A Concept of Operations (ConOps) and Design Considerations for an In-time Aviation Safety Management System (IASMS) for Advanced Air Mobility (AAM)
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RealTerm Energy and NREL have identified a shared vision to evaluate opportunities to facilitate the organization and assignment of metadata to building control system (BCS) data via industry-informed machine learning (ML). Manual metadata assignment is labor intensive and costly, slowing down any Energy Management and Information System (EMIS) deployment in the building space. This project aims to develop methodologies to accurately assign this metadata and significantly decrease the level of effort associated with deploying EMIS. The objective of this project is to identify/design methodologies to assign metadata to HVAC control points automatically. The identified methodologies will be programmed in analytics algorithms so they can ingest a list of points and produce a detailed tagging following the Haystack classification nomenclature. To validate the efficacy of each methodology, tagging results will be compared utilizing a list of points extracted from RealTerm's building database-as well as data extracted from the NREL campus via the Intelligent Campus program-enabling testing against large datasets with real world challenges. The developed methodologies may leverage building manager/operator input on a limited basis to add context to the classifying algorithms. The partnership aims to advance global efforts in areas related to the DOE missions through improving operational performance of commercial buildings. It is well documented that buildings fall out of commission after they are occupied, wasting significant energy and incurring associated costs simply due to poor operational performance. Emerging EMIS technologies that perform continuous commissioning help to address this issue, yet integration of these systems can be labor intensive both for the technology vendor and the building owner/operator. This project will enable more efficient and cost-effective analytics for buildings, enabling improvement in building operations at lower cost points.
On-orbit Servicing, Assembly, and Manufacturing (OSAM) will revolutionize the space industry by transforming the concept of operations of space systems and enabling new, radically different system implementations. These new implementations will benefit from a novel persistent asset design paradigm which focuses on evolvable designs that are tailored to the operational environment, not the launch environment. In addition, the ability to launch sub-systems independently enable future persistent assets to economically expand in capability and size, achieving cost effective and productive operations lasting for decades like terrestrial observatories. With few exceptions (International Space Station, Hubble Space Telescope, Mission Extension Vehicle customers), current space systems are not visited once they are operational. Leveraging emerging low cost commercial launch provides the ability to repeatedly and routinely revisit space systems. Thus, revolutionary new approaches for space system design are possible, creating completely new opportunities for small businesses and accelerating the growth of already established space industries. To usher in the revolutionary new operational paradigm, two things are needed. First, to build confidence in the technology and new paradigm, there must be a leading example, a bellwether persistent asset, that demonstrates the reliability and maturity of the new persistent asset paradigm (where repeated visits are common). Second, in order to rapidly advance and validate OSAM capabilities, an efficient means is required to conduct tests in the space environment. A persistent platform testbed satisfies both these needs. The space environment exhibits a plethora of characteristics that are difficult and costly to accurately simulate for a full system in a terrestrial laboratory, such as near zero gravity, a wide range of ionizing radiation types, atomic oxygen, and micro-meteoroids and space debris traveling at high velocity. In addition, since persistent assets range in mass from a few grams to several metric tons, it is difficult to accurately simulate interactions between these systems and visiting vehicles (that also exhibit a wide range of varying masses and capabilities). These interactions include the transmission of forces and/or exchanging mass (in the form of instruments, fuel, robotic assets, etc.). Thus, a rapid, versatile and cost efficient in-space testing capability that includes a persistent test platform and a surrounding in-space test zone is needed to mature technologies through experimentation. The testbed can provide common services, such as: power, thermal control, vibration isolation, data transmission between experiments and terrestrial experimenters, station-keeping, pointing, and robotic agents that can be leveraged by customer experiments. The onboard robotic agents can be used to provide payload handling services, such as: assembly, change out or upgrade, relocation, connecting/disconnecting utilities, inspection, repair or servicing, etc. Since the persistent platform cost will be amortized over many hosted payloads, its services can eventually be offered at a price much lower than if one were to design a unique and dedicated spacecraft and mission for those few experiments. The key to achieving an effective testbed is providing efficient cost effective access and infrastructure to a variety of commercial, academic and government customers coupled with extensibility, in the capability of an individual persistent platform test bed or replication of the test bed in a different operational regime. Three potential options for implementing a test bed were developed and evaluated in this study.
