摘要 :
Commuter operations refer to the transportation of passengers on small capacity aircraft over short distances. These services have historically been plagued by high operating costs, yielding marginally profitable operations. The r...
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Commuter operations refer to the transportation of passengers on small capacity aircraft over short distances. These services have historically been plagued by high operating costs, yielding marginally profitable operations. The recent convergence of new technologies in autonomy and electric propulsion brings a step change in aircraft operating efficiency which may significantly improve the economics and environmental footprint of these vehicles. The next challenge is to optimize operations to match the demand and best utilize these assets. To do so, forecasting the demand for these new services is critical. First, we develop a demand model based on an empirically-calibrated generalized cost of travel combined with a multinomial logit model. Next, we investigate the design of sustainable and profitable regional air mobility operations by optimizing the network, schedule, and assignment of aircraft to flights. We develop a novel half-leg half-itinerary mixed-integer linear program to solve the integrated flight scheduling and fleet assignment problem using a multi-objective hierarchical optimization balancing the maximization of operating profits and the minimization of operating emissions. This helps identify promising regional air mobility markets, assess their economical viability, and select an optimum fleet composition to best serve these markets. The proposed framework is implemented in the United States Northeast Corridor using a fleet of three small-gauge electrified aircraft ranging in size from 9 passengers to 48 passengers. Several what-if scenarios are investigated with respect to the battery technology, the relative importance of profit and carbon emission objectives, and the network topology to identify their impact on the operations in terms of number of profitable routes, number of airports served, number of passengers carried, operating profits and operating emissions. Results highlight a significant potential for profitable regional air mobility services that will dramatically improve the connectivity and reach of rural and under-served communities, provided efficient and environmentally friendly aircraft are used.
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摘要 :
Commuter operations refer to the transportation of passengers on small capacity aircraft over short distances. These services have historically been plagued by high operating costs, yielding marginally profitable operations. The r...
展开
Commuter operations refer to the transportation of passengers on small capacity aircraft over short distances. These services have historically been plagued by high operating costs, yielding marginally profitable operations. The recent convergence of new technologies in autonomy and electric propulsion brings a step change in aircraft operating efficiency which may significantly improve the economics and environmental footprint of these vehicles. The next challenge is to optimize operations to match the demand and best utilize these assets. To do so, forecasting the demand for these new services is critical. First, we develop a demand model based on an empirically-calibrated generalized cost of travel combined with a multinomial logit model. Next, we investigate the design of sustainable and profitable regional air mobility operations by optimizing the network, schedule, and assignment of aircraft to flights. We develop a novel half-leg half-itinerary mixed-integer linear program to solve the integrated flight scheduling and fleet assignment problem using a multi-objective hierarchical optimization balancing the maximization of operating profits and the minimization of operating emissions. This helps identify promising regional air mobility markets, assess their economical viability, and select an optimum fleet composition to best serve these markets. The proposed framework is implemented in the United States Northeast Corridor using a fleet of three small-gauge electrified aircraft ranging in size from 9 passengers to 48 passengers. Several what-if scenarios are investigated with respect to the battery technology, the relative importance of profit and carbon emission objectives, and the network topology to identify their impact on the operations in terms of number of profitable routes, number of airports served, number of passengers carried, operating profits and operating emissions. Results highlight a significant potential for profitable regional air mobility services that will dramatically improve the connectivity and reach of rural and under-served communities, provided efficient and environmentally friendly aircraft are used.
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摘要 :
Commuter operations refer to the transportation of passengers on small capacity aircraft over short distances. These services have historically been plagued by high operating costs, yielding marginally profitable operations. The r...
展开
Commuter operations refer to the transportation of passengers on small capacity aircraft over short distances. These services have historically been plagued by high operating costs, yielding marginally profitable operations. The recent convergence of new technologies in autonomy and electric propulsion brings a step change in aircraft operating efficiency which may significantly improve the economics and environmental footprint of these vehicles. The next challenge is to optimize operations to match the demand and best utilize these assets. To do so, forecasting the demand for these new services is critical. First, we develop a demand model based on an empirically-calibrated generalized cost of travel combined with a multinomial logit model. Next, we investigate the design of sustainable and profitable regional air mobility operations by optimizing the network, schedule, and assignment of aircraft to flights. We develop a novel half-leg half-itinerary mixed-integer linear program to solve the integrated flight scheduling and fleet assignment problem using a multi-objective hierarchical optimization balancing the maximization of operating profits and the minimization of operating emissions. This helps identify promising regional air mobility markets, assess their economical viability, and select an optimum fleet composition to best serve these markets. The proposed framework is implemented in the United States Northeast Corridor using a fleet of three small-gauge electrified aircraft ranging in size from 9 passengers to 48 passengers. Several what-if scenarios are investigated with respect to the battery technology, the relative importance of profit and carbon emission objectives, and the network topology to identify their impact on the operations in terms of number of profitable routes, number of airports served, number of passengers carried, operating profits and operating emissions. Results highlight a significant potential for profitable regional air mobility services that will dramatically improve the connectivity and reach of rural and under-served communities, provided efficient and environmentally friendly aircraft are used.
