fidelity simulations in order to obtain efficient aerothermodynamic models of the re-entering vehicles. Multi fidelity approaches allow to accelerate the exploration and evaluation of design alternatives through the use of different representations of a physical system/process, each characterized by a different level of fidelity and associated computational expense. By efficiently combining less-expensive information from low- fidelity models with a principled selection of few expensive simulations, multi fidelity methods allow to incorporate high-fidelity costly information for design analysis and optimization. Speci fically, we propose a multifi delity active learning strategy to accelerate the multidisciplinary design optimization (MDO) of a re-entry vehicle. The active learning scheme is formulated to be both data driven and domain-aware, and is implemented for the design of an Orion-like re-entry capsule. The MDO problem comprises trajectory analysis, propulsion system model, aerothermodynamic models, and structural model of the thermal protection systems (TPS). The design objectives are the minimization of the propellant mass burned during the entry maneuver, the structural mass of the TPS and the temperature reached by the TPS structure. The results show that our multifidelity scheme allows to efficiently improve the design solution through a limited number of high- fidelity evaluations

Observing Grasping Actions Directed to Emotion-Laden Objects: Effects upon Corticospinal Excitability

The motor system is recruited whenever one executes an action as well as when one observes the same action being executed by others. Although it is well established that emotion modulates the motor system, the effect of observing other individuals acting in an emotional context is particularly elusive. The main aim of this study was to investigate the effect induced by the observation of grasping directed to emotion-laden objects upon corticospinal excitability (CSE). Participants classified video-clips depicting the right-hand of an actor grasping emotion-laden objects. Twenty video-clips differing in terms of valence but balanced in arousal level were selected. Motor evoked potentials (MEPs) were then recorded from the first dorsal interosseous using transcranial magnetic stimulation (TMS) while the participants observed the selected emotional video-clips. During the video-clip presentation, TMS pulses were randomly applied at one of two different time points of grasping: (1) maximum grip aperture, and (2) object contact time. CSE was higher during the observation of grasping directed to unpleasant objects compared to pleasant ones. These results indicate that when someone observes an action of grasping directed to emotion-laden objects, the effect of the object valence promotes a specific modulation over the motor system.Fil: Nogueira Campos, Anaelli A.. Universidade Federal de Juiz de Fora; BrasilFil: Saunier, Ghislain. Universidade Federal do Pará; BrasilFil: Della Maggiore, Valeria Monica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Fisiología y Biofísica Bernardo Houssay. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Fisiología y Biofísica Bernardo Houssay; ArgentinaFil: De Oliveira, Laura A. S.. Centro Universitário Augusto Motta; BrasilFil: Rodrigues, Erika C.. Centro Universitário Augusto Motta; BrasilFil: Vargas, Claudia D.. Universidade Federal do Rio de Janeiro; Brasi

Probability of the most massive cluster under non-Gaussian initial conditions

Very massive high redshift clusters can be used to constrain and test the Lambda CDM model. Taking into account the observational constraints of Jee et al. (2009) we have calculated the probability for the most massive cluster to be found in the range (5.2-7.6)e14 M_sun, between redshifts 1.4-2.2, with a sky area of 11 sqdeg and under non-Gaussian initial conditions. Clusters constrain the non-Gaussianity on smaller scales than current cosmic microwave background or halo bias data and so can be used to test for running of the non-Gaussianity parameter f_NL.Comment: Matches MNRAS accepted versio

Free and cued selective reminding test predicts progression to Alzheimer's disease in people with mild cognitive impairment

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A Comprehensive Phenotypic and Functional Immune Analysis Unravels Circulating Anti-Phospholipase A2 Receptor Antibody Secreting Cells in Membranous Nephropathy Patients

Introduction: Primary membranous nephropathy (MN) is characterized by the presence of antipodocyte antibodies, but studies describing phenotypic and functional abnormalities in circulating lymphocytes are limited. Methods: We analyzed 68 different B- and T-cell subsets using flow cytometry in 30 MN patients (before initiating immunosuppression) compared with 31 patients with non-immune-mediated chronic kidney disease (CKD) and 12 healthy individuals. We also measured 19 serum cytokines in MN patients and in healthy controls. Lastly, we quantified the ex vivo production of phospholipase A2 receptor (PLA2R)-specific IgG by plasmablasts (measuring antibodies in culture supernatants and by the newly developed FluoroSpot assay [AutoImmun Diagnostika, Strasberg, Germany]) and assessed the circulating antibody repertoire by phage immunoprecipitation sequencing (PhIP-Seq). Results: After adjusting for multiple testing, plasma cells and regulatory B cells (BREG) were significantly higher (P < 0.05) in MN patients compared with both control groups. The percentages of circulating plasma cells correlated with serum anti-PLA2R antibody levels (P = 0.042) and were associated with disease activity. Ex vivo-expanded PLA2R-specific IgG-producing plasmablasts generated from circulating PLA2R-specific memory B cells (mBCs) correlated with serum anti-PLA2R IgG antibodies (P < 0.001) in MN patients. Tumor necrosis factor-alpha (TNF-alpha) was the only significantly increased cytokine in MN patients (P < 0.05), whereas there was no significant difference across study groups in the autoantibody and antiviral antibody repertoire. Conclusion: This extensive phenotypic and functional immune characterization shows that autoreactive plasma cells are present in the circulation of MN patients, providing a new therapeutic target and a candidate biomarker of disease activity

Gastrointestinal presentation of kawasaki disease: A red flag for severe disease?

Background Kawasaki disease (KD) is a febrile systemic vasculitis of unknown etiology and the main cause of acquired heart disease among children in the developed world. To date, abdominal involvement at presentation is not recognized as a risk factor for a more severe form of the disease. Objective To evaluate whether presenting abdominal manifestations identify a group at major risk for Intravenous immunoglobulin (IVIG)-resistance and coronary lesions. Methods Retrospective study of KD patients diagnosed between 2000 and 2015 in 13 pediatric units in Italy. Patients were divided into 2 groups according to the presence or absence of abdominal manifestations at onset. We compared their demographic and clinical data, IVIG-responsiveness, coronary ectasia/aneurysms, laboratory findings from the acute and subacute phases. Results 302 patients (181 boys) were enrolled: 106 patients with, and 196 patients without presenting abdominal features. Seasonality was different between the groups (p = 0.034). Patients with abdominal manifestations were younger (p = 0.006) and more frequently underwent delayed treatment (p = 0.014). In the acute phase, patients with abdominal presentation had higher platelet counts (PLT) (p = 0.042) and lower albuminemia (p = 0.009), while, in the subacute phase, they had higher white blood cell counts (WBC) and PLT (p = 0.002 and p < 0.005, respectively) and lower red blood cell counts (RBC) and hemoglobin (Hb) (p = 0.031 and p 0.009). Moreover, the above mentioned group was more likely to be IVIG-resistant (p < 0.005) and have coronary aneurysms (p = 0.007). In the multivariate analysis, presenting abdominal manifestations, age younger than 6 months, IVIG- resistance, delayed treatment and albumin concentration in the acute phase were independent risk factors for coronary aneurysms (respectively p<0.005, <0.005, = 0.005 and 0.009). Conclusions This is the first multicenter report demonstrating that presenting gastrointestinal features in KD identify patients at higher risk for IVIG-resistance and for the development of coronary aneurysms in a predominantly Caucasian population