On-orbit Servicing, Assembly, and Manufacturing (OSAM) will revolutionize the space industry by transforming the concept of operations of space systems and enabling new, radically different system implementations. These new implementations will benefit from a novel persistent asset design paradigm which focuses on evolvable designs that are tailored to the operational environment, not the launch environment. In addition, the ability to launch sub-systems independently enable future persistent assets to economically expand in capability and size, achieving cost effective and productive operations lasting for decades like terrestrial observatories. With few exceptions (International Space Station, Hubble Space Telescope, Mission Extension Vehicle customers), current space systems are not visited once they are operational. Leveraging emerging low cost commercial launch provides the ability to repeatedly and routinely revisit space systems. Thus, revolutionary new approaches for space system design are possible, creating completely new opportunities for small businesses and accelerating the growth of already established space industries. To usher in the revolutionary new operational paradigm, two things are needed. First, to build confidence in the technology and new paradigm, there must be a leading example, a bellwether persistent asset, that demonstrates the reliability and maturity of the new persistent asset paradigm (where repeated visits are common). Second, in order to rapidly advance and validate OSAM capabilities, an efficient means is required to conduct tests in the space environment. A persistent platform testbed satisfies both these needs. The space environment exhibits a plethora of characteristics that are difficult and costly to accurately simulate for a full system in a terrestrial laboratory, such as near zero gravity, a wide range of ionizing radiation types, atomic oxygen, and micro-meteoroids and space debris traveling at high velocity. In addition, since persistent assets range in mass from a few grams to several metric tons, it is difficult to accurately simulate interactions between these systems and visiting vehicles (that also exhibit a wide range of varying masses and capabilities). These interactions include the transmission of forces and/or exchanging mass (in the form of instruments, fuel, robotic assets, etc.). Thus, a rapid, versatile and cost efficient in-space testing capability that includes a persistent test platform and a surrounding in-space test zone is needed to mature technologies through experimentation. The testbed can provide common services, such as: power, thermal control, vibration isolation, data transmission between experiments and terrestrial experimenters, station-keeping, pointing, and robotic agents that can be leveraged by customer experiments. The onboard robotic agents can be used to provide payload handling services, such as: assembly, change out or upgrade, relocation, connecting/disconnecting utilities, inspection, repair or servicing, etc. Since the persistent platform cost will be amortized over many hosted payloads, its services can eventually be offered at a price much lower than if one were to design a unique and dedicated spacecraft and mission for those few experiments. The key to achieving an effective testbed is providing efficient cost effective access and infrastructure to a variety of commercial, academic and government customers coupled with extensibility, in the capability of an individual persistent platform test bed or replication of the test bed in a different operational regime. Three potential options for implementing a test bed were developed and evaluated in this study.
The Next Generation Air Transportation System (NextGen) concept for 2025 envisions the movement of large numbers of people and goods in a safe, efficient, and reliable manner. The NextGen will remove many of the constraints in the current air transportation system, support a wider range of operations, and deliver an overall system capacity up to 3 times that of current operating levels. In order to achieve the NextGen vision, research is necessary in the areas of surface traffic optimization, maximum runway capacity, reduced runway occupancy time, simultaneous single runway operations, and terminal area conflict prevention, among others. The National Aeronautics and Space Administration (NASA) is conducting Collision Avoidance for Airport Traffic (CAAT) research to develop technologies, data, and guidelines to enable Conflict Detection and Resolution (CD&R) in the Airport Terminal Maneuvering Area (ATMA) under current and emerging NextGen operating concepts. In this report, an initial concept for an aircraft-based method for CD&R in the ATMA is presented. This method is based upon previous NASA work in CD&R for runway incursion prevention, the Runway Incursion Prevention System (RIPS). CAAT research is conducted jointly under NASA's Airspace Systems Program, Airportal Project and the Aviation Safety Program, Integrated Intelligent Flight Deck Project.
