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- Machine Learning (ML) has enabled models that can improve efficiency and decrease computational cost - ML models are crucial in enabling Integrated Computational Materials Engineering (ICME) - Large data sets require robust means of storing ML data and models
Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.
The International Space Station (ISS) is presently equipped with an elaborate, heavy, and high-power consuming system that recovers approximately 50% of O 2 from metabolic CO 2 as part of the atmospheric revitalization (AR) at the ISS habitat. Future long-duration missions will require a sustainable and efficient system capable of yielding a minimum of 75% O 2 recovery to reach the self-sufficiency required for long space missions beyond earth’s low orbit. A Macrofluidic Electrochemical Reactor (MFECR) technology development effort is currently underway at NASA Marshall Space Flight Center (MSFC) to not only increase significantly current O 2 recovery efficiency, improving self-sufficiency on AR at the ISS habitat and future long missions, but also reduce the complexity of the system. The authors have developed and deployed a comprehensive 3D multiphysics model that thoroughly replicates the actual configuration and fluid/material domains of the MFECR. The coupled physics in this multiphysics model include multicomponent-multiphase electrochemical-driven reactions, non-ideal mass transport mechanism, free and porous flow, heat transfer, CO 2 solubility on alkaline electrolyte, water condensation on porous medium, and DC electrical current generation along with Joule heating effect. This model is aimed to conduct quantitive benchmark on three different MFECR’s layouts, one without serpentine paths (plain) and two with serpentines leading to four and twelve paths respectively. Once experimental data is generated via a test matrix of 200 tests, the model will be validated to conduct MFECR’s process optimization and revalidate the quantitive benchmark on three different MFECR’s layouts.
Cyberphysical systems with autonomous functions are complex pieces of software, consisting of many components, some of which implement autonomous functionality and some may use AI or machine learning algorithms. Software bugs in an autonomous system are of particular concern, as they can have catastrophic consequences. However, detailed studies based on empirical data are rare and therefore these bugs are not well understood. This paper aims to contribute towards filling that gap by investigating the software changes and bugs in Autonomy Operating System (AOS) for Unmanned Aircraft Systems (UAS), which consist of 26 components containing about 103,000 lines of code and having a total of 772 bugfixes. Based on the data extracted from the code repository and semi-structured interviews with the developers of AOS, we explore the differences among autonomous software components, components developed using Model-based Software Engineering, and reuse with respect to change proneness, fault proneness, distribution of bugfixes among AOS components and files of these components, and characteristics of bugs of different AOS components. Our results show that the autonomous components were significantly more change prone (measured in number of commits and code churn) and fault prone (measured in bugfixes per KLoC) than non-autonomous components. The distribution of the locations of bugfixes was skewed, both at component and file level (i.e., a small number of components / files contained the majority of bugs). These evidence-based findings provide important insights to researchers and practitioners alike and can be used to efficiently improve the quality and reliability of autonomous systems.
Simulations performed by an unstructured-grid, cell-centered, finite-volume, mixed-element flow solver USM3D-MEwere presented at the Seventh Drag Prediction Workshop (DPW-7). Reynolds-averaged Navier-Stokes (RANS) solutions for the NASA high-speed common research model wing-body configuration were computed using a new implementation of the negative variant of the Spalart-Allmaras turbulence model (SA-neg) with hierarchical adaptive nonlinear iteration method (HANIM).ThisUSM3D-ME enhancement improved efficiency and robustness of RANS solutions resulting in fast iterative convergence to machine zero residuals on mixed-element unstructured grids provided by the DPW-7 organizing committee. This paper presents theoretical foundations for the new implementation of the SA-neg model and analyzesUSM3D-ME solutions for flow conditions corresponding to the required DPW-7 cases.
Since landing on the Martian surface, the Perseverance rover has relied on a distributed team to generate commands for exploring its new environment each sol (Martian day). The team uses a complex suite of software tools to accomplish this challenging task in time for the next window of opportunity to send commands to the rover. A key piece of this software ecosystem is COCPIT (Component-based Campaign Planning, Implementation, and Tactical). COCPIT is part of the next generation of planning and scheduling software tools developed by NASA's Jet Propulsion Laboratory in partnership with NASA's Ames Research Center. COCPIT is a web-based application that allows users to collaboratively view and update the Perseverance rover's activity plans, continuously verify that the plan satisfies constraints, assign targets for directing scientific instruments, document science intent, and model power and data resources. Mars Surface Operations requires diverse expertise from team members within the Engineering, Science, Robotic, and Instrument Operations groups, distributed across North America and Europe. In order to improve efficiency and reduce risk, all teams are able to review and edit their activities simultaneously and see the effects on the plan in its entirety. As part of the Ground Data System (GDS) tool suite, COCPIT is responsible for the activity plan. It provides specialized views that allow operators to understand where there may be room for additional observations, see whether any planning constraints are being violated, and confirm that energy usage and data generation are within the defined limits. It contains details such as which filters a camera will use for a given observation, what the resolution of the images should be, where to store the data onboard, and how long the observation is expected to take. It predicts when specific data will be downlinked from the rover to a passing orbiter, so that the team knows when to expect that data on Earth for evaluation in future planning. Ultimately the information from the COCPIT plan is translated to sequences that will be bundled and radiated to Perseverance for execution. The COCPIT tool is used throughout all planning phases.
Analysis of communication systems is crucial to NASA objectives, whether located here on Earth or on a cosmic scale. The Glenn Research Center Communication Analysis Suite (GCAS) is software designed for analysis of communication systems throughout the solar system. This suite includes web-based visualization software that is used to simulate the data generated from the GCAS MATLAB® (The MathWorks, Inc.) capability. This report focuses on recent additions to the visualization portion of GCAS, including the motivation and methodology behind them. Notable additions include improved camera controls, such as novel first- and third-person settings, methods for displaying pertinent analysis information, finer control over timing mechanisms, and automated features designed to improve efficiency and memory usage.
For almost 100 years, commercial aircraft have been fabricated using riveted aluminum alloy structures. Aside from refining aircraft designs for better aerodynamic efficiency, improving alloy compositions, and automating assembly steps, fuselage construction remains largely unchanged today. The intent of the lightweight metallic fuselage prototype undertaken in NASA’s Advanced Air Transport Technology project was to demonstrate innovative forming and joining processes that advance the design paradigm, i.e. achieve high-rate manufacturing and reduce assembly time/costs. Over the past decade, researchers at NASA Langley Research Center have evaluated the potential to modify the flow forming process to produce near-net shaped cylinders, integrally stiffened along the cylinder axis, for space launch vehicles. The production of integral stiffeners in a formed aluminum cylinder, the size of the Space Shuttle external tank, replaced machined thick plate and welding steps to eliminate > 500,000 pounds of machining chips and ~ 0.5 miles of welds. The flow forming method, called the Integrally Stiffened Cylinder (ISC) process, was successfully demonstrated up to the 10-foot diameter scale, which laid the groundwork for the current investigation of metallic fuselage structures. Using the ISC process as the basis for the re-design of a metallic fuselage eliminates hundreds of thousands of holes and rivets, while significantly reducing assembly time and crack initiation sites in the integral structure. However, the ISC process is not currently configured to fabricate circumferential ring frames for carrying fuselage internal pressure loads. Consequently, a trade study was performed to assess existing and advanced manufacturing processes, including additive manufacturing, forming, and welding. The approach with the lowest barriers to success, while simultaneously improving manufacturing time and cost, was to use formed ring frame segments attached to the ISC via Refill Friction Stir Spot Welding (RFSSW). The RFSSW process is five times faster than drilling, reaming, and riveting and provides similar mechanical performance. Finishing the fuselage structure with conventional windows, floor beams, and floor panels can then be accomplished using incumbent assembly methods, thereby maximizing reuse of existing infrastructure for aircraft construction. Structural analyses were performed to assess and optimize the geometric variables of integrated skin and stiffener configurations. A cost benefit and manufacturing rate analysis was also performed to compare against the current state-of-the-art for single aisle transport class aircraft. The resulting structure offers a weight reduction that rivals current graphite-epoxy composite fuselage structures. The projected manufacturing rate is close to double current metallic fuselages and six times faster than current composite manufacturing practices. The damage tolerance properties of a monocoque fuselage structure have yet to be assessed, but past integral airframe structural work has exploited geometric features to blunt or turn cracks. This concept offers promise that integrated structures can meet stringent aircraft durability specifications. An important benefit is that such aluminum fuselage structures may be inspected and repaired using established practices and existing expertise. Finally, pursuit of advanced manufacturing processes for future aluminum fuselages minimizes waste and the structure is 100% recyclable at the end-of-life for maximum sustainability.
BACKGROUND The Artemis campaign is a Moon exploration program with a series of six planned missions, five of which will be crewed. These five crewed missions will contain a single mission segment (space flight), or multiple mission segments involving space flight (Orion), lunar landing (LTV) and/or space habitat (Gateway). Each crewed segment faces the risk of unique medical conditions, necessitating medical sets/kits tailored to those specificities. To support and enable a data-driven and evidence-based decision-making process through out a mission’s life cycle, a software tool called IMPACT was developed. Using probabilistic risk assessment (PRA) methodologies, IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool built for analyzing the possibility of encountering complex medical risks during space flight, and for identifying the medical resources and capabilities needed to treat those potential at-risk medical conditions. IMPACT achieves this by performing hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. During an extended simulation modeling phase, IMPACT generated analytical results for medical risks, and the medical resources and capabilities to address those risks, for every segment of every crewed Artemis mission. This presentation will highlight the reliability, consistency and validity of IMPACT’s computational modeling techniques and will showcase the library of analytical outcomes generated for the Artemis missions. OVERVIEW During the early stages of IMPACT’s design, architecture and technical requirements collection, “scenarios” (use cases) - achievement goals required for acceptance testing, were identified by stakeholders. IMPACT successfully completed the scenario testing requirements and undertook an extensive operational run phase utilizing a wide range of input combinations with a goal of delivering a cohesive, trustworthy, reliable, vast, and diverse body of evidence. The intent of these modeling runs was to validate consistency in output, ensure solidity of executable operations and to streamline processes by identifying areas requiring efficiency improvements. Using the many missions of Artemis, IMPACT ran variations of operational runs to assess the output for acceptable, as well as unusual characteristics. This rigorous long-term “shakedown” analysis was implemented to help build a collective body of evidence to aid in securing a high level of confidence, reliability, and validity in the output, whether from the applicational components of IMPACT, or the entirety of the operational process. ANTICIPATED ANALYSIS AND CONCLUSION This presentation will discuss the various categories of input criteria; the comparisons in the application of these input criteria to various Artemis missions; the preparation and collection of the body of evidence, and reliability of the computational modeling techniques. This paper serves as an initial analytical overview of IMPACT’s probabilistic risk assessment (PRA) medical risk outputs covering Artemis missions and is not intended to be deemed the official medical response for the Artemis campaign.
The NASA Glenn Research Center Environmental Effects and Coatings Branch is developing a passive coating for lunar dust mitigation. This simple, transparent, conductive coating will be capable of reducing lunar dust adhesion to surfaces and dissipating surface charge. Anticipated benefits include compatibility with active dust mitigation strategies, improved efficiency and performance of solar cells and thermal control surfaces, and protection of astronauts and equipment on the lunar surface.
The Spatial Targeting and Alignment Rig for Scanning (STARScan) is a 3D photogrammetry system developed at NASA Ames Research Center to address bottlenecks in pre/post-test scanning of arcjet test articles. It reduces scan time from 15 minutes with handheld laser scanners to under 2 minutes, while maintaining high accuracy (±0.2-0.5 mm). STARScan integrates an array of cameras, a 3D-printed rack, turntable, and LED light panels, all controlled via a user-friendly graphical user interface (GUI). The system offers tools for scan visualization, mesh analysis, and data export, automating tasks such as alignment of pre/post-test scans, material recession measurements, surface roughness assessment, and curvature analysis. By integrating scanning, imaging, and post-processing into one application, STARScan significantly improves efficiency in scanning and analyzing arcjet test samples.
Arcjet Computer Vision (arcjetCV) has been significantly upgraded to enhance accuracy and performance in tracking material recession and shock-material standoff in test videos. These improvements include integrating new machine learning models, developing a specialized edge detection class, and incorporating a more comprehensive training dataset. These upgrades have refined the software’s ability to automate time-resolved recession tracking, making it more precise and reliable for analyzing complex physical processes. In parallel, a new tool called STARscan (Spatial Targeting and Alignment Rig for Scanning) is being developed to capture detailed 3D surface data before and after testing. By comparing these pre- and post-test scans with arcjetCV’s automated video analysis results, users can achieve a more comprehensive assessment of material recession. This method enables cross-validation of results, improving confidence in the analysis of tested materials. The expanded capabilities of arcjetCV have been successfully demonstrated on videos from various facilities, including the NASA Ames arcjets, UIUC’s PlasmatronX, and the VKI Plasmatron. It has been adopted as a new standard for in-situ recession tracking by the Mars Sample Return Project and Orion. ArcjetCV’s improved efficiency and accuracy are critical for reducing testing uncertainties and validating heatshield material performance under extreme conditions. The software’s user-friendly graphical interface ensures ease of use, enabling seamless processing and precise analysis of arcjet videos, providing deeper insights into material behavior in hypersonic environments. ArcjetCV is now available on both PyPI and Conda, allowing easy installation via "pip install arcjetCV" or through the Conda package manager, ensuring broad accessibility and streamlined deployment for users across various platforms.
Composite materials have become ubiquitous in the aerospace industry due to their exceptionally light weight and high strength characteristics, as well as their unique ability to be engineered and tailored to meet specific loading conditions and performance requirements. These advanced materials offer superior strength-to-weight ratios compared to traditional metallic materials, making them particularly valuable in weight-critical aerospace applications where every pound saved translates to improved efficiency and performance. In currently operating fleets of commercial and military aircraft, composite materials have been successfully applied to critical structural components, including primary load-bearing elements such as the fuselage sections and flooring structures, which must withstand significant in-flight loads and provide passenger safety. Additionally, these materials have been specifically tailored and optimized for aerodynamic components such as wings and tail assemblies, where their ability to be molded into complex shapes while maintaining structural integrity is particularly advantageous. The application of composite materials extends beyond primary structural elements into the realm of internal cabin components, most notably in innovative seat designs where weight reduction and structural integration are paramount concerns. