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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

A Verification Framework for Runtime Assurance of Autonomous UAS

Runtime Assurance (RTA) is a design-time architecture for safety-critical systems where an internal monitor acts upon detecting a violation of a property. The simplex architecture is an instance of RTA, where the action taken is to hand control of the overall system to a trusted controller when an untrusted one violates a safety property. Simplex RTA is emerging as a method for allowing AI/ML and other unverified software to be integrated into safety-critical applications like aircraft. To this end, the American Society for Testing and Materials (ASTM) and NASA have each published guidelines on the use of RTA in such systems. In the simplex RTA framework, a system has an advanced controller (AC) and a reversionary controller (RC). The system is allowed to operate with the AC until a runtime monitor detects that some property has been violated and then the RC takes over. Assuming that the sample rate of the monitor will detect improper functioning with enough time for the RC to correct the impending problem, and that the RC is trusted, the system will operate as intended. This use of the simplex RTA framework can allow for the integration of untrusted, but possibly more performant, controllers in a safe way. This paper presents a formalization of a simplex RTA framework in the Prototype Verification System (PVS) theorem prover using an embedding of differential dynamic logic (DDL) called Plaidypvs. A novel feature of this framework is that it can be instantiated at different levels of abstraction. This feature allows for the formal verification of a system with an untrusted black box component, such as an AI/ML controller. This paper does not address the many difficulties in deploying RTA in an industrial-level system. Instead, the focus is on the formal verification of the simplex RTA framework in the language of hybrid programs. Hybrid programs are programs that include both discrete and continuous dynamics and can be used to model complex cyber-physical systems. Plaidypvs is a tool that enables formalization of hybrid programs in the PVS theorem prover. Plaidypvs enables the verification of the general simplex RTA framework and then, by specializing some components of the hybrid program, verifying instances of the framework while treating the untrusted component as a black box. A selection of Unmanned Aircraft Systems (UAS) operations are shown as instances of the general RTA framework in PVS. This offers the benefit of design time verification of relevant safety properties to the system, and it also gives requirements on the sample rate of sensors that determine the time interval in which the ‘switch’ property of the RTA framework is checked.

PVS↗
NASA NTRS2007

SPARTAN: A High-Fidelity Simulation for Automated Rendezvous and Docking Applications

bd Systems (a subsidiary of SAIC) has developed the Simulation Package for Autonomous Rendezvous Test and ANalysis (SPARTAN), a high-fidelity on-orbit simulation featuring multiple six-degree-of-freedom (6DOF) vehicles. SPARTAN has been developed in a modular fashion in Matlab/Simulink to test next-generation automated rendezvous and docking guidance, navigation,and control algorithms for NASA's new Vision for Space Exploration. SPARTAN includes autonomous state-based mission manager algorithms responsible for sequencing the vehicle through various flight phases based on on-board sensor inputs and closed-loop guidance algorithms, including Lambert transfers, Clohessy-Wiltshire maneuvers, and glideslope approaches The guidance commands are implemented using an integrated translation and attitude control system to provide 6DOF control of each vehicle in the simulation. SPARTAN also includes high-fidelity representations of a variety of absolute and relative navigation sensors that maybe used for NASA missions, including radio frequency, lidar, and video-based rendezvous sensors. Proprietary navigation sensor fusion algorithms have been developed that allow the integration of these sensor measurements through an extended Kalman filter framework to create a single optimal estimate of the relative state of the vehicles. SPARTAN provides capability for Monte Carlo dispersion analysis, allowing for rigorous evaluation of the performance of the complete proposed AR&D system, including software, sensors, and mechanisms. SPARTAN also supports hardware-in-the-loop testing through conversion of the algorithms to C code using Real-Time Workshop in order to be hosted in a mission computer engineering development unit running an embedded real-time operating system. SPARTAN also contains both runtime TCP/IP socket interface and post-processing compatibility with bdStudio, a visualization tool developed by bd Systems, allowing for intuitive evaluation of simulation results. A description of the SPARTAN architecture and capabilities is provided, along with details on the models and algorithms utilized and results from representative missions.

Turbe, Michael A.↗
NASA NTRS2005

Reconfigurable Processing Module

To accommodate a wide spectrum of applications and technologies, NASA s Exploration System's Missions Directorate has called for reconfigurable and modular technologies to support future missions to the moon and Mars. In response, Langley Research Center is leading a program entitled Reconfigurable Scaleable Computing (RSC) that is centered on the development of FPGA-based computing resources in a stackable form factor. This paper details the architecture and implementation of the Reconfigurable Processing Module (RPM), which is the key element of the RSC system. The RPM is an FPGA-based, space-qualified printed circuit assembly leveraging terrestrial/commercial design standards into the space applications domain. The form factor is similar to, and backwards compatible with, the PCI-104 standard utilizing only the PCI interface. The size is expanded to accommodate the required functionality while still better than 30% smaller than a 3U CompactPCI(TradeMark)card and without the overhead of the backplane. The architecture is built around two FPGA devices, one hosting PCI and memory interfaces, and another hosting mission application resources; both of which are connected with a high-speed data bus. The PCI interface FPGA provides access via the PCI bus to onboard SDRAM, flash PROM, and the application resources; both configuration management as well as runtime interaction. The reconfigurable FPGA, referred to as the Application FPGA - or simply "the application" - is a radiation-tolerant Xilinx Virtex-4 FX60 hosting custom application specific logic or soft microprocessor IP. The RPM implements various SEE mitigation techniques including TMR, EDAC, and configuration scrubbing of the reconfigurable FPGA. Prototype hardware and formal modeling techniques are used to explore the performability trade space. These models provide a novel way to calculate quality-of-service performance measures while simultaneously considering fault-related behavior due to SEE soft errors.

Somervill, Kevin↗
NASA NTRSDate not supplied

Unsupervised Change Detection for Space Habitats Using 3D Point Clouds

This work presents an algorithm for scene change detection from point clouds to enable autonomous robotic caretaking in future space habitats. Autonomous robotic systems will help maintain future deep-space habitats, such as the Gateway space station, which will be uncrewed for extended periods. Existing scene analysis software used on the International Space Station (ISS) relies on manually-labeled images for detecting changes. In contrast, the algorithm presented in this work uses raw, unlabeled point clouds as inputs. The algorithm first applies modified Expectation-Maximization Gaussian Mixture Model (GMM) clustering to two input point clouds. It then performs change detection by comparing the GMMs using the Earth Mover’s Distance. The algorithm is validated quantitatively and qualitatively using a test dataset collected by an Astrobee robot in the NASA Ames Granite Lab comprising single frame depth images taken directly by Astrobee and full-scene reconstructed maps built with RGB-D and pose data from Astrobee. The runtimes of the approach are also analyzed in depth. The source code is publicly released to promote further development.

robotics↗
NASA NTRS2000

HTMT-class Latency Tolerant Parallel Architecture for Petaflops Scale Computation

Computational Aero Sciences and other numeric intensive computation disciplines demand computing throughputs substantially greater than the Teraflops scale systems only now becoming available. The related fields of fluids, structures, thermal, combustion, and dynamic controls are among the interdisciplinary areas that in combination with sufficient resolution and advanced adaptive techniques may force performance requirements towards Petaflops. This will be especially true for compute intensive models such as Navier-Stokes are or when such system models are only part of a larger design optimization computation involving many design points. Yet recent experience with conventional MPP configurations comprising commodity processing and memory components has shown that larger scale frequently results in higher programming difficulty and lower system efficiency. While important advances in system software and algorithms techniques have had some impact on efficiency and programmability for certain classes of problems, in general it is unlikely that software alone will resolve the challenges to higher scalability. As in the past, future generations of high-end computers may require a combination of hardware architecture and system software advances to enable efficient operation at a Petaflops level. The NASA led HTMT project has engaged the talents of a broad interdisciplinary team to develop a new strategy in high-end system architecture to deliver petaflops scale computing in the 2004/5 timeframe. The Hybrid-Technology, MultiThreaded parallel computer architecture incorporates several advanced technologies in combination with an innovative dynamic adaptive scheduling mechanism to provide unprecedented performance and efficiency within practical constraints of cost, complexity, and power consumption. The emerging superconductor Rapid Single Flux Quantum electronics can operate at 100 GHz (the record is 770 GHz) and one percent of the power required by convention semiconductor logic. Wave Division Multiplexing optical communications can approach a peak per fiber bandwidth of 1 Tbps and the new Data Vortex network topology employing this technology can connect tens of thousands of ports providing a bi-section bandwidth on the order of a Petabyte per second with latencies well below 100 nanoseconds, even under heavy loads. Processor-in-Memory (PIM) technology combines logic and memory on the same chip exposing the internal bandwidth of the memory row buffers at low latency. And holographic storage photorefractive storage technologies provide high-density memory with access a thousand times faster than conventional disk technologies. Together these technologies enable a new class of shared memory system architecture with a peak performance in the range of a Petaflops but size and power requirements comparable to today's largest Teraflops scale systems. To achieve high-sustained performance, HTMT combines an advanced multithreading processor architecture with a memory-driven coarse-grained latency management strategy called "percolation", yielding high efficiency while reducing the much of the parallel programming burden. This paper will present the basic system architecture characteristics made possible through this series of advanced technologies and then give a detailed description of the new percolation approach to runtime latency management.

