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Monitoring Airspace Complexity and Determining Contributing Factors

The national airspace has evolved over many years to accommodate increased traffic demand [1] while simultaneously maintaining one of the safest forms of transportation [2], [3]. One of the reasons for this success is the ability of the system and the operators to adapt and accommodate to situations that routinely disrupt optimal operations. These situations may include: adverse weather, delays, early arrivals, equipment outages, and other factors that are outside the operators ability to control. These factors can lead to states where automation is unable to properly handle these issues and therefore air traffic controllers and pilots have to intervene, ultimately increasing communication between operators resulting in higher workload. As controller workload increases to handle sub-optimal operating conditions this can be viewed as an increase in complexity. The reasoning for this is because humans are now required to make tactical decisions in response to external factors, resulting in a departure from the strategic plan where operations would be more efficiently managed. Human operators control airspace complexity under rigid regulations that are constantly changing. The airspace is divided into sectors and the number of aircraft assigned to each controller is limited for safe handling. There has been past work that devised airspace complexity metrics in commercial aviation and related these metrics to controller workload (e.g., [4],[5]). The upper bounds on the system load are pre-determined. Such bounds on complexity make for a safe system, but the system cannot scale and adapt to autonomous, dense, and heterogeneous traffic, including the many types of Unmanned Aerial Vehicles (UAVs) envisioned to be added to the operations. We hypothesize that, as traffic density and heterogeneity grow, and other key metrics change, there will be phase transitions at which the way traffic should be managed changes significantly [6]. We offer a method for in-time detection of contributing factors that lead to phase transitions, characterized by increased complexity. To the best of our knowledge, there is no tool similar to our proposed effort that identifies such contributing factors or precursor patterns. To define the scope we are proposing to measure complexity from the viewpoint of the Terminal Radar Approach Control Facilities (TRACON) controller’s perspective. In particular we are analyzing arrivals into KSFO. With safety as the top concern for airspace operators, it is important to recognize that as density and heterogeneity grow, the focus of the system will change. Times of the day when the airspace has low density and heterogeneity, the flights will follow more efficient paths where the aircraft move on established routes that are more or less directly to the destination. However, when density and heterogeneity increases, the system will begin changing focus to avoiding conflicts and collisions and route the flights in a more flexible way. Higher flexibility requires more communication and coordination between controllers and pilots which the current automation is unable to handle. This paper proposes a novel approach that monitors airspace complexity at multiple scales, uses a Machine Learning-based tool that predicts when operations will transition to a regime of greater complexity, and identifies actions that can reduce the complexity while still maintaining efficient and safe operations. We demonstrate our proposed approach using data from multiple complementary sources. This includes, but is not limited to: historical aircraft surveillance data from NASA’s Sherlock Data Warehouse [7], METAR weather data, and airport configuration data from Aviation System Performance Metrics (ASPM). The surveillance data flight paths are sampled at a variable sample rate — increasing as the aircraft approaches the airport. This is due to how Sherlock manages flight track stitching between different radar facilities which have different sampling rates. The weather and performance data are logged at defined intervals throughout the day at a courser refresh rate. In addition to the logged data and metrics, we leverage pre-defined Standard Terminal Arrival Routes (STARs) procedures to characterize the path of each flight. Each flight files for one of these routes in the flight plan well before entering the terminal airspace, and approximately follows the route until it leaves the STAR, typically on the final fix of a runway transition. However, most flights do not always fly the full STAR procedure to completion [8], but the majority do adhere to the fixes within the common route of the procedure. Our approach leverages fixes in the common route of each of the STARs to build a reference path to the airport. This allows us to characterize the flight paths in what we are defining as the “maneuvering area” (the airspace between the STAR and before the flight is lined up on the runway’s final approach) to determine how off nominal the flights are to calculate its complexity score. Determining airspace complexity is a concept that does not have a concrete answer. In