SEARCH · Engineering Papers

Results for “demand uncertainty”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9

All sources NASA NTRS DOE OSTI

NASA NTRS2020

In-Time UAV Flight-Trajectory Estimation and Tracking Using Bayesian Filters

Rapid increase of UAV operation in the next decade in areas of on-demand delivery, medical transportation services, law enforcement, traffic surveillance and several others pose potential risks to the low altitude airspace above densely populated areas. Safety assessment of airspace demands the need for a novel UAV traffic management (UTM) framework for regulation and tracking of the vehicles. Particularly for low-altitude UAV operations, quality of GPS measurements feeding into the UAV is often compromised by loss of communication link caused by presence of trees or tall buildings in proximity to the UAV flight path. Inaccurate GPS locations may yield to unreliable monitoring and inaccurate prognosis of remaining battery life and other safety metrics which rely on future expected trajectory of the UAV. This work therefore proposes a generalized monitoring and prediction methodology for autonomous UAVs using in-time GPS measurements. Firstly, a typical 4D smooth trajectory generation technique from a series of waypoint locations with associated expected times-of-arrival based on B-spline curves is presented. Initial uncertainty in the vehicle's expected cruise velocity is quantified to compute confidence intervals along the entire flight trajectory using error interval propagation approach. Further, the generated planned trajectory is considered as the prior knowledge which is updated during its flight with incoming GPS measurements in order to estimate its current location and corresponding kinematic profiles. Estimation of position is denoted in dicrete state-space representation such that position at a future time step is derived from position and velocity at current time step and expected velocity at the future time step. A linear Bayesian filtering algorithm is employed to efficiently refine position estimation from noisy GPS measurements and update the confidence intervals. Further, a dynamic re-planning strategy is implemented to incorporate unexpected detour or delay scenarios. Finally, critical challenges related to uncertainty quantification in trajectory prognosis for autonomous vehicles are identified, and potential solutions are discussed at the end of the paper. The entire monitoring framework is demonstrated on real UAV flight experiments conducted at the NASA Langley Research Center.

Banerjee, Portia↗

NASA NTRS2021

Highland Lakes Water Resources: Using NASA Earth Observations to Improve Detection Systems for Harmful Algal Events in the Highland Lakes in Central Texas

Beginning in 2019, harmful algal events in Austin, Texas, caused canine deaths in the Lady Bird Lake and Lake Travis reservoirs. These reservoirs are part of the larger Highland Lakes chain, managed by the Lower Colorado River Authority (LCRA) and the City of Austin Department of Watershed Protection (CoA DWP), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable algal event conditions in central Texas, the LCRA and CoA DWP partnered with NASA DEVELOP to improve algal event early-warning systems through the application of remote sensing and machine learning. An Earth observation-based algal monitoring system will assist the responsible agencies in predicting algal conditions and communicating hazards to the public. The NASA DEVELOP team utilized Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data to produce products including chlorophyll-a concentrations, cyanobacteria detections, turbidity, and water surface temperature. Chlorophyll-a concentrations were retrieved with a pre-trained machine learning model (mixture density network) and spectral indices, while the other products were derived from spectral indices. In situ field data were used to validate and quantify uncertainties for each product. The validations show strong correlations for chlorophyll-a and water surface temperature. Time series analyses of chlorophyll-a concentrations show peaks in the severe drought years (2015 and 2016). This project's resulting products enable monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain and will ultimately support water management, decision making, and risk communication.

Kaitlynn Hietpas↗

NASA NTRSDate not supplied

The Application of Remote Sensing and Machine Learning to Improve Early Warning Systems for Harmful Algal Events in the Highland Lake Chain, TX

Beginning in 2019, harmful algal events have caused canine deaths in both Lady Bird Lake and Lake Travis located near Austin, Texas. These two reservoirs are part of the larger Highland Lakes chain, managed by the City of Austin Department of Watershed Protection (COA DWP) and the Lower Colorado River Authority (LCRA), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable environmental conditions for algal events in central Texas, NASA DEVELOP partnered with LCRA and COA DWP to improve monitoring and early detection of algal events, utilizing satellite remote sensing and machine learning. Spatially and temporally varied chlorophyll a concentrations, cyanobacteria detections, turbidity, and water surface temperature products are used as environmental proxies. Landsat 8 Operational Land Imager (OLI) and the Sentinel-2 MultiSpectral Instrument (MSI) data were used to provide a combined revisit time of up to ~3 days and < 30 m per pixel products. Chlorophyll a concentrations were estimated using a pre-trained Mixture Density Network, and cyanobacteria detection was accomplished using the Broad Wavelength Algae Index, which can differentiate algal blooms from algal proliferations (mats). In situ data were used to validate remotely sensed measurements and quantify uncertainties. Preliminary results show a good fit between the modeled output and in situ observations, suggesting that remote sensing data can be used to retrieve biogeochemical properties and/or inherent optical properties (IOPs) of water columns in these inland human-made lakes. Uncertainties were introduced from the sensitivity to atmospheric correction, inherent mismatch between satellite and sampling data, and a relatively lower signal-to-noise ratio over water. The resulting products enable near real-time monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain, and will ultimately support water management, decision making, and risk communication.

