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NASA NTRS2023

Vertiport Dynamic Density

Advanced Air Mobility (AAM) is envisioned to be another spoke in a region’s transportation system, supplementing ground-based travel with air-based travel. Eventually, air taxis will be pervasive, convenient, and affordable. As demand and operations tempo increase, congestion may arise. Several characteristics of AAM reduce the applicability of techniques used in today’s National Airspace System (NAS) to manage congestion. AAM will include both scheduled and non-scheduled, on-demand operations, challenging strategic deconfliction algorithms. Flights will be fairly short, traversing an urban area, not hundreds or thousands of miles, with corresponding low energy reserves, prohibiting excessive delays. Flight operators will need flexibility in operations, scheduling a flight only minutes before departure, or diverting to an alternate vertiport if it becomes advantageous, further adding to trajectory uncertainty that would challenge strategic deconfliction. Finally, operators will desire privacy to protect sensitive information or preserve competitive advantage, limiting early access to intent information. In this paper, we present an approach for managing congestion at vertiports by providing insight into the traffic situation to support operationally-advantageous and safe land or divert decisions. We propose a metric designed with usefulness and usability in AAM operations in mind. The metric uses the sociology concept of dynamic density (DD) that takes into consideration not only number of flights, but also the interaction of those flights with the vertiport’s limited resources, namely the landing pads and the parking spots. DD supports a Pilot in Command (PIC) with decisions about whether to proceed, expedite, delay, or divert; and supports air traffic control (ATC) and vertiport operators in airspace management and vertiport usage. We demonstrate the metric on a notional vertiport scenario. We also show that DD provides better insight into congestion and resulting flight delays than an aircraft count metric used in traditional air traffic management.

Lilly Spirkovska↗

DOE OSTI2026

RxnRover/amlro

AMLRO (Active Machine Learning Reaction Optimizer) is an open-source framework designed to accelerate chemical reaction optimization using active learning with classical machine learning regression models. AMLRO integrates space-filling sampling strategies (e.g., Sobol and Latin Hypercube sampling) with iterative model training, prediction, and experiment selection to efficiently navigate complex reaction spaces. The platform supports multiple regression models, flexible multi-objective definitions, and user-defined parameter bounds, enabling data-efficient optimization from small initial datasets. AMLRO is designed for ease of use by experimentalists and can operate as a standalone decision-support tool or be integrated into closed-loop automated experimentation workflows.

Kulathunga, Dulitha Prasanna [Iowa State Universit↗

DOE OSTI2024

Digital Twin + AI: Control Room of the Future

A digital twin enhances power grid control room operations by providing real-time monitoring, predictive insights, simulation capabilities, remote control, training opportunities, data integration, and decision support. This technology empowers control room operators to effectively manage the grid, optimize performance, and ensure reliable and efficient energy distribution.

control room of the future↗

NASA NTRS2013

Effects of Airport Tower Controller Decision Support Tool on Controllers Head-Up Time

Despite that aircraft positions and movements can be easily monitored on the radar displays at major airports nowadays, it is still important for the air traffic control tower (ATCT) controllers to look outside the window as much as possible to assure safe operations of traffic management. The present paper investigates whether an introduction of the NASA's proposed Spot and Runway Departure Advisor (SARDA), a decision support tool for the ATCT controller, would increase or decrease the controllers' head-up time. SARDA provides the controller departure-release schedule advisories, i.e., when to release each departure aircraft in order to minimize individual aircraft's fuel consumption on taxiways and simultaneously maximize the overall runway throughput. The SARDA advisories were presented on electronic flight strips (EFS). To investigate effects on the head-up time, a human-in-the-loop simulation experiment with two retired ATCT controller participants was conducted in a high-fidelity ATCT cab simulator with 360-degree computer-generated out-the-window view. Each controller participant wore a wearable video camera on a side of their head with the camera facing forward. The video data were later used to calculate their line of sight at each moment and eventually identify their head-up times. Four sessions were run with the SARDA advisories, and four sessions were run without (baseline). Traffic-load levels were varied in each session. The same set of user interface - EFS and the radar displays - were used in both the advisory and baseline sessions to make them directly comparable. The paper reports the findings and discusses their implications.

airport surface operations↗

DOE OSTI2025

Meta‐Analysis and Regression Modeling of the Impacts of Four Indoor Environmental Quality Metrics on Office Performance

