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DOE OSTI2019

ASCR Workshop on In Situ Data Management: Enabling Scientific Discovery from Diverse Data Sources

In January 2019, the U.S. Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions for in situ data management (ISDM). The workshop defined ISDM as the practices, capabilities, and procedures to control the organization of data and enable the coordination and communication among heterogeneous tasks, executing simultaneously in a high-performance computing system, cooperating toward a common objective. The workshop revealed two primary, interdependent motivations for processing and managing data in situ. The first motivation is that the in situ methodology enables scientific discovery from a broad range of data sources over a wide scale of computing platforms: leadership-class systems, clusters, clouds, workstations, and embedded devices at the edge. The successful development of ISDM capabilities will benefit real-time decision-making, design optimization, and data-driven scientific discovery. The second motivation is the need to decrease data volumes. ISDM can make critical contributions to managing large data volumes from computations and experiments to minimize data movement, save storage space, and boost resource efficiency, often while simultaneously increasing scientific precision. A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery across the Office of Science mission space. Applications wanting to use the in situ capabilities include those where data analysis feeds back to the simulation, decisions are made autonomously, big data or machine learning is among the tasks to be coordinated, and computations need to be completed in real time. The workshop identified six priority research directions that highlight the components and capabilities needed for ISDM to be successful for the wide variety of applications discussed: making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

SIRIUS: Enabling Progressive Data Exploration for Extreme-Scale Scientific Data

Scientific simulations on high performance computing (HPC) platforms generate large quantities of data. To bridge the widening gap between compute and I/O, and enable data to be more efficiently stored and analyzed, simulation outputs need to be refactored, reduced, and appropriately mapped to storage tiers. However, a systematic solution to support these steps has been lacking in the current HPC software ecosystem. To that end, this paper develops SIRIUS, a progressive JPEG-like data management scheme for storing and analyzing big scientific data. It co-designs data decimation, compression, and data storage, taking the hardware characteristics of each storage tier into considerations. With reasonably low overhead, our approach refactors simulation data, using either topological or uniform decimation, into a much smaller, reduced-accuracy base dataset, and a series of deltas that is used to augment the accuracy if needed. The base dataset and deltas are compressed and written to multiple storage tiers. Data saved on different tiers can then be selectively retrieved to restore the level of accuracy that satisfies data analytics. Thus, SIRIUS provides a paradigm shift towards elastic data analytics and enables end users to make trade-offs between analysis speed and accuracy on-the-fly. This paper further develops algorithms to preserve statistics for data decimation, a common requirement for reducing data. Here, we assess the impact of SIRIUS on unstructured triangular meshes, a pervasive data model used in scientific simulations. In particular, we evaluate two realistic use cases: the blob detection in fusion and high-pressure area extraction in computational fluid dynamics.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2015

Achieving TASAR Operational Readiness

NASA has been developing and testing the Traffic Aware Strategic Aircrew Requests (TASAR) concept for aircraft operations featuring a NASA-developed cockpit automation tool, the Traffic Aware Planner (TAP), which computes traffic/hazard-compatible route changes to improve flight efficiency. The TAP technology is anticipated to save fuel and flight time and thereby provide immediate and pervasive benefits to the aircraft operator, as well as improving flight schedule compliance, passenger comfort, and pilot and controller workload. Previous work has indicated the potential for significant benefits for TASAR-equipped aircraft, and a flight trial of the TAP software application in the National Airspace System has demonstrated its technical viability. This paper reviews previous and ongoing activities to prepare TASAR for operational use.

Wing, David J.↗

DOE OSTI2015

Framework for Identifying Cybersecurity Risks in Manufacturing

Increasing connectivity, use of digital computation, and off-site data storage provide potential for dramatic improvements in manufacturing productivity, quality, and cost. However, there are also risks associated with the increased volume and pervasiveness of data that are generated and potentially accessible to competitors or adversaries. Enterprises have experienced cyber attacks that exfiltrate confidential and/or proprietary data, alter information to cause an unexpected or unwanted effect, and destroy capital assets. Manufacturers need tools to incorporate these risks into their existing risk management processes. This article establishes a framework that considers the data flows within a manufacturing enterprise and throughout its supply chain. The framework provides several mechanisms for identifying generic and manufacturing-specific vulnerabilities and is illustrated with details pertinent to an automotive manufacturer. Finally, in addition to providing manufacturers with insights into their potential data risks, this framework addresses an outcome identified by the NIST Cybersecurity Framework.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Strong Local-Nonlocal Coupling for Integrated Fracture Modeling