Shuyu Chang↗

NASA NTRSDate not supplied

NASA Earth Systems Digital Twins (ESDT)

"Similarly to artificial intelligence, which is now revolutionizing many aspects of our daily lives, Earth system digital twin technologies have the potential to revolutionize the way Earth Science research will be conducted in the future, and how results and knowledge from this research will provide information to support decision making and yield impactful societal benefits. An Earth System Digital Twin or ESDT is a dynamic and interactive information system that first provides a digital replica of the past and current states of the Earth or Earth system as accurately and timely as possible; second, allows for computing forecasts of future states under nominal assumptions and based on the current replica; and third, offers the capability to investigate many hypothetical scenarios under varying impact assumptions. In other words, an ESDT provides the integrated What-Now, What-Next, and What-If pictures of the Earth or Earth system, by continuously ingesting newly observed data and by leveraging multiple interconnected models, machine learning as well advanced computing and visualization capabilities. Digital twins have been developed in engineering since 2002, but the interest in digital twins for the Earth domain is more recent and stems from the convergence of several developments: - The huge amount of diverse data that has now been collected continuously for more than 50 years, and that is becoming more and more difficult to access, understand, and utilize. - At the same time, because of climate change and its impacts the information produced by all of this data is becoming of interest to many new non-traditional users for analyzing and predicting various phenomena. - Because of advances in computational and visualization capabilities and the parallel unprecedented development of machine learning (ML), extracting relevant information from these large amounts of data and running complex models faster has become possible. As a result, it is becoming necessary and possible to build intuitive and interactive frameworks that will enable users with various skill levels and/or organizational hierarchy levels to easily access large amounts of targeted information along with the relevant tools and models (Earth system and human activity models), to support them in analyzing and visualizing this information, to help them understand interactions among models, to visualize the potential outcomes of various impacts, and to support decision or policy making. The full power of digital twins is that, through an integrated representation and standardized tools and software technologies, the same digital replica can address the needs of multiple users at various resolutions (spatial and temporal) and for various applications (science, economic, policy, etc.) – “from farmer to scientist”. With all these interests at stake, the challenges of building optimal digital twins are many and complex. The first challenge is to determine if a Digital Twin should be global or local, and multi-domain or thematic. For example, some domains such as Climate or Weather will require a global Digital Twin or Digital Twin capabilities while science areas such as Biodiversity might be more local. We can also envision that multiple thematic ESDTs, e.g., Air Quality, Wildfires, Hydrology could be federated or provide input to other ESDTs, either on a regional level or to a more global ESDT. Overall, we can imagine a future “web” of Digital Twins co-existing in a hierarchy or in a network, and capable of being connected or federated depending on the needs. This last point brings up the very important challenge of interoperability, including standards and protocols that will need to be built into these systems from the beginning. Each individual digital twin would have full flexibility in internal construction but would need standards-based interfaces (input and output) or hooks to make it compatible with others. Another challenge when building digital twins will be to decide how to organize each digital replica. Based on the applications targeted by the DT under implementation, various amounts and types of raw data, Analysis Ready Data (ARD) and information will need to be incorporated. Depending on the required latencies and needs of the users, various solutions can be considered, including Data Cubes, Data Lakes, pointers, or computing information on demand. We envision that each ESDT will choose a solution adapted to its specific objectives. Another important challenge is the type(s) of visualization that will be used, as well as the level of interactivity and refresh rate that will be required. Again, this will depend on the objectives of the ESDT, but also on the various users’ needs. In most cases, several types of visualizations and human interfaces will need to be offered depending on the projected users of that system. In parallel to the challenges highlighted above, there are also many tools and technologies that will need to be developed or improved for all types of digital twins. Among those are improved machine learning technologies, for example providing explainability, but also ML techniques for causality and providing a better integration of physics models. Additionally, reliable uncertainty quantification methods will be needed for all ESDT components, from validating data fusion and assimilation to assessing the accuracy of ML models and weighing the values of decisions supported by those systems. This presentation introduces the ESDT concept, presents several ESDT use cases, and a proposed ESDT architecture framework, as well as various technologies being developed by the Advanced Information Systems Technology (AIST) Program."

Earth Science Remote Sensing; Information Systems↗

NASA NTRS2000

Fabrication, Metrology and Modeling of the Space-Based Lidar Telescope for SPARCLE

Over the past 7 years, NASA Marshall Space Flight Center (MSFC) through the Global Hydrology and Climate Center (GHCC) has been working; in collaboration with the University of Alabama in Huntsville (UAH) Center for Applied Optics (CAO), and others; towards demonstrating a solid state coherent Doppler lidar instrument for space-based global measurement of atmospheric winds. The Space Readiness Coherent Lidar Experiment (SPARCLE) was selected by NASA's New Millennium Program to demonstrate the feasibility and technology readiness of space-based coherent wind lidar. The CAO was responsible for the design, development, integration, and testing of the SPARCLE optical system. Operating at 2-micron wavelength, SPARCLE system performance is dominated by the optical quality of the transmitter/receiver optical system. The stringent optical performance requirements coupled with the demanding physical and environmental constraints of a space-based instrument necessitate extensive characterization of the telescope optical performance that is critical to predicting the lidar system efficiency and operation in space. Individual components have been measured prior to assembly and compared to the designed specifications. Based on the individual components, the telescope design was optimized to produce a suitable telescope. Once the telescope is completed, it will be tested and evaluated and the data shall be used to anchor computer based models of the optical system. Commercial optical modeling codes were used to evaluate the performance of the telescope under a variety of anticipated on-orbit environments and will eventually be compared to environmental tests conducted in the course of qualifying the telescope for flight. Detailed analysis using the "as built" data will help to reduce uncertainties within the lidar system model and will increase the accuracy of the lidar performance predictions.

