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Aerospace Systems Design Laboratory (ASDL)
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ItemDevelopment of a Parametric Drag Polar Approximation for Conceptual Design(Georgia Institute of Technology. School of Aerospace Engineering, 2023-06) Sampaio Felix, Barbara ; Perron, Christian ; Ahuja, Jai ; Mavris, Dimitri N.The present work proposes an efficient parametric approximation of mission drag polars by combining multi-fidelity surrogate models with parametric reduced order modeling techniques. Traditionally, semi-empirical aerodynamic analyses are used to provide drag polars needed for mission analysis during the conceptual design of aircraft. The database needed for these methods is unavailable for unconventional vehicles, and for this reason, many studies rely on higher-fidelity models typical of preliminary design to perform design space exploration for novel vehicle geometries. Due to the high computational cost and evaluation time of these higher-fidelity models, researchers constrain the design space exploration of vehicles by either relying on single discipline optimization or obtaining mission drag polars for a few vehicle geometries within their design loop. The present work demonstrates the application of Hierarchical Kriging surrogate models to obtain mission drag polars for fixed vehicle geometries. Then, the proper orthogonal decomposition reduced order model with Kriging interpolation is used to approximate the coherent structure of mission drag polars. The proposed method is demonstrated on a supersonic commercial aircraft. Experiments showed that both the multi-fidelity surrogate model and the reduced order model are able to emulate vehicle drag polars well for fixed and varying vehicle geometries, respectively.
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ItemDesign Space Reduction using Multi-Fidelity Model-Based Active Subspaces(Georgia Institute of Technology, 2023-06) Mufti, Bilal ; Perron, Christian ; Gautier, Raphaël ; Mavris, Dimitri N.The parameterization of aerodynamic design shapes often results in high-dimensional design spaces, creating challenges when constructing surrogate models for aerodynamic coefficients. Active subspaces offer an effective way to reduce the dimensionality of such spaces, but existing approaches often require a substantial number of gradient evaluations, making them computationally expensive. We propose a multi-fidelity, model-based approach to finding an active subspace that relies solely on direct function evaluations. By using both high- and low-fidelity samples, we develop a model-based approximation of the projection matrix of the active subspace. We evaluate the proposed method by assessing its active subspace recovery characteristics and resulting model prediction accuracy for airfoil and wing drag prediction problems. Our results show that the proposed method successfully recovers the active subspace with an acceptable model prediction error. Furthermore, a cost vs. accuracy comparison with the multi-fidelity gradient-based active subspace method demonstrates that our approach offers comparable predictive performance with lower computational costs. Our findings provide strong evidence supporting the usage of the proposed method to reduce the dimensionality of design spaces when gradient samples are unavailable or expensive to obtain.
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ItemDevelopment of an Open Rotor Propulsion System Model and Power Management Strategy(Georgia Institute of Technology, 2023-01) Clark, Robert A. ; Perron, Christian ; Tai, Jimmy C. M. ; Airdo, Benjamin ; Mavris, Dimitri N.The development of an open rotor propulsion system architecture model and fuel burn-minimizing power management strategy is investigated. The open rotor architecture consists of a single-rotor open rotor (SROR) connected to the low speed shaft of a traditional turbojet engine in a puller configuration. The proposed architecture is modeled in the Numerical Propulsion System Simulation (NPSS) tool, and performance is evaluated across a complete flight envelope typical for a narrow body commercial airliner. Rotor performance maps are generated using a custom blade element momentum theory (BEMT) code, while compressor performance maps are created using CMPGEN. The performance of the overall propulsion system is detailed in the context of a notional 150 passenger aircraft mission, and a method for scheduling rotor power across the flight envelope is developed in order to minimize aircraft mission fuel burn. It is demonstrated that the power absorbed by the rotor can be optimized by scheduling rotor blade pitch angle versus fan speed. A power management technique using the optimal blade pitch angle at only six points in the flight envelope was shown to provide significant computational benefits without sacrificing any fuel burn when compared to a method using a schedule generated from data across the complete flight envelope.
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ItemA Multi-Fidelity Approximation of the Active Subspace Method for Surrogate Models with High-Dimensional Inputs(Georgia Institute of Technology, 2022-06) Mufti, Bilal ; Chen, Mengzhen ; Perron, Christian ; Mavris, Dimitri N.Modern design problems routinely involve high-dimensional inputs and the active subspace has been recognized as a potential solution to this issue. However, the computational cost for collecting training data with high-fidelity simulations can be prohibitively expensive. This paper presents a multi-fidelity strategy where low-fidelity simulations are leveraged to extract an approximation of the high-fidelity active subspace. Both gradient-based and gradient-free active subspace methods are incorporated with the proposed multi-fidelity strategy and are compared with the equivalent single-fidelity method. To demonstrate the effectiveness of our proposed multi-fidelity strategy, the aerodynamic analysis of an airfoil and a wing are used to define two application problems. The effectiveness of the current approach is evaluated based on its prediction accuracy and training cost improvement. Results show that using a low-fidelity analysis to approximate the active subspace of high-fidelity data is a viable solution and can provide substantial computational savings, yet this is counterbalanced with slightly worse prediction accuracy.