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Lunar habitation missions are currently being planned to have astronauts return to the moon by the mid 2020's with a sustained lunar presence by the end of the decade. The various landed modules needed to support the missions are ...
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Lunar habitation missions are currently being planned to have astronauts return to the moon by the mid 2020's with a sustained lunar presence by the end of the decade. The various landed modules needed to support the missions are expected to be distributed around Shacklcton Crater at distances ranging from 1 to 15 km and with power needs ranging from 10 kW to 50 kW. Current plans call for a solar array to be installed on the rim of the crater that receives near-constant sunlight year around with a power distribution system that transfers power from the source to consumers. This paper details several power distribution systems: DC transmission lines, radio frequency power beaming, and optical power beaming. Sizing algorithms for each of these distributions systems along with their necessary subsystems were developed from literature and subject matter expertise input. Several experiments were then conducted to determine the performance of the systems along with their sensitivities to changes in assumptions for various sub-components. The defined Figures of Merit enable mission designers to select the best power distribution system for each possible power consumer mission scenario. The experimental results were analyzed and compiled into a set of figures that highlight the conditions for which a certain system outperforms the other.
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Airworthiness certification is challenging for novel aircraft concepts. To avoid the significant cost associated with the redesign for certification compliance, incorporating certification requirements into early aircraft design i...
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Airworthiness certification is challenging for novel aircraft concepts. To avoid the significant cost associated with the redesign for certification compliance, incorporating certification requirements into early aircraft design is desired for unconventional aircraft. However, epistemic uncertainties arising from modeling assumptions and aleatory uncertainties stemming from uncontrollable noise factors may have impacts on the certification analysis and certification-constrained design process. This paper presents an uncertainty quantification study based on a certification-constrained design and optimization study previously conducted for NASA's PEGASUS concept. The epistemic uncertainties are modeled through a set of multiplicative factors applied to intermediate disciplinary variables. The sensitivity analysis between design metrics and multiplicative factors reveals that uncertainties in stability and control derivatives can significantly affect certification constraint predictions, while uncertainties in drag approximations have considerable impacts on the vehicle sizing process. The aleatory uncertainties added to flight dynamics simulations include wind velocities and variations of weight and center of gravity. Four representative design candidates are evaluated for their robustness against aleatory uncertainties based on the Monte Carlo simulation performed on noise factors. The results show that aleatory uncertainties can affect the aircraft dynamic responses in flight test simulations, thus compromising certification compliance.
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In recent years, the use of data mining and machine learning techniques for safety analysis, incident and accident investigation, and fault detection has gained traction among the aviation community. Flight data collected from rec...
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In recent years, the use of data mining and machine learning techniques for safety analysis, incident and accident investigation, and fault detection has gained traction among the aviation community. Flight data collected from recording devices contains a large number of heterogeneous parameters, sometimes reaching up to thousands on modern commercial aircraft. More data is being collected continuously which adds to the ever-increasing pool of data available for safety analysis. However, among the data collected, not all parameters are important from a risk and safety analysis perspective. Similarly, in order to be useful for modern analysis techniques such as machine learning, using thousands of parameters collected at a high frequency might not be computationally tractable. As such, an intelligent and repeatable methodology to select a reduced set of significant parameters is required to allow safety analysts to focus on the right parameters for risk identification. In this paper, a step-by-step methodology is proposed to down-select a reduced set of parameters that can be used for safety analysis. First, correlation analysis is conducted to remove highly correlated, duplicate, or redundant parameters from the data set. Second, a pre-processing step removes metadata and empty parameters. This step also considers requirements imposed by regulatory bodies such as the Federal Aviation Administration and subject matter experts to further trim the list of parameters. Third, a clustering algorithm is used to group similar flights and identify abnormal operations and anomalies. A retrospective analysis is conducted on the clusters to identify their characteristics and impact on flight safety. Finally, analysis of variance techniques are used to identify which parameters were significant in the formation of the clusters. Visualization dashboards were created to analyze the cluster characteristics and parameter significance. This methodology is employed on data from the approach phase of a representative single-aisle aircraft to demonstrate its application and robustness across heterogeneous data sets. It is envisioned that this methodology can be further extended to other phases of flight and aircraft.
