Estimating Mass of Inflatable Aerodynamic Decelerators Using Dimensionless Parameters

This paper describes a technique for estimating mass for inflatable aerodynamic decelerators. The technique uses dimensional analysis to identify a set of dimensionless parameters for inflation pressure, mass of inflation gas, and mass of flexible material. The dimensionless parameters enable scaling of an inflatable concept with geometry parameters (e.g., diameter), environmental conditions (e.g., dynamic pressure), inflation gas properties (e.g., molecular mass), and mass growth allowance. This technique is applicable for attached (e.g., tension cone, hypercone, and stacked toroid) and trailing inflatable aerodynamic decelerators. The technique uses simple engineering approximations that were developed by NASA in the 1960s and 1970s, as well as some recent important developments. The NASA Mars Entry and Descent Landing System Analysis (EDL-SA) project used this technique to estimate the masses of the inflatable concepts that were used in the analysis. The EDL-SA results compared well with two independent sets of high-fidelity finite element analyses

A Multidisciplinary Tool for Systems Analysis of Planetary Entry, Descent, and Landing (SAPE)

SAPE is a Python-based multidisciplinary analysis tool for systems analysis of planetary entry, descent, and landing (EDL) for Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune, and Titan. The purpose of SAPE is to provide a variable-fidelity capability for conceptual and preliminary analysis within the same framework. SAPE includes the following analysis modules: geometry, trajectory, aerodynamics, aerothermal, thermal protection system, and structural sizing. SAPE uses the Python language-a platform-independent open-source software for integration and for the user interface. The development has relied heavily on the object-oriented programming capabilities that are available in Python. Modules are provided to interface with commercial and government off-the-shelf software components (e.g., thermal protection systems and finite-element analysis). SAPE runs on Microsoft Windows and Apple Mac OS X and has been partially tested on Linux

A Survey of Shape Parameterization Techniques

This paper provides a survey of shape parameterization techniques for multidisciplinary optimization and highlights some emerging ideas. The survey focuses on the suitability of available techniques for complex configurations, with suitability criteria based on the efficiency, effectiveness, ease of implementation, and availability of analytical sensitivities for geometry and grids. The paper also contains a section on field grid regeneration, grid deformation, and sensitivity analysis techniques

Multidisciplinary Tool for Systems Analysis of Planetary Entry, Descent, and Landing

Systems analysis of a planetary entry (SAPE), descent, and landing (EDL) is a multidisciplinary activity in nature. SAPE improves the performance of the systems analysis team by automating and streamlining the process, and this improvement can reduce the errors that stem from manual data transfer among discipline experts. SAPE is a multidisciplinary tool for systems analysis of planetary EDL for Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune, and Titan. It performs EDL systems analysis for any planet, operates cross-platform (i.e., Windows, Mac, and Linux operating systems), uses existing software components and open-source software to avoid software licensing issues, performs low-fidelity systems analysis in one hour on a computer that is comparable to an average laptop, and keeps discipline experts in the analysis loop. SAPE uses Python, a platform-independent, open-source language, for integration and for the user interface. Development has relied heavily on the object-oriented programming capabilities that are available in Python. Modules are provided to interface with commercial and government off-the-shelf software components (e.g., thermal protection systems and finite-element analysis). SAPE currently includes the following analysis modules: geometry, trajectory, aerodynamics, aerothermal, thermal protection system, and interface for structural sizing

A Novel Shape Parameterization Approach

This paper presents a novel parameterization approach for complex shapes suitable for a multidisciplinary design optimization application. The approach consists of two basic concepts: (1) parameterizing the shape perturbations rather than the geometry itself and (2) performing the shape deformation by means of the soft objects animation algorithms used in computer graphics. Because the formulation presented in this paper is independent of grid topology, we can treat computational fluid dynamics and finite element grids in a similar manner. The proposed approach is simple, compact, and efficient. Also, the analytical sensitivity derivatives are easily computed for use in a gradient-based optimization. This algorithm is suitable for low-fidelity (e.g., linear aerodynamics and equivalent laminated plate structures) and high-fidelity analysis tools (e.g., nonlinear computational fluid dynamics and detailed finite element modeling). This paper contains the implementation details of parameterizing for planform, twist, dihedral, thickness, and camber. The results are presented for a multidisciplinary design optimization application consisting of nonlinear computational fluid dynamics, detailed computational structural mechanics, performance, and a simple propulsion module

