Alexia P. Payan

Uncovering local magnetospheric processes governing the morphology and variability of Ganymede's aurora using three-dimensional multifluid simulations of Ganymede's magnetosphere

We investigate local magnetospheric processes governing the morphology and variability of Ganymedes aurora depending on its position with respect to the center of the Jovian plasma sheet. We couple an existing three-dimensional multifluid simulation to a new aurora brightness model developed for this study. With this, we are able to qualitatively and quantitatively show that the short- and long-period variabilities observed in Ganymedes auroral footprint at Jupiter are also predicted to be present in the brightness and morphology of the aurora at Ganymede. We also examine the relationship between acceleration structures and precipitation of electrons in Ganymedes neutral atmosphere by looking at the component of the electric field parallel to Ganymedes magnetic field. Our results confirm that regions of electron accelerations coincide with regions of brightest auroral emissions, as expected. Finally, we identify the likely source regions of electrons generating the aurora at Ganymede and discuss the plasma dynamic mechanisms likely responsible for these accelerations.

Effect of plasma torus density variations on the morphology and brightness of the Io footprint

We develop a 2-D-layered model of the Io plasma torus to study the apparent “shutoff” of the Io footprint in 2007, when it disappeared beneath a region of diffuse emissions, roughly coincident with a massive eruption of Tvashtar Paterae. First, we investigate the effects of Ios location in the plasma torus and validate our model results against Hubble UV observations of the Io footprint. We are able to qualitatively reproduce variations in the morphology of the footprint due to Ios changing latitudinal location with respect to the center of the plasma torus, capturing the bright leading spot and the dimmer tail. Then, we consider the effects of an increase in the local plasma density on the brightness and morphology of the Io footprint. Our results show a correlation between a local density increase in the plasma torus and the dimming of the Io footprint as observed in 2007. In particular, we find that a local density enhancement at Io of fivefold compared to the nominal value is sufficient to produce the observed shutoff of the footprint.

Improvement of Rotorcraft Safety Metrics Using Performance Models and Data Integration

Historically, rotorcraft have featured higher accident and incident rates compared to fixed-wing air carrier and general aviation aircraft. Safety improvements are currently sought through the adoption of flight data monitoring, a voluntary and proactive safety program where retrospective analysis of flight data records identifies precursors of safety-critical events and tracks improvements pursuant of corrective measures. This paper provides a review of flight data monitoring and elaborates on the unique and significant challenges of its implementation for rotorcraft operations. Two enhancements to current practice are presented in the form of physics-based model integration for improved safety event definition and detection and integration with external data sources to evaluate meaningful safety metrics supplemental to a priori operational event detection. For the former, it is demonstrated that a model-based approach to helicopter autorotation significantly improves characterization of operational safe...

Parametric Assessment of Aviation Environmental Goals: Implications on R&D Decision Making

The prospects of the U.S. commercial aviation sector remain positive with a long-term outlook of growth, driven by the U.S. and world economies. The National Airspace System (NAS) is anticipated to become congested and flight delays are likely to propagate throughout the system. This will likely increase the consumption of aviation fuel, and consequently, nitrogen oxide (NOx) and carbon dioxide (CO2) emissions. Several U.S. research initiatives are addressing the sustainability needs of the future NAS. Meanwhile, the International Air Transport Association (IATA) has set system level goals to guide future research and development (R&D) programs. This paper proposes a quantitative approach that bridges the gap between R&D initiatives and IATA’s system level goals. The objective is to provide valuable insight into required contributions from technologies, operations, and bio-fuels to assess the applicability of IATA’s goals.

Framework Development for Performance Evaluation of the Future National Airspace System

Sustainability of the National Airspace System (NAS) continues to be a major concern to its governing body, the Federal Aviation Administration (FAA). Several research efforts are addressing the sustainability needs of the future NAS, including the Continuous Lower Emissions, Energy and Noise (CLEEN) program under the FAA, and the Environmentally Responsible Aviation (ERA) and Fixed Wing (FW) projects of the National Aeronautics and Space Administration (NASA). These initiatives are focused on developing and maturing technologies that would mitigate the environmental impacts of aviation. Alternatively, the Next Generation air transportation system (NextGen) is to provide substantial efficiency improvements from an operational perspective. Also, bio-fuels continue to be an attractive alternative to conventional jet fuels given their reduced life cycle emissions. This paper proposes an integrated framework that would evaluate the performance of the future NAS under different scenarios that consider varying technology, operation, and biofuel contributions. The objective is to assess whether or not the system level goals laid out by the International Air Transport Association (IATA) will be met.

Meeting Emissions Reduction Targets: A Probabilistic Lifecycle Assessment of the Production of Alternative Jet Fuels

In 2009, the aviation industry announced its commitment to address aviation’s environmental footprint by defining a four-pillar strategy to reduce greenhouse gas emissions. One of these pillars concerns the development of more efficient technologies for aircraft and engines, and the replacement of fossil carbon-based energy sources by renewable and sustainable solutions. Novel fuel-efficient technologies with promising emission reduction potential are currently being developed around the world. For instance, new lighter materials, new engine architectures, and futuristic aircraft concepts are studied to decrease fuel consumption and thus improve the carbon footprint of the future worldwide fleet. However, the projected 5% average annual growth in air transportation will most likely offset the expected carbon savings from new technologies alone. Another option for the aviation industry is to reduce its carbon footprint by considering the use of bio-jet fuels produced from renewable biomass feedstocks. Cost is however a barrier to large-scale commercial deployment of alternative fuels in aviation. In addition, selecting sustainable biomass options for the production of alternative jet fuels is challenging due to uncertain social, economic, environmental, and climatic factors. In this paper, we first examine the potential of new aircraft and engine technologies to reduce aviation-related carbon emissions. We show that without additional measures, technology infusions are not sufficient to meet the ambitious carbon emissions reduction goal set forth by IATA. Next, we study how changing the fuel source by introducing biofuels has potential to alleviate the environmental footprint of aviation. In this analysis, we account for the uncertainties associated with the selection of biomass feedstock options and their corresponding refining processes to produce suitable “drop-in” bio-jet fuels. A probabilistic analysis encompassing likely scenarios is carried out and a visualization interface is proposed to substantiate and facilitate decision making by aviation industry stakeholders.