Peridynamics, a nonlocal extension of continuum mechanics, is unique in its ability to capture pervasive material failure. Its use in the majority of system-level analyses carried out at Sandia, however, is severely limited, due in large part to computational expense and the challenge posed by the imposition of nonlocal boundary conditions. Combined analyses in which peridynamics is employed only in regions susceptible to material failure are therefore highly desirable, yet available coupling strategies have remained severely limited. This report is a summary of the Laboratory Directed Research and Development (LDRD) project “Strong Local-Nonlocal Coupling for Integrated Fracture Modeling,” completed within the Computing and Information Sciences (CIS) Investment Area at Sandia National Laboratories. A number of challenges inherent to coupling local and nonlocal models are addressed. A primary result is the extension of peridynamics to facilitate a variable nonlocal length scale. This approach, termed the peridynamic partial stress, can greatly reduce the mathematical incompatibility between local and nonlocal equations through reduction of the peridynamic horizon in the vicinity of a model interface. A second result is the formulation of a blending-based coupling approach that may be applied either as the primary coupling strategy, or in combination with the peridynamic partial stress. This blending-based approach is distinct from general blending methods, such as the Arlequin approach, in that it is specific to the coupling of peridynamics and classical continuum mechanics. Facilitating the coupling of peridynamics and classical continuum mechanics has also required innovations aimed directly at peridynamic models. Specifically, the properties of peridynamic constitutive models near domain boundaries and shortcomings in available discretization strategies have been addressed. The results are a class of position-aware peridynamic constitutive laws for dramatically improved consistency at domain boundaries, and an enhancement to the meshfree discretization applied to peridynamic models that removes irregularities at the limit of the nonlocal length scale and dramatically improves convergence behavior. Finally, a novel approach for modeling ductile failure has been developed, motivated by the desire to apply coupled local-nonlocal models to a wide variety of materials, including ductile metals, which have received minimal attention in the peridynamic literature. Software implementation of the partial-stress coupling strategy, the position-aware peridynamic constitutive models, and the strategies for improving the convergence behavior of peridynamic models was completed within the Peridigm and Albany codes, developed at Sandia National Laboratories and made publicly available under the open-source 3-clause BSD license.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Preface to the special volume on the second Sandia Fracture Challenge

In this study, ductile failure of structural metals is a pervasive issue for applications such as automotive manufacturing, transportation infrastructures, munitions and armor, and energy generation. Experimental investigation of all relevant failure scenarios is intractable, requiring reliance on computation models. Our confidence in model predictions rests on unbiased assessments of the entire predictive capability, including the mathematical formulation, numerical implementation, calibration, and execution.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources

In January 2019, the US Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions (PRDs) for in situ data management (ISDM). A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery. This article describes six PRDs identified by the workshop, which highlight the components and capabilities needed for ISDM to be successful for a wide variety of applications—making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2003