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摘要 :
In recent years, the use of data mining and machine learning techniques for safety analysis, incident and accident investigation, and fault detection has gained traction among the aviation community. Flight data collected from rec...
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In recent years, the use of data mining and machine learning techniques for safety analysis, incident and accident investigation, and fault detection has gained traction among the aviation community. Flight data collected from recording devices contains a large number of heterogeneous parameters, sometimes reaching up to thousands on modern commercial aircraft. More data is being collected continuously which adds to the ever-increasing pool of data available for safety analysis. However, among the data collected, not all parameters are important from a risk and safety analysis perspective. Similarly, in order to be useful for modern analysis techniques such as machine learning, using thousands of parameters collected at a high frequency might not be computationally tractable. As such, an intelligent and repeatable methodology to select a reduced set of significant parameters is required to allow safety analysts to focus on the right parameters for risk identification. In this paper, a step-by-step methodology is proposed to down-select a reduced set of parameters that can be used for safety analysis. First, correlation analysis is conducted to remove highly correlated, duplicate, or redundant parameters from the data set. Second, a pre-processing step removes metadata and empty parameters. This step also considers requirements imposed by regulatory bodies such as the Federal Aviation Administration and subject matter experts to further trim the list of parameters. Third, a clustering algorithm is used to group similar flights and identify abnormal operations and anomalies. A retrospective analysis is conducted on the clusters to identify their characteristics and impact on flight safety. Finally, analysis of variance techniques are used to identify which parameters were significant in the formation of the clusters. Visualization dashboards were created to analyze the cluster characteristics and parameter significance. This methodology is employed on data from the approach phase of a representative single-aisle aircraft to demonstrate its application and robustness across heterogeneous data sets. It is envisioned that this methodology can be further extended to other phases of flight and aircraft.
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Modal regulators and deformation trackers are designed for an open-loop fluttering wing model. The regulators are designed with modal coordinate and accelerometer inputs respectively. The modal coordinates are estimated with simul...
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Modal regulators and deformation trackers are designed for an open-loop fluttering wing model. The regulators are designed with modal coordinate and accelerometer inputs respectively. The modal coordinates are estimated with simulated fiber optics. The robust stability of the closed-loop systems is compared in a structured singular-value vector analysis. Performance is evaluated and compared in a gust alleviation and flutter suppression simulation. For the same wing and flight condition two wing-shape-tracking control architectures are presented, which achieve deformation control at any point on the wing.
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摘要 :
Modal regulators and deformation trackers are designed for an open-loop fluttering wing model. The regulators are designed with modal coordinate and accelerometer inputs respectively. The modal coordinates are estimated with simul...
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Modal regulators and deformation trackers are designed for an open-loop fluttering wing model. The regulators are designed with modal coordinate and accelerometer inputs respectively. The modal coordinates are estimated with simulated fiber optics. The robust stability of the closed-loop systems is compared in a structured singular-value vector analysis. Performance is evaluated and compared in a gust alleviation and flutter suppression simulation. For the same wing and flight condition two wing-shape-tracking control architectures are presented, which achieve deformation control at any point on the wing.
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Conceptual design of boundary layer ingesting (BLI) aircraft requires a methodology that captures the aero-propulsive interactions in a parametric fashion. This entails modeling the impacts of BLI as a function of the airframe and...
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Conceptual design of boundary layer ingesting (BLI) aircraft requires a methodology that captures the aero-propulsive interactions in a parametric fashion. This entails modeling the impacts of BLI as a function of the airframe and propulsor design. Previous work has analyzed the sensitivity of these BLI effects to the propulsor size and throttle. This paper assesses the sensitivity of the BLI effects to the airframe design through a series of experiments, using CFD. The scope of this analysis is restricted to tube and wing type BLI concepts. Results from these studies help identify the critical airframe design space that needs to be considered when generating a parametric model of the BLI effects. Guidelines regarding the level of detail required for the airframe geometry model are discussed.
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