Sterling, Thomas↗
NASA NTRSDate not supplied

Verifying PLC Programs via Monitors: Extending the Integration of FRET and PLCverif

Verification of Programmable Logic Controller (PLC) programs requires reasoning about propositions qualified in terms of time. CERN’s PLCverif, an open-source tool for the analysis of safety-critical PLC systems, uses Linear Temporal Logic (LTL) for the specification of properties. Until now, PLCverif depended on third-party tools that accept LTL specifications to perform verification. However, our experience with industrial PLC programs shows that, to overcome analysis limitations, a wide range of techniques are needed to successfully verify complex properties. In this paper, we extend PLCverif to enable PLC program verification of pure-past LTL (PLTL) safety properties with assertion-based verification tools. To this end, we take an algorithm from the runtime-monitoring domain, apply it to bounded model checking of PLC programs, and implement it in PLCverif. We extend the integration of NASA’s Formal Requirements Elicitation Tool (FRET) into PLCverif to use PLTL properties generated with FRET. In addition, we leverage the program structure induced by the PLC scan-cycle for a state-space reduction. Finally, we expose the algorithm to a real-world case study of critical systems at CERN.

Formal verification↗
NASA NTRS2021

Concurrent Runtime Verification of Data Rich Events

This paper presents the open source runtime verification tool MESA (MEssage-based System Analysis), implemented in Scala, which supports concurrent monitors using the Actor model. Furthermore, the tool supports indexing (slicing) on the data values occurring in data-carrying events, for each individual monitor. The tool is generic in the sense that any monitoring system can be used for creating monitors. In this paper, we use the internal Scala DSL Daut for programming such in data parameterized state machines and temporal logic. To illustrate MESA/Daut, we present a case study that monitors flights from live U.S. airspace data streams, verifying that they conform to planned routes. With base in the case study, we then perform an extensive empirical study of the potential benefits from monitoring slices of a single property in concurrently executing actors. Due to the overhead of scheduling “small” actors (one for each slice or a small number of slices), it is not obvious that concurrent execution of such is beneficial. However, as a main result, we demonstrate that concurrent monitoring of slices to handle data-carrying events can provide considerable speed gains.

finite state machines↗
NASA NTRSDate not supplied

Initial Evaluation of CropManage Decision-Support Model for Vineyard ET Estimation

The CropManage(CM) decision-support web application was originally developed by U.C. Cooperative Extension to support evapotranspiration (ET) based irrigation scheduling and nutrient management of cool-season vegetables. The model uses prescribed crop phenology curves to develop daily estimates of fractional green canopy cover (Fc) within the field. Periodic Fc observations acquired by ground-based methods or imported from NASA’s Satellite Irrigation Management Support (SIMS) can be used to adjust the prescribed timeseries for such factors as weather anomalies or non-standard agronomic practice, as needed. Fc is then converted to daily crop coefficient (fraction of reference ET) values. The crop coefficient is combined with reference evapotranspiration, collected by the California Irrigation Management Information System, to derive daily ET estimates for the given field. Irrigation runtime recommendations are issued for a given date based on total ET since the last irrigation event (less any rainfall), and corrected for distribution uniformity of the water delivery system. In this project, CM was adapted to vineyards by adding sub-models to account for early-season depletion of stored soil moisture, cover crop presence, and intentional water stress. An initial trial was performed on a Central Coast winegrape vineyard, where an eddy covariance tower measured daily ET during from May-Dec 2020. Total ET for the period showed strong agreement between the tower-based measurements (443 mm) and the CM model (431 mm). The model tended to overestimate cumulative ET during mid-June by up to 30 mm (about 15%) and later underestimated cumulative ET by as much as 65 mm (about 23%) in late September, suggesting that additional model calibration is needed to improve simulation of within-season variability. Results will be reported for trials on additional vineyard sites conducted during the 2021 season.

Evaluation↗
NASA NTRS2023

Launch Complex 34, SWMU CCO542022 DNAPL Source Zone Operations, Maintenance, and Monitoring, Site-Wide Long-Term Monitoring, and Hot Spot 6 Air Sparge System Annual Performance Monitoring Report Cape Canaveral Space Force Station, Florida