The Next Generation Air Transportation System (NextGen) concept for 2025 envisions the movement of large numbers of people and goods in a safe, efficient, and reliable manner. The NextGen will remove many of the constraints in the current air transportation system, support a wider range of operations, and deliver an overall system capacity up to 3 times that of current operating levels. In order to achieve the NextGen vision, research is necessary in the areas of surface traffic optimization, maximum runway capacity, reduced runway occupancy time, simultaneous single runway operations, and terminal area conflict prevention, among others. The National Aeronautics and Space Administration (NASA) is conducting Collision Avoidance for Airport Traffic (CAAT) research to develop technologies, data, and guidelines to enable Conflict Detection and Resolution (CD&R) in the Airport Terminal Maneuvering Area (ATMA) under current and emerging NextGen operating concepts. The term ATMA was created to reflect the fact that the CD&R concept area of operation is focused near the airport within the terminal maneuvering area. In the following, an initial concept for an aircraft-based method for CD&R in the ATMA is presented. This method is based upon previous NASA work in CD&R for runway incursion prevention, the Runway Incursion Prevention System (RIPS).
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Most of the existing traffic simulation packages require significant calibration work to be able to reflect reality. To evaluate special operations including emerging technologies, a microscopic simulation that tracks detailed interactions of all the elements of the traffic systems is usually needed. This type of simulation is usually computationally demanding. This work developed an Airport Shuttle Planning and Improved Routing Event-driven Simulation (ASPIRES) package to simulate and evaluate current, potential, and future airport shuttle operations. Here, the simulation was driven by data and thus did not require much calibration effort. The discrete-event simulation nature of ASPIRES makes the simulation computationally efficient. Simulating 1 day of shuttle operations takes less than 2s. The study site of this work is the Dallas/Fort Worth International Airport in the U.S. The shuttle service that connects the five terminals of the airport and the rental car center was studied. Travel times, dwell times, and passenger arrivals were simulated using empirical distributions derived mainly from real data to capture the stochastic nature of the rental car center shuttle bus operations. Data on bus miles traveled, bus energy consumption, passenger wait times, and passengers left behind at stops were collected to study the trade-off between energy use and passenger experience. Electric bus and on-demand bus operations were also included. The simulation outputs can show passengers statistics at terminals, shuttles statistics, and charging station statistics. ASPIRES cannot be used to model a generic traffic system but is well-suited for fleet systems.
Wildland fires take place most often in remote areas without access to communications infrastructure. Communication during wildfire operations is crucial for safe and effective command and control of air assets, ground-based firefighters, and fire management. Without connectivity, Incident Command (IC) cannot exchange information, receive alerts or work with all parties involved in the wildfire suppression operation. Current policy directs IC to use voice radio communications, which require no prior infrastructure, to provide information needed for the command, control and safety of personnel and resources. Cellular communications are used only for logistical purposes unless no other method is available. To address some of the shortcomings of the current state of communications, the Advanced Capabilities for Emergency Response Operations (ACERO) Second Shift (SS) technical team is developing characteristics of an air-to-ground Mesh Radio System (MRS) and preparing for a future demonstration. The MRS will allow air and ground assets to automatically join a radio network without relying on existing infrastructure and exchange information critical to maintaining situation awareness and air traffic management by the onsite IC. The MRS will also give users more options for communications in addition to voice communications.
In this paper we study a problem of hurricane emergency preparedness via placement of emergency storage assets prior to its strike. We present a two-stage stochastic model for choosing locations and quantities of emergency storage to help support the power grid through a hurricane event. The expectation of losses in our two-stage model is estimated using the sample average approximation. We construct damage scenarios for sample average approximation using WIND Toolkit meteorological data and fragility curves of various electric grid components. We demonstrate the efficacy of our two-stage planning model by simulating operations during Hurricane Dolly on the 2000-bus transmission test system. Our model, coupled with our scenario selection strategy, is effective at mitigating loss of load when compared to a model without emergency storage assets placed prior to an extreme event.
