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NASA NTRS2024

Implementation of a First-Order Quadratic Program Solver in C

This paper details a translation of a first order quadratic program (QP) solver from MATLAB to C. NASA could use this QP solver to generate online flight path trajectories for powered descent vehicles during landing. Over 12 weeks, the team designed, implemented, and tested two iterations of the QP solver for accuracy and runtime on 104 benchmark QP tests. The final iteration was 541.07% faster than the first, handling most tests in under one second. Additionally, it solved four more QP tests for N≥1383, and all outputs for cost and D_x matched the MATLAB reference values.

Optimization↗
NASA NTRSDate not supplied

Benchmarking Computational Tools for Calling SNPs and Indels in Complex Microbial Populations

The NASA BioNutrients missions seek to understand the suitability of microorganisms for bioproduction during space flight. One topic of interest is the stability of microbial genomes during long-term ambient storage and subsequent rehydration and growth. To address these questions, samples from 8 species were flown to ISS for 5 years of desiccated storage at ambient temperature (Stasis Packs) and 2 species were packaged along with powdered media inside a bioreactor system to allow hydration and growth in microgravity (Production Packs). For both systems, Whole Genome Sequencing (WGS) of the DNA extracted from the returned samples and paired ground controls will be conducted to identify changes in genome stability due to time, storage conditions and growth in space. Across the technical replicates, ground controls, 10 timepoints, and multiple experimental conditions, ~300 samples have been selected for initial analysis with WGS sequencing to 100x coverage. A flexible and resource efficient mutation calling pipeline is needed to process this large dataset and allow for comparisons between species. Many bioinformatics tools for calling Indels and Single Nucleotide Variants (SNVs) are designed for use with pure isolates, where true variations from the reference genome are expected to dominate the reads aligning to the location of mutation. In contrast, DNA from the Stasis Pack (SP) samples was collected directly after recovery from desiccated storage and the Production Pack (PP) samples were collected after fermentation. In this context, reads with mutations are expected to be less frequent than reads that align with the reference genome, as each sample will include multiple lines of cells. Thus, BioNutrients samples are expected to be similar to samples from cancer cell or “pooled” sequencing approaches. In preparation for the analysis of the BioNutrients samples, we have tested three mutation calling tools (GATK for Microbes, BreSeq and DiscoSNP) designed for complex samples. A challenge of validating mutation identification pipelines is a lack of “Ground Truth” datasets, especially for complex samples. To compare these three tools, we sought to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO as part of the Space Algae-1 mission. Here we present a summary of these tools against the analysis originally conducted using the CRISP tool. Critical metrics are compared such as runtime, the number of SNPs, the number and size of Indels, and patterns of transversion and transitions identified by each tool are reported. By sharing these benchmarking results collected in support of the BioNutrients mission, we aim to guide others seeking to identify SNVs in similarly complex microbial samples.

Biology↗
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↗
NASA NTRS2025

Robust Trajectory Optimization for NRHO Rendezvous Using SPICE Kernel Relative Motion

In this paper, robust optimization is performed on trajectory correction maneuvers during the lunar lander return phase of an Artemis mission, treating the trajectory from one hour after low lunar orbit departure to arrival in the vicinity of the lunar Gateway as a relative motion problem. To enable rapid stochastic optimization techniques requiring many candidate trajectories, SPICE kernel relative motion as implemented by the Quadratic Interpolated State Transition (QIST) system is used as the underlying dynamics propagation. The optimization is performed with a genetic optimizer using linear covariance (LinCov) software in a simplified operational context, taking into account the availability of navigation sensors with varying measurement models, ranges, and accuracies. No numerical integration is used, since the relative motion around Gateway is fully characterized with the a priori computation of the QIST coefficients. Maneuver placements are computed to optimize the minimum 3σ delta-v of the trajectory, the position dispersion at a target point, and a convex combination of these two metrics. An order of magnitude runtime improvement is provided over legacy methods with less than 10% error introduced. All QIST results are shown to be in-family with legacy methods. The tradespace for optimal delta-v design is found to range from 77.0 to 93.9 m/s, while the range of optimal dispersion is between 1.4 and 11.7 km.

