Modeling the properties of complex alloys such as nickel superalloys is an extremely challenging scientific and engineering task. The model should take into account a large number of uncorrelated factors, for many of which information may be missing or vague. The individual contribution of one or another chemical element out of a dozen possible ligants cannot be determined by traditional methods. Moreover, there are no general analytical models describing the influence of elements on the characteristics of alloys. Artificial neural networks are one of the few statistical modeling tools that can account for many implicit correlations and establish correspondences that cannot be identified by other more familiar mathematical methods. However, such networks require careful tuning to achieve high performance, which is time-consuming. Data preprocessing can make model training much easier and faster. This article focuses on combining physics-based deep network configuration and input data engineering to simulate the solvus temperature of nickel superalloys. The used deep artificial neural network shows good simulation results. Thus, this method of numerical simulation can be easily applied to such problems.

The aim of this work is a numerical comparison (FEM) between lattice pyramidal-core panel and honeycomb core panel for different core thicknesses. By evaluating the mid-span deflection, the shear rigidity and the shear modulus for both core types and different core thicknesses, it is possible to define which core type has got the best mechanical behaviour for each thickness and the evolution of that behaviour as far as the thickness increases. Since a specific base geometry has been used for the lattice pyramidal core, the comparison gives us the opportunity to investigate the unit cell strut angle giving the higher mechanical properties. The presented work considers a detailed FEM modelling of a standard 3-point bending test (ASTM C393/C393M Standard Practice). Detailed FEM modelling addresses to detailed discretization of cores by means of beam elements for lattice core and shell elements for honeycomb core. Facings, instead, have been modelled by using shell elements for both sandwich panels. On lattice core structure, elements of core and facings are directly connected, to better simulate the additive manufacturing process. Otherwise, an MPC-based constraint between facings and core has been used for honeycomb core structure. Both sandwich panels are entirely built of Aluminium alloy. Prior to compare the two models, the FEM sandwich panel model with lattice pyramidal core needs to be validated with 3-point bending test experimental results, in order to ensure a good reliability of the FEM approach and of the comparison. Furthermore, the analytical validation has been performed according to Allen's theory. The FEM analysis is linear static with an increasing midspan load ranging from 50N up to 500N.

In the present paper, the optimal selection of batteries for an electric pump-fed hybrid rocket engine is analyzed. A two-stage Mars Ascent Vehicle, suitable for the Mars Sample Return Mission, is considered as test case. A single engine is employed in the second stage, whereas the first stage uses a cluster of two engines. The initial mass of the launcher is equal to 500 kg and the same hybrid rocket engine is considered for both stages. Ragone plot-based correlations are embedded in the optimization process in order to chose the optimal values of specific energy and specific power, which minimize the battery mass ad hoc for the optimized engine design and ascent trajectory. Results show that a payload close to 100 kg is achievable considering the current commercial battery technology.

Secondary flows have a huge impact on losses generation in modern low pressure gas turbines (LPTs). At design point, the interaction of the blade profile with the end-wall boundary layer is responsible for up to 40% of total losses. Therefore, predicting accurately the end-wall flow field in a LPT is extremely important in the industrial design phase. Since the inlet boundary layer profile is one of the factors which most affects the evolution of secondary flows, the first main objective of the present work is to investigate the impact of two different inlet conditions on the end-wall flow field of the T106A, a well known LPT cascade. The first condition, labeled in the paper as C1, is represented by uniform conditions at the inlet plane and the second, C2, by a flow characterized by a defined inlet boundary layer profile. The code used for the simulations is based on the Discontinuous Galerkin (DG) formulation and solves the Reynolds-averaged Navier-Stokes (RANS) equations coupled with the Spalart Allmaras turbulence model. Secondly, this work aims at estimating the influence of viscosity and turbulence on the T106A end-wall flow field. In order to do so, RANS results are compared with those obtained from an inviscid simulation with a prescribed inlet total pressure profile, which mimics a boundary layer. A comparison between C1 and C2 results highlights an influence of secondary flows on the flow field up to a significant distance from the end-wall. In particular, the C2 end-wall flow field appears to be characterized by greater over turning and under turning angles and higher total pressure losses. Furthermore, the C2 simulated flow field shows good agreement with experimental and numerical data available in literature. The C2 and inviscid Euler computed flow fields, although globally comparable, present evident differences. The cascade passage simulated with inviscid flow is mainly dominated by a single large and homogeneous vortex structure, less stretched in the spanwise direction and closer to the end-wall than vortical structures computed by compressible flow simulation. It is reasonable, then, asserting that for the chosen test case a great part of the secondary flows details is strongly dependent on viscous phenomena and turbulence.