In this paper we study a problem of hurricane emergency preparedness via placement of emergency generation assets prior to its strike. We present a two-stage stochastic model for choosing locations and quantities of emergency storage to help support the power grid through a hurricane event. The expectation of losses in our two-stage model is estimated using the sample average approximation. We construct damage scenarios for sample average approximation using WIND Toolkit meteorological data and fragility curves of various electric grid components. We demonstrate the efficacy of our two-stage planning model by simulating operations during Hurricane Dolly on the 2000-bus transmission test system. Our model, coupled with our scenario selection strategy, is effective at mitigating loss of load when compared to a model without emergency generation assets placed prior to an extreme event.
Autonomous platforms already make observations over a wide range of temporal and spatial scales, measuring salinity, temperature, nitrate, pressure, oxygen, biomass; and many other parameters. However, the observations are not comprehensive. Future autonomous systems need to be more affordable, more modular, more capable and easier to operate. Creative new types of platforms and new compact, low power, calibrated and stable sensors are under development to expand autonomous observations. Communications and recharging need bandwidth and power which can be supplied by standardized docking stations. In situ power generation will also extend endurance for many types of autonomous platforms, particularly autonomous surface vehicles. Standardized communications will improve ease of use, interoperability, and enable coordinated behaviors. Improved autonomy and communications will enable adaptive networks of autonomous platforms. Improvements in autonomy will have three aspects: hardware, control, and operations. As sensors and platforms have more onboard processing capability and energy capacity, more measurements become possible. Control systems and software will have the capability to address more complex states and sophisticated reactions to sensor inputs, which allows the platform to handle a wider variety of circumstances without direct operator control. Operational autonomy is increased by reducing operating costs. To maximize the potential of autonomous observations new standards and best practices are needed. In some applications, focus on common platforms and volume purchases could lead to significant cost reductions. Cost reductions could enable order-of-magnitude increases in platform operations and increase sampling resolution for a given level of investment. Energy harvesting technologies should be integral to the system design, for sensors, platforms, vehicles, and docking stations. Connections are needed between the marine energy and ocean observing communities to coordinate among funding sources, researchers, and end users. Regional teams should work with global organizations such as IOC/GOOS in governance development. International networks such as EGO for the emerging glider operations should also provide a forum for addressing governance. Networks of multiple vehicles can improve operational efficiencies and transform operational patterns. There is a need to develop operational architectures at regional and global scales to provide a backbone for active networking of autonomous platforms.
Natural disasters can result in the loss of life and cost governments and private industry billions to recover each year. Over the past decade the rate and severity of natural disasters such as wildfires and hurricanes have resulted in increasingly negative impacts to communities, public health, natural ecosystems, and the economy. To help reduce these impacts, NASA’s Aeronautics Research Mission Directorate is working to advance technologies and enable the safe and efficient inclusion of novel aviation applications to better assist in disaster response. To execute on these efforts, NASA’s Advanced Capabilities for Emergency Response Operations (ACERO) and System-Wide Safety (SWS) projects have developed coordinated strategic research plans focused on aviation operations for disaster response. The ACERO project will be a multi-year effort that focuses on enabling the use of uncrewed aircraft systems (UAS) to improve firefighter safety and efficiency and enable the use of UAS to conduct new missions such as logistics and aerial suppression. The ACERO project will demonstrate technologies that support the Second Shift concept, enabling UAS and ground technologies to support aerial suppression in degraded visual conditions (e.g., heavy smoke, nighttime). The SWS project will be a multi-year effort that focuses on addressing the key safety barriers that are preventing the authorization of UAS operations in a variety of increasingly complex disaster response applications: post-hurricane response, medical courier, and urban disaster response. The SWS project will demonstrate an In-Time Aviation Safety Management System (IASMS) designed to effectively monitor, assess, and mitigate safety risks associated with hazards to UAS operations for disaster response. This paper will provide a deeper insight into NASA’s research and development plans and discuss how solutions developed in partnership with industry stakeholders and federal agencies will improve disaster response across the globe.