Case for Deploying Complex Systems Utilizing Commodity Components

When the International Space Station (ISS) finally reached an operational state, many of the Payload Operations and Integration Facility (POIF) hardware components were reaching end of life, COTS product costs were soaring, and the ISS budget was becoming severely constrained. However, most requirement development was complete. In addition, the ISS program is a fully functioning program with at least fifteen years of operational life remaining. Therefore it is critical that any upgrades, refurbishments, or enhancements be accomplished in realtime with minimal disruptions to service. For these and other reasons, it was necessary to ensure the viability of the POIF. Due to the to the breadth of capability of the POIF (a NASA ground station), it is believed that the lessons to be learned by other complex systems are applicable and any solutions garnered by the POIF are applicable to other complex systems as well. With that in mind, a number of new approaches have been investigated to increase the portability of the POIF and reduce the cost of refurbishment, operations, and maintenance. These new approaches were directed at the Total Cost of Ownership (TCO); not only the refurbishment but also current operational difficulties, licensing, and anticipation of the next refurbishment. Our basic premise is that technology had evolved dramatically since the concept of the POIF ground system and we should leverage our experience on this new technological landscape. Fortunately, Moore's law and market forces have changed the landscape considerably. These changes are manifest in five (5) ways that are particularly relevant to POIF: 1. Complex Instruction Set Computing (CISC) processors have advanced to unprecedented levels of compute capacity with a dramatic cost break, 2. Linux has become a major operating system supported by most vendors on a broad range of platforms, 3. Windows(TradeMark) based desktops are pervasive in the office environment, 4. Stable and affordable WindowsTM development environments and tools are available and offer a rich set of capabilities, 5. The WindowsTM 2000 provides a stable client platform, Therefore, five studies were proposed, developed, and are in the current process of deployment which dramatically reduces the cost of operations, maintenance, refurbishment, and deployment of a ground system. Restating and refining the basic premise stated earlier, it is possible to enhance operations through the replacement of hardware and software components with commodity based items wherever applicable. This will dramatically reduce the overall lifecycle cost of the project. The first study leveraged the POIF S secure, three-tier, web architecture to replace the client workstations with lower cost PC platforms. A second study initiated a review of COTS products to examine the level of added value of each product. This study included replacement of some COTS products with custom code, deletions, substitutions, and consolidation of COTS products. Studies three and four reviewed the server architectures of the data distribution systems and Enhanced HOSC System (EHS) command and telemetry system to propose migration to new platforms, both software and hardware. The final study reviewed current IP communication technologies, developed an operational model for flight operations, and demonstrated that voice over IP was practical and could be integrated into operations.

Bryant, Barry S.↗

NASA NTRS2018

Initial Implementation and Operational Use of TASAR in Alaska Airlines Flight Operations

NASA has been developing and testing the Traffic Aware Strategic Aircrew Requests (TASAR) concept for aircraft operations featuring a NASA-developed cockpit automation tool, the Traffic Aware Planner (TAP), which computes route changes compatible with nearby traffic and airspace constraints to improve flight efficiency. The TAP technology is anticipated to save fuel, flight time, and operating costs and thereby provide immediate and pervasive benefits to the aircraft operator. Alaska Airlines is partnered with NASA to implement and evaluate TASAR in revenue service. This paper will describe activities undertaken to achieve TASAR operational status at Alaska Airlines, and it will present preliminary results from initial flight operations.

Wing, David J.↗

DOE OSTI2019

Environmental stimuli drive a transition from cooperation to competition in synthetic phototrophic communities

Phototrophic communities of photosynthetic algae or cyanobacteria and heterotrophic bacteria or fungi are pervasive throughout the environment. How interactions between members contribute to the resilience and affect the fitness of phototrophic communities is not fully understood. In this work, we integrated metatranscriptomics, metabolomics and phenotyping with computational modelling to reveal condition-dependent secretion and cross-feeding of metabolites in a synthetic community. We discovered that interactions between members are highly dynamic and are driven by the availability of organic and inorganic nutrients. Environmental factors, such as ammonia concentration, influenced community stability by shifting members from collaborating to competing. Moreover, overall fitness was dependent on genotype and streamlined genomes improved growth of the entire community. Our mechanistic framework offers insights into the physiology and metabolic response to environmental and genetic perturbation of these ubiquitous microbial associations.

09 BIOMASS FUELS↗

DOE OSTI2014

Validation of a Monte Carlo model used for simulating tube current modulation in computed tomography over a wide range of phantom conditions/challenges