Awareness of how buildings interact with the occupant experience—especially human performance—is becoming more prevalent, as seen by increasing interest and investment in healthy built environments. However, there is a need to synthesize the wide array of existing indoor environmental assessment and performance research in a way that can translate directly to building design and operation. Existing research in this area typically focuses on a single isolated metric and has not focused on making the results utilizable by building practitioners. The aim of this research is to investigate existing office performance literature through meta‐analyses and produce regression models for four indoor environmental quality (IEQ) metrics to support critical decision‐making for building operation and renovation. To reach this aim, a literature review was conducted to identify studies that measure the impact of changing ventilation rate, temperature, horizontal illuminance, and noise level in offices on occupant task performance. This repository of field and laboratory studies was analyzed to visualize the trends between the selected IEQ metrics and task performance. The temperature, ventilation rate, and horizontal illuminance regression models showed clear improvement potential when modifying indoor conditions toward the defined high‐performance range, while the regression model for noise level was inconclusive. The discussion notes the importance of designing holistically for all components of these IEQ categories to utilize the results, for example, good filtration on outdoor air for quantifying ventilation impact and uniform overhead lighting with low contrast for quantifying horizontal illuminance impact. The novelty of this work is in considering multiple facets of the indoor environment under a single, unified analysis schema and producing IEQ‐based performance gains that can directly inform cost‐benefit analyses of building design and renovation.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2014

Real-Time Science Operations to Support a Lunar Polar Volatiles Rover Mission

Future human exploration of the Moon will likely rely on in situ resource utilization (ISRU) to enable long duration lunar missions. Prior to utilizing ISRU on the Moon, the natural resources (in this case lunar volatiles) must be identified and characterized, and ISRU demonstrated on the lunar surface. To enable future uses of ISRU, NASA and the CSA are developing a lunar rover payload that can (1) locate near subsurface volatiles, (2) excavate and analyze samples of the volatile-bearing regolith, and (3) demonstrate the form, extractability and usefulness of the materials. Such investigations are important both for ISRU purposes and for understanding the scientific nature of these intriguing lunar volatile deposits. Temperature models and orbital data suggest near surface volatile concentrations may exist at briefly lit lunar polar locations outside persistently shadowed regions. A lunar rover could be remotely operated at some of these locations for the approx. 2-14 days of expected sunlight at relatively low cost. Due to the limited operational time available, both science and rover operations decisions must be made in real time, requiring immediate situational awareness, data analysis, and decision support tools. Given these constraints, such a mission requires a new concept of operations. In this paper we outline the results and lessons learned from an analog field campaign in July 2012 which tested operations for a lunar polar rover concept. A rover was operated in the analog environment of Hawaii by an off-site Flight Control Center, a rover navigation center in Canada, a Science Backroom at NASA Ames Research Center in California, and support teams at NASA Johnson Space Center in Texas and NASA Kennedy Space Center in Florida. We find that this type of mission requires highly efficient, real time, remotely operated rover operations to enable low cost, scientifically relevant exploration of the distribution and nature of lunar polar volatiles. The field demonstration illustrated the need for science operations personnel in constant communications with the flight mission operators and the Science Backroom to provide immediate and continual science support and validation throughout the mission. Specific data analysis tools are also required to enable immediate data monitoring, visualization, and decision making. The field campaign demonstrated that this novel methodology of real-time science operations is possible and applicable to providing important new insights regarding lunar polar volatiles for both science and exploration.

Moon; Volatiles; Rover; Missions↗

DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗

NASA NTRS2018

Air Traffic Management Technology Demostration-1 (ATD-1) Avionics Phase 2 Flight Test and Results

NASA’s first Air Traffic Management Technology Demonstration (ATD-1) subproject successfully completed 19 days of flight test validation in January and February 2017 of an Interval Management (IM) avionics prototype and the procedures used to conduct IM arrival and approach operations. IM is one of the three elements integrated into NASA’s ATD-1 concept of operations with the subproject goal of improving aircraft efficiency and airport throughput during high-density arrival operations. The ATD-1 concept of operations combines advanced arrival scheduling, controller decision support tools, and interval management (IM) avionics to enable merging of multiple, time-based, efficient arrival streams. IM contributes to the operation by calculating speeds that enable an aircraft to precisely achieve a specific time or distance behind another aircraft. When precise spacing intervals can be calculated, achieved, and then maintained during high-density operations, aircraft efficiency should be improved by enabling the aircraft to remain closer to the optimum descent trajectory instead of using vectors and step-down altitudes, and airport throughput should be maintained or improved by each aircraft arriving at the runway threshold closer to the assigned spacing interval. This avionics development and flight test was conducted under a NASA contract by Boeing Research and Technology, with Boeing Commercial Aircraft, Honeywell, United Airlines, and Jeppesen as sub-contractors. The Honeywell built IM avionics were the first ever prototype built based on NASA requirements as well as developing and non-flight tested international IM standards, integrated into two test aircraft, and then flown in real-world conditions at the Grant County International Airport (KMWH). The IM prototype flown in the flight test used data from the Ownship and the assigned lead, or Target, aircraft to calculate the airspeed necessary for the Ownship to achieve the desired spacing. The flight test demonstrated that the IM avionics prototype generally met the IM requirement for spacing accuracy. However, the control laws implemented require further development to reduce the high IM speed command rate and the number of speed reversals observed during the test. Pilots assessed the IM procedure as acceptable, and issues requiring further attention were identified. In summary, the IM avionics prototype showed significant promise in contributing to the goals of improving aircraft efficiency and airport throughput. The flight test results also provided important data to the FAA and the working group developing the follow-on version of the international IM standards.