Peters, Bruce R.↗

NASA NTRS1993

A fault-tolerant intelligent robotic control system

This paper describes the concept, design, and features of a fault-tolerant intelligent robotic control system being developed for space and commercial applications that require high dependability. The comprehensive strategy integrates system level hardware/software fault tolerance with task level handling of uncertainties and unexpected events for robotic control. The underlying architecture for system level fault tolerance is the distributed recovery block which protects against application software, system software, hardware, and network failures. Task level fault tolerance provisions are implemented in a knowledge-based system which utilizes advanced automation techniques such as rule-based and model-based reasoning to monitor, diagnose, and recover from unexpected events. The two level design provides tolerance of two or more faults occurring serially at any level of command, control, sensing, or actuation. The potential benefits of such a fault tolerant robotic control system include: (1) a minimized potential for damage to humans, the work site, and the robot itself; (2) continuous operation with a minimum of uncommanded motion in the presence of failures; and (3) more reliable autonomous operation providing increased efficiency in the execution of robotic tasks and decreased demand on human operators for controlling and monitoring the robotic servicing routines.

Marzwell, Neville I.↗

NASA NTRS2013

Precision Departure Release Capability (PDRC) Technology Description

After takeoff, aircraft must merge into en route (Center) airspace traffic flows which may be subject to constraints that create localized demand-capacity imbalances. When demand exceeds capacity, Traffic Management Coordinators (TMCs) often use tactical departure scheduling to manage the flow of departures into the constrained Center traffic flow. Tactical departure scheduling usually involves use of a Call for Release (CFR) procedure wherein the Tower must call the Center TMC to coordinate a release time prior to allowing the flight to depart. In present-day operations release times are computed by the Center Traffic Management Advisor (TMA) decision support tool based upon manual estimates of aircraft ready time verbally communicated from the Tower to the Center. The TMA-computed release is verbally communicated from the Center back to the Tower where it is relayed to the Local controller as a release window that is typically three minutes wide. The Local controller will manage the departure to meet the coordinated release time window. Manual ready time prediction and verbal release time coordination are labor intensive and prone to inaccuracy. Also, use of release time windows adds uncertainty to the tactical departure process. Analysis of more than one million flights from January 2011 indicates that a significant number of tactically scheduled aircraft missed their en route slot due to ready time prediction uncertainty. Uncertainty in ready time estimates may result in missed opportunities to merge into constrained en route flows and lead to lost throughput. Next Generation Air Transportation System (NextGen) plans call for development of Tower automation systems capable of computing surface trajectory-based ready time estimates. NASA has developed the Precision Departure Release Capability (PDRC) concept that uses this technology to improve tactical departure scheduling by automatically communicating surface trajectory-based ready time predictions to the Center scheduling tool. The PDRC concept also incorporates earlier NASA and FAA research into automation-assisted CFR coordination. The PDRC concept helps reduce uncertainty by automatically communicating coordinated release times with seconds-level precision enabling TMCs to work with target times rather than windows. NASA has developed a PDRC prototype system that integrates the Center's TMA system with a research prototype Tower decision support tool. A two-phase field evaluation was conducted at NASA's North Texas Research Station (NTX) in Dallas-Fort Worth. The field evaluation validated the PDRC concept and demonstrated reduced release time uncertainty while being used for tactical departure scheduling of more than 230 operational flights over 29 weeks of operations. This paper presents the Technology Description. Companion papers include the Final Report and a Concept of Operations.

tactical departure scheduling↗

NASA NTRS2013

Precision Departure Release Capability (PDRC) Concept of Operations

After takeoff, aircraft must merge into en route (Center) airspace traffic flows which may be subject to constraints that create localized demandcapacity imbalances. When demand exceeds capacity Traffic Management Coordinators (TMCs) often use tactical departure scheduling to manage the flow of departures into the constrained Center traffic flow. Tactical departure scheduling usually involves use of a Call for Release (CFR) procedure wherein the Tower must call the Center TMC to coordinate a release time prior to allowing the flight to depart. In present-day operations release times are computed by the Center Traffic Management Advisor (TMA) decision support tool based upon manual estimates of aircraft ready time verbally communicated from the Tower to the Center. The TMA-computed release is verbally communicated from the Center back to the Tower where it is relayed to the Local controller as a release window that is typically three minutes wide. The Local controller will manage the departure to meet the coordinated release time window. Manual ready time prediction and verbal release time coordination are labor intensive and prone to inaccuracy. Also, use of release time windows adds uncertainty to the tactical departure process. Analysis of more than one million flights from January 2011 indicates that a significant number of tactically scheduled aircraft missed their en route slot due to ready time prediction uncertainty. Uncertainty in ready time estimates may result in missed opportunities to merge into constrained en route flows and lead to lost throughput. Next Generation Air Transportation System (NextGen) plans call for development of Tower automation systems capable of computing surface trajectory-based ready time estimates. NASA has developed the Precision Departure Release Capability (PDRC) concept that uses this technology to improve tactical departure scheduling by automatically communicating surface trajectory-based ready time predictions to the Center scheduling tool. The PDRC concept also incorporates earlier NASA and FAA research into automation-assisted CFR coordination. The PDRC concept helps reduce uncertainty by automatically communicating coordinated release times with seconds-level precision enabling TMCs to work with target times rather than windows. NASA has developed a PDRC prototype system that integrates the Center's TMA system with a research prototype Tower decision support tool. A two-phase field evaluation was conducted at NASA's North Texas Research Station (NTX) in DallasFort Worth. The field evaluation validated the PDRC concept and demonstrated reduced release time uncertainty while being used for tactical departure scheduling of more than 230 operational flights over 29 weeks of operations. This paper presents the Concept of Operations. Companion papers include the Final Report and a Technology Description. ? SUBJECT:

tactical departure scheduling↗

NASA NTRS2013

Precision Departure Release Capability (PDRC) Final Report

After takeoff, aircraft must merge into en route (Center) airspace traffic flows that may be subject to constraints that create localized demand/capacity imbalances. When demand exceeds capacity, Traffic Management Coordinators (TMCs) and Frontline Managers (FLMs) often use tactical departure scheduling to manage the flow of departures into the constrained Center traffic flow. Tactical departure scheduling usually involves a Call for Release (CFR) procedure wherein the Tower must call the Center to coordinate a release time prior to allowing the flight to depart. In present-day operations release times are computed by the Center Traffic Management Advisor (TMA) decision support tool, based upon manual estimates of aircraft ready time verbally communicated from the Tower to the Center. The TMA-computed release time is verbally communicated from the Center back to the Tower where it is relayed to the Local controller as a release window that is typically three minutes wide. The Local controller will manage the departure to meet the coordinated release time window. Manual ready time prediction and verbal release time coordination are labor intensive and prone to inaccuracy. Also, use of release time windows adds uncertainty to the tactical departure process. Analysis of more than one million flights from January 2011 indicates that a significant number of tactically scheduled aircraft missed their en route slot due to ready time prediction uncertainty. Uncertainty in ready time estimates may result in missed opportunities to merge into constrained en route flows and lead to lost throughput. Next Generation Air Transportation System plans call for development of Tower automation systems capable of computing surface trajectory-based ready time estimates. NASA has developed the Precision Departure Release Capability (PDRC) concept that improves tactical departure scheduling by automatically communicating surface trajectory-based ready time predictions and departure runway assignments to the Center scheduling tool. The PDRC concept also incorporates earlier NASA and FAA research into automation-assisted CFR coordination. The PDRC concept reduces uncertainty by automatically communicating coordinated release times with seconds-level precision enabling TMCs and FLMs to work with target times rather than windows. NASA has developed a PDRC prototype system that integrates the Center's TMA system with a research prototype Tower decision support tool. A two-phase field evaluation was conducted at NASA's North Texas Research Station in Dallas/Fort Worth. The field evaluation validated the PDRC concept and demonstrated reduced release time uncertainty while being used for tactical departure scheduling of more than 230 operational flights over 29 weeks of operations. This paper presents research results from the PDRC research activity. Companion papers present the Concept of Operations and a Technology Description.

tactical departure scheduling↗

NASA NTRS2013

Precision Departure Release Capability (PDRC): NASA to FAA Research Transition

After takeoff, aircraft must merge into en route (Center) airspace traffic flows which may be subject to constraints that create localized demand-capacity imbalances. When demand exceeds capacity, Traffic Management Coordinators (TMCs) and Frontline Managers (FLMs) often use tactical departure scheduling to manage the flow of departures into the constrained Center traffic flow. Tactical departure scheduling usually involves use of a Call for Release (CFR) procedure wherein the Tower must call the Center to coordinate a release time prior to allowing the flight to depart. In present-day operations release times are computed by the Center Traffic Management Advisor (TMA) decision support tool based upon manual estimates of aircraft ready time verbally communicated from the Tower to the Center. The TMA-computed release time is verbally communicated from the Center back to the Tower where it is relayed to the Local controller as a release window that is typically three minutes wide. The Local controller will manage the departure to meet the coordinated release time window. Manual ready time prediction and verbal release time coordination are labor intensive and prone to inaccuracy. Also, use of release time windows adds uncertainty to the tactical departure process. Analysis of more than one million flights from January 2011 indicates that a significant number of tactically scheduled aircraft missed their en route slot due to ready time prediction uncertainty. Uncertainty in ready time estimates may result in missed opportunities to merge into constrained en route flows and lead to lost throughput. Next Generation Air Transportation System plans call for development of Tower automation systems capable of computing surface trajectory-based ready time estimates. NASA has developed the Precision Departure Release Capability (PDRC) concept that improves tactical departure scheduling by automatically communicating surface trajectory-based ready time predictions and departure runway assignments to the Center scheduling tool. The PDRC concept also incorporates earlier NASA and FAA research into automation-assisted CFR coordination. The PDRC concept reduces uncertainty by automatically communicating coordinated release times with seconds-level precision enabling TMCs and FLMs to work with target times rather than windows. NASA has developed a PDRC prototype system that integrates the Center's TMA system with a research prototype Tower decision support tool. A two-phase field evaluation was conducted at NASA's North Texas Research Station in Dallas-Fort Worth. The field evaluation validated the PDRC concept and demonstrated reduced release time uncertainty while being used for tactical departure scheduling of more than 230 operational flights over 29 weeks of operations.