Purpose: Monte Carlo (MC) simulation methods have been widely used in patient dosimetry in computed tomography (CT), including estimating patient organ doses. However, most simulation methods have undergone a limited set of validations, often using homogeneous phantoms with simple geometries. As clinical scanning has become more complex and the use of tube current modulation (TCM) has become pervasive in the clinic, MC simulations should include these techniques in their methodologies and therefore should also be validated using a variety of phantoms with different shapes and material compositions to result in a variety of differently modulated tube current profiles. The purpose of this work is to perform the measurements and simulations to validate a Monte Carlo model under a variety of test conditions where fixed tube current (FTC) and TCM were used. Methods: A previously developed MC model for estimating dose from CT scans that models TCM, built using the platform of MCNPX, was used for CT dose quantification. In order to validate the suitability of this model to accurately simulate patient dose from FTC and TCM CT scan, measurements and simulations were compared over a wide range of conditions. Phantoms used for testing range from simple geometries with homogeneous composition (16 and 32 cm computed tomography dose index phantoms) to more complex phantoms including a rectangular homogeneous water equivalent phantom, an elliptical shaped phantom with three sections (where each section was a homogeneous, but different material), and a heterogeneous, complex geometry anthropomorphic phantom. Each phantom requires varying levels of x-, y- and z-modulation. Each phantom was scanned on a multidetector row CT (Sensation 64) scanner under the conditions of both FTC and TCM. Dose measurements were made at various surface and depth positions within each phantom. Simulations using each phantom were performed for FTC, detailed x–y–z TCM, and z-axis-only TCM to obtain dose estimates. This allowed direct comparisons between measured and simulated dose values under each condition of phantom, location, and scan to be made. Results: For FTC scans, the percent root mean square (RMS) difference between measurements and simulations was within 5% across all phantoms. For TCM scans, the percent RMS of the difference between measured and simulated values when using detailed TCM and z-axis-only TCM simulations was 4.5% and 13.2%, respectively. For the anthropomorphic phantom, the difference between TCM measurements and detailed TCM and z-axis-only TCM simulations was 1.2% and 8.9%, respectively. For FTC measurements and simulations, the percent RMS of the difference was 5.0%. Conclusions: This work demonstrated that the Monte Carlo model developed provided good agreement between measured and simulated values under both simple and complex geometries including an anthropomorphic phantom. This work also showed the increased dose differences for z-axis-only TCM simulations, where considerable modulation in the x–y plane was present due to the shape of the rectangular water phantom. Results from this investigation highlight details that need to be included in Monte Carlo simulations of TCM CT scans in order to yield accurate, clinically viable assessments of patient dosimetry.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2019

Parsl: Pervasive Parallel Programming in Python

High-level programming languages such as Python are increasingly used to provide intuitive interfaces to libraries written in lower-level languages and for assembling applications from various components. This migration towards orchestration rather than implementation, coupled with the growing need for parallel computing (e.g., due to big data and the end of Moore's law), necessitates rethinking how parallelism is expressed in programs. Here, we present Parsl, a parallel scripting library that augments Python with simple, scalable, and flexible constructs for encoding parallelism. These constructs allow Parsl to construct a dynamic dependency graph of components that it can then execute efficiently on one or many processors. Parsl is designed for scalability, with an extensible set of executors tailored to different use cases, such as low-latency, high-throughput, or extreme-scale execution. We show, via experiments on the Blue Waters supercomputer, that Parsl executors can allow Python scripts to execute components with as little as 5 ms of overhead, scale to more than 250000 workers across more than 8000 nodes, and process upward of 1200 tasks per second. Other Parsl features simplify the construction and execution of composite programs by supporting elastic provisioning and scaling of infrastructure, fault-tolerant execution, and integrated wide-area data management. We show that these capabilities satisfy the needs of many-task, interactive, online, and machine learning applications in fields such as biology, cosmology, and materials science.

parallel programming↗

DOE OSTI2026

Computational Discovery of Ultralow Thermal Conductivity in the Energy-Degenerate Polymorphic Crystal Family A 2 M 2 M’Q 4

Crystalline materials, characterized by their well-defined lattices, typically exhibit a unique global thermodynamic minimum for a specific composition. However, in this study, we discover a quaternary chalcogenide family, A 2 M 2 M’Q 4 (A: alkali metals; M: coinage metal; M’: transition or group-IVA metals; Q: chalcogens), that exhibits pervasive energy (near-)degeneracy. For a given composition, multiple structurally distinct polymorphs exist within a formation enthalpy window of only a few milli-electron volts per atom. We quantify this inherent structural flexibility using a dedicated descriptor, σ f : the standard deviation of formation enthalpies among degenerate (meta)stable polymorphs. The consistently low σf observed across the A 2 M 2 M’Q 4 family signifies a characteristically shallow and frustrated potential energy landscape, which drives pronounced lattice anharmonicity, marking these materials as prime candidates for ultralow lattice thermal conductivity (κ L ). Employing an advanced high-throughput computational framework that integrates thermodynamics, lattice dynamics, and thermal conductivity calculations, we screen 1215 A 2 M 2 M’Q 4 compounds, identifying 30 stable candidates with κ L < 0.5 W m –1 K –1 at 300 K. Among them, Rb 2 Ag 2 SnTe 4 and Rb 2 Au 2 HfTe 4 , two representatives from the IVA and TM subgroups, are predicted to show ultralow room-temperature κ L of 0.174 W m –1 K –1 and 0.295 W m –1 K –1 , respectively. A systematic analysis suggests that the nonbonding and antibonding states induced by “dual rattlers” are the origin of low thermal conductivity in these compounds. Our results position the A 2 M 2 M’Q 4 family as a rich source of intrinsic thermal insulators and suggest that polymorphic energy degeneracy may serve as a valuable signpost for identifying crystalline families with potential anharmonicity.