Protection of Critical Infrastructures: A Methodology for Facilitating Modeling and Simulation of Notional Scenarios

The fear of terrorist attacks whether it be at a small or large scale keeps increasing and people struggle to find a way to protect civil vital assets, population areas, land and coastal sites, as well as strategic sites and critical infrastructures. The use of sophisticated methods, and advanced technologies by individuals engaged in criminal activities, pose an extreme challenge to the detection of potential malevolent systems, especially in the case of aerial systems flying at low or very low altitude. Therefore, enhancing the capabilities of detection of aerial systems that could be a threat to the nations’ critical infrastructures and key assets is a necessity. This involves identifying the best positioning of detection systems enabling the protection of a particular asset in a timely fashion. However, this is hindered by the complex nature of the system of systems required to provide an efficient protection. Moreover, it is difficult to fully protect a densely populated area where the detection of suspicious and potentially harmful activities is highly compromised. Therefore, current systems used for the protection of critical infrastructures and key assets are generally not adapted to and not optimized for the problem at hand. Besides, the assets requiring efficient protection against potential malevolent systems are highly heterogeneous in nature, as are the potential threats to these assets. The complexity and challenges associated to this problem may explain why a methodology enabling the definition of detection systems of systems able to provide accurate scanning of the low altitude air domain, in a specific geographic and climatic environment, is currently lacking. Finally, the combinatorial aspect of the problem usually hinders the conduct of a thorough study of all available possibilities of protection. That may be one reason why, to date, a structured methodology for the design of network-enabled systems does not exist. This paper thus proposes a methodology aimed at addressing the aforementioned gaps and challenges. This methodology particularly reformulates the problem in clear terms so as to facilitate the subsequent modeling and simulation of potential threat scenarios. This paper describes the steps involved in the implementation of the proposed approach, as well as the modeling and simulation of a notional scenario, using an agent-based modeling and simulation tool. The results of the simulation help gain insight into the reality of the problem.

Multilevel Probabilistic Morphological Analysis for Facilitating Modeling and Simulation of Notional Scenarios

Despite advances in computational power, simulation-based experiments of complex problems remain time-consuming. Significant efforts are required to define an accurate representation of the problem and draw relevant conclusions about relationships between input parameters and output performance measures. To reduce the computational burden without sacrificing accuracy, it is customary to design a set of relevant scenarios. Doing so necessitates that the problem be decomposed into relevant parameters and that the consistency of these parameters be analyzed. To address this challenge, we propose a multilevel probabilistic morphological analysis. Traditionally, morphological analysis documents the decomposition of a system into its main components, and performs binary compatibility assessments to document relational data between component options. However, this approach leads to a rigid definition of the problem space and a static solution space that does not account for uncertain and dynamic behaviors associated with complex systems-of-systems. This research incorporates two important improvements. First, it explicitly incorporates a multilevel approach accommodating any successive decomposition steps that may be required when dealing with a complex system-of-systems problem. Second, this research introduces probabilistic cross-consistency assessments instead of traditional binary assessments to account for uncertain future states of the world. This probabilistic scheme is used to describe degrees of likelihood that two elements may coexist in a given scenario. This way, more complex interactions may also be captured and more realistic scenarios may be defined. Finally, the proposed method is applied to a problem investigating the design of architectures of detection systems for the protection of homeland critical assets.

Initial Feasibility Test of Exploring the Air Traffic management Design Tradespace through Surrogate Modeling

The National Airspace System (NAS) is a complex system-of-systems and modeling it is not a trivial task. However, a desire for accurate models of the NAS to enable the simulation of potential improvements to the management of the airspace and the systems that operate within it is desired. This paper describes an approach for the assembly of an integrated, variable-fidelity analysis capability that captures aspects of the NAS relevant to specific use cases. An on-demand mobility concept, the “Thin Haul” concept as implemented in the Boston/New England area airspace, was selected as the use case for this paper. The technical approach was implemented and an integrated, variable-fidelity modeling environment was created. A parametric assessment was then conducted on the environment and it was demonstrated that rapid, real-time assessments can be performed by utilizing surrogate models to wrap around the different modules in the environment. Surrogates were produced for airspace performance metrics, defined as the average airspace density in a given area and the average number of conflicts identified in the airspace, as a function of the parameters varied within the economic model.

Long-term Alternative Fuels Scenarios

Alternative fuels have been proposed as one of the solutions to significantly reduce the Greenhouse Gas footprint of aviation over longer time future timeframes. This modeling approach attempts to quantify alternative fuels quantities that the US market will support in a given time frame for specific marginal cost functions for the different alternative fuels production pathways that currently seem viable. The modeling approach uses the approach of a Cournot-Nash equilibrium oligopoly model that represents each of the production pathways as a competing firm with a specific marginal cost trade-off function. The model chooses the best market response of each firm that maximizes the payoff of each competing firm. The result is a fuel quantity of each production pathway at a given market clearing price. Additionally, a number of sensitiies that explore the sentivity to assumptions such as fuel price, elasticities, and assumed technology improvements were studied.