ATM↗

NASA NTRS2018

Air Traffic Management Technology Demonstration - 3 (ATD-3) Dynamic Weather Routes Domestic En Route Concept of Operations Synopsis Version 1.0

NASA conducted a survey in 2015 of stakeholders in industry, flight operators, and others to collect data on the most pressing air traffic management (ATM) issues impacting en route operations. Weather related delays and interruptions were identified as the most significant and costly problem impacting en route operations in today's system. Additionally, future traffic demands will place greater demands on the FAA's air traffic control system, necessitate a more efficient and integrated traffic management system and provide controller tools to leverage human productivity. Consequently, NASA is assembling a suite of integrated ground and aircraft-based technologies and decision-making aids under the Airspace Technology Demonstrations 3 (ATD-3) project to continuously search for more efficient weather-avoidance routes and to rapidly respond to weather changes and associated traffic management initiatives. These route efficiency enhancements are targeted at en route airspace and will enable continued use of en route and arrival metering in the presence of weather. NASA's Dynamic Weather Routes (DWR) concept represents a subset of those technologies and focuses primarily on the cruise phase of flight. It leverages existing air traffic weather, airspace, and traffic data, as well as improvements in navigation, surveillance, communication and digital network technologies, and builds upon existing ATM automation to address the shortcomings associated with strategic traffic flow management initiatives and weather forecasting uncertainties. The potential benefits in the form of time, fuel, and cost savings are significant. This concept of operations synopsis provides a detailed description of DWR, its potential benefits and notional implementation paths.

DWR↗

NASA NTRSDate not supplied

A Framework for Assessment of Autonomy Challenges in Air Traffic Management

Traditionally, air traffic management services have been provided by air traffic controllers and managers stationed in ground facilities, employed or contracted by the public sector, and supported by automation. These centralized, human-centric air traffic management services do not scale to accommodate increasing demands from conventional and new entrant operations for access to the national airspace system. One transformation that provides much needed scalability is increasing the level of autonomy of air traffic management by enabling edge agents of the system, including vehicles, operators, and third-party service suppliers, to collectively self-manage independently from the centralized service providers and enabling the automation to also take on more independent traffic management responsibility from the human agents. This paper identifies challenges to increasing the level of autonomy of air traffic management services. It describes a framework to enable a systematic identification of these challenges. The framework consists of a functional breakdown of air traffic management services and several dimensions characterizing different autonomy scales. The autonomy dimensions include the automation level between human and machine agents, the locus of control between centralized and distributed edge agents, cognitive activities for autonomous situation awareness and decision making, intelligence levels ranging from skill-based to expertise-based autonomous behavior, and uncertainty levels of the dynamics and environment in which autonomous agents operate. Several challenges are identified and categorized using the different dimensions of the autonomy framework.

automation, autonomy framework, collective autonom↗

NASA NTRS2022

An Overall Assessment of JPSS-3 VIIRS Radiometric Performance Based on Pre-Launch Testing

Satellite imagery and data are playing an increasingly important role in scientific studies of the Earth and its climate. The scientific community has been demanding ever-increasing capabilities and accuracy from the data provided by these satellites. One key instrument on board a series of satellite platforms is the Visible Infrared Imaging Radiometer Suite (VIIRS), which provides high-quality data of the Earth from low Earth orbit covering the visible to long-wave infrared parts of the spectrum. The fourth build in the series, set to be launched on the Joint Polar-orbiting Satellite System 3 (JPSS-3) platform, has recently completed its main ground calibration program and is set to be integrated into the satellite bus in the near future. This calibration program covered a comprehensive series of performance metrics designed to demonstrate the quality of the science data and ensure the instrument can maintain its calibration successfully once on-orbit. The subject of this work covers the radiometric calibration metrics including dynamic range, signal-to-noise ratio/noise equivalent differential temperature (SNR/NEdT), polarization sensitivity, scattered light response, relative spectral response, response versus scan angle, and uniformity, as well as uncertainties; all key metrics met or exceeded their design requirements with some minor exceptions. Comparisons to previous builds will also be provided.