cations↗

NASA NTRS2018

Climate Lecture 20: The Educational Global Climate Model (EdGCM)

Climate change will profoundly impact Earth's environmental health as well as the world's economic and geopolitical landscape over the coming decades. The impacts of climate change are, in fact, already beginning to be experienced and have the potential to affect every living plant and animal on Earth within decades. Given this reality, every citizen of this planet should have the right to knowledge about the Earth's climate system and have the option to adapt to, or help mitigate the profound changes that are coming. In addition, a portion of the workforce needs to be capable of interpreting and analyzing climate information because, since the impacts of climate change will be widespread, pervasive, and continue to change over time, more professions will be interacting with climate data. We are already at, or past, the point where educators and their students require access to the scientific and technological resources - computer models, data, and visualization tools - that scientists use daily in the study of climate change. Although scientists use many methods to study Earth's climate system, global climate models (GCMs) have become the primary tools for exploring the complex interactions between components of the entire system: atmosphere, oceans, and land. GCMs are used to make projections of future climate change, to simulate climates of the past, and even to help scientists look for life on other planets. Like any model, a GCM can help people evaluate actions before they are taken. Like Business Intelligence software, they are Climate Intelligence tools. Unfortunately, GCMs are black boxes to most people. A previous chapter in this book by Gary Russell, entitled Building a Climate Model, is one example of the growing body of literature aimed at the general public describing the inner workings of global climate models. This literature goes a long way toward explaining climate model fundamentals. However, it will not be enough to alleviate their black-box nature unless people are afforded hands-on, authentic learning experiences as well.

Chandler, Mark A.↗

DOE OSTI2023

A Fourth-Order Embedded Boundary Finite Volume Method for the Unsteady Stokes Equations with Complex Geometries

A fourth-order finite volume embedded boundary (EB) method is presented for the unsteady Stokes equations. The algorithm represents complex geometries on a Cartesian grid using EB, employing a technique to mitigate the ``small cut-cell"" problem without mesh modifications, cell merging, or state redistribution. Spatial discretizations are based on a weighted least-squares technique that has been extended to fourth-order operators and boundary conditions, including an approximate projection to enforce the divergence-free constraint. Solutions are advanced in time using a fourth-order additive implicit-explicit Runge-Kutta method, with the viscous and source terms treated implicitly and explicitly, respectively. Formal accuracy of the method is demonstrated with several grid convergence studies, and results are shown for an application with a complex bio-inspired material. In conclusion, the developed method achieves fourth-order accuracy and is stable despite the pervasive small cells arising from complex geometries.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations

Many High Performance Computing (HPC) facilities have developed and deployed frameworks in support of continuous monitoring and operational data analytics (MODA) to help improve efficiency and throughput. Because of the complexity and scale of systems and workflows and the need for low-latency response to address dynamic circumstances, automated feedback and response have the potential to be more effective than current human-in-the-loop approaches which are laborious and error prone. Progress has been limited, however, by factors such as the lack of infrastructure and feedback hooks, and successful deployment is often site- and case-specific. In this position paper we report on the outcomes and plans from a recent Dagstuhl Seminar, seeking to carve a path for community progress in the development of autonomous feedback loops for MODA, based on the established formalism of similar (MAPE-K) loops in autonomous computing and self-adaptive systems. By defining and developing such loops for significant cases experienced across HPC sites, we seek to extract commonalities and develop conventions that will facilitate interoperability and interchangeability with system hardware, software, and applications across different sites, and will motivate vendors and others to provide telemetry interfaces and feedback hooks to enable community development and pervasive deployment of MODA autonomy loops.