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ItemMulti-Fidelity Reduced-Order Modeling Applied to Fields with Inconsistent Representations(Georgia Institute of Technology, 2020-12-06) Perron, ChristianOur ever-increasing capacity for high-performance computing has progressively elevated the role of physics-based simulations in the conceptual and preliminary phases of aircraft design. This virtualization of the early design process has allowed for additional design freedom and shorter development time while engineers continuously strive for cleaner and quieter aircraft. While modern high-fidelity simulations can provide results with great accuracy, their application is often hindered by their steep computational cost and the limited availability of computing resources. This is especially prohibitive for design problems requiring the analysis of many aircraft configurations and at several flight conditions. To overcome the overwhelming cost of high-fidelity simulations, these are often replaced in practice by cheaper surrogate models generated using a handful of previously obtained solutions. When applied to physics-based results, surrogate models are typically associated with the prediction of integrated quantities. Recently, a new form of surrogate modeling, referred to as Reduced-Order Modeling (ROM), was developed for the prediction of high-dimensional field quantities. In addition to providing physically richer results than conventional surrogate models, this form of approximation is especially relevant for multi-disciplinary applications where the physical quantities exchanged between the disciplines are typically fields. As with most empirical models, the accuracy of a ROM is contingent on the amount of data used for their construction. While these models offer fast predictions, collecting a sufficiently large dataset to achieve the desired accuracy can be impractical when applied to high-fidelity simulations, especially when considering many design parameters. Hence, the main objective of this dissertation is to improve current ROM methods by requiring less high-fidelity data while maintaining adequate accuracy. Specifically, we consider a multi-fidelity approach that enhances a few high-fidelity solutions with results from an inexpensive low-fidelity simulation. While various multi-fidelity solutions exist for conventional surrogate models, few are available for reduced-order modeling. A major factor behind the scarcity of multi-fidelity ROMs is that simulations of different fidelity generally produce fields with disparate representations. As a result, this work focuses on this issue and investigate methods to allow the fusion of inconsistent fields. This dissertation contributes to the field of reduced-order modeling by proposing a multi-fidelity method that employs manifold alignment to find a common low-dimensional representation of two datasets with heterogeneous fields. Once aligned, a single prediction model combines the multi-fidelity datasets with an approach inspired by existing fusion-based multi-fidelity techniques. Therefore, the developed method can combine fields from various models irrespective of their representations. The produced ROM then potentially has better performance than a single-fidelity model trained with the same computational budget. The viability of the proposed method is validated using two practical problems, i.e., the aerodynamic analysis of a transonic airfoil and a transonic wing. Multiple multi-fidelity scenarios are considered with different fidelity combinations, various model configurations, and inconsistent fields. In many cases, the developed method can effectively provide improved predictions compared to an equivalent single-fidelity approach despite fusing results with inconsistent representations. At worst, when the proposed method is applied to datasets with a large fidelity difference, the accuracy of the resulting ROM tends to that of a single-fidelity model. Also, the results show that the developed method behaves similarly to existing multi-fidelity ROM methods when joining high- and low-fidelity fields with a consistent representation.
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ItemDevelopment of a Multi-Fidelity Reduced-Order Model Based on Manifold Alignment(Georgia Institute of Technology, 2020-06) Perron, Christian ; Rajaram, Dushhyanth ; Mavris, Dimitri N.This work presents the development of a novel multi-fidelity, parametric, and non-intrusive Reduced Order Modeling~(ROM) method to tackle the problem of achieving an acceptable predictive accuracy under a limited computational budget, i.e., with expensive simulations and sparse training data. Traditional multi-fidelity surrogate models that predict scalar quantities address this issue by leveraging auxiliary data generated by a computationally cheaper lower fidelity code. However, for the prediction of field quantities, simulations of different fidelities may produce high-dimensional responses with inconsistent dimensionality and topology, rendering the direct application of common multi-fidelity techniques challenging. The proposed approach uses manifold alignment to fuse inconsistent fields from high- and low-fidelity simulations by individually projecting their solution onto a common shared latent space. Hence, simulations using incompatible grids or geometries can be combined into a single multi-fidelity ROM without additional manipulation of the data. This method is applied to a variety of multi-fidelity scenarios using a transonic airfoil problem. In most cases, the new multi-fidelity ROM achieves comparable predictive accuracy at a substantially lower computational cost. Furthermore, it is demonstrated that the proposed method can readily combine disparate fields without any adverse effect on model performance.
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ItemEconomics of Advanced Thin-Haul Concepts and Operations(Georgia Institute of Technology, 2016) Harish, Anusha ; Perron, Christian ; Bavaro, Daniel ; Ahuja, Jai ; Ozcan, Melek D. ; Justin, Cedric Y. ; Briceno, Simon ; German, Brian J. ; Mavris, Dimitri N.The thin-haul commuter concept refers to an envisioned class of four to nine passenger aircraft operating very short flights and providing scheduled and on-demand air services from smaller airports. Its objective is to enhance regional mobility reach by combining the flexibility of automobile travel with the shorter commute times associated with air travel. To achieve economic viability, the thin-haul commuter concept must provide appreciable economic advantages when compared to current commuter aircraft. This may be achieved by increasing the revenue potential through innovative pricing and scheduling, while drastically reducing operating costs, in particular, energy, maintenance, and labor costs. These ambitious objectives require the infusion of new cutting edge technologies. The use of distributed electric propulsion is investigated to reduce both energy and maintenance expenditures. New avionics systems are considered to enable simplified operations and thus to reduce both labor and training costs. The purpose of this on-going research is to assess the viability of the thin-haul aviation concept by investigating both the operational and economic impact of introducing a fleet of distributed electric propulsion aircraft into the operations of a commuter airline. This paper presents the development of an integrated economics and operations model that incorporates preliminary estimates of a distributed electric propulsion vehicle performance as well as some aspects of typical commuter operator schedules. The model helps compare advanced electric vehicles with more conventional commuters, and therefore enables a preliminary assessment of the expected cost savings.