designing this metric, we consider what increases the workload for the air traffic controllers. Consequently more specialized vectoring maneuvers results in higher workload. Accordingly, we start with a theory: each flight has a direct path it takes from the STAR’s common route to the final approach’s outer marker fix for the flight’s landing runway. It is important to note that the direct path is only used as a reference. If the majority of the flights have a large consistent offset as compared to other routes it does not necessarily mean that those flights have higher complexity. We are merely building a distribution based on this direct path for that particular STAR and runway pair to determine the normal mode of operations for that route. Flights that are in the upper tail of these distributions will result in higher complexity scores and flights that fly in the median will represent the normal mode of operations and therefore will have lower complexity scores. Since flights following each STAR route take different paths to the airport, we have a different distribution for each STAR route and therefore can model these distributions to compute a complexity score from their respective normalized distributions. To evaluate the effectiveness of our proposed airspace complexity metric we will compare against an established approach based on trajectory clustering [9]. This unsupervised learning technique consists of the following steps: (1) identify the general maneuvering areas (waypoints) by performing $\kappa$-means or DBSCAN clustering on locations where aircraft frequently turn based on the surveillance radar track data, (2) map flight trajectories onto sequences of waypoints, and (3) cluster the sequences based on their common subsequences. From a high-level perspective, this baseline model learns nominal operations in the airspace through the sequence of waypoints that are representative of where aircraft change direction and defines deviations from the nominal operations as “complex.” Therefore, more deviations from the nominal operations correspond to higher complexity values. For our validation, we re-implemented this technique and tune model hyper-parameters to correctly detect waypoints for the arrival traffic into the San Francisco bay area. We will compute the complexity measure over a one-year period using our proposed technique as well as the baseline. Our validation will be based on each technique’s ability to detect a set of undesirable outcomes (e.g., go-arounds, holding patterns, average time in the airspace, etc.). Since our current complexity metric is derived from the offset from the direct reference path, it’s important to understand what causes these offsets. In many of the flights with high offset distance, flights performing holding patterns and S turns can be observed. These maneuvering tactics are utilized to add distance between the aircraft and the destination runway to prevent multiple flights from having conflicting arrival times. In order to predict a rise in complexity (or the precursor to complexity), it’s necessary to be able to identify these potential conflicts (which in turn, result in higher offsets). To do this, we define a “representative flight” for each STAR route and runway pair. This flight is approximately the path the flight would take if there was a clear path with no other flights in the airspace — including the time remaining to the airport. We first identify the flights for a given STAR runway pair using the offset to the reference path distributions that fall between the 44-55 percentiles. This yields the flights that conform to the most normal mode of operation. Each of these flights is partitioned based on the percent complete from the entry point into the maneuvering areas from 0\% – 100\% complete. Then for each percent “bin”, we take the median value of the flight’s latitude/longitude coordinates, airspeed, and (non causal) time remaining to the airport to construct a lookup table for each percent complete bin on a given route. As a flight enters the maneuvering area, we can find the estimated arrival time of a flight to the airport by finding the closest point to the representative path’s percent complete bin (relative to the flight’s current position at any snapshot in the airspace) and therefore retrieve the corresponding remaining time left on the “representative path”. We assume that the flight will follow the representative path to completion when deriving these estimates. We can then compare these estimated arrival times against other flights for the same snapshot in time to identify potential conflicts. If more flights are estimated to arrive within a tolerance window than there are runways available, then we have a potential conflict. We can use this derived measure along with other factors expected to add disruption to the operation such as weather and runway configuration changes as an input to machine learning tools to detect precursors that increases in our complexity measure. This novel method will assist in uncovering insights into the contributing factors that lead to increased complexity that may allow for in-time responses to avoid reaching a high complexity state in the airspace.