autonomy loops↗

DOE OSTI2022

Multi‐Layered Systems for Permanent Geologic Storage of CO 2 at the Gigatonne Scale

Abstract The effectiveness of Carbon Capture and Storage (CCS) as an imperative decarbonization technology relies on the sealing capacity of a fine‐grained caprock to permanently store CO 2 deep underground. Uncertainties in assessing the caprock sealing capacity increase with the spatial and temporal scales and may delay CCS deployment at the gigatonne scale. We have developed a computationally efficient transport model to capture the dynamics of basin‐wide upward CO 2 migration in a multi‐layered setting over geological time scales. We find that massive capillary breakthrough and viscous flow of CO 2 , even through pervasively fractured caprocks, are unlikely to occur and compromise the storage security. Potential leakage from the injection reservoir is hampered by repetitive layering of overlying caprocks. This finding agrees with geologic intuition and should be understandable by the public, contributing to the development of climate policies around this technology with increased confidence that CO 2 will be indefinitely contained in the subsurface.

58 GEOSCIENCES↗

NASA NTRS2016

Cyber-Threat Assessment for the Air Traffic Management System: A Network Controls Approach

Air transportation networks are being disrupted with increasing frequency by failures in their cyber- (computing, communication, control) systems. Whether these cyber- failures arise due to deliberate attacks or incidental errors, they can have far-reaching impact on the performance of the air traffic control and management systems. For instance, a computer failure in the Washington DC Air Route Traffic Control Center (ZDC) on August 15, 2015, caused nearly complete closure of the Centers airspace for several hours. This closure had a propagative impact across the United States National Airspace System, causing changed congestion patterns and requiring placement of a suite of traffic management initiatives to address the capacity reduction and congestion. A snapshot of traffic on that day clearly shows the closure of the ZDC airspace and the resulting congestion at its boundary, which required augmented traffic management at multiple locations. Cyber- events also have important ramifications for private stakeholders, particularly the airlines. During the last few months, computer-system issues have caused several airlines fleets to be grounded for significant periods of time: these include United Airlines (twice), LOT Polish Airlines, and American Airlines. Delays and regional stoppages due to cyber- events are even more common, and may have myriad causes (e.g., failure of the Department of Homeland Security systems needed for security check of passengers, see [3]). The growing frequency of cyber- disruptions in the air transportation system reflects a much broader trend in the modern society: cyber- failures and threats are becoming increasingly pervasive, varied, and impactful. In consequence, an intense effort is underway to develop secure and resilient cyber- systems that can protect against, detect, and remove threats, see e.g. and its many citations. The outcomes of this wide effort on cyber- security are applicable to the air transportation infrastructure, and indeed security solutions are being implemented in the current system. While these security solutions are important, they only provide a piecemeal solution. Particular computers or communication channels are protected from particular attacks, without a holistic view of the air transportation infrastructure. On the other hand, the above-listed incidents highlight that a holistic approach is needed, for several reasons. First, the air transportation infrastructure is a large scale cyber-physical system with multiple stakeholders and diverse legacy assets. It is impractical to protect every cyber- asset from known and unknown disruptions, and instead a strategic view of security is needed. Second, disruptions to the cyber- system can incur complex propagative impacts across the air transportation network, including its physical and human assets. Also, these implications of cyber- events are exacerbated or modulated by other disruptions and operational specifics, e.g. severe weather, operator fatigue or error, etc. These characteristics motivate a holistic and strategic perspective on protecting the air transportation infrastructure from cyber- events. The analysis of cyber- threats to the air traffic system is also inextricably tied to the integration of new autonomy into the airspace. The replacement of human operators with cyber functions leaves the network open to new cyber threats, which must be modeled and managed. Paradoxically, the mitigation of cyber events in the airspace will also likely require additional autonomy, given the fast time scale and myriad pathways of cyber-attacks which must be managed. The assessment of new vulnerabilities upon integration of new autonomy is also a key motivation for a holistic perspective on cyber threats.

Complex Networks↗