Relative Motion↗
NASA NTRSDate not supplied

Assembly and Integration Status of a High Fidelity Ground Test Bed for the Water Processor Assembly

The Water Recovery System (WRS) is a critical component of life support aboard the International Space Station (ISS) and will play an essential role in future missions beyond Low Earth Orbit (LEO). Its primary functional units – the Urine Processor Assembly (UPA), Brine Processor Assembly (BPA), and Water Processor Assembly (WPA) – must be evaluated for extended operation, dormancy resilience, material obsolescence, and reliability under exploration-driven constraints. Ground testing is vital for developing these technologies and generating statistically relevant reliability assessments, which requires extended runtime under integrated, Flight-like conditions. Currently, no high-fidelity, fully integrated WPA ground test bed exists to support these objectives. To address this gap, NASA is developing a WPA test bed at Marshall Space Flight Center (MSFC) that combines downgraded ISS flight hardware with functionally flight-like components in a cost-effective configuration while maintaining priority hardware investigations. This paper describes the current status of hardware assembly and integration, outlines key challenges such as simulating microgravity effects and mitigating obsolescence, and presents future test objectives including software development, reliability assessments, dormancy studies, and exploration-oriented upgrades.

Mary-Elizabeth Davis↗
NASA NTRSDate not supplied

Assembly and Integration Status of a High Fidelity Ground Test Bed for the Water Processor Assembly

The Water Recovery System (WRS) is a critical component of life support aboard the International Space Station (ISS) and will play an essential role in future missions beyond Low Earth Orbit (LEO). Its primary functional units – the Urine Processor Assembly (UPA), Brine Processor Assembly (BPA), and Water Processor Assembly (WPA) – must be evaluated for extended operation, dormancy resilience, material obsolescence, and reliability under exploration-driven constraints. Ground testing is vital for developing these technologies and generating statistically relevant reliability assessments, which requires extended runtime under integrated, Flight-like conditions. Currently, no high-fidelity, fully integrated WPA ground test bed exists to support these objectives. To address this gap, NASA is developing a WPA test bed at Marshall Space Flight Center (MSFC) that combines downgraded ISS flight hardware with functionally flight-like components in a cost-effective configuration while maintaining priority hardware investigations. This paper describes the current status of hardware assembly and integration, outlines key challenges such as simulating microgravity effects and mitigating obsolescence, and presents future test objectives including software development, reliability assessments, dormancy studies, and exploration-oriented upgrades.

Water Processor Assembly↗
NASA NTRS2013

Maintaining the Health of Software Monitors

Software health management (SWHM) techniques complement the rigorous verification and validation processes that are applied to safety-critical systems prior to their deployment. These techniques are used to monitor deployed software in its execution environment, serving as the last line of defense against the effects of a critical fault. SWHM monitors use information from the specification and implementation of the monitored software to detect violations, predict possible failures, and help the system recover from faults. Changes to the monitored software, such as adding new functionality or fixing defects, therefore, have the potential to impact the correctness of both the monitored software and the SWHM monitor. In this work, we describe how the results of a software change impact analysis technique, Directed Incremental Symbolic Execution (DiSE), can be applied to monitored software to identify the potential impact of the changes on the SWHM monitor software. The results of DiSE can then be used by other analysis techniques, e.g., testing, debugging, to help preserve and improve the integrity of the SWHM monitor as the monitored software evolves.

Runtime Monitor↗
NASA NTRS2014

Monitoring with Data Automata

We present a form of automaton, referred to as data automata, suited for monitoring sequences of data-carrying events, for example emitted by an executing software system. This form of automata allows states to be parameterized with data, forming named records, which are stored in an efficiently indexed data structure, a form of database. This very explicit approach differs from other automaton-based monitoring approaches. Data automata are also characterized by allowing transition conditions to refer to other parameterized states, and by allowing transitions sequences. The presented automaton concept is inspired by rule-based systems, especially the Rete algorithm, which is one of the well-established algorithms for executing rule-based systems. We present an optimized external DSL for data automata, as well as a comparable unoptimized internal DSL (API) in the Scala programming language, in order to compare the two solutions. An evaluation compares these two solutions to several other monitoring systems.

log analysis↗
NASA NTRSDate not supplied

Establishing the Assurance Efficacy of Automated Risk Mitigation Strategies

Verification and validation of increasingly autonomous aviation systems is a major challenge. Traditional techniques for the assurance of high-confidence, safety-critical systems are not equipped to handle the complexity, uncertainty, and lack of predictability inherent in non-deterministic systems. Techniques such as run time monitoring, formal methods, and testing and simulation have been applied to some effect, but it is difficult to properly assess the success of such measures. The authors propose the concept of Assurance Efficacy to address this gap. Assurance Efficacy is seen as a parameter, criteria, or perspective by which to evaluate, identify and explore safety risk mitigation strategies and operational assurance architectures. Validation of the utility of this concept through flight testing is a first step in determining its potential role in assessing the overall safety of complex, increasingly autonomous systems that cannot be fully assured in the design phase.

system safety↗
NASA NTRSDate not supplied

Dynamic Assurance of Autonomous Systems through Ground Control Software∗

Assurance cases are being increasingly acknowledged as a way to build trust in complex systems with autonomous capabilities [1]. An assurance case is a comprehensive, defensible, and valid justification that a system will function as intended for the specific mission and operating environment. Such justifications for systems with autonomous capabilities are often based on various probabilistic quantifications [2]. Due to the dynamic nature of the environmental conditions in which these systems operate, as well as the changing nature of the autonomous systems themselves, these probabilistic quantifications cannot be simply estimated once during design time. Rather, they need to be continually evaluated during systems operations to ensure that the assurance case justifications are valid. We refer to the assurance case that combines both the static and dynamic elements as a Dynamic Assurance Case (DAC).

dynamic assurance case↗
NASA NTRS2023

Wildfire-fighting Use Case Requirements to Monitor

In this technical report, we provide requirements for a wildfire-fighting use-case, towards the Safety Demonstrator 1. The use case will incorporate ground and airborne assets operating in a coordinated fashion, and will comprise five activities, from detection to the execution of the initial attack. Depending on the activity and the data involved, the requirements identified may be non-probabilistic or probabilistic. In both cases, we first identify some of the requirements we wish to monitor, and then present a formalization using the language of requirements of the NASA requirements elicitation tool FRET. To formalize probabilistic requirements, we use a novel extension to FRET’s requirements language that incorporates notions of probability, and discuss how requirements can be translated into existing probabilistic temporal logics like PCTL. We exemplify how some of the requirements presented can be monitored using the existing tools Ogma and Copilot. We close with a summary and future directions.

Requirements↗
NASA NTRSDate not supplied

Dynamic Assurance of Autonomous Systems Through Ground Control Software

Assurance cases have emerged as a way to build trust in complex autonomous systems. Many assurance case justifications for such systems need to be constantly reevaluated based on the current system context and performance. Autonomous systems, especially those deployed in remote environments, often have a ground control system that enables monitoring and remote operations. In this paper, we propose a dynamic assurance framework that aims at connecting the assurance case with the ground control system. We use the ground control system to facilitate dynamic evaluation of quantitative assurance measures that support various justifications in the assurance case. We demonstrate the proposed dynamic assurance framework on the NASA Ames Research Center project Troupe. We use a combination of in-house and external tools to identify the assurance measures, formalize the related requirements, and generate monitors that feed the data to the external ground control system.

dynamic assurance case↗
NASA NTRSDate not supplied

Assuring and Securing Machine Learning

A short presentation highlighting using machine learning and topological data analysis to address the challenges of assuring and securing machine learning enabled systems.

Machine Learning↗