complexity↗
DOE OSTI2026

A Scalable, Distribution Network-Aware, Customer Privacy-Preserving Framework for Operation of Virtual Power Plants

This poster presents a hierarchical control framework for a virtual power plant that leverages behind-the-meter resources for grid services while maintaining customer privacy during setpoint disaggregation. Unlike many existing approaches, the virtual power plant model uses a hierarchical control strategy and an iterative approach to determine the optimal set point dis-aggregation without direct load control while maintaining system-level power flow and voltage constraints. The proposed approach is numerically validated on a synthetic distribution feeder in San Francisco, demonstrating the ability of the framework to provide privacy-preserving virtual power plant services.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

How Distributed Energy Resources Can Support Resilience in Utility Distribution Networks

The goal of this webinar is to engage with electric utilities in the Midwest, particularly small public utilities, to understand the industry's needs for science tools to plan for winter resilience in the future, designing tools that will benefit electric power resilience in all communities. Michigan Tech leads this project with partners from multiple academic, government, and industry groups and asked NLR to present on DERs and laboratory tools and resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2003

Development of a Dynamically Configurable, Object-Oriented Framework for Distributed, Multi-modal Computational Aerospace Systems Simulation: Second Year Progress Report

Mesh generation has long been recognized as a bottleneck in the CFD process. While much research on automating the volume mesh generation process have been relatively successful,these methods rely on appropriate initial surface triangulation to work properly. Surface discretization has been one of the least automated steps in computational simulation due to its dependence on implicitly defined CAD surfaces and curves. Differences in CAD peometry engines manifest themselves in discrepancies in their interpretation of the same entities. This lack of "good" geometry causes significant problems for mesh generators, requiring users to "repair" the CAD geometry before mesh generation. The problem is exacerbated when CAD geometry is translated to other forms (e.g., IGES )which do not include important topological and construction information in addition to entity geometry. One technique to avoid these problems is to access the CAD geometry directly from the mesh generating software, rather than through files. By accessing the geometry model (not a discretized version) in its native environment, t h s a proach avoids translation to a format which can deplete the model of topological information. Our approach to enable models developed in the Denali software environment to directly access CAD geometry and functions is through an Application Programming Interface (API) known as CAPRI. CAPRI provides a layer of indirection through which CAD-specific data may be accessed by an application program using CAD-system neutral C and FORTRAN language function calls. CAPRI supports a general set of CAD operations such as truth testing, geometry construction and entity queries.

Afjeh, Abdollah A.↗
DOE OSTI2024

Evolution of parton distribution functions in the short-distance factorization scheme

Lattice QCD offers the possibility of computing parton distributions from first principles, although not in the usual $\overline{MS}$ factorization scheme. Calculations are therefore matched to $\overline{MS}$ using a perturbative procedure which is the source of significant uncertainty within the currently accessible kinematics. We present the possibility of computing the z 2 evolution of non-singlet pseudo-parton distribution functions within the short factorization scheme in a numerically improvable way. The goal is to have tools to evolve a calculation to a scale where perturbative uncertainties are less pronounced. We compare a numerical extraction of the evolution operator from lattice data to the computation of z 2 dependence in perturbation theory. Finally, we discuss how this numerical work may be extended to address the two-scale problem that arises when the Ioffe time range must be made large to extend the reach of the calculation of the pseudo-PDF to smaller values of the momentum fraction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2016

Investigating Global Ion and Neutral Atom Populations with IBEX and Voyager

The main objective of this project was to investigate pickup ion (PUI) production in the solar wind and heliosheath (the region between the termination shock and the heliopause) and compute the distributed energetic neutral atom fluxes throughout the helioshpere. The simulations were constrained by comparing the model output against observations from Ulysses, New Horizons, Voyager 1 and 2, and IBEX space probes. As evidenced by the number of peer reviewed journal publications resulting from the project (13 plus three submitted) and their citation rate (156 citations over three years), the project has made a lasting contribution to the field. The outcome is a significant improvement of our understanding of the pickup ion production and distribution in the distant heliosphere. The team has accomplished the entire set of tasks A-H set forth in the proposal. Namely, the transport modeling framework has been augmented with two populations of pickup ions (PUIs), the boundary conditions for the plasma and interstellar neutral hydrogen were verified against Ulysses and New Horizons PUI and an optimal set of velocity diffusion parameters established. The multi-component fluxes of PUIs were computed and isotropic velocity distributions generated for each cell in the computer simulation that covered the heliosphere from 1.5 AU to the heliopause. The distributions were carefully compared with in situ measurements at 3 AU (Ulysses), 12 AU (New Horizons), and 80-90 AU (Voyager 1 and 2) as well as those inferred from ENA fluxes measured by Cassini and IBEX (Wu et al., 2016). Some examples of modeldata comparison are shown in Figure 1. We have used coupled MHD-plasma and kinetic-neutral code to investigate the likely range of plasma and magnetic field parameters in the local interstellar medium (LISM), based on the assumption that the shape of the IBEX ribbon could be used to determine the orientation of the interstellar magnetic field. While the magnetic field is believed to be oriented toward the center of the ribbon, constraining its strength requires comparing the model-predicted angular diameter and circularity of the ribbon with the observations. The study, published in Heerikhuisen et al. (2014), found that the most likely range for the LISM magnetic field strength is between 0.2 and 0.3 nT, which is less than previously thought. Figure 2 shows the IBEX data (left) and compares it to the simulation with a 0.2 nT interstellar magnetic field (center) and a 0.4 nT (right).