Baxley, Brian T.↗

NASA NTRS1987

A PC-Based Free Text DSS for Health Care

A free Decision Support System(DST) has been constructed for health care professional that allows the analysis of complex medical cases and the creation of diagnostic list of potential diseases for clinical evaluation.The system uses a PC-based text management system specifically designed for desktop operation. The texts employed in the decision support package include the Merck Manual (published by Merck Sharpe & Dohme) and Control of Communicable Diseas in Man (published by the American Public Health Association). The background and design of the database are discussed along with a structured analysis procedure for handling free text DSS system. A case study is presented to show the application of this technology and conclusions are drawn in the summary that point to expanded areas of professional intention and new frontiers yet to be explored in this rapidly progressing field.

Grams, Ralph R.↗

NASA NTRS2019

In-Time Non-Participant Casualty Risk Assessment to Support Onboard Decision Making for Autonomous Unmanned Aircraft

Numerous operational paradigms, technologies, and missions are emerging as newcomers to the National Airspace System (NAS) develop small Unmanned Aircraft Systems (sUAS), personal air vehicles and other Urban Air Mobility (UAM) concepts. As the list of applications expands, maintaining the safety of the current airspace system remains one of the core concerns preventing widespread commercial implementation of these concepts. Further, the risks associated with unmanned aircraft operations themselves have to be recognized and mitigated in a timely manner. Safety-critical risks include, but are not limited to, flight outside of approved airspace, unsafe proximity to people or property, critical system failures, loss-of control, and cyber-security related risks. Instead of reacting to accidents, a set of predictive and data-driven risk monitoring, assessment, and mitigation capabilities are envisioned to help capture and eliminate hazards as these systems become operational. NASA’s System-wide Safety project is performing R&D on such a safety assurance concept. As part of this concept, this paper describes an architecture that continuously monitors a diverse set of onboard and ground-based sources to estimate and predict non-participant casualty risk during flight. Timely identification of the changing nature of this risk can inform decision making processes to mitigate current and impending situations.

Ancel, Ersin↗

NASA NTRS2015

Expanding the Operational Use of Total Lightning Ahead of GOES-R

NASA's Short‐term Prediction Research and Transition Center (SPoRT) has been transitioning real‐time total lightning observations from ground‐based lightning mapping arrays since 2003. This initial effort was with the local Weather Forecast Offices (WFO) that could use the North Alabama Lightning Mapping Array (NALMA). These early collaborations established a strong interest in the use of total lightning for WFO operations. In particular the focus started with warning decision support, but has since expanded to include impact‐based decision support and lightning safety. SPoRT has used its experience to establish connections with new lightning mapping arrays as they become available. The GOES‐R / JPSS Visiting Scientist Program has enabled SPoRT to conduct visits to new partners and expand the number of operational users with access to total lightning observations. In early 2014, SPoRT conducted the most recent visiting scientist trips to meet with forecast offices that will used the Colorado, Houston, and Langmuir Lab (New Mexico) lightning mapping arrays. In addition, SPoRT met with the corresponding Center Weather Service Units (CWSUs) to expand collaborations with the aviation community. These visits were an opportunity to learn about the forecast needs of each office visited as well as to provide on‐site training for the use of total lightning, setting the stage for a real‐time assessment during May‐July 2014. With five lightning mapping arrays covering multiple geographic locations, the 2014 assessment has demonstrated numerous uses of total lightning in varying situations. Several highlights include a much broader use of total lightning for impact‐based decision support ranging from airport weather warnings, supporting fire crews, and protecting large outdoor events. The inclusion of the CWSUs has broadened the operational scope of total lightning, demonstrating how these data can support air traffic management, particularly in the Terminal Radar Approach Control Facilities (TRACON) region around an airport. These collaborations continue to demonstrate, from the operational perspective, the utility of total lightning and the importance of continued training and preparation in advance of the Geostationary Lightning Mapper.

GLM↗

NASA NTRS2009

Probabilistic Risk Assessment for Decision Making During Spacecraft Operations

Decisions made during the operational phase of a space mission often have significant and immediate consequences. Without the explicit consideration of the risks involved and their representation in a solid model, it is very likely that these risks are not considered systematically in trade studies. Wrong decisions during the operational phase of a space mission can lead to immediate system failure whereas correct decisions can help recover the system even from faulty conditions. A problem of special interest is the determination of the system fault protection strategies upon the occurrence of faults within the system. Decisions regarding the fault protection strategy also heavily rely on a correct understanding of the state of the system and an integrated risk model that represents the various possible scenarios and their respective likelihoods. Probabilistic Risk Assessment (PRA) modeling is applicable to the full lifecycle of a space mission project, from concept development to preliminary design, detailed design, development and operations. The benefits and utilities of the model, however, depend on the phase of the mission for which it is used. This is because of the difference in the key strategic decisions that support each mission phase. The focus of this paper is on describing the particular methods used for PRA modeling during the operational phase of a spacecraft by gleaning insight from recently conducted case studies on two operational Mars orbiters. During operations, the key decisions relate to the commands sent to the spacecraft for any kind of diagnostics, anomaly resolution, trajectory changes, or planning. Often, faults and failures occur in the parts of the spacecraft but are contained or mitigated before they can cause serious damage. The failure behavior of the system during operations provides valuable data for updating and adjusting the related PRA models that are built primarily based on historical failure data. The PRA models, in turn, provide insight into the effect of various faults or failures on the risk and failure drivers of the system and the likelihood of possible end case scenarios, thereby facilitating the decision making process during operations. This paper describes the process of adjusting PRA models based on observed spacecraft data, on one hand, and utilizing the models for insight into the future system behavior on the other hand. While PRA models are typically used as a decision aid during the design phase of a space mission, we advocate adjusting them based on the observed behavior of the spacecraft and utilizing them for decision support during the operations phase.

dynamic fault trees↗

DOE OSTI2024

Performance of a Drifting Acoustic Instrumentation SYstem (DAISY) for characterizing radiated noise from marine energy converters

Marine energy converters can generate electricity from energetic ocean waves and water currents. Because sound is extensively used by marine animals, the radiated noise from these systems is of regulatory interest. However, the energetic nature of these locations poses challenges for performing accurate passive acoustic measurements, particularly with stationary platforms. The Drifting Acoustic Instrumentation SYstem (DAISY) is a modular hydrophone recording system purpose-built for marine energy environments. Using a flow shield in currents and mass–spring–damper suspension system in waves, we demonstrate that DAISYs can effectively minimize the masking effect of flow noise at frequencies down to 10 Hz. In addition, we show that groups of DAISYs can utilize time-delay-of-arrival post-processing to attribute radiated noise to a specific source. Consequently, DAISYs can rapidly measure radiated noise at all frequencies of interest for prototype marine energy converters. Furthermore, the resulting information from future operational deployments should support regulatory decision-making and allow technology developers to make design adjustments that minimize the potential for acoustic impacts as their systems are scaled up for utility-scale power generation.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

A HPC Theory-Guided Machine Learning Cyberinfrastructure for Communicating Hydrometeorological Data Across Scales

High-resolution predictions of hydrometeorological variables are critical for supporting hydropower generation decisions and flood control at hydroelectric power plants. Traditional climate and hydrologic models rely on the numerical simulation of detailed physical processes. Therefore, running these simulations is time-, labor-, and computation-intensive. Improving the spatial and temporal resolution in these modeling outputs could lead to cubic increases in both the simulation time and computational demands, rendering high-resolution hydrometeorological predictions expensive and impractical. Many past studies apply the super resolution (SR) technique to downscale climate models using deep learners. However, deep learners are deemed “black-boxes,” as their derivation processes from low-resolution outputs to high-resolution outputs are often hidden. Their results are difficult for domain scientists to interpret and validate. Thus, there is a need for an exploratory machine learning approach that can partially integrate domain-specific theory and knowledge into the data-driven mapping process between simulation outputs of different spatial scales. The domain-specific theory and knowledge can be incorporated into the data model through an inductive approach in which process-related environmental variables are used and analyzed as key drivers (i.e., environmental surrogates) to reflect the complex physical processes. Many of these variables, such as land use land cover, soil types, topography, digital elevation, air temperature, and various watershed characteristics, can be directly measured through sensors or remote sensing techniques. Additionally, SR applications that can downscale hydrological and hydrodynamics models to efficiently produce high-resolution (1 m) flood depth grids are still rare. Since the flood depth grid can be used to support critical decisions for flood control operation at hydroelectric power plants, it is crucial to enable an SR-based capability for interpolating high-resolution flood inundation maps.