JPSS-3↗

NASA NTRSDate not supplied

Additional Large-Drop Ice Accretion Test Results for a Large Scale Swept Wing Section from January 2022

In-flight icing is an important consideration that affects aircraft design, performance, certification and safety. Newer regulations combined with increasing demand to reduce fuel burn, emissions and noise are driving a need for improvements in icing simulation capability. To that end, this paper presents the results of additional ice accretion testing conducted in the NASA Icing Research Tunnel in January 2022 with a large swept wing section typical of a modern commercial transport. The model was based upon a section of the Common Research Model wing at the 64% semispan station with a streamwise chord length of 136 in. The test conditions were developed with an icing scaling analysis to generate similar conditions for a small median volumetric diameter (MVD) = 25 μm cloud and a large MVD = 110 μm cloud. A series of tests were conducted over a range of total temperature from -23.8 ˚C to -1.4 ˚C with all other conditions held constant. Another series of tests explored cloud MVD variations from 50 μm to 230 μm while holding constant certain scaling parameters. The variation in ice mass and scanned ice volume across repeat conditions was approximately 50% lower than the uncertainty in the cloud MVD and liquid water content. The measured ice mass and volume calculated from the 3D scans were used to compute the mass to volume ratio that is sometimes referred to as ice density or void fraction. When the ice volume based on the ice shape maximum combined cross section was used to determine this ratio, the resulting values were in the range of 240 to 455 kg/m 3 . This is consistent with analogous values previously reported in the literature. The ice shape mass and volume increased with MVD from 50 to 230 μm at fixed values of the scaling parameters. The ice mass to volume ratio was approximately constant for all of the cases which showed that the accreted mass and volume increased at approximately the same rate. These results demonstrate the significance of cloud MVD on ice shape mass and volume.

Ice accretion, Large drops, Wind tunnel↗

NASA NTRSDate not supplied

Additional Large-Drop Ice Accretion Test Results for a Large Scale Swept Wing Section from January 2022

In-flight icing is an important consideration that affects aircraft design, performance, certification and safety. Newer regulations combined with increasing demand to reduce fuel burn, emissions and noise are driving a need for improvements in icing simulation capability. To that end, this paper presents the results of additional ice accretion testing conducted in the NASA Icing Research Tunnel in January 2022 with a large swept wing section typical of a modern commercial transport. The model was based upon a section of the Common Research Model wing at the 64% semispan station with a streamwise chord length of 136 in. The test conditions were developed with an icing scaling analysis to generate similar conditions for a small median volumetric diameter (MVD) = 25 μm cloud and a large MVD = 110 μm cloud. A series of tests were conducted over a range of total temperature from -23.8 ˚C to -1.4 ˚C with all other conditions held constant. Another series of tests explored cloud MVD variations from 50 μm to 230 μm while holding constant certain scaling parameters. The variation in ice mass and scanned ice volume across repeat conditions was approximately 50% lower than the uncertainty in the cloud MVD and liquid water content. The measured ice mass and volume calculated from the 3D scans were used to compute the mass to volume ratio that is sometimes referred to as ice density or void fraction. When the ice volume based on the ice shape maximum combined cross section was used to determine this ratio, the resulting values were in the range of 240 to 455 kg/m3. This is consistent with analogous values previously reported in the literature. The ice shape mass and volume increased with MVD from 50 to 230 μm at fixed values of the scaling parameters. The ice mass to volume ratio was approximately constant for all of the cases which showed that the accreted mass and volume increased at approximately the same rate. These results demonstrate the significance of cloud MVD on ice shape mass and volume.