This Annual Performance Monitoring Report (PMR) for the Dense Non-Aqueous Phase Liquid (DNAPL) Source Zone (DSZ), Site-Wide Long-Term Monitoring (LTM), and Hot Spot (HS) 6 Air Sparge (AS) System presents the results of Year 13 operation of the hydraulic containment (HC) Interim Measure (IM), the results of performance monitoring direct-push technology (DPT) sampling and monitoring well sampling conducted in the DSZ, results of the biennial site-wide LTM event, and the results of operations and performance sampling of the HS 6 AS IM at Launch Complex 34 (LC34), located at Cape Canaveral Space Force Station (CCSFS), Florida. This site has been designated Solid Waste Management Unit (SWMU) CC054 under the Kennedy Space Center (KSC) Resource Conservation and Recovery Act (RCRA) Corrective Action Program. For the site-wide biennial LTM event, a total of 55 monitoring wells were sampled for volatile organic compounds (VOCs) in February 2023 and one well was sampled for polychlorinated biphenyls (PCBs) in December 2022. One well planned for VOC sampling was found to be destroyed and could not be sampled (CW0002). The LTM wells are screened in two lithologic zones: Layer 1 (0 to 25 feet below land surface [bls]) and Layer 2 (25 to 30 ft bls), and are located in the outlying areas of LC34 to monitor groundwater conditions within the Low-Concentration Plume (LCP), defined as concentrations exceeding Groundwater Cleanup Target Levels (GCTLs), and the High-Concentration Plume (HCP), defined as concentrations exceeding Natural Attenuation Default Concentrations (NADCs). Results from the biennial sampling event indicated overall plume stability and delineation for the plume, which extends over 300 acres. The operational period for Year 13 of the HCS was from April 1, 2022 to March 31, 2023. Operational runtime for the system was 90 percent during Year 13, with downtime events attributed to planned maintenance, system repairs, and power outages. As of March 31, 2023, a total of 313,673,241 cumulative gallons of groundwater containing 88,337 pounds of VOCs have been removed by the HCS. Influent concentrations of trichloroethene (TCE) have decreased since startup from approximately 280,000 μg/L (January 2010) to 12,000 μg/L (March 2023). During the reporting period, all effluent concentrations from the HCS (aqueous and vapor) were below regulatory reporting limits, indicating the system continues to operate as intended. Performance monitoring was conducted in December 2022 within the DSZ to evaluate TCE contamination. Groundwater samples were collected via DPT at nine locations, consistent with previous events between 2017 and 2021. Full vertical profile sampling was completed at each DPT from 8 to 98 feet bls, at 5-foot intervals. The DPT performance monitoring results are summarized in this PMR. The results revealed TCE remains at concentrations greater than 11,000 μg/L in the DSZ (1-percent solubility, indicative of DNAPL) at eight of the nine DPT locations and at depths ranging from 8 to 98 feet bls. In addition to DPT sampling, groundwater samples were collected from deep monitoring wells in the DSZ area (Layers 7 and 8) in December 2022 to verify vertical delineation. Layer 7/8 monitoring well results were non-detect in December 2022, with exception of three wells (IW0162, IW043D2, and IW044D2), where TCE, cis-1,2-dichloroethene (cDCE), and/or vinyl chloride (VC) were detected above GCTLs. These wells are screened 105 to 115 feet bls, which is below the existing recovery well capture zone. TCE was first detected in IW0162 in December 2021 and has since been sampled at least monthly to monitor TCE concentrations. The maximum TCE concentration during this operational period was 15,000 μg/L at IW0162 in March 2023. Because of the increased TCE concentrations in this well, a new recovery well (RW21D), screened 86 to 106 feet bls, was installed in January 2023 and incorporated into existing HCS operations. The HS 6 AS IM was initiated in 2018 with 160 AS wells, and expanded in 2019 with an additional 140 AS wells. Quarterly performance monitoring was reduced to semi-annual in 2020. The HS 6 AS system remained operational during the reporting period covered under this report. The results of the HS 6 system operation and semi-annual performance monitoring are summarized in this report. Semi-annual monitoring results collected in April and October 2022 show concentrations of contaminants of concern (cDCE, trans-1,2-dichloroethene, and vinyl chloride) are generally decreasing and not impacting the surface water drainage canal, indicating the HS 6 IM is meeting objectives. A Phase Two Expansion of the HS 6 AS IM was recently completed. As of the date of this report, the expansion became operational in August 2023 and the first quarter of monitoring was conducted in November 2023. Details of the construction, start-up, and performance monitoring will be included in a future Annual PMR. Overall, the tasks associated with Year 13 operation of the HC IM, operation of the HS 6 AS IM, and biennial site-wide sampling were performed in accordance with recommendations included in the previous 2021 LC34 (Year 12) annual report. Evaluation of results from the HC IM and HS 6 IM show that these systems are operating as designed and meeting performance objectives. Results from the site-wide biennial LTM program also show that the overall network of monitoring wells is adequate to continue monitoring plume-wide conditions.

Complex 34↗
NASA NTRS2018

Applications of the Dynamic N-Dimensional K-Vector

The n-dimensional k-vector (NDKV) is an appealing alternative to binary tress for resolving complex queries in large relational databases. The method has excelled in several applications involving static databases. The present paper extends the theory supporting the NDKV to handle dynamic databases, where the data is updated frequently. This includes deleting records, adding new entries, or editing existing elements. The merit of this new version of the NDKV, the dynamic n-dimensional k-vector (DNDKV), is that it is no longer necessary to recompute the entire k-vector (the main structure that indexes the data) every time a record changes. The algorithm updates the four constituents of the standard NDKV on the fly: the database, sorted database, index, and k-vector tables. As a result, the DNDKV becomes comparable in terms of capabilities and flexibility to stateof-the-art storage engines relying on structured query languages (SQL). The performance of the DNDKV is assessed by running typical read/write operations on a database that contains millions of pre-computed missions to celestial bodies. This database requires frequent updates whenever an orbit solution is refined or new bodies are discovered. The DNDKV is faster than rebuilding the k-vector tables completely, provided that the number of elements being added or removed is not excessively large. Direct runtime comparisons with MySQL suggest that the DNDKV is several times faster for reading but might be slower for writing and updating the database. One limit of the technique is the elements being added must be within the range of the current k-vector tables. If this is not the case, the technique cannot be used and the k-vector tables must be rebuilt from scratch.