Florinski, Vladimir↗
NASA NTRS1997

A Comparison of PETSC Library and HPF Implementations of an Archetypal PDE Computation

Two paradigms for distributed-memory parallel computation that free the application programmer from the details of message passing are compared for an archetypal structured scientific computation a nonlinear, structured-grid partial differential equation boundary value problem using the same algorithm on the same hardware. Both paradigms, parallel libraries represented by Argonne's PETSC, and parallel languages represented by the Portland Group's HPF, are found to be easy to use for this problem class, and both are reasonably effective in exploiting concurrency after a short learning curve. The level of involvement required by the application programmer under either paradigm includes specification of the data partitioning (corresponding to a geometrically simple decomposition of the domain of the PDE). Programming in SPAM style for the PETSC library requires writing the routines that discretize the PDE and its Jacobian, managing subdomain-to-processor mappings (affine global- to-local index mappings), and interfacing to library solver routines. Programming for HPF requires a complete sequential implementation of the same algorithm, introducing concurrency through subdomain blocking (an effort similar to the index mapping), and modest experimentation with rewriting loops to elucidate to the compiler the latent concurrency. Correctness and scalability are cross-validated on up to 32 nodes of an IBM SP2.

Hayder, M. Ehtesham↗
NASA NTRS2018

Computational Component Build-Up for the X-57 Distributed Electric Propulsion Aircraft

A computational study of the wing for the distributed electric propulsion X-57 Maxwell airplane configuration at cruise and takeoff/landing conditions was completed. Three unstructured-mesh, Navier-Stokes computational fluid dynamics methods, FUN3D, USM3D and Kestrel, were used to predict the performance buildup of components to the full X-57 configuration. The goal of the X-57 wing and distributed electric propulsion system design was to meet or exceed the required lift coefficient of 3.95 for a stall speed of 58 knots. The X-57 Maxwell airplane was designed with a small, high aspect ratio cruise wing that was designed for a high cruise lift coefficient of 0.75 at a cruise speed of 150 knots and altitude of 8,000 ft, with an angle of attack of approximately 0deg. The computational data indicates that the X-57 full aircraft drag would meet the cruise drag goal with a 25 count drag margin. The cruise configuration maximum lift coefficient is 2.07 and without including the stabilator is 1.86 at an angle of attack of 14 deg, predicted with the USM3D flow solver using the Spalart-Allmaras turbulence model. The maximum lift coefficient for the high-lift wing (with the 30deg flap deflection) without the stabilator contribution is 2.60 at an angle of attack of 13 deg. For high-lift blowing conditions with 13.7 hp/prop, the maximum lift coefficient excluding the stabilator is 4.426 at (alpha) = 13 deg. Therefore, the lift augmentation from the high-lift propellers is 1.7 and the total lift augmentation from the high-lift system (30 deg flap deflection and the high-lift blowing) is 2.38. The drag for the high-lift wing with 30 deg flap deflection is much higher than the cruise wing configuration, but the high-lift system is used only during a small portion of the flight envelope. The pitching moment is relatively constant for both blown and unblown conditions when the stabilator is excluded. Modeling the full geometry has indicated some adverse effects from the fuselage on the wing and stabilator. At high angles of attack, the solutions with the USM3D flow solver using the Spalart-Allmaras turbulence model indicates large flow separation on the wing upper surface between the two high-lift nacelles near the fuselage, and also a reduction in sectional lift on the stabilator in the first 50 percent of the stabilator semispan. However, the large flow separation near the fuselage is mostly eliminated in the solutions predicted with two codes, USM3D and Kestrel, using Hybrid Reynolds-averaged Navier Stokes/Large Eddy Simulation turbulence models.

Deere, Karen A.↗
DOE OSTI2025

Unorthodox parallelization for Bayesian quantum state estimation

Quantum state tomography (QST) allows for the reconstruction of quantum states through measurements and some inference technique under the assumption of repeated state preparations. Bayesian inference provides a promising platform to achieve both efficient QST and accurate uncertainty quantification, yet is generally plagued by the computational limitations associated with long Markov chains. In this work, we present a novel Bayesian QST approach that leverages modern distributed parallel computer architectures to efficiently sample a D-dimensional Hilbert space. Using a parallelized preconditioned Crank–Nicholson Metropolis–Hastings algorithm, we demonstrate our approach on simulated data and experimental results from IBM Quantum systems up to four qubits, showing significant speedups through parallelization. Although highly unorthodox in pooling independent Markov chains, our method proves remarkably practical, with validation ex post facto via diagnostics like the intrachain autocorrelation time. We conclude by discussing scalability to higher-dimensional systems, offering a path toward efficient and accurate Bayesian characterization of large quantum systems.

