Rapid Integrated Interdependent Fleet-Level Environmental Model

Bernardo, Jose Enrique; Zaidi, Turab; Levine, Martin D.; Jiménez, Hernando; Mavris, Dimitri N.

doi:10.2514/1.c033572

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…For a constant travel demand, this productivity loss has to be covered by carrying more passengers per flight or by increasing the number of aircraft in the fleet. Several research projects have highlighted the importance of a complete fleet-level analysis in the assessment of new technologies and have proposed advanced modeling frameworks [19][20][21] in which the aircraft flight profiles have to be simulated [22]. Research by Moolchandani et al [23] and Jimenez et al [24] employed fleet-level models to assess the environmental impact of future technologies considered in the ERA project.…”

mentioning

confidence: 99%

Airplane Design Optimization for Minimal Global Warming Impact

Proesmans

Vos

2022

Journal of Aircraft

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This paper presents a method to assess the key performance indicators of aircraft designed for minimum direct operating costs and aircraft designed for minimum global warming impact. The method comprises a multidisciplinary aircraft optimization algorithm capable of changing wing, engine, and mission design variables while including constraints on flight and field performance. The presented methodology uses traditional class-I methods augmented with dedicated class-II models to increase the sensitivity of the performance indicators to relevant design variables. The global warming impact is measured through the average temperature response caused by several emission species (including carbon dioxide, nitrogen oxides, and contrail formation) over a prolonged period of 100 years. The analysis routines are verified against experimental data or higher-order methods. The design algorithm is subsequently applied to a single-aisle medium-range aircraft, demonstrating that a 57% reduction in average temperature response can be achieved as compared to an aircraft optimized for minimal operating costs. This reduction is realized by flying at 7.6 km and Mach 0.60, and by lowering the engine overall pressure ratio to approximately 37. However, to compensate for the lower productivity, it is estimated that 13% more climate-optimized aircraft have to be operated for the hypothetical fleet under consideration.

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mentioning

confidence: 99%

Airplane Design Optimization for Minimal Global Warming Impact

Proesmans

Vos

2022

Journal of Aircraft

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“…62 They are used for technology evaluation to reduce the overall uncertainty of the analysis. [62][63][64] Copula based sampling for a Bayesian Network is employed by Liang 65,66 for stochastic multidisciplinary analysis with high-dimensional coupling. Bayesian Networks 67 are ubiquitously applied for modeling dependency structures of systems and combined with copulas to specify the joint distribution functions.…”

Section: Validation Of Expert Judgment For Estimation Of Input Distrimentioning

confidence: 99%

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Roelofs

Vos

2018

2018 AIAA Aerospace Sciences Meeting

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Evaluation and assessment of novel technologies for aerospace applications is essential for business strategy and decision making regarding development efforts. Since technology is evaluated in the conceptual design phase and little is known about the technology, large uncertainty is present. This uncertainty needs to be accurately assessed and managed. To investigate the research efforts that have been performed to perform technology evaluation under uncertainty, a literature review was conducted, focusing on methods and modeling approaches to assign and quantify these uncertainties. It is found that probability theory is still the most popular theory for representing uncertainty. Polynomial Chaos Expansions and Stochastic Collocation methods are gaining popularity for propagating uncertainty through a modeling environment, but Monte Carlo Simulations are still widely used. Commonly, surrogate models are used to reduce computational effort. Other efforts focus on the use of multifidelity approaches to reduce computational effort when high-fidelity methods are required. Four issues that may need to be addressed in future research were identified.

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“…ANGIM and GREAT are both tools developed at the Aerospace Systems Design Laboratory at the Georgia Institute of Technology under FAA PARTNER/ASCENT funding. An overview of these tools will be presented in Sub-Section G, and further details of these tools can be found in Bernardo et al [17] [18] [19]. The outputs module takes the outputs of ANGIM and GREAT, and converts them into noise contour maps, and other relevant environmental metrics.…”

Section: Methodsmentioning

confidence: 99%

“…However, combining ANGIM and GREAT together yields a modeling and simulation environment that meets all the requirements. Detailed description of ANGIM and GREAT can be found in Bernardo et al [17] [18] [19]. At the time of this publication, only the ANGIM module in REACT was developed and integrated, and the GREAT module was not yet integrated.…”

Section: G Environmental Calculationsmentioning

confidence: 99%

REACT: A Rapid Environmental Impact on Airport Community Tradeoff Environment

Monteiro

Prem

Kirby

et al. 2018

2018 AIAA Aerospace Sciences Meeting

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Community noise exposure is the one of the key challenges to the potential growth of the aviation industry. Various forecasts have shown that the industry is expected to grow in the future years, and with this comes a predicted increase in noise levels around airports without a significant influx of noise reduction technologies across the fleet. In order to plan for these future scenarios, airports, airlines, the FAA, and local governments must analyze various noise mitigation strategies, operational restrictions, and changes to airport states, whilst still increasing capacity. Thus, it has become important for these entities to be able to rapidly tradeoff different noise mitigation strategies to effectively manage community noise exposure in current and future airport scenarios. However, noise modeling can be a cumbersome task due to the potential for high computational times and its case dependency. Each entity, airport, and community have their own concerns, conditions, and population structure. Current noise modeling software can take multiple hours to set up an airport case study and run a single operational state. REACT, a tradeoff environment developed in this paper, aims to provide the key stakeholders with airport-specific information, an accessible visual user interface, and most importantly, rapid computations. REACT connects the various mitigation strategies and techniques most important to each of the key stakeholders, and allows these authorities to investigate solutions that are most important to them in current and future airport states.

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Rapid Integrated Interdependent Fleet-Level Environmental Model

Cited by 4 publications

References 22 publications

Airplane Design Optimization for Minimal Global Warming Impact

Airplane Design Optimization for Minimal Global Warming Impact

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

REACT: A Rapid Environmental Impact on Airport Community Tradeoff Environment

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