A modelling framework to support power architecture trade-off studies for More-Electric Aircraft

Garriga, Ana Garcia; Govindaraju, Parithi; Ponnusamy, Sangeeth Saagar; Cimmino, Nicola; Mainini, Laura

doi:10.1016/j.trpro.2018.02.013

Cited by 12 publications

(11 citation statements)

References 10 publications

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“…Additional works developed in the context of the MISSION project propose test cases to demonstrate MISSION framework capabilities being developed to handle aircraft-system interaction, multifidelity models integration and trade-off studies. 5,6,72 In terms of validation, two different types of test scenarios are considered. The first is the evaluation of SUT impact on other systems or on the aircraft, and the second is the aircraft's or other system's impact on the SUT.…”

Section: Aircraft-level Integrated Simulationmentioning

confidence: 99%

“…This is mainly due to the one-way requirements flow (usually top-down), that leads to suboptimal solution at system level and is unable to capture the impact that a change at system level has on the aircraft level, thus limiting the adoption of novel system architectures. 5,6 Literature comprises of a wide range of alternative approaches to support system integration for aircraft design. Major efforts focus on the development and formalization of methodologies to support system integration and design optimization.…”

Section: Introductionmentioning

confidence: 99%

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A modelling and simulation framework for the integrated design of aircraft systems

Cimmino

Ponnusamy

Garriga

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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New technologies and complex systems are being developed in commercial aviation to meet strict requirements regarding fuel consumption, emissions and noise constraints. This motivates the development of multidisciplinary environments to efficiently manage the increasing complexity of the design process. Under the Clean Sky 2 initiative, the ModellIng and Simulation tools for Systems IntegratiON on Aircraft (MISSION) project aims to develop an integrated framework to holistically support the aircraft design, development and validation processes. Within the MISSION framework, this paper proposes a methodology to handle the integration between the aircraft level and the system level in the early phase of aircraft design. The methodology is demonstrated for the case of the Landing Gear System in the rejected take-off scenario.

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Section: Aircraft-level Integrated Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A modelling and simulation framework for the integrated design of aircraft systems

Cimmino

Ponnusamy

Garriga

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…In addition, the power requirements during emergency operations (e.g., rejected takeoff and one-engine landing) drive the sizing of the subsystems for power generation and transportation. Previous work demonstrated that the modeling framework used in this paper effectively captures the compound effects of changing multiple technological solutions and discussed implementation details for an actuation use case [53].…”

Section: Architecture Evaluationmentioning

confidence: 99%

“…Previous work in the context of the MISSION project focuses on the integration of the system-level dynamics with the aircraft-level [51] and the design of controls for multiple aircraft systems [52]. This paper leverages the modeling framework proposed in Garcia Garriga et al [53] to enable trade-off studies among multiple power architectures in the evaluation of aircraft system architectures and proposes a principled approach to reduce the architecture design space and speed up the identification of the optimal architecture.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Enabled Multi-Fidelity Platform for the Integrated Design of Aircraft Systems

Garriga

Mainini

Ponnusamy

2019

Journal of Mechanical Design

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The push toward reducing the aircraft development cycle time motivates the development of collaborative frameworks that enable the more integrated design of aircraft and their systems. The ModellIng and Simulation tools for Systems IntegratiON on Aircraft (MISSION) project aims to develop an integrated modelling and simulation framework. This paper focuses on some recent advancements in the MISSION project and presents a design framework that combines a filtering process to down-select feasible architectures, a modeling platform that simulates the power system of the aircraft, and a machine learning-based clustering and optimization module. This framework enables the designer to prioritize different designs and offers traceability on the optimal choices. In addition, it enables the integration of models at multiple levels of fidelity depending on the size of the design space and the accuracy required. It is demonstrated for the electrification of the Primary Flight Control System (PFCS) and the landing gear braking system using different electric actuation technologies. The performance of different architectures is analyzed with respect to key performance indicators (fuel burn, weight, power). The optimization process benefits from a data-driven localization step to identify sets of similar architectures. The framework demonstrates the capability of optimizing across multiple, different system architectures in an efficient way that is scalable for larger design spaces and larger dimensionality problems.

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“…In addition, with the development of artificial intelligence and information technologies, model-based system engineering (MBSE) [8][9][10] and optimization-assisted design [11] have become the focus of researchers, becoming increasingly mature, and will be particularly powerful for the design of aircraft power systems. In this context, models of the electrical environment control system (ECS) [12,13], electromagnetic actuators (EMAs) [14][15][16][17], and electro-hydraulic actuators (EHAs) [16,18,19] were established to support the trade-offs between the weight and power loss of More-electric systems. To implement these analyses, machine models for optimal designs have been widely investigated.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Modeling Methodology for Optimal Design of Electric Machines Based on Multi-Disciplinary Design Optimization

Dai

Wang

Meng³

et al. 2019

Energies

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The transportation sector is undergoing electrification to gain advantages such as lighter weight, improved reliability, and enhanced efficiency. As contributors to the safety of embedded critical functions in electrified systems, better sizing of electric machines in vehicles is required to reduce the cost, volume, and weight. Although the designs of machines are widely investigated, existing studies are mostly complicated and application-specific. To satisfy the multi-level design requirements of power systems, this study aims to develop an efficient modeling method of electric machines with a background of aircraft applications. A variable-speed variable-frequency (VSVF) electrically excited synchronous generator is selected as a case study to illustrate the modular multi-physics modeling process, in which weight and power loss are the major optimization goals. In addition, multi-disciplinary design optimization (MDO) methods are introduced to facilitate the optimal variable selection and simplified model establishment, which can be used for the system-level overall design. Several cases with industrial data are analyzed to demonstrate the effectiveness and superior performance of the modeling method. The results show that the proposed practices provide designers with accurate, fast, and systematic means to develop models for the efficient design of aircraft power systems.

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A modelling framework to support power architecture trade-off studies for More-Electric Aircraft

Cited by 12 publications

References 10 publications

A modelling and simulation framework for the integrated design of aircraft systems

A modelling and simulation framework for the integrated design of aircraft systems

A Machine Learning Enabled Multi-Fidelity Platform for the Integrated Design of Aircraft Systems

Multi-Level Modeling Methodology for Optimal Design of Electric Machines Based on Multi-Disciplinary Design Optimization

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