Bayesian inference↗
NASA NTRS1988

An analysis for high speed propeller-nacelle aerodynamic performance prediction. Volume 1: Theory and application

A computer program, the Propeller Nacelle Aerodynamic Performance Prediction Analysis (PANPER), was developed for the prediction and analysis of the performance and airflow of propeller-nacelle configurations operating over a forward speed range inclusive of high speed flight typical of recent propfan designs. A propeller lifting line, wake program was combined with a compressible, viscous center body interaction program, originally developed for diffusers, to compute the propeller-nacelle flow field, blade loading distribution, propeller performance, and the nacelle forebody pressure and viscous drag distributions. The computer analysis is applicable to single and coaxial counterrotating propellers. The blade geometries can include spanwise variations in sweep, droop, taper, thickness, and airfoil section type. In the coaxial mode of operation the analysis can treat both equal and unequal blade number and rotational speeds on the propeller disks. The nacelle portion of the analysis can treat both free air and tunnel wall configurations including wall bleed. The analysis was applied to many different sets of flight conditions using selected aerodynamic modeling options. The influence of different propeller nacelle-tunnel wall configurations was studied. Comparisons with available test data for both single and coaxial propeller configurations are presented along with a discussion of the results.

Egolf, T. Alan↗
DOE OSTI2024

Systematic Uncertainties from Gribov Copies in Lattice Calculation of Parton Distributions in the Coulomb Gauge

Recently, a new method has been proposed to compute parton distributions using boosted correlators fixed in the Coulomb gauge (CG) within the framework of large-momentum effective theory. This approach, which does not involve Wilson lines, could greatly improve the efficiency and precision of lattice quantum chromodynamics calculations. However, concerns remain regarding whether systematic uncertainties from Gribov copies, which correspond to ambiguities in lattice gauge-fixing, are adequately controlled. This work assesses the effects of Gribov copies on Coulomb-gauge-fixed quark correlators. We utilize different strategies for Coulomb-gauge fixing, selecting two different groups of Gribov copies based on lattice gauge configurations. We examine the differences in the resulting spatial quark correlators in both vacuum and pion states. Our findings indicate that the statistical errors of the matrix elements from both Gribov copies, regardless of the correlation range, decrease proportionally to the square root of the number of gauge configurations. The difference between the strategies does not show statistical significance compared to the gauge noise, demonstrating that the effect of the Gribov copies can be neglected in practical lattice calculations of quark parton distributions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1991

The sensitivity of fracture location distribution in brittle materials

The Weibull weak-link theory allows for the computation of distribution functions for both the fracture location and the applied far-field stress. Several authors have suggested using the fracture location information from tests to infer Weibull parameters, and others have used the predictive capabilities of the theory to calculate average fracture locations for brittle bodies. By a simple set of example calculations, it is shown that the fracture location distribution function is distinctly more sensitive to perturbations in the stress state than the fracture stress distribution function is. In general, the average fracture location is more subject to stress perturbations than the average fracture stress. The results indicate that care must be exercised in applying fracture location theory.

Wetherhold, Robert C.↗
DOE OSTI2025

Cosmic Reionization on Computers: Statistical Properties of the Distributions of Mean Opacities

Quasar absorption lines provide a unique window to the relationship between galaxies and the intergalactic medium during the Epoch of Reionization. In particular, high redshift quasars enable measurements of the neutral hydrogen content of the universe. However, the limited sample size of observed quasar spectra, particularly at the highest redshifts, hampers our ability to fully characterize the intergalactic medium during this epoch from observations alone. In this work, we characterize the distributions of mean opacities of the intergalactic medium in simulations from the Cosmic Reionization on Computers (CROC) project. We find that the distribution of mean opacities along sightlines follows a non-trivial distribution that cannot be easily approximated by a known distribution. When comparing the cumulative distribution function of mean opacities measurements in subsamples of sample sizes similar to observational measurements from the literature, we find consistency between CROC and observations at redshifts $z\lesssim 5.7$. However, at higher redshifts ($z\gtrsim5.7$), the cumulative distribution function of mean opacities from CROC is notably narrower than those from observed quasar sightlines implying that observations probe a systematically more opaque intergalactic medium at higher redshifts than the intergalactic medium in CROC boxes at these same redshifts. This is consistent with previous analyses that indicate that the universe is reionized too early in CROC simulations.

79 ASTRONOMY AND ASTROPHYSICS↗