Parametric Mass Modeling for Mars Entry, Descent and Landing System Analysis Study

This paper provides an overview of the parametric mass models used for the Entry, Descent, and Landing Systems Analysis study conducted by NASA in FY2009-2010. The study examined eight unique exploration class architectures that included elements such as a rigid mid-L/D aeroshell, a lifting hypersonic inflatable decelerator, a drag supersonic inflatable decelerator, a lifting supersonic inflatable decelerator implemented with a skirt, and subsonic/supersonic retro-propulsion. Parametric models used in this study relate the component mass to vehicle dimensions and mission key environmental parameters such as maximum deceleration and total heat load. The use of a parametric mass model allows the simultaneous optimization of trajectory and mass sizing parameters

Thermal Protection System Mass Estimating Relationships For Blunt-Body, Earth Entry Spacecraft

Mass estimating relationships (MERs) are developed to predict the amount of thermal protection system (TPS) necessary for safe Earth entry for blunt-body spacecraft using simple correlations that are non-ITAR and closely match estimates from NASA's highfidelity ablation modeling tool, the Fully Implicit Ablation and Thermal Analysis Program (FIAT). These MERs provide a first order estimate for rapid feasibility studies. There are 840 different trajectories considered in this study, and each TPS MER has a peak heating limit. MERs for the vehicle forebody include the ablators Phenolic Impregnated Carbon Ablator (PICA) and Carbon Phenolic atop Advanced Carbon-Carbon. For the aftbody, the materials are Silicone Impregnated Reusable Ceramic Ablator (SIRCA), Acusil II, SLA- 561V, and LI-900. The MERs are accurate to within 14% (at one standard deviation) of FIAT prediction, and the most any MER can under predict FIAT TPS thickness is 18.7%. This work focuses on the development of these MERs, the resulting equations, model limitations, and model accuracy

Systems Analysis of Carbon Nanotubes: Opportunities and Challenges for Space Applications

Recent availability of carbon nanotubes (CNTs) in quantities and formats amenable to producing macroscale components invites consideration of these materials in space applications where their attractive properties can enable the realization of bold concepts for affordable space exploration. The challenge is to identify relevant systems and quantify the benefits at the systems level. Before significant investment or adoption of CNTs for large aerospace systems can be justified, there must be a plausible path to attain the perceived systems level benefits. This challenging step requires a close collaboration among experts on CNTs and aerospace system communities. This paper provides an overview of a few relevant potential CNTs applications for space systems and the gap that must be overcome for deployment of CNTs. It also provides a simple engineering-level systems analysis approach to quantify the benefits of using CNTs over state of the art material solutions

Training Knowledge Bots for Physics-Based Simulations Using Artificial Neural Networks

Millions of complex physics-based simulations are required for design of an aerospace vehicle. These simulations are usually performed by highly trained and skilled analysts, who execute, monitor, and steer each simulation. Analysts rely heavily on their broad experience that may have taken 20-30 years to accumulate. In addition, the simulation software is complex in nature, requiring significant computational resources. Simulations of system of systems become even more complex and are beyond human capacity to effectively learn their behavior. IBM has developed machines that can learn and compete successfully with a chess grandmaster and most successful jeopardy contestants. These machines are capable of learning some complex problems much faster than humans can learn. In this paper, we propose using artificial neural network to train knowledge bots to identify the idiosyncrasies of simulation software and recognize patterns that can lead to successful simulations. We examine the use of knowledge bots for applications of computational fluid dynamics (CFD), trajectory analysis, commercial finite-element analysis software, and slosh propellant dynamics. We will show that machine learning algorithms can be used to learn the idiosyncrasies of computational simulations and identify regions of instability without including any additional information about their mathematical form or applied discretization approaches