Mortari, Daniele↗
NASA NTRS2022

Launch Complex 34, SWMU Cc054 2021 DNAPL Source Zone Operations, Maintenance, and Monitoring, and Hot Spot 6 Air Sparge System Annual Performance Monitoring Report Cape Canaveral Space Force Station, Florida

This Annual Performance Monitoring Report (PMR) for the Dense Non-Aqueous Phase Liquid (DNAPL) Source Zone (DSZ) and Hot Spot 6 (HS 6) Air Sparge (AS) System presents the results of Year 12 operation of the hydraulic containment (HC) Interim Measure (IM), the results of performance monitoring direct-push technology (DPT) sampling and monitoring well sampling conducted in the DSZ, and the results of operations and performance sampling of the HS 6 AS IM at Launch Complex 34 (LC34), located at Cape Canaveral Space Force Station (CCSFS), Florida. Site-wide biennial LTM sampling was not conducted during this reporting period and is scheduled to be conducted in December 2022. The timeframe for activities documented in this PMR extends from April 1, 2021 to March 31, 2022. LC34 has been designated Solid Waste Management Unit CC054 under the Kennedy Space Center (KSC) Resource Conservation and Recovery Act Corrective Action Program. The objective of the HC IM at LC34 is to contain the DSZ and deep dissolved-phase trichloroethene (TCE) high concentration plume via operation of a hydraulic containment system (HCS). The pre-IM design 300 micrograms per liter (μg/L) TCE groundwater contour was used to establish the deep zone capture area for deep recovery wells, and the shallow zone capture area was defined by the DSZ. The system began operating in 2010, and in 2015, the system was expanded to provide HC for areas within the 300 μg/L TCE groundwater isocontours of HS 3 and 4. In 2018 and 2019, an investigation was conducted to re-characterize the DSZ, which included investigating TCE mass in Layer 7. This data was subsequently used to optimize the pumping rates of the HCS to more adequately capture residual contaminant mass. The operational period for Year 12 of the HCS was from April 1, 2021 to March 31, 2022. Operational runtime for the system was 94 percent during Year 12, with downtime events attributed to planned maintenance, system repairs, and power outages. As of March 31, 2022, a total of 285,712,801 gallons of groundwater containing 84,933 pounds of VOCs have been removed by the HCS. Influent concentrations of TCE have decreased since startup from approximately 280,000 µg/L (January 2010) to 12,000 µg/L (March 2022). During the reporting period, all effluent concentrations from the HCS (aqueous and vapor) were below regulatory reporting limits, indicating the system continues to operate as intended. Performance monitoring was conducted in December 2021 within the DSZ to evaluate TCE contamination. Groundwater samples were collected via DPT at nine locations, consistent with previous events in 2017, 2018, 2019, and 2020. Full vertical profile sampling was completed at each DPT from 8 to 98 ft bls, at 5 foot intervals. The DPT performance monitoring results are summarized in this PMR. The results revealed TCE remains at concentrations greater than 11,000 µg/L in the DSZ (1-percent solubility, indicative of DNAPL) at eight of the nine DPT locations at depths ranging from 8 to 98 ft bls. An overall increasing trend of TCE concentrations was observed in DPT samples during this reporting period, which may be due to several recovery wells that were turned off during the AS Pilot Study in the DSZ that operated from July 2021 to February 2022 (documented separately from this report). The maximum TCE concentration in 2021 was 15,400,000 µg/L in the 58 ft bls depth interval at DPT597 (previous maximum result in 2020 was 1,690,000 at 48 ft bls at DPT596). During the 2021 DPT event, the overall majority of TCE contamination was identified in the 58 ft bls interval (below Layer 4), where in the previous year the majority of mass was observed in Layer 4. This trend appears to indicate continued mass discharge from Layer 4 (fine-grained unit). In addition to DPT sampling, monitoring well samples were collected from deep wells in the DSZ area (Layers 7 and 8) to verify vertical delineation. All monitoring well results were non-detect or below cleanup levels, with exception of one well (IW0162, screened 105 to 115 ft bls, which is below the existing recovery well capture zone) where TCE was identified above cleanup target levels. The HS 6 AS system remained operational during the reporting period covered under this report. The HS 6 AS IM was initiated in 2018 with 160 AS wells, and expanded in 2019 with an additional 140 AS wells. Quarterly performance monitoring was reduced to semi-annual prior to this operational period. The results of the HS 6 system operation and semi-annual performance monitoring are summarized in this report. Semi-annual monitoring results collected in April and October 2021 show concentrations of contaminants of concern (cis-1,2-dichloroethene, trans-1,2- dichloroethene, and vinyl chloride) are generally decreasing and not impacting the surface water drainage canal, indicating the HS 6 IM is meeting objectives. Overall, the tasks associated with Year 12 operation of the HC IM and operation of the HS 6 AS IM were performed in accordance with the recommendations of the 2020 LC34 (Year 11) Operations, Maintenance, and Monitoring Report for DNAPL Source Zone, Site Wide LongTerm Monitoring, and Hot Spot 6 Air Sparging System PMR (NASA, 2021c). Evaluation of results from the HC IM and HS 6 IM show that these systems are operating as designed and meeting performance objectives.

