Design and Evaluation of Guidance Algorithms for 4D-Trajectory-Based Terminal Airspace Operations

The work under this research deals with the development of a computational framework suitable for the design and analysis of 4D green trajectories for terminal airspace operations. First, a 4D-trajectory-based operational concept for terminal area operation consisting of ground-side automation and flight-deck-side automation is presented. The focus of the current paper is the development of 4D-trajectory design tools as part of the ground-side automation. The paper first identifies aircraft aerodynamic, fuel consumption, emissions, and noise models necessary for trajectory optimization based on open-source data such as the Base of Aircraft DAta (BADA). A numerical trajectory optimization framework is then proposed for the design of 4D-trajectories. The framework is able to accommodate aircraft performance constraints, separation constraints, and airport capacity considerations, and it can model "green" considerations such as fuel & emissions minimization, and noise reduction. The trajectory optimization framework is demonstrated on single and multiple aircraft scenarios. Using parametric optimization approach the paper explores the relationship between the time-of-arrival at runway threshold and the fuel consumption for a B737 aircraft. In the multi-aircraft scenario the paper illustrates the implementation of 3 nmi separation criteria between a pair of aircraft. A companion paper deals with the flight-deck-side automation that tracks the 4D trajectory clearances created by the groundside automation.Automation concepts for NextGen terminal area operations can be categorized as (i) ground-side automation systems, or (ii) flight-deck-side automation systems. The research in the current paper is focused on ground-side automation. Reference 6 describes a concept for future high-density terminal air traffic operations that has been developed by the Airspace Super Density Operations (ASDO) researchers at NASA Ames Research Center. The concept described in Reference 6 includes five core capabilities: 1) Extended Terminal Area Routing, 2) Precision Scheduling Along Routes, 3) Merging and Spacing, 4) Tactical Separation, and 5) Off-Nominal Recovery. The first two capabilities are strategic planning tools and the remaining three are tactical decision support tools. In general tools developed for ground-side operations could be classified as (i) Stategic Planning Tools, and (ii) Tactical Decision Support Tools. The following sub-sections describe the past research under these two categories. A. Strategic Planning ToolsStrategic planning tools deal with (i) route and runway assignment, and (ii) scheduling and sequencing at key points such as the meter fix and the runway threshold. The Traffic Management Advisor (TMA) 7 is one of earliest scheduling tools developed at NASA Ames Research Center. The TMA supports en route controllers and managers with scheduling, spacing, and arrival flow management and is currently deployed at multiple Air Route Traffic Control Centers. The TMA uses a timeline graphical user interface (TGUI) t...

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“…The propagation time step is chosen as second. Position and altitude model: (34) Equality Constraints:…”

Section: Trajectory Final Conditionsmentioning

confidence: 99%

4D Green Trajectory Design for Terminal Area Operations Using Nonlinear Optimization Techniques

Vaddi

Sweriduk

Tandale

2012

AIAA Guidance, Navigation, and Control Conference

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“…The flight planning and optimization problems presented in literature [27][28][29] consider the wind components (north and east) stationary, deterministic, and computed through polynomial regression from RUC predictions. In Vaddi et al, 30 closed-loop tests evaluate the robustness of a descent guidance algorithm to atmospheric data uncertainties. The wind is considered to have two components: a deterministic component, obtained from the forecast data and assumes it only has a linear variation with altitude, and a stochastic component constructed using an autoregressive model 31 based on forecast data and flight data from the same airspace and a lag of no more than 15 min.…”

Section: Introductionmentioning

confidence: 99%

New atmospheric data model for constant altitude accelerated flight performance prediction calculations and flight trajectory optimization algorithms

Dancila

Botez

2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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This article presents a new method for storing and computing the atmospheric data used in time-critical flight trajectory performance prediction calculations, such as flight performance prediction calculations in flight management systems and/or flight trajectory optimization, of constant altitude cruise segments. The proposed model is constructed based on the forecast data provided by Meteorological Service Agencies, in a GRIB2 data file format, and the set of waypoints that define the lateral component of the evaluated flight profile(s). The atmospheric data model can be constructed/updated in the background or off-line, when new atmospheric prediction data are available, and subsequently used in the flight performance computations. The results obtained using the proposed model show that, on average, the atmospheric parameter values are computed six times faster than through 4D linear interpolations, while yielding identical results (value differences of the order of 10e−14). When used in flight trajectory performance calculations, the obtained results show that the proposed model conducts to significant computation time improvements. The proposed model can be extended to define the atmospheric data for a set of cruise levels (usually multiple of 1000 ft).

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“…1) LNAV & VNAV [4][5][6][7][8][9][10] features of FMS that enable 3D-path tracking capability 2) Required Time-of-Arrival [11][12][13][14] (RTA) feature of FMS that enables an explicit TOA specification at waypoints such as the Meterfix and runway 3) Interval Management [15][16][17][18][19] (IM) tools that enable the capability to maintain spatial and temporal spacing with another aircraft 4) 4-Dimensional FMS (4DFMS) [20][21][22][23] capability that enables full 4D-trajectory tracking The focus of the current research is to develop a model of IM and evaluate it in a high-fidelity simulation environment in order to establish the accuracy of its capability to maintain temporal spacing. Section II summarizes IM capabilities from published literature.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Temporal Spacing Errors Associated with Interval Management Algorithms

Bai

Vaddi

Mulfinger

2014

14th AIAA Aviation Technology, Integration, and Operations Conference

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This paper seeks to characterize the temporal spacing errors resulting from the use of Interval Management (IM) algorithms. The focus of the current paper is IM concepts and algorithms that realize a specified temporal spacing between a Target aircraft and an Ownship aircraft at the runway threshold. The paper presents an IM algorithm consisting of the following four modules: (i) Target-Landing-Time Estimation Module, (ii) Ownship-Landing-Time Estimation Module, (iii) Ownship Speed Command Computation Module, and (iv) Ownship Thrust Command Computation Module. The overall guidance module is evaluated on a simulation that models aircraft point-mass dynamics, bank-angle auto-pilot dynamics, pitch-axis auto-pilot dynamics, and engine lag dynamics. The simulation environment also consists of actual atmospheric forecasts and realistic spatio-temporally correlated wind uncertainty models. Results obtained from single case simulation as well as Monte-Carlo simulations are presented in the paper. The modeled scenario consisted of an A320 Target equipped with "Lateral Navigation"/"Vertical Navigation" (LNAV/VNAV) capabilities followed by an A320 Ownship equipped with the IM algorithm. Both aircraft fly the BIGSUR route to SFO airport using a RAP-13 1-hr wind forecast. 500 Monte-Carlo simulations were conducted with realistic wind uncertainty models. The IM algorithm for this case is seen to have a 90% probability landing time error range of 5.9 seconds, compared to the no-IM solution, which has a 90% probability landing time error range of 33.4 seconds.

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Design and Evaluation of Guidance Algorithms for 4D-Trajectory-Based Terminal Airspace Operations

Cited by 8 publications

References 16 publications

4D Green Trajectory Design for Terminal Area Operations Using Nonlinear Optimization Techniques

4D Green Trajectory Design for Terminal Area Operations Using Nonlinear Optimization Techniques

New atmospheric data model for constant altitude accelerated flight performance prediction calculations and flight trajectory optimization algorithms

Evaluation of Temporal Spacing Errors Associated with Interval Management Algorithms

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