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NASA NTRSDate not supplied

Initial Performance Evaluation of Flight Path Management Onboard Automation

Significant developments in automation are necessary to achieve safe and efficient operations in advanced aerial mobility related concepts. Urban Air Mobility (UAM) is rapidly growing, emerging field that poses a challenging use case with a tighter scale of operations compared to the traditional commercial transport paradigm. A large part of the challenge is the uncharted territory; as of this paper, no set of operational standards or guidelines for UAM operations have been established and automated en route operations for UAM level 4 (UML-4) have not been studied. Flight Path Management (FPM) automation provides a set of capabilities that are critical toward enabling airborne vehicles to achieve mission success while maintaining operational safety. An initial performance evaluation of FPM automation was conducted using a UAM-adapted version of the Autonomous Operations Planner (AOP), an onboard trajectory management capability developed over years of research targeting commercial transport operations, as its reference implementation. This paper describes the evaluation, including the approach and methodology for simulating FPM automation in UML-4, key results, future work, and conclusions.

flight path management↗

NASA NTRSDate not supplied

Initial Performance Evaluation of Flight Path Management Onboard Automation

flight path management↗

DOE OSTI2026

Conditional distribution estimation of building characteristics with diffusion models for urban energy modeling

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulation Datasets

Publication For a thorough description of the methods, see the peer-reviewed paper: Jones, A.D., Rastogi, D., Vahmani, P. et al. Continental United States climate projections based on thermodynamic modification of historical weather. Sci Data 10, 664 (2023). https://doi.org/10.1038/s41597-023-02485-5 Overview The IM3 / HyperFACETS climate simulations provide 40-year historical (1980-2019) as well as four 80-year future simulations (2020-2099) over the U.S. The future simulations are split into near (2020-2059) and far future (2060-2099) segments. The future scenarios span a range of plausible changes in future climate (both Global Circulation Model (GCM) and Representative Concentration Pathways/Shared Socioeconomic Pathway (RCP/SSP) dimensions). The simulations provide climate variables with high spatiotemporal resolution (25 hourly variables and 207 3-hourly variables at 12 km2). The datasets are generated using dynamical downscaling with the WRF (Weather Research and Forecasting) model (version 4.2.1) and therefore preserve physical consistency across variables. WRF is a state-of-the-art, fully compressible, non-hydrostatic, mesoscale numerical weather prediction model. WRF is coupled with an urban canopy model (UCM), which resolves urban surfaces. The future scenarios were developed using a thermodynamic global warming approach where past events are replayed under a range of future warming conditions. These scenarios therefore provide a perspective on potential increases in extreme event intensity, geographic scope, and duration, with previously non-extreme conditions potentially crossing new thresholds to be considered extreme by today's standards. This approach is not intended to estimate future changes in extreme event frequency that might result from changes in large-scale atmospheric dynamics. This dataset has NOT been bias corrected. A bias corrected version of selected variables is under development and will be released here when available. Scenarios Files Data for each scenario is provided in weekly NetCDF files. 25 variables are available at hourly resolution, and 207 variables are available at three-hourly resolution. Spatial resolution is 12km and spans the conterminous United States (CONUS), including some areas of Canada and Mexico, resulting in a grid of 424 by 299 cells. The spatial projection is a Lambert Conformal Conic with the following proj-string: "+proj=lcc +lat_0=40.0000076293945 +lon_0=-97 +lat_1=30 +lat_2=45 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs". The available scenarios and simulation periods are listed below: historical | 1980 - 2019 rcp45cooler | 2020 - 2059 rcp45cooler | 2060 - 2099 rcp45hotter | 2020 - 2059 rcp45hotter | 2060 - 2099 rcp85cooler | 2020 - 2059 rcp85cooler | 2060 - 2099 rcp85hotter | 2020 - 2059 rcp85hotter | 2060 - 2099 * The first year (1979, 2019, and 2059) of data within each scenario represents a model warmup period and should not be used. These are located in the `spinup_files` directory. Historical year 2020 is considered an extra year of data beyond the simulation period and can be found in the `additional_files` directory. For information on specific variables and a more in-depth discussion of methodology, please refer to the data landing page at https://tgw-data.msdlive.org. Delta Warming Files The global and CONUS warming deltas for each scenario are provided in degrees Celsius annually and monthly. Restart Files Yearly restart files are provided for each scenario which can be used to restart the WRF model at a particular point in time. Spinup Files The first year of data within each simulation period represents a model warmup period and should not be used. The files are provided here for the sake of reproducibility. Additional Files Additional years of data are provided as an extension of the historic simulation.