Ice accretion↗

NASA NTRSDate not supplied

A Combined Al-Mg/Pb-Pb Age of the Solar System

Astrophysical models of planet formation and protoplanetary disk evolution demand precise and accurate timing of the sequence of events in the solar nebula, relative to a time t=0, usually taken to be during the short epoch of CAI (Ca-rich, Al-rich inclusion) formation. Most CAIs formed withlive26Al (mean-life τ26= 1.034 Myr [1]), with an abundance 26Al/27Al ≈ (26Al/27Al)SS= 5.23 × 10-5[2]. We adopt this as the widespread level of 26Al in the solar nebula at t=0. Assuming spatial homogeneity of 26Al, an inclusion that had less 26Al, (26Al/27Al)0, formed a time Δt26= τ26ln[(26Al/27Al)SS/ (26Al/27Al)0] after t=0.These ages are typical precise to within ±0.1 Myr. Igneous bulk meteorites and inclusions can be relatively dated by the Al-Mg chronometer, but only ifΔt26<6 Myr. The Pb-Pb system is useful as a longer relative chronometer. It yields absolute ages tPb using 207Pb/206Pb, 206Pb/204Pb, and 238U/235U ratios measured indifferent portions of a sample, assuming certain half-lives [4]. These absolute ages are uncertain to within ±9 Myr due to uncertainties in the 235U half-life[3], but times of formation ΔtPb= tCAI–tPb relative to t=0, are more precise(±0.5Myr),iftCAI can be found. Here, tCAI means the Pb-Pb age that would be measured in CAIs using the half-lives the community typically uses, if they achieved isotopic closure at t=0. Unfortunately, direct Pb-Pb dating of CAIs has not definitively determined tCAI. Based on four CAIs with canonical (26Al/27Al)0,[5,6] found tPb= 4567.30 ± 0.16 Myr. No other CAI ages with measured 238U/235U have been reported in the refereed literature, but there are hints of other CAIs with ages tPb= 4568.0 ± 0.2 Myr [7] and tPb= 4568.3 ± 0.2 Myr [8].It is unclear whether anyof these igneous type B CAIs isotopically closed at t=0 or represents tCAI. Instead of measurements, we advocate finding tCAI by minimizing the discrepancies between the Al-Mg and Pb-Pb chronometers. Assuming Δt26=ΔtPb, we find the implied t’CAI= tPb+Δt26, then define t*CAIas the weighted mean of the t’CAI. t*CAIis the best guess for the Pb-Pb age of t=0; the assumption of homogeneity is justified if the t’CAI cluster within errors around t*CAI. This statistical approach is similar to, but improves on, that of[9]. We find t*CAI= 4568.73 ± 0.16 Myr. Below we discuss our methodology and the implications of this age for CAIs, 1.4 Myr older than the reported and typically used age 4567.30±0.16 Myr. Methods: We base our estimate of t*CAIon five achondrites for which published (26Al/27Al)0and Pb-Pb ages exist: the quenched angrites D’Orbigny, Sahara 99555 (SAH 99555), and Northwest Africa (NWA) 1670; the pseudo-eucrite Asuka 881394; and the inner disk achondrite. All are “NC” (non-carbonaceous) achondrites that likely cooled quickly enough that the Al-Mg and Pb-Pb systems achieved isotopic closure simultaneously. We also considered the “CC” (carbonaceous chondrite-like) achondrites NWA 2796 and NWA 6704, butdo not include them in our fit. Al-Mg and Pb-Pb seem not to have closed simultaneously, possibly because formation in the outer disk from volatile-rich composition led to slower cooling. Of the 8 chondrules from NWA 5697 measured by [20,21], we also consider the 4 for which 238U/235U was measured: 2-C1, 5-C2, 3-C5, 11-C1.Depending on their post-formation thermal histories, the Al-Mg and Pb-Pb systems in chondrules may or may not have closed simultaneously. Table 1: (26Al/27Al)0, Pb-Pb ages of selected samplesSample(26Al/27Al)0/ 10-6RefPb-PbRefD’Orbigny3.98±0.15104563.43±0.19♮10-12SAH 995553.64±0.18104563.88±0.2712NWA16705.92±0.59104564.39±0.24*10Asuka 88139413.1±0.5613-154564.98±0.1715NWA 73253.03±0.14164563.4±2.616NWA 27963.94±0.16174562.89±0.5917NWA 67043.03±0.14184562.76±0.26192-C17.56±1.53204567.57±0.56*215-C27.04±1.51204567.54±0.52*213-C58.85±1.83204566.20±0.63*2111-C15.55±1.84204565.84±0.72*21*regression based on one subset of data points ♮weighted mean of two datasets Pb-Pb ages are proportional to the intercept of the line formed by linear regression of 207Pb/206Pb vs. 206Pb/204Pb data from various washes, leachates and residues of acid dissolution of a sample. Because contamination by terrestrial or primordial Pb is pervasive, some fractions must be excluded from regressions to ensure a fit with acceptable mean squares weighted deviation (MSWD). Usually points are excluded based on low [Pb], or low 206Pb/204Pb ratio(low radiogenic component), with single outliers identified [11,12,15,16,17]. In the starred examples (Table 1)and the case of 3 CAI Pb-Pb ages [5], up to half the points were excluded solely because did not fit a pre-determined line. This approach is vulnerable to confirmation bias and produces fits with low MSWD and too-low Pb-Pb age uncertainty. Regressing the same data points as [10], were produce the Pb-Pb age of NWA 1670 of 4564.39±0.24 Myr. But selecting other combinations of data points, other, equally valid, isochrons yield ages from 4563.77±0.21 Myr to 4564.64±0.23 Myr. Similar arguments apply to the Pb-Pb isochrons built by [21] for chondrules 2-C1 (we find 4567.33±0.44 to 4567.85±0.46 Myr), 5-C2 (4566.84±0.53 to 4567.70±0.44 Myr), 3-C5 (4565.84±0.54to 4567.04±0.54)and 11-C1 (4565.36±0.51 to 4565.74±0.45 Myr). Our adopted ages for these and NWA 1670 are listed in Table 2.Table 2. tCAI estimated from various components, using our regressions for the chondrules & NWA 1670.SampleΔt26(Myr)tPb(Myr)t’CAI(Myr)D’Orbigny5.05±0.044563.43±0.194568.48±0.19SAH 995555.14±0.054563.88±0.274569.02±0.27NWA16704.64±0.104564.21±0.634568.85±0.67Asuka 8813943.81±0.044564.98±0.174568.79±0.17NWA 73255.33±0.054563.4±2.64568.7±2.6NWA 27965.06±0.044562.89±0.594567.95±0.59NWA 67045.29±0.134562.76±0.264568.05±0.292-C12.00±0.214567.59±0.704569.59±0.725-C22.07±0.224567.23±0.914569.30±0.933-C51.84±0.214566.44±1.124568.28±1.1411-C12.32±0.344565.52±0.664567.84±0.73achondrite4568.72±0.16chondrules4568.76±0.58combined4568.73±0.16A weighted average of the five NC achondrites(or just D’Orbigny, SAH 99555 and Asuka 881394)yields t*CAI= 4568.72 ± 0.16Myr. All are consistent with this value to within 1.8σ, and MSWD=1.5. Including the 4 U-corrected chondrules, t*CAI= 4568.73± 0.16Myrwith MSWD=1.66, which is statistically significant. All chondrules and NC achondrites are consistent with this to within 1.8σ, (Figure 1).Figure 1. Al-Mg formation times after t=0 vs. Pb-Pb ages. The five NC achondrites and four chondrules are consistent with a Pb-Pb age of t=0 of 4568.7 Myr. Discussion: The data from achondrites and chondrules are consistent with a single Pb-Pb age at t=0, justifying the assumption of 26Al homogeneity. The age, 4568.7 Myr, is ≈1.4 Myr older than the commonly accepted Pb-Pb age of CAIs that formed with canonical 26Al/27Al at t=0 [3]. Others have interpreted the discrepancy to signify 26Al heterogeneity in the CAI-forming region[5,21]. We suggest instead that CAIs were exposed to transient heating events that reset the Pb-Pb system without disturbing the Al-Mg system. Notably, chondrules typically experienced transient heating at these times in the nebula [22]. If so, direct measurements of CAIs will not yield as reliable a Pb-Pb age of t=0 as statistical approaches like this and that of [9].References:[1] Auer et al. 2009. [2] Jacobsen, B et al. 2008, EPSL 272, 353-364. [3] Tissot, Fet al. 2017, GCA 213, 593-617.[4] Villa, I et al. 2016, GCA 172, 387-392.[5] Amelin, Y et al. 2010, EPSL 300, 343-350.[6] Connelly, Jet al. 2012, Science 338, 651.[7] Bouvier, Aet al. 2011, LPICo 1639, 9054. [8] Bouvier, Aand Wadhwa, M2010, Nat Geosci 3, 637-641.[9] Nyquist, Let al. 2009, GCA 73, 5115-5136.[10] Schiller et al. 2015. [11] Wadhwa & Brennecka 2012. [12] Tissot et al. 2017. [13] Nyquist et al. 2003. [14] Wadhwa et al. 2009. [15] Wimpenny et al. 2019, GCA 244, 478-501.[16] Koefoed et al. 2016, GCA 183, 31-45. [17] Bouvier, A et al. 2011, GCA 75, 5310-5323. [18] Sanborn, Met al. 2019, GCA 245, 577-596. [19] Amelin, Yet al. 2019, GCA 245, 628-642.[20] Bollard, Jet al. 2017, Sci Adv 3 ,e1700407. [21] Bollard, Jet al. 2019, GCA 260, 62-83.[22] Villeneuve, J et al. 2009, Science 325, 985