trichloroethene↗
NASA NTRS2018

Imaging for Hypersonic Experimental Aeroheating Testing (IHEAT) Version 4.0: User Manual

The IHEAT v4.0 software is a data reduction code for global thermography data acquired in the NASA Langley Aerothermodynamics Laboratory (LAL) hypersonic wind tunnels. IHEAT uses red and green color-intensity data from two-dimensional images of wind tunnel models to compute temperatures and heat-transfer rates using a semi-infinite, one-dimensional heat transfer approximation at each image pixel. Multiple automated tools in IHEAT v4.0 decrease the time required to reduce the data from a phosphor thermography wind tunnel run. Data at one or all of the image pixel locations can be exported to computer files for further analysis. The prior version of IHEAT, v3.2, was written in PV-WAVE® (now owned by Rogue Wave® Software) in 1994 and was limited in functionality to fit within the memory constraints of the available computers at the time. IHEAT v4.0 is written in MATLAB® by MathWorks® and contains several new features that leverage the increase in available memory of the current computers. A Piecewise tool permits the user to extract data along a segmented line cut that can follow interesting features in the image better than the single, straight line cuts that were possible with the legacy Length and Profile tools. The new Load Run and Batch tools facilitate batch processing by loading in all of the input files and images for a run at the same time. Load Run permits the user to process the available run images manually, while Batch automatically saves heat transfer data from all of the images based on the analysis previously performed on a single frame. IHEAT v4.0 also can automatically calculate the temporal collapse of reference line cuts from the time history heating data for a run to indicate the appropriate frame to reduce for each run. The IHEAT v4.0 source code was compiled into a standalone executable file that can be accessed remotely from several computers with different operating systems, simultaneously. The software is run through the MATLAB® Compiler Runtime engine, and therefore, IHEAT does not require a software license to run. Any software commands executed in the IHEAT v4.0 code will not affect other similar applications running on the same machine. Similarly, changes to the parent software do not affect a compiled code. These features of IHEAT v4.0 are improvements over the legacy v3.2 code, which required regular maintenance to avoid losing functionality as the PVWAVE ® programming language was upgraded.

Mason, Michelle L.↗
NASA NTRS2003

Experimental Evaluation of Verification and Validation Tools on Martian Rover Software

We report on a study to determine the maturity of different verification and validation technologies (V&V) on a representative example of NASA flight software. The study consisted of a controlled experiment where three technologies (static analysis, runtime analysis and model checking) were compared to traditional testing with respect to their ability to find seeded errors in a prototype Mars Rover. What makes this study unique is that it is the first (to the best of our knowledge) to do a controlled experiment to compare formal methods based tools to testing on a realistic industrial-size example where the emphasis was on collecting as much data on the performance of the tools and the participants as possible. The paper includes a description of the Rover code that was analyzed, the tools used as well as a detailed description of the experimental setup and the results. Due to the complexity of setting up the experiment, our results can not be generalized, but we believe it can still serve as a valuable point of reference for future studies of this kind. It did confirm the belief we had that advanced tools can outperform testing when trying to locate concurrency errors. Furthermore the results of the experiment inspired a novel framework for testing the next generation of the Rover.

Brat, Guillaume↗
NASA NTRSDate not supplied

CropManage Application for Vineyard Irrigation Decision-Support

CropManage is a free web-application developed by U.C. Cooperative Extension to support evapotranspiration based irrigation scheduling and nutrient management for major specialty crops. Prescribed phenology curves are used to develop daily estimates of canopy cover within a given field, based on days since planting (annual crops) or budbreak (trees, vines). These curves are modulated by a MaxCan parameter representing seasonal maximum canopy cover. Crop development observations can be used to adjust for such factors as weather anomalies or non-standard agronomic practice, as needed. Canopy cover is converted to crop coefficient and combined with reference evapotranspiration to derive daily water consumption. Guidance on crop water requirement is then conveyed to users in terms of system runtime issued on-demand for a given date, largely based on total evapotranspiration since last irrigation event. In this study, CropManage was adapted to vineyards by adding modules accounting for early-season soil moisture depletion and cover crop presence. A crop stress parameter was added to accommodate deficit irrigation practice, allowing the user to specify percentage departure from full water requirement along with start/stop dates. An initial verification exercise was performed on three winegrape vineyards located in California’s Central Coast (2020), North Coast (2020) and Central Valley (2019). Daily crop evapotranspiration was monitored by eddy-covariance fluxtowers. MaxCan was measured by ground and satellite observation. Stress regime was specified by grower practice where available, otherwise stress levels were inferred from applied water records. Mean absolute error and mean bias error of modeled cumulative evapotranspiration were computed with respect to the eddy covariance measurements collected throughout the growing season. Results indicate the modified CropManage water management module performs reasonably well for winegrape. Additional effort is planned to modify the nutrient module for vineyard use.

