Organizational Unit:

Daniel Guggenheim School of Aerospace Engineering

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https://hdl.handle.net/1853/70742

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College of Engineering

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Aerospace Design Group

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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https://finding-aids.library.gatech.edu/agents/corporate_entities/106

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Publication Search Results

Now showing 1 - 10 of 232

Exploring the Design Space of an Electric Ship using a Probabilistic Technology Evaluation Methodology

(Georgia Institute of Technology, 2019-08) McNabb, Jeffrey ; Robertson, Nicole A. ; Steffens, Michael J. ; Sudol, Alicia ; Mavris, Dimitri N. ; Chalfant, Julie

With the advent of new technologies for electric ships, there is a need for a robust methodology to quantitatively evaluate their impact on the performance of a ship, while accounting for the uncertain nature of their parameters. To that end, this paper gives an overview of the Technology Identification, Evaluation, and Selection, or TIES, methodology as applied a 10kton surface combatant. This case study highlights the ability of TIES to aid in a broad exploration of the design space, by giving designers key tools that allow them to show in a traceable manner the tradeoffs involved in infusing technologies and making other design choices, as well as which designs best meet different sets of Figures of Merit. This ultimately allows decision-makers to determine what technologies or design choices to invest in to yield a ship with the performance parameters that will best serve the needs of its stakeholders.
Power Management Optimization for Off-Design Performance Assessment of Ducted Electric Fan with Boundary Layer Ingestion

(Georgia Institute of Technology, 2019-06) Brucculeri, Joseph ; Salas Nunez, Luis ; Gladin, Jonathan ; Mavris, Dimitri N.

This work consists on the examination of the system level benefits of a partially turboelectric propulsion architecture with Boundary Layer Ingestion (BLI) in a 150-pax class size commercial aircraft. This propulsion system consists of an electric ducted fan that ingests the boundary layer from the aft-end of the fuselage. The power for this aft-fan is extracted from two underwing turbofan engines. The BLI effects are quantified in CFD using the power balance method, which are then integrated using surrogate models into an aircraft sizing and mission analysis environment to obtain mission performance metrics. With the overarching goal of minimizing fuel consumption, an approach to optimize the motor power during off-design analysis is presented. The effect that different power management strategies have on system performance are also analyzed. The results showed that such novel propulsion architecture not only presents benefits with respect to a conventional system, but also that the application of an optimized power schedule offers additional fuel burn reductions.
Evaluating Optimal Paths for Aircraft Subsystem Electrification in Early Design

(Georgia Institute of Technology, 2019-06) Bendarkar, Mayank ; Rajaram, Dushhyanth ; Yu, Cai ; Briceno, Simon ; Mavris, Dimitri N.

The aerospace industry’s push for More-Electric Aircraft (MEA) has motivated numerous studies to quantify and optimize the impact of subsystem electrification in early design phases. Past studies on multi-objective optimization of MEA show a clear benefit over conventional architectures when no constraints are placed on the number of subsystems electrified at once. In reality however, aircraft manufacturers are more likely to progressively electrify subsystems over multiple aircraft generations. While step-by-step electrification may lead to sub-optimal intermittent MEA architectures when compared with scenarios with no such imposition on number of subsystems electrified, little or no literature was found to address the optimal paths towards such electrification changes. The primary aim of this study is the creation of a mathematically defensible methodology that provides decision makers with the ability to analyze several paths for electrification of MEA subsystems while considering Pareto-optimality and other metrics based on objectives of interest in early design. It is hoped that decision makers will be able to understand the performance trade-offs between different electrification paths under different scenarios, constraints, and uncertainties. The resulting methodology is demonstrated on an exercise in the electrification of Small Single Aisle aircraft.
Network-Optimized Design of a Notional Hybrid Electric Airplane for Thin-Haul Operations

(Georgia Institute of Technology, 2019-06) Weit, Colby J. ; Justin, Cedric Y. ; Mavris, Dimitri N.

