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The simulation combines existing fracture mechanics based damage propagation techniques with a discrete approach to modeling discontinuities in finite elements. Additionally, the use of an advanced laminate theory recovers deformation and stress information that would normally require a high fidelity model. To accomplish this, the same theoretic and analytical concepts that a high fidelity numerical simulation tool utilizes for laminate damage simulation are placed in the context of a low fidelity finite element. In taking this approach, a laminate can be modeled as a single layer low fidelity shell mesh that has the ability to locally increase fidelity and represent a delamination based damage process but only if it is determined that one should occur. The numerical simulation tool's performance has been validated against numerical benchmarks as well as experimental data.

Modeling additive manufacturing processes can be difficult due to the scale difference between the active processing point (e.g., a sub-millimeter melt pool) and the part itself. Typically, the tools used to model these processes are either too computationally intensive (due to high physical fidelity or inefficient computations) or are focused solely on either the microscale (e.g., microstructure) or macroscale (e.g., cracks). These pitfalls make the tools unsuitable for fast and efficient evaluations of additive manufacturing build files and parts. Failures in parts made by laser powder bed fusion (L-PBF) often come when there is a lack of fusion or overheating of the metal powder that causes areas of high porosity. AM-PM uses a point field-based method to model L-PBF process conditions from either the build instructions (pre-build) or in situ measurements (during the build). The AM-PM modeling technique has been tested in several builds including a Ti-6Al-4V test article that was divided into 16 parts, each with different build conditions. With AM-PM, calculations are performed faster than similar methods and the technique can be generalized to other additive manufacturing processes. The AM-PM method is at technology readiness level (TRL) 6 (system/subsystem model or prototype demonstration in a relevant environment) and is available for patent licensing.

The term Advanced Air Mobility (AAM) refers to a new mode of transportation utilizing highly automated airborne vehicles for transporting goods and/or people. The adoption of widespread use of AAM vehicles will necessitate a network of vertiports located throughout a geographical region. A vertiport refers to a physical structure for the departure, arrival, and parking/storage of AAM vehicles. NASA-developed Vertiport Assessment and Mobility Operations System (VAMOS!) enables identifying geographical locations suitable for locating a vertiport or assessing suitability of pre-selected locations. For example, suitability evaluation factors include zoning, land use, transit stations, fire stations, noise, and time-varying factors like congestion and demand. The vertiport assessment system assigns suitability values to these factors based on user-input, and types, including location-based (e.g., proximity to mass transit stations), level-based (e.g., noise levels), characteristic-based (e.g., residential zoning), and time-based (e.g., demand). Based on user input, the system spreads a grid over the geographical area, specifies importance criteria and weights for scaling the impact of the suitability factors, and identifies specific sub-regions as candidate locations. The candidate sub-regions are shown on a user interface map overlay in a color-coded gradient that reflects the suitability strength for a sub-region. Vertiport locations are selected within these sub-regions. These candidate vertiport locations are refined by establishing feasibility of flight between them. VAMOS! includes a modeling component and a simulation component. The modeling component assists a user to identify one or more geographical locations at which a vertiport may be physically built. The simulation component of the technology displays, in real-time, the simulated operational behavior of AAM vehicles and in the context of their projected flight paths combined with data dynamically obtained from live sources. These data sources can be from the Federal Aviation Administration (FAA) or other private or public governing bodies, from one or more AAM vehicles in flight, and from weather sources.

For rapid parachute deployment simulation, the framework and methodology provided by the simulation database uses parametrized aerodynamic data for a variety of environmental conditions, air taxi design parameters, and landing system designs. The database also includes a compilation of drag coefficients, thrust and lift forces, and further relevant aerodynamic parameters utilized in the simulated flight of a proposed air taxi. The database and framework can be constructed using simulated data that accounts for oscillatory breathing of parachutes. The methodology can further employ an overset grid of body-fitted meshes to accurately capture deployment of an internally-stored parachute, as well as descent of the air taxi and deployed parachute. The systems and methods of the disclosed technology can be utilized with existing CFD solvers in a plug-and-play manner, such that the framework can be integrated to directly improve the performance of these solvers and the machines on which they are installed. The framework itself can employ parallelization to enable distributed solution of intensive CFD simulations to build a robust database of simulated data. Further, as up to 90% of computational time is spent in the calculation of aerodynamic parameters for use in coupled trajectory equations, the framework can significantly reduce the computational costs and design time for safe landing systems for air taxis. These reductions can lead to lower costs for design processes, while enabling rapid design and testing prior to physical prototyping.

The digital twin NAS simulator provides a complete digital copy of the individual systems that comprise the NAS to allow for the creation of offline simulations to test proposed changes to one or more individual systems based on actual historical data from the NAS or on real-time data from the NAS. The NAS is composed of a collection of systems, including source systems such as weather stations from various locations or airports, which are used by other systems such as individual aircraft flight data and airline operators. Other systems may include management systems such as the FAA, air traffic control centers, and flight traffic monitors. Operational data from each of these systems may be archived by a central information sharing platform such as the System Wide Information Management (SWIM) Program operated by the FAA. The digital twin NAS simulator can access archived SWIM data to create a digital twin NAS system to provide a virtual environment that may operate in real-time alongside the actual NAS, with the digital twin receiving live data updates from the actual NAS. A dedicated application programming interface (API) is used to facilitate communication between various distributed external components and the testbed. The testbed receives NAS data during a test and feeds the data to the simulation manager for use with a digital twin of the NAS system. The result is a virtual environment that is an exact twin of the actual operational system and is able to function identically to the actual NAS system because it is based on and uses the same data archived from the actual NAS system. A primary function of the virtual twin NAS is that it will allow for changes to one or more systems to be simulated against the archived NAS data and subsequently allow for a comparison between the simulated results and the actual results from the operational system. The digital twin simulator may also function in a distributed network environment, allowing for simulations of different elements to run simultaneously, which speeds up and improves the testing and evaluation of proposed changes.