13 HYDRO ENERGY↗

NASA NTRS2005

Handling Trajectory Uncertainties for Airborne Conflict Management

Airborne conflict management is an enabling capability for NASA's Distributed Air-Ground Traffic Management (DAG-TM) concept. DAGTM has the goal of significantly increasing capacity within the National Airspace System, while maintaining or improving safety. Under DAG-TM, autonomous aircraft maintain separation from each other and from managed aircraft unequipped for autonomous flight. NASA Langley Research Center has developed the Autonomous Operations Planner (AOP), an onboard decision support system that provides airborne conflict management (ACM) and strategic flight planning support for autonomous aircraft pilots. The AOP performs conflict detection, prevention, and resolution from nearby traffic aircraft and area hazards. Traffic trajectory information is assumed to be provided by Automatic Dependent Surveillance Broadcast (ADS-B). Reliable trajectory prediction is a key capability for providing effective ACM functions. Trajectory uncertainties due to environmental effects, differences in aircraft systems and performance, and unknown intent information lead to prediction errors that can adversely affect AOP performance. To accommodate these uncertainties, the AOP has been enhanced to create cross-track, vertical, and along-track buffers along the predicted trajectories of both ownship and traffic aircraft. These buffers will be structured based on prediction errors noted from previous simulations such as a recent Joint Experiment between NASA Ames and Langley Research Centers and from other outside studies. Currently defined ADS-B parameters related to navigation capability, trajectory type, and path conformance will be used to support the algorithms that generate the buffers.

Barhydt, Richard↗

NASA NTRS2012

Key Decision Record Creation and Approval Module

Retaining good key decision records is critical to ensuring the success of a project or operation. Having adequately documented decisions with supporting documents and rationale can greatly reduce the amount of rework or reinvention over a project's, vehicle's, or facility's lifecycle. Stennis Space Center developed and uses a software tool that automates the Key Decision Record (KDR) process for its engineering and test projects. It provides the ability for a user to log key decisions that are made during the course of a project. By customizing Parametric Technology Corporation's (PTC) Windchill product, the team was able to log all information about a decision, and electronically route that information for approval. Customizing the Windchill product allowed the team to directly connect these decisions to the engineering data that it might affect and notify data owners of the decision. The user interface was created in JSP and Javascript, within the OOTB (Out of the Box) Windchill product, allowing users to create KDRs. Not only does this interface allow users to create and track KDRs, but it also plugs directly into the OOTB ability to associate these decision records with other relevant engineering data such as drawings, designs, models, requirements, or specifications

Hebert, Barrt↗

NASA NTRS2013

Ubiquitous Wireless Smart Sensing and Control

Need new technologies to reliably and safely have humans interact within sensored environments (integrated user interfaces, physical and cognitive augmentation, training, and human-systems integration tools). Areas of focus include: radio frequency identification (RFID), motion tracking, wireless communication, wearable computing, adaptive training and decision support systems, and tele-operations. The challenge is developing effective, low cost/mass/volume/power integrated monitoring systems to assess and control system, environmental, and operator health; and accurately determining and controlling the physical, chemical, and biological environments of the areas and associated environmental control systems.

Wagner, Raymond↗

NASA NTRS2014

A Simplified Approach to Cloud Masking with VIIRS in the S-NPP/JPSS Era

The quantitative detection of clouds in satellite imagery has a number of important applications in weather analysis. The proper interpretation of satellite imagery for improved situational awareness depends on knowing where the clouds are at all times of the day. Additionally, many products derived from infrared measurements need accurate cloud information to mask out regions where retrieval of geophysical parameters in the atmosphere or on the surface are not possible. Thus, the accurate detection of the presence of clouds in satellite imagery on a global basis is important to the product developers and the operational weather community to support their decision‐making process. This abstract describes an application of a two‐channel bispectral composite threshold (BCT) approach applied to VIIRS imagery. The simplified BCT approach uses only the 10.76 and 3.75 micrometer spectral channels in two spectral tests; a straightforward infrared threshold test with the longwave channel and a shortwave minus longwave channel difference test. The key to the success of this approach as demonstrated in past applications to GOES and MODIS data is the generation of temporally and spatially dependent thresholds used in the tests from a previous number of days at similar observations to the current data. The presentation will present an overview of the approach and intercomparison results with other satellites, methods, and against verification data.

Jedlovec, Gary J.↗