Recently the Emergency Management Operations Center (EMOC) of St. Tammany Parish turned to the Technology Development and Transfer Office (TDTO) of NASA's Stennis Space Center (SSC) for help in combating the problems associated with water inundation. Working through a Dual-Use Development Agreement the Technology Development and Transfer Office, EMOC and a small geospatial applications company named Nvision provided the parish with a new front-line defense. REACT, Real-time Emergency Action coordination Tool is a decision support system that integrates disparate information to enable more efficient decision making by emergency management personnel.
An intelligent emergency response virtual environment (ERVE) that provides emergency first responders, response planners, and managers with situational awareness as well as training and support for safe schools is presented. ERVE incorporates an intelligent agent facility for guiding and assisting the user in the context of the emergency response operations. Response information folders capture key information about the school. The system enables interactive 3D visualization of schools and academic campuses, including the terrain and the buildings' exteriors and interiors in an easy to use Web..based interface. ERVE incorporates live camera and sensors feeds and can be integrated with other simulations such as chemical plume simulation. The system is integrated with a Geographical Information System (GIS) to enable situational awareness of emergency events and assessment of their effect on schools in a geographic area. ERVE can also be integrated with emergency text messaging notification systems. Using ERVE, it is now possible to address safe schools' emergency management needs with a scaleable, seamlessly integrated and fully interactive intelligent and visually compelling solution.
An autonomous system refers to the system that has the power and ability for self-governance in the performance of system functions. Autonomous systems have been actively pursued in a variety of domains such as automotive, aviation, maritime, medicine, and nuclear fields. As an unmanned concept employing the highest automation level, the autonomous system basically performs most of the work in normal operations or emergency situations. However, despite advances in technology, many researchers have noted these systems still require human actions. The nature of human actions on autonomous systems is different than the human actions that are considered in existing systems. Nevertheless, only a few studies have been conducted on 1) characterizing the different types of errors and risks associated with human actions interacting with autonomous systems and 2) how to evaluate human actions in the autonomous operations. As a starting point, this study aims to investigate differences of tasks in autonomous operation compared to those in existing nuclear power plant operation using the Event Modeling Risk Assessment Using Linked Diagram (EMRALD) software. In this paper, insights aspect of human error and time are derived out and discussed based on the output of the EMRALD models.
Neural operators have emerged as powerful surrogates for modeling complex physical problems. However, they suffer from spectral bias making them oblivious to high-frequency modes, which are present in multiscale physical systems. Therefore, they tend to produce over-smoothed solutions, which is particularly problematic in modeling turbulence and for systems with intricate patterns and sharp gradients such as multi-phase flow systems. In this work, we introduce a new approach named high-frequency scaling (HFS) to mitigate spectral bias in convolutional-based neural operators. By integrating HFS with proper variants of UNet, we demonstrate a higher prediction accuracy by mitigating spectral bias in single and two-phase flow problems. Unlike Fourierbased techniques, HFS is directly applied to the latent space, thus eliminating the computational cost associated with the Fourier transform. Additionally, we investigate alternative spectral bias mitigation through a diffusion model conditioned on neural operators. While the diffusion model integrated with the standard neural operator may still suffer from significant errors, these errors are substantially reduced when the diffusion model is integrated with a HFS-enhanced neural operator.
Accurate measurement of exposure rate from gamma-ray spectral data remains a critical challenge during radiological emergency response operations. Conventional methods rely on pre-defined static conversion factors derived from fixed geometries and isotopic compositions, which often fail to capture real-world environmental variability. This study presents a generalized approach as a "next-step" for converting gamma-ray spectral data into exposure rate using polynomial response models. The method introduces a flexible weighting scheme based on the in-situ detector response to distributed sources, enabling a pathway towards improved correspondence between measured spectra and "ground-truth" exposure rates. Experimental data from sodium iodide NaI(Tl) detectors were used to validate the approach as, at least equivalent to the current count-to-exposure method employed in emergency response CONOPS. Results show that the polynomial weighting model is sufficiently equal to the count-to-exposure method and may help improve accuracy given its adaptability to real-world conditions.