Electric propulsion for aviation applications has gained significant momentum in the past few years owing to a convergence of technologies enabling the design of competitive aircraft. This excitement also highlights the expectations for how electrification enables novel airplane architectures and powertrains leading to significant reductions in energy usage, emissions, and ultimately operating costs. Thin-haul operations is a natural application for electric propulsion owing to the relatively short flights mitigating the need for large batteries. Hybridization of electric airplanes further mitigates the need for large batteries and enables an earlier entry into service. Aircraft designs often have capabilities that significantly exceed the needs of many missions making up their day-to-day operations. This study considers the optimization of a hybrid-electric aircraft for thin-haul operations and investigates how the airplane design can be modularized to enable a multi-design-point optimization. This allows the vehicle to operate as close as possible to its many design points. The objective is to maximize the aircraft direct operating profit by optimizing the hybridization ratio while accounting for the ability to trade payload for additional range. The analysis is then applied over a wide range of routes representative of the network of a thin-haul operator. This yields a network-optimized vehicle that maximizes direct operating profits. This aircraft is then compared to a baseline turboprop aircraft. Depending on the number of routes operated and profit margins being sought, the resulting design exhibits optimal hybridization ratios ranging nominally from 52% to 96%. The study also investigates the opportunity to trade payload for additional range by swapping some payload for additional batteries. The impact of various levels of battery specific energy densities on operating economics are also studied.
Integrated Sizing and Optimization of HybridWing Body Aircraft in Conceptual Design

(Georgia Institute of Technology, 2019-06) Xie, Jiacheng ; Cai, Yu ; Chen, Mengzhen ; Mavris, Dimitri N.

The hybrid wing body (HWB) configuration is a paradigm shift in commercial transport aircraft design in terms of environmentally responsible characteristics and significant performance improvements over the conventional tube-and-wing configuration. However, the sizing methods and analysis tools used in conceptual design of tube-and-wing aircraft are not fully compatible with HWB due to the highly integrated fuselage and wing. This paper proposes a novel approach to perform parametric sizing and optimization of HWB aircraft at the conceptual design phase, and develops an interdisciplinary design framework which integrates preliminary aerodynamic analysis, weight estimation, propulsion system sizing, and mission analysis. Enabled by the techniques of Design of Experiments and surrogate modeling, a design space exploration is conducted over the top-level aircraft design variables, including sensitivity assessment, feasible design space identification, and constrained multi-objective optimization. The impact of uncertainties in disciplinary analyses and novel technologies on aircraft-level performance is investigated through an uncertainty analysis.
Predicting The Occurrence of Weather And Volume Related Ground Delay Program

(Georgia Institute of Technology, 2019-06) Mangortey, Eugene ; Pinon, Olivia J. ; Puranik, Tejas G. ; Mavris, Dimitri N.

Traffic Management Initiatives (TMI) such as Ground Delay Programs (GDP) are instituted by traffic management personnel to address and reduce the impacts of constraints in the National Airspace System. Ground Delay Programs are initiated whenever demand is projected to exceed an airport’s acceptance rate over a lengthy period of time. Such instances occur when an airport is affected by conditions such as inclement weather, aircraft congestion, runway-related incidents, equipment failures, and other causes that do not fall in these categories. Over the years, efforts have been made to reduce the impact of Ground Delay Programs on airports and flight operations by predicting their occurrence. However, these efforts have largely focused on weather-related Ground Delay Programs, primarily due to a lack of access to comprehensive Ground Delay Program data. There has also been limited benchmarking of Machine Learning algorithms to predict the occurrence of Ground Delay Programs. Consequently, this research 1)fused data from the Traffic Flow Management System (TFMS), Aviation System Performance Metrics (ASPM), and Automated Surface Observing Systems (ASOS) datasets, and 2) leveraged supervised Machine Learning algorithms to develop prediction models as a means to predict the occurrence of weather and volume-related Ground Delay Programs. The Kappa Statistic evaluation metric revealed that Boosting Ensemble was the best suited algorithm for predicting the occurrence of weather and volume-related Ground Delay Programs.
A Model-Based System Engineering Approach to Normal Category Airplane Airworthiness Certification

(Georgia Institute of Technology, 2019-06) Bleu-Laine, Marc-Henri ; Bendarkar, Mayank ; Xie, Jiacheng ; Briceno, Simon ; Mavris, Dimitri N.