CropManage↗
NASA NTRS1992

Completable scheduling: An integrated approach to planning and scheduling

The planning problem has traditionally been treated separately from the scheduling problem. However, as more realistic domains are tackled, it becomes evident that the problem of deciding on an ordered set of tasks to achieve a set of goals cannot be treated independently of the problem of actually allocating resources to the tasks. Doing so would result in losing the robustness and flexibility needed to deal with imperfectly modeled domains. Completable scheduling is an approach which integrates the two problems by allowing an a priori planning module to defer particular planning decisions, and consequently the associated scheduling decisions, until execution time. This allows a completable scheduling system to maximize plan flexibility by allowing runtime information to be taken into consideration when making planning and scheduling decision. Furthermore, through the criteria of achievability placed on deferred decision, a completable scheduling system is able to retain much of the goal-directedness and guarantees of achievement afforded by a priori planning. The completable scheduling approach is further enhanced by the use of contingent explanation-based learning, which enables a completable scheduling system to learn general completable plans from example and improve its performance through experience. Initial experimental results show that completable scheduling outperforms classical scheduling as well as pure reactive scheduling in a simple scheduling domain.

Gervasio, Melinda T.↗
NASA NTRS1993

Runtime support and compilation methods for user-specified data distributions

This paper describes two new ideas by which an HPF compiler can deal with irregular computations effectively. The first mechanism invokes a user specified mapping procedure via a set of compiler directives. The directives allow use of program arrays to describe graph connectivity, spatial location of array elements, and computational load. The second mechanism is a simple conservative method that in many cases enables a compiler to recognize that it is possible to reuse previously computed information from inspectors (e.g. communication schedules, loop iteration partitions, information that associates off-processor data copies with on-processor buffer locations). We present performance results for these mechanisms from a Fortran 90D compiler implementation.

Ponnusamy, Ravi↗
NASA NTRS2013

Generalized Ocean Color Inversion Model for Retrieving Marine Inherent Optical Properties

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future ensemble applications.

Werdell, P. Jeremy↗
NASA NTRS2024

Launch Complex 34, SWMU CC054 2023 DNAPL Source Zone Operations, Maintenance, and Monitoring, Site-Wide Long-Term Monitoring, and Hot Spot 6 Air Sparge System Annual Performance Monitoring and Phase Two Expansion Construction Completion Report Cape Canaveral Space Force Station, Florida