This work deals with an explicit guidance and control architecture for autonomous lunar ascent and orbit injection, i.e., the locally-flat near-optimal guidance, accompanied by nonlinear reduced-attitude control. This is a new explicit guidance scheme, based on the local projection of the position and velocity variables, in conjunction with the real-time solution of the associated minimum-time problem. A recently-introduced quaternion-based reduced-attitude control algorithm, which enjoys quasi-global stability properties, is employed to drive the longitudinal axis of the ascent vehicle toward the desired direction. Actuation, based on thrust vectoring, is modeled as well. Extensive Monte Carlo simulations prove the effectiveness of the guidance, control, and actuation architecture proposed in this study for precise lunar orbit insertion, in the presence of nonnominal flight conditions.

Space-telescopes placed in the Sun-Earth second Lagrange point (L2) observe the sky following a scan strategy that is usually based on a spin-precession motion. Knowing which regions of the sky will be more observed by the instrument is important for the science operations and the instrument calibration. Computing sky observation parameters numerically (discretizing time and the sky) can consume large amounts of time and computational resources, especially when high resolution isrequired.This problem becomesmore critical if quantities are evaluated at detector level instead of considering the instrument entire Field of View (FoV). In previous studies, the authors have derived analytic solutions for quantities that characterize the observation of each point in the sky in terms of observation time according to the scan strategy parameters and the instrument FoV. Analytic solutions allow to obtain results faster than using numerical methods as well as capture detailed characteristics which can be overseen due to discretization limitations. The original approach is based on the analytic expression of the instrument trace over the sky. Such equations are implicit and thusrequiresthe use of numeric solversto compute the quantities.In this work, a new and simpler approach for computing one ofsuch quantities(mean observation time) is presented.The quantity is first computed for pure spin motion and then the effect of the spin axis precession is incorporated under the assumption that the precession motion is slow compared to the spin motion.In this sense, this new approach further simplifies the analytic approach, sparing the use of numeric solvers, which reduces the complexity of the implementation and the computing time.

This study compares various ways of calculating flows for the problems with the presence of shock waves by first-order schemes and higher-order DG method on the tests from the Quirk list, namely: Quirk's problem and its modifications, shock wave diffraction at a 90 degree corner, the problem of double Mach reflection. It is shown that the use of HLLC and Godunov's numerical schemes flows in calculations can lead to instability, the Rusanov-Lax-Friedrichs scheme flow can lead to high dissipation of the solution. The most universal in heavy production calculations are hybrid schemes flows, which allow the suppression of the development of instability and conserve the accuracy of the method.

This work concerns the investigation of a Q1D methodology employed to study pressure oscillations in solid rocket motors driven by hydrodynamic instabilities. A laboratory-scale solid motor designed to develop vortex-shedding phenomena is analyzed for the whole firing time. The comparison between numerical results and experimental data shows good agreement regarding pressure oscillations signature, especially in the flute-mode behavior, the typical oscillations frequency trend present in any motor liable to hydrodynamic instabilities. Such result ensures the model capability to cope with this particular kind of pressure oscillations source, allowing the investigation of the phenomenon with a lighter and cost savings methodology than CFD simulations.