Airworthiness certification is to ensure the safety of aircraft. With the surge in novel general aviation aircraft configurations and technologies, the Federal Aviation Administration replaced prescriptive design requirements with performance-based airworthiness standards in Federal Aviation Regulations Part 23 that governs the airworthiness of normal category airplane. The amendment ported over the accepted means of compliance (MoC) from prescriptive advisory circulars to a number of consensus standards from aviation community. Because these MoCs are scattered in multiple documents and cross-reference one another, the certification practice with this new format may be cumbersome and time-consuming.This paper proposes a Model Based System Engineering (MBSE) approach that is envisioned to parametrically transform the document-centric exercise to a model-based process. The approach helps collect the FAR23 regulations and the associated MoC in an integrated system model along with the relevant mappings between them. This allows users to automatically generate a compliance checklist for any specific certification requirement. Other benefits of the MBSE approach include circular referencing check, automatically propagating any future changes to the FARs or MoC standards through the model, and potential incorporation with early aircraft design.
Application Of Data Fusion And Machine Learning To The Analysis Of The Relevance Of Recommended Flight Reroutes

(Georgia Institute of Technology, 2019-06) Dard, Ghislain ; Mangortey, Eugene ; Pinon, Olivia J.

One of the missions of the Federal Aviation Administration (FAA) is to maintain the safety and efficiency of the National Airspace System (NAS). One way to do so is through Traffic Management Initiatives (TMIs). Traffic Management Initiatives, such as reroute advisories, are issued by Air Traffic Controllers whenever there is a need to balance demand with capacity in the National Airspace System. Indeed, rerouting flights ensures that aircraft operate with the flow of traffic, remain away from special use airspace, and avoid saturated areas of the airspace and areas of inclement weather. Reroute advisories are defined by their level of urgency i.e. Required, Recommended or For Your Information (FYI). While pilots almost always comply with required reroutes, their decisions to follow recommended reroutes vary. Understanding the efficiency and relevance of recommended reroutes is key to the identification and definition of future reroute options. In addition, because traffic in the National Airspace System can be forecasted through airline schedules and flight plans, it is also possible to predict the issuance of volume-related reroute advisories. Consequently, the objectives of this work is two-fold: 1) Assess the relevance of existing recommended reroutes, and 2) predict the issuance and the type of volume-related reroute advisories. This was achieved by 1) fusing data from relevant datasets, extracting statistics, and identifying trends and patterns within the data, and 2) developing models to predict the issuance of volume-related reroute advisories. It is expected that the capabilities developed may ultimately contribute to reducing unnecessary flight reroutes.
Development of A Certification Module for Early Aircraft Design

(Georgia Institute of Technology, 2019-06) Xie, Jiacheng ; Briceno, Simon ; Mavris, Dimitri N. ; Chakraborty, Imon

The airworthiness certification process of civil transportation aircraft is expensive, timeconsuming, and subject to uncertainty. To reduce the cost and time spent on the certification process, this paper proposes an approach to incorporate certification considerations into early design stages using virtual certification techniques. As a proof of concept, this paper focuses on flight performance certification requirements and developed a certification analysis module for aircraft conceptual and early preliminary design based on FAR-25 Subpart B. The module transforms the regulations from textual documents to quantitative constraint functions and ensures the certification constraint check of the design through physics-based analysis. To validate the module, a Small Single-aisle Aircraft testing model is developed and virtually certified by the module. The certification analysis result of the testing model is benchmarked with public domain data.
A Multi-UAS Trajectory optimization Methodology for Complex Enclosed Environments

(Georgia Institute of Technology, 2019-06) Barlow, Sarah ; Choi, Youngjun ; Briceno, Simon ; Mavris, Dimitri N.

This paper explores a multi-UAV trajectory optimization methodology for confined environments. One potential application of this technology is performing warehouse inventory audits; this application is used to evaluate the methodology's impact on minimizing total mission times. This paper investigates existing algorithms and improves upon them to better address the constraints of warehouse-like environments. An existing inventory scanning algorithm generates sub-optimal, collision free paths for multi-UAV operations, which has two sequential processes: solving a vehicle routing problem, and determining optimal deployment time without any collision. To improve the sub-optimal results, this paper introduces three possible improvements on the multi-UAV inventory tracking scenario. First, a new algorithm logic which seeks to minimize the total mission time once collision avoidance has been ensured rather than having separate processes. Next, an objective function that seeks to minimize the maximum UAV mission time rather than minimizing the total of all UAV mission times. Last, an operational setup consisting of multiple deployment locations instead of only one. These algorithms are evaluated individually and in combination with one another to assess their impact on the overall mission time using a representative inventory environment. The best combination will be further analyzed through a design of experiments by varying several inputs and examining the resulting fleet size, computation time, and overall mission time.