This Annual Performance Monitoring Report (PMR) for the Dense Non-Aqueous Phase Liquid (DNAPL) Source Zone (DSZ), Site-Wide Long-Term Monitoring (LTM), and Hot Spot 6 (HS 6) Air Sparge (AS) System presents the results of Year 14 operations and performance monitoring of the hydraulic containment (HC) Interim Measure (IM), details associated with construction and implementation of the HS 6 AS system expansion (Phase Two), and the results of operations and performance sampling of the HS 6 AS IM at Launch Complex 34 (LC34), located at Cape Canaveral Space Force Station (CCSFS), Florida. The timeframe for activities documented in this PMR extends from April 1, 2023 to March 31, 2024. LC34 has been designated Solid Waste Management Unit CC054 under the Kennedy Space Center (KSC) Resource Conservation and Recovery Act Corrective Action Program. The objective of the HC IM at LC34 is to contain the shallow and deep DSZ and surrounding dissolved-phase trichloroethene (TCE) high concentration plume via operation of a hydraulic containment system (HCS). The pre-IM design 300 micrograms per liter (μg/L) TCE groundwater contour was used to establish the deep zone capture area for deep recovery wells, and the shallow zone capture area was defined by the DSZ. The system began operating in 2010, and in 2015, the system was expanded to provide HC for areas within the 300 μg/L TCE groundwater isocontours of HS 3 and 4. In 2018 and 2019, an investigation was conducted to recharacterize the DSZ, which included investigating TCE mass in Layer 7. This data was subsequently used to optimize the pumping rates of the HCS and install additional recovery wells in Layer 7 to more adequately capture residual contaminant mass. The operational period for Year 14 of the HCS was from April 1, 2023 to March 31, 2024. Operational runtime for the system was 94 percent during Year 14, with downtime events attributed to planned maintenance, system repairs, and power outages. As of March 31, 2024, a total of 344,849,634 cumulative gallons of groundwater containing 94,656 pounds of chlorinated volatile organic compounds (CVOCs) have been removed by the HCS. During the reporting period covered under this report, the HCS recovered 31,176,393 gallons and approximately 6,319 pounds of CVOC mass. Total combined influent concentrations of TCE have decreased since startup from approximately 280,000 µg/L (January 2010) to 25,000 µg/L (March 2024). During the reporting period, all effluent concentrations from the HCS (aqueous and vapor) were below regulatory reporting limits, indicating the system continues to operate as intended. Performance monitoring was conducted in January 2024 within the DSZ to evaluate TCE contamination. Groundwater samples were collected via DPT at nine locations, consistent with previous events between 2017 and 2022. Full vertical profile sampling was completed at each DPT from 8 to 98 feet below land surface (bls), at 5-foot intervals. The DPT performance monitoring results are summarized in this PMR. The results revealed TCE remains at concentrations greater than 11,000 µg/L in the DSZ (1-percent solubility, indicative of DNAPL) at eight of the nine DPT locations and at depths ranging from 28 to 98 feet bls. An overall decreasing trend of TCE concentrations was observed in DPT samples during this reporting period, which is a reduction from the previous event (December 2022) and the peak event in December 2021, where TCE percentages appeared to increase in all depth zones because several recovery wells were turned off during the AS pilot study in the DSZ. The maximum TCE concentration in January 2024 was 1,600,000 µg/L in the 53 feet bls depth interval at DPT594 (previous maximum result in 2022 was 1,800,000 µg/L in the 48 feet bls depth interval at DPT599). This maximum concentration in the 53 feet bls depth interval is in the deep capture zone. During the January 2024 DPT event, the largest portion of TCE mass was observed in the 48 feet bls interval above/within Layer 4. This trend remains consistent with previous years and appears to indicate continued mass discharge from Layer 4 (fine-grained unit). In addition to DPT sampling, annual groundwater samples were collected from 11 deep monitoring wells in the DSZ area (Layers 7 and 8) in December 2023 to verify vertical and horizontal delineation. Three of the wells were also sampled biweekly to evaluate operations of recovery well RW21D (screened 86 to 106 feet bls), which was installed in January 2023. Of the Layer 7/8 monitoring sampled only annually, results were non-detect or less than groundwater cleanup target levels GCTLs in December 2023, with the exception of one well, IW45D2, which had a cis-1,2-dichloroethene (cDCE), detection greater than the GCTL. Of the three wells sampled biweekly during the operational period, the well located closest to Layer 7 recovery well RW21D (IW44D2, screened 105 to 115 feet bls) had concentrations of TCE, cDCE and vinyl chloride (VC) greater than GCTLs throughout the operational period, but displayed a decreasing trend since the peak concentrations in September 2023. The maximum TCE concentration during this operational period was 190,000 µg/L at IW44D2 in September 2023, but reduced to 700 µg/L in March 2024, indicating the HCS is still effectively removing mass from the source area. Expansion of the HCS and addition of new recovery wells is ongoing and will continue to be evaluated as the groundwater recovery scheme is optimized. Details of the expansion and optimization will be provided in a future PMR. The HS 6 AS IM was initiated in 2018 with 160 AS wells and expanded in 2019 with another 140 AS wells. An additional expansion of the HS 6 AS IM was completed during the reporting period covered under this report and details of the construction implementation and startup of the expansion are detailed in Section III of this report. The new expansion, referred to as Phase Two, was implemented between August 17, 2022 and August 28, 2023, and included the installation of 190 air sparge wells to treat an additional 11.2 acres. The original configuration (referred to as Phase One) operated until Phase Two came online, then all but 52 AS wells were turned off so the components could be moved and utilized in the Phase Two area. The 52 AS wells that remain on are in a barrier configuration preventing contaminated groundwater from impacting the treated area. The HS 6 AS system (both Phase One and Two) operated normally during the reporting period covered under this report. Semi-annual performance monitoring of the Phase One configuration was conducted in April and November 2023, consistent with previous years. For the Phase Two configuration, 21 new monitoring wells were installed and sampled quarterly, with a baseline event in July 2023, and quarterly events in November 2023 and February 2024 summarized in this report. Semi-annual monitoring results collected in April and October 2023 show concentrations of contaminants of concern (cDCE, trans-1,2-dichloroethene, and VC) have decreased to less than GCTLs in nearly all wells and not impacting the surface water drainage canal, indicating the HS 6 IM continues to meet objectives. The baseline and quarterly sampling for the Phase Two configuration indicate generally decreasing concentrations in wells within and around the perimeter of the treatment area. At least two more quarters of monitoring will be conducted and once those results are evaluated a reduced the sampling frequency may be considered. Overall, the tasks associated with Year 14 operation of the HC IM and operation of the HS 6 AS IM were performed in accordance with recommendations included in the previous 2022 LC34 (Year 13) PMR. Evaluation of results from the HC IM and HS 6 IM show that these systems are operating as designed and meeting performance objectives.

groundwater remediation↗