S. J. Desch↗

NASA NTRS2008

Fomalhaut's Debris Disk and Planet: Constraining the Mass of Formalhaut B from Disk Morphology

Following the optical imaging of exoplanet candidate Fomalhaut b (Fom b), we present a numerical model of how Fomalhaut's debris disk is gravitationally shaped by a single interior planet. The model is simple, adaptable to other debris disks, and can be extended to accommodate multiple planets. If Fom b is the dominant perturber of the belt, then to produce the observed disk morphology it must have a mass M(sub pl) < 3M(sub J), an orbital semimajor axis a(sub pl) > 101.5AU, and an orbital eccentricity e(sub pl) = 0.11 - 0.13. These conclusions are independent of Fom b's photometry. To not disrupt the disk, a greater mass for Fom b demands a smaller orbit farther removed from the disk; thus, future astrometric measurement of Fom b's orbit, combined with our model of planet-disk interaction, can be used to determine the mass more precisely. The inner edge of the debris disk at a approximately equals 133AU lies at the periphery of Fom b's chaotic zone, and the mean disk eccentricity of e approximately equals 0.11 is secularly forced by the planet, supporting predictions made prior to the discovery of Fom b. However, previous mass constraints based on disk morphology rely on several oversimplifications. We explain why our constraint is more reliable. It is based on a global model of the disk that is not restricted to the planet's chaotic zone boundary. Moreover, we screen disk parent bodies for dynamical stability over the system age of approximately 100 Myr, and model them separately from their dust grain progeny; the latter's orbits are strongly affected by radiation pressure and their lifetimes are limited to approximately 0.1 Myr by destructive grain-grain collisions. The single planet model predicts that planet and disk orbits be apsidally aligned. Fomalhaut b's nominal space velocity does not bear this out, but the astrometric uncertainties are difficult to quantify. Even if the apsidal misalignment proves real, our calculated upper mass limit of 3 M(sub J) still holds. Parent bodies are evacuated from mean-motion resonances with Fom b; these empty resonances are akin to the Kirkwood gaps opened by Jupiter. The belt contains at least 3M(sub Earth) of solids that are grinding down to dust, their velocity dispersions stirred so strongly by Fom b that collisions are destructive. Such a large mass in solids is consistent with Fom b having formed in situ.

Chiang, E.↗

NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