OpenMDAO User Workshop 2022
Conference videos from the OpenMDAO Workshop 2022, including numerous talks from government and academia.
Optimization↗
Engineering topics
Eliot Aretskin-Hariton
Publications and source records attributed to Eliot Aretskin-Hariton.
Gondola For High Altitude Planetary Science (GHAPS) Guide System Trade Study
The Gondola for High-Altitude Planetary Science (GHAPS) project is a balloon-borne astronomical observatory designed operate in the ultraviolet, Visible, and near-mid infrared spectral region. The GHAPS Optical Telescope Assembly (OTA) is designed around a 1-m aperture narrow field-of-view telescope with near-diffraction-limited performance. GHAPS will utilize Wallops Arc-Second Pointing System (WASP) for pointing the OTA with an accuracy of 1 arcsec or better. WASP relies heavily on a self-contained star tracker assembly to determine the OTA line of sight. Preliminary structural analysis indicates that potential misalignments could be present between the OTA line of sight and the star tracker FOV center during the expected flight conditions that could compromise GHAPS pointing accuracy.
Telescope Image Guider↗
Multidisciplinary Optimization of an Electric Quadrotor Urban Air Mobility Aircraft
Audio and video are playable when viewing powerpoint file in presentation mode.
Optimization,OpenMDAO,Dymos,Quadrotor↗
Advancement of the General Aviation Synthesis Program Using Python to Enable Optimization-Based Hybrid-Propulsion Aircraft Design
In support of the Electrified Powertrain Flight Demonstrator and Advanced Air Transport Technologies projects at NASA, a new tool has been developed at NASA's Ames and Glenn Research Centers to enable coupled engine and airframe optimization and analysis. The new tool combines the engineering-level analysis methods and empirical models of the FORTRAN General Aviation Synthesis Program (GASP) with the Python-based OpenMDAO framework to provide a modular framework for efficient gradient-based optimization with the aim of incorporating new subsystem models for unconventional configurations. The tool has been verified against GASP analyses of several aircraft models and mission formulations. Preliminary efforts have been made to integrate pyCycle, a thermodynamic cycle analysis tool, to enable simultaneous optimization of hybrid propulsion system and vehicle parameters while taking full mission performance and constraints into account. This will improve current capabilities to assess impacts of electrified powertrain technologies on future aircraft designs.
Kenneth R Lyons↗
NASA MDAO Efforts in 2024
Explore the source record for details and available documents.
optimization↗
Aviary: An Open-Source Multidisciplinary Design, Analysis, and Optimization Tool for Modeling Aircraft With Analytic Gradients
Demands on aircraft design methods in recent years have begun to require increasingly higher amounts of coupling between disciplines and have also begun to require optimization in order to satisfy competing objectives involving large numbers of parameters that define unconventional configurations. These expanding requirements have amplified a need for new and improved aircraft design, analysis, and optimization codes that are capable of performing coupled design exploiting analytic gradients where possible. Aviary is a multidisciplinary design optimization and analysis framework which allows for tightly coupled simultaneous aircraft and subsystem design using analytic gradients. Aviary has employed the methods of two legacy aircraft analysis tools to provide native analytically differentiated calculations for five different disciplines, and it also has the ability to couple in external discipline analysis tools, whether or not those tools can provide analytic gradients. Preliminary examples and modeling efforts have shown Aviary’s ability to effectively model novel concepts and explore large and non-intuitive design spaces. Finally, a multi-level user interface in Aviary creates an easy entry point for users with any level of multidisciplinary design, analysis, and optimization experience.
multidisciplinary↗
Multidisciplinary Optimization of A Transonic Truss-Braced Wing Aircraft Using Aviary
The continuous push to decrease fuel burn of single-aisle commercial aircraft has led to interest in a Transonic Truss-Braced Wing (TTBW) concept vehicle. Sporting high-slung wings that are long and slender to increase aerodynamic efficiency, a TTBW can also accommodate higher-bypass turbine engines. The combination of these two changes potentially leads to an overall decrease in fuel consumption. In this paper, a TTBW concept vehicle is assembled in the Aviary open-source tool for conceptual aircraft design. The conceptual-level aerodynamics and propulsion systems that come prepackaged with Aviary are replaced with higher-fidelity vortex lattice method for aerodynamics (VSPAERO) and a one-dimensional cycle analysis tool for propulsion (pyCycle). The vehicle is then optimized to minimize fuel burn for a representative commercial mission. Design parameters for the vehicle include the electrified turbine size, the size of the electric motors which are used for takeoff and climb assist, and battery capacity. The TTBW concept vehicle presented in this paper represents the first application of Aviary to an aircraft design problem.
Optimization↗
End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty
Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.
Ben Phillips↗
Multidisciplinary Optimization of a Transonic Truss Braced Wing Aircraft with Hybrid-Electric Propulsion
Next generation aircraft concepts have subsystems that are increasingly inter-connected. This necessitates advanced design tools using gradient based optimization to properly account for strong subsystem coupling in these aircraft. Additionally, these design tools require accurate modeling capability to support the increased interest in electrified propulsion. Design studies for these aircraft must consider the trade-offs between electric propulsion and turbojet engines at all points in the flight envelope to determine the optimal balance between propulsion options. This paper describes the development of an electric propulsion subsystem model designed to work within Aviary - an open source aircraft design tool. We will demonstrate the electric propulsion model operating with Aviary by showing results from an optimized Transonic Truss-Braced Wing (TTBW) concept using assisted electric propulsion during climb. First, we present results with and without electrification to show the overall system impacts of hybrid electric propulsion during climb. We also present two distinct battery models compatible with this optimization framework, and compare results using both. Next, we ran the same problem using a slightly different cell type, and demonstrate a considerable change in the result. Finally, we vary the cell energy density of the batteries, and provide an illustrative trend for the system-level impact for improving cell technology.
optimization↗
Aviary: An Open-Source Multidisciplinary Design, Analysis, and Optimization Tool for Modeling Aircraft with Analytic Gradients
In recent years demands on aircraft design methods have begun to require higher degrees of coupling between disciplines and optimization in order to satisfy competing objectives involving large numbers of design parameters that define unconventional configurations. These expanding requirements have amplified a need for new and improved aircraft design, analysis, and optimization codes that are capable of performing coupled design and exploiting analytic gradients to perform gradient-based optimization where possible. To address this need, a new multidisciplinary design optimization and analysis tool called Aviary is presented. This tool, built on OpenMDAO, allows for tightly coupled, simultaneous aircraft and subsystem design using analytic gradients. Aviary includes methods from two NASA developed legacy aircraft analysis tools and provides native analytically differentiated calculations for five different disciplines (weights and sizing, aerodynamics, geometry, propulsion, and mission analysis), while also allowing external discipline analysis tools to be coupled, regardless of whether those tools can provide analytic gradients. Verification and preliminary examples and modeling efforts show Aviary’s ability to effectively model novel concepts and explore large and non-intuitive design spaces. Additionally, a multi-level user interface in Aviary creates an easy entry point for users with any level of multidisciplinary design, analysis, and optimization experience.
optimization↗
Multidisciplinary Optimization of a Transonic Truss Braced Wing Aircraft with Hybrid-Electric Propulsion
Explore the source record for details and available documents.
multidisciplinary↗
Multidisciplinary Design Optimization of a Transonic Truss-Braced Wing using Physics-Based Models
Economic and environmental forces have placed pressure on the aviation industry to produce future aircraft designs with substantial improvements over those flying today. In order to meet these ambitious goals, new concepts such as the Transonic Truss-Braced Wing (TTBW) are being considered which diverge from the traditional tube-and-wing design. While this concept offers potential performance benefits over its traditional counterpart, it also features more coupling between various physics disciplines that must be considered during the design of the concept. This unique challenge provides a ripe opportunity for the application of multidisciplinary design optimization tool sets. The final work will couple together the mission analysis capability of Aviary with OpenAeroStruct for aerodynamic analysis and TACS for structural analysis. Using these tools we will preform a design optimization on the TTBW concept minimizing design fuel burn. The optimization will include design variables for mission trajectory, wing geometry, and structural sizing variables.
mbsae↗
Design Under Uncertainty with Design-Dependent Uncertain Variables
Uncertainty quantification (UQ) can provide a more robust understanding of a system, leading to better informed decisions earlier in the design process. The additional information that UQ provides can be leveraged during a design optimization process known as design under uncertainty that, when incorporated with multidisciplinary design and optimization, can become computationally infeasible due to the large number of responses required for meaningful results. Previous work addressed reducing the computational expense in design under uncertainty by incorporating analytic derivatives throughout polynomial chaos expansion. Although this allows design under uncertainty to be feasible for more systems, some multidisciplinary systems have design-dependent uncertain variables. This paper details an implementation of design dependent uncertain variables in a manner than preserves derivatives required for efficient gradient-based optimization throughout the process. Two analytic examples of design-dependent uncertain variables are given: the first transforms a uniform uncertain variable with one design variable and the second transforms a normal uncertain variable with two design variables. The polynomial chaos expansion (PCE) results are comparable to both the Monte Carlo (MC) results and the analytic results for the two examples. A case study that maximizes the lift-to-drag ratio with a design-dependence between the wing leading edge sweep angle and uncertain parameter percentage of laminar flow is compared to a MC and alternative optimization formulations. This paper demonstrates that design-dependent uncertain variables are valid and hold throughout PCE.
robust design↗
Design Under Uncertainty for Conceptual Aircraft Design Leveraging Analytical Gradients
The purpose of this paper is to extend previously demonstrated methodologies for design under uncertainty, leveraging analytical gradients to higher fidelity analysis for use in conceptual aircraft design. Previous work developed methods to generate analytical derivatives through polynomial chaos expansion, eliminating the need to estimate derivatives via complex step or finite difference. In this research, the authors build upon the methods to include physics-based aircraft design codes for aircraft design under uncertainty. This extends the previous work’s case study, which employed analytical aerodynamics and Breguet range estimations for wing design, to a higher fidelity level. In addition, this work extends previous work on interface development between the Uncertainty Quantification with Polynomial Chaos Expansion (UQPCE) software and Model-Based Systems Analysis and Engineering (MBSA&E) frameworks. This paper will discuss the development work necessary to perform multidisciplinary design under uncertainty as well as demonstrate the mechanics of interfacing UQPCE and conceptual aircraft design tools such as NASA’s Aviary code. In a case study, a conceptual aircraft design under uncertainty was conducted and compared against a traditional deterministic design. When given information about the uncertainty space from UQPCE, the optimizer was able to shape the output distribution and produce a more robust design.
UQ↗