Jones, Andrew D.↗

NASA NTRS2026

Introduction to the Special Issue on Advanced Air Mobility Noise: Predictions, Measurements and Perception

This Special Issue focuses on noise associated with Advanced Air Mobility (AAM), an emerging class of predominantly electric distributed-propulsion aircraft designed for urban and regional transportation. As these vehicles move toward certification and deployment, noise has become a central challenge for regulatory approval and public acceptance, particularly due to operations in densely populated areas and at low altitudes. The 24 contributions in this issue address three key aspects of AAM noise: prediction, measurement, and human perception. Prediction studies span a wide range of modeling fidelities, from high-resolution simulations to improved semi-analytical approaches, and examine complex aeroacoustic mechanisms including rotor interactions, turbulence ingestion, and broadband noise generation. Measurement studies, largely at model scale, provide new insights into tonal and broadband noise characteristics across configurations and operating conditions, while supporting model validation. Perception-focused contributions investigate annoyance, sound quality metrics, and auralization, emphasizing the role of context and operational factors in shaping human response. Together, these works highlight the interdisciplinary nature of AAM noise research and the need for integrated approaches to enable quieter vehicle design.

Perception↗

NASA NTRS1991

Simulations of microphysical, radiative, and dynamical processes in a continental-scale forest fire smoke plume

The impact of a large forest fire smoke plume on atmospheric processes is studied through a numerical model of meteorology, aerosols, and radiative transfer. The simulated smoke optical depths at 0.63-micron wavelength are in agreement with analyses of satellite data and show values as high as 1.8. The smoke has an albedo of 35 percent, or more than double the clear-sky value, and cools the surface by as much as 5 K. An imaginary refractive index, n sub im, of 0.01 yields results which closely match the observed cooling, single scattering albedo, and the Angstrom wavelength exponent. An n exp im of 0.1, typical of smoke from urban fires, produces 9 K cooling. Coagulation causes the geometric mean radius by number to increase from the initial value of 0.08 micron to a final value of 0.15 micron, while the specific extinction and absorption increase by 40 and 25 percent, respectively.

Westphal, Douglas L.↗

NASA NTRS2020

Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: the Case of Rio deJaneiro, Brazil

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001-2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12-35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

IMERG↗

NASA NTRS2017

Synthetic Scenarios from CMIP5 Model Simulations for Climate Change Impact Assessments in Managed Ecosystems and Water Resources: Case Study in South Asian Countries

Increasing population, urbanization, and associated demand for food production compounded by climate change and variability have important implications for the managed ecosystems and water resources of a region. This is particularly true for south Asia, which supports one quarter of the global population, half of whom live below the poverty line. This region is largely dependent on monsoon precipitation for water. Given the limited resources of the developing countries in this region, the objective of our study was to empirically explore climate change in south Asia up to the year 2099 using monthly simulations from 35 global climate models (GCMs) participating in the fifth phase of the Climate Model Inter-comparison Project (CMIP5) for two future emission scenarios (representative concentration pathways RCP4.5 and RCP8.5) and provide a wide range of potential climate change outcomes. This was carried out using a three-step procedure: calculating the mean annual, monsoon, and non-monsoon precipitation and temperatures; estimating the percent change from historical conditions; and developing scenario funnels and synthetic scenarios. This methodology was applied for the entire south Asia region; however, the percent change information generated at 1.5deg grid scale can be used to generate scenarios at finer spatial scales. Our results showed a high variability in the future change in precipitation (-23% to 52%, maximum in the non-monsoon season) and temperature (0.8% to 2.1%) in the region. Temperatures in the region consistently increased, especially in the Himalayan region, which could have impacts including a faster retreat of glaciers and increased floods. It could also change rivers from perennial to seasonal, leading to significant challenges in water management. Increasing temperatures could further stress groundwater reservoirs, leading to withdrawal rates that become even more unsustainable. The high precipitation variability (with higher propensity for localized intense rainfall events) observed in the region can be a key factor for managed ecosystems and water management and could also lead to more incidence of severe urban flooding. The results could be used to assess both mitigation and adaptation alternatives to reduce vulnerabilities in managed ecosystems (agricultural and urban) and water resources.

agriculture↗

NASA NTRS2023

An Urban Drainage Scheme for Large-Scale Flood Models

As flood modeling spatial resolutions get finer, physical processes normally neglected, such as urban drainage, must be accounted for. Here, we describe and evaluate an urban drainage scheme for large-scale flood models. The parameterization accounts for urban imperviousness, and water flow over streets and through a prescribed urban drainage network. A parameter sensitivity analysis is performed during three major extreme floods over Rio de Janeiro city, Brazil, at ∼200 m spatial resolution. Results show that, compared to a hypothetical case without urban drainage, representing a drainage network decreases urban flooding during selected extreme events across Rio de Janeiro by 31–53 %. Such a decrease is caused by an underground water storage of up to 2.5 billion m3 across the city during flood peaks. Underground water storage and transport smooth out and delay peak flows by a few hours over major rivers and channels draining the city. Simulations also indicate that the number of residents exposed to flooding drops by 60–80 %, from ∼5 million to 1–2 million, when an urban drainage system is considered during extreme events. Similar proportions are found for social infrastructure (i.e., schools and hospitals) exposed to flooding. Results reveal that racial minority and low-income populations could disproportionally be exposed to extreme floodings across the city. We conclude that representing urban drainage has a substantial impact on flood exposure and should be accounted for in fine resolution modeling. The proposed scheme is particularly useful in poorly monitored cities and where extreme floods are a frequent hazard yet to be tackled.

Urban drainage↗

DOE OSTI2025

Hestia-SWIFL: hourly anthropogenic fossil fuel CO2 and heat on the 2km WRF grid, version 1.1

The Hestia-SWIFL version 1.1 anthropogenic heat (AH) and fossil fuel CO2 (FFCO2) emissions data product represent emissions due to the combustion of fossil fuel and cement production within the state of Arizona from 2019 to 2022. This product was developed as part of the Southwest Urban Corridor Integrated Field Laboratory (SW-IFL) project, which aims to provide new knowledge and tools that address extreme heat, air quality, climate change and related urban environmental issues by integrating high-resolution observations, modeling, and civic engagement. The emissions are generated using a bottom-up/engineering approach and are tied to results generated by the Vulcan Project version 4, an effort to quantify space/time-resolved FFCO2 & AH emissions for the entire United States landscape. A large number of data sources are combined to best estimate the emissions at fine scales such as air quality emissions data, traffic flow data, building information, sociodemographic information, and fuel statistics. The AH product provides emissions for two emissions sources (transportation and point source emissions) in units of Watts per hour per square meter (W/m2) per year (annual files) or per hour (hourly files). The FFCO2 product provides emissions from nine individual emission sectors as well as the total, and in units of tons of carbon (tC) per grid cell per year or per hour. The output made available here places the native spatial resolution of the Hestia FFCO2 & AH emissions data product (points, lines, and polygons) into a regularized 2km x 2km grid at hourly and annual temporal resolutions, and stored in netCDF files. The exact spatial extent is defined by the ASU Weather Research Forecast (WRF) simulation grid. All data are processed using R/Python pm high-performance computing system. 2-27-2026 updates: Bugs in airport hourly profile (both AH and FFCO2) and building spatial patterns (FFCO2 only) were fixed. Hourly emissions are reprocessed for all years to reflect those changes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2020

High Resolution Global Coupled Chemistry-Meteorology Simulations Using the NASA GEOS Composition Forecast System, GEOS-CF

We will give an overview of the NASA Global Earth Observing System Composition Forecast system (GEOS-CF), a high-resolution (0.25 degree) global composition model developed by the NASA Global Modeling and Assimilation Office (GMAO). This system combines the GEOS weather and aerosol model with the GEOS-Chem chemistry module (version 12) to provide a holistic view of atmospheric composition that captures a wide range of air pollutants such as ozone, nitrogen oxides, volatile organic compounds, and fine particulate matter. The spatial resolution of 0.25 degrees (approx. 25 km) is fine enough to resolve local features such as nighttime ozone titration previously resolved only by urban or regional models. Furthermore, since there are no boundary conditions for a global model, the GEOS-CF captures large-scale processes such as long-range transport of air pollutants from forest fires. Comparisons against surface observations highlight the model’s overall capability to reproduce the diurnal variability of air pollutants under a variety of meteorological conditions. In addition, we show how machine learning techniques can be used to correct for sub-grid variability, which further improves model estimates at a given surface observation site. The GEOS-CF system offers a new tool for scientists and the public health community alike and is being developed jointly with several government and non-profit partners. As an example, we will show the use of GEOS-CF during the Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE). The campaign, conducted in collaboration between NASA and the Bureau of Ocean Energy Management (BOEM), aims to investigate the response of onshore air quality to Outer Continental Shelf (OCS) oil and gas exploration, development and production. Detailed gas-phase chemistry, as provided by GEOS-CF, is critical to understand the formation of air pollution related to hydrocarbon emissions from offshore oil and gas activities. The accuracy of GEOS-CF can be further improved by incorporating detailed offshore emissions compiled by BOEM.

Knowland, K. Emma↗

NASA NTRS2021

The simulation of the impact of the spatial distribution of vegetation on the urban microclimate: A case study in Mostaganem

Increasing vegetation is one of the solutions proposed to mitigate the phenomenon of urban heat islands. The present research aims to study the effect of the spatial distribution of vegetation on the urban microclimate during the summer period. The city of Mostaganem with its Mediterranean climate was chosen as a case study. Based on meteorological data collected on a hot day in July, the study is carried out using the ENVI-met 4.4.5. Scenarios of different vegetation layouts were carried out. Air, mean radiant and surface temperatures are the microclimatic parameters that have been analyzed for each of the scenarios, and in order to study the outdoor thermal comfort, the Predicted Mean Vote (PMV) is chosen as the comfort index, it compares the average value of the votes of a large group of people on the thermal sensation scale. The study revealed that tree alignments can have a cooling effect on the city temperature close to 1.2◦C. However, its impact on reducing the surface temperature can reach up to 4 ◦C. The introduction of vegetation on the site had the overall effect of lowering the air temperature and therefore relatively improving the level of comfort compared to the reference scenario. But, in order to reduce discomfort to an acceptable level during the day, vegetation can be simultaneously accommodated by city parameters such as urban form.

Nora Bachir↗

NASA NTRS2016

Lightning NOx and Impacts on Air Quality

Lightning generates relatively large but uncertain quantities of nitrogen oxides, critical precursors for ozone and hydroxyl radical (OH), the primary tropospheric oxidants. Lightning nitrogen oxide strongly influences background ozone and OH due to high ozone production efficiencies in the free troposphere, effecting small but non-negligible contributions to surface pollutant concentrations. Lightning globally contributes 3-4 ppbv of simulated annual-mean policy-relevant background (PRB) surface ozone, comprised of local, regional, and hemispheric components, and up to 18 ppbv during individual events. Feedbacks via methane may counter some of these effects on decadal time scales. Lightning contributes approximately 1 percent to annual-mean surface particulate matter, as a direct precursor and by promoting faster oxidation of other precursors. Lightning also ignites wildfires and contributes to nitrogen deposition. Urban pollution influences lightning itself, with implications for regional lightning-nitrogen oxide production and feedbacks on downwind surface pollution. How lightning emissions will change in a warming world remains uncertain.

Reactive nitrogen oxides↗

DOE OSTI2024

DEPLOYING FAST CHARGING INFRASTRUCTURE FOR ELECTRIC VEHICLES IN URBAN NETWORKS: AN ACTIVITY-BASED APPROACH

This paper explores an important problem under the domain of network modeling, the optimal configuration of charging infrastructure for electric vehicles (EVs) in urban networks considering EV users' daily activities and charging behavior. This study proposes a charging behavior simulation model considering different initial state of charge (SOC), travel distance, availability of home chargers, and the daily schedule of trips for each traveler. The proposed charging behavior simulation model examines the complete chain of trips for EV users as well as the interdependency of trips traveled by each driver. The problem of finding the optimum charging configuration is then formulated as a mixed-integer nonlinear programming problem that considers the dynamics of travel time and travel distance, the interdependency of trips made by each driver, limited range of EVs, remaining battery capacity for recharging, waiting time in queue, and detour to access a charging station. This problem is solved using a metaheuristic approach for a large-scale case network. A series of examples are presented to demonstrate the model efficacy and explore the impact of energy consumption on the final SOC and the optimum charging infrastructure.

Chain of Trips↗

DOE OSTI2025

Kinetic Modeling of Secondary Organic Aerosol in a Weather-Chemistry Model: Parameterizations, Processes, and Predictions for GOAmazon

Secondary organic aerosol (SOA) forms and evolves in the atmosphere through many pathways and processes, over diverse spatial and time scales. Hence, there is a need to represent these widely-varying kinetic processes in large-scale atmospheric models to allow for accurate predictions of the abundance, properties, and impacts of SOA. In this work, we integrated a kinetic, process-level model (simpleSOM-MOSAIC) into a weather-chemistry model (WRF-Chem) to simulate the oxidation chemistry and microphysics of atmospheric SOA. simpleSOM-MOSAIC simulates multigenerational gas-phase chemistry, autoxidation reactions, heterogeneous oxidation, oligomerization, and phase-state-influenced gas/particle partitioning of SOA. As a case study, the integrated WRF-Chem-simpleSOM-MOSAIC (WC-SSM) model was used to simulate the photochemical evolution downwind of a large city (Manaus, Brazil) in the Amazon and, in turn, study the anthropogenic and biogenic interactions in an otherwise pristine environment. Consistent with previous work, we found that OA was enhanced by up to a factor of four in the urban plume due to elevated hydroxyl radical (OH) concentrations, relative to the background, and that this OA was dominated by SOA from biogenic precursors (80%). Further, in addition to accurately simulating the OA enhancement in the urban plume, the model reproduced the magnitude of the OA oxygen-to-carbon (O:C) ratio and broadly tracked the evolution of the aerosol size distribution. Our work highlights the importance of including an integrated, kinetic representation of SOA processes in an atmospheric model

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1975

Land use classification for hydrologic models using interactive machine classification of LANDSAT data

Models designed to simulate the hydrology of urban areas require input parameters describing the land use and degree of imperviousness of the watershed. Unfortunately, the magnitude and spatial distribution of these parameters are rather difficult to estimate when a large watershed is involved. Trade-offs between accuracy of the model parameters and the time or money available for their determination must be made. Because of the necessity of such trade-offs, a study was developed to investigate the use of computer aided analysis of LANDSAT multispectral data in estimating percent of imperviousness and associated land uses needed in urban hydrologic modeling. An interactive computer was used to delineate seven land use classifications in the 342 sq. km. Maryland portion of the Anacostia River Basin from LANDSAT data. These results compared favorably with those of an earlier study which obtained the same information through analysis of aerial photographs having a scale of 1:4800. Approximately 94 man days were required to complete the land use analysis using the aerial photographs while less than three man days were required to accomplish similar tasks using the LANDSAT data.

Thomas J. Jackson↗

NASA NTRSDate not supplied

Sub-City-Scale Air Quality Forecasts Combining Models, Satellites, and Surface Measures

Poor air quality is a global major concern, especially in cities with their higher emissions and large numbers of exposed people. Air quality monitoring has traditionally relied on ground-based measurements from a few accurate but expensive regulatory-grade monitors, leading to limited spatial data coverage. More recently, these have been supplemented with other data sources, including satellite observations of pollutants, atmospheric chemistry model simulations, and low-cost monitors allowing for denser spatial data collection at the expense of lower accuracy compared to regulatory-grade monitors. Each of these air quality data sources have their own benefits and drawbacks, and so there is an opportunity to combine these data together while respecting their relative strengths and weaknesses in order to generate a more comprehensive and detailed picture of local air quality. I will present our current work towards such a combination, with a focus on producing higher spatial resolution estimates and near-term forecasts of Nitrogen Dioxide in urban areas in the United States. Our method combines data from the GEOS Composition Forecasting (GEOS-CF) model system, TROPOMI tropospheric NO2 satellite data products, and ground measurements from the EPA regulatory monitoring network using a combination of simple intuitive relationships and machine learning techniques. I will show the performance of this proposed method in several urban areas in the United States, comparing it with baseline approaches using single data sources separately. Overall, we find that combining these disparate datasets together leads to more accurate air quality forecasts in the target areas than is possible using each data source separately.

Air quality↗

NASA NTRSDate not supplied

SIRIUS: Simulation Infrastructure for Research on Interoperating Unmanned Systems

This paper presents the Simulation Infrastructure for Research on Interoperating Unmanned Systems (SIRIUS), a research framework for simulation and analysis of future conceptual Urban Air Mobility (UAM) operations. SIRIUS is being developed under the auspices of the NASA Air Traffic Management eXploration project, UAM subproject (ATM-X UAM). SIRIUS provides an intuitive, highly configurable graphical user interface to design complex traffic scenarios and airspace configurations representative of conceptual UAM operations. Aircraft simulated with SIRIUS can be equipped with flight-tested capabilities for detect and avoid (DAA), geofencing, distributed merging and spacing, path conformance, and path planning while executing time-constrained, 4D trajectories generated by a UAM ground operations system. Central to the design of the SIRIUS simulation framework is the capability to evaluate the integration and interoperability of ground-based separation services (e.g., strategic separation) with extended DAA functionality (e.g., path monitoring, separation provision, merging and spacing, etc.) The simulation environment also supports modelling of wind, navigation, and sensor uncertainties, as well as communication delays. SIRIUS enables distributed simulation of large-scale scenarios. An interactive graphical analysis capability helps isolate, visualize, and compare relevant vehicle state data and widely used measures of performance metrics across multiple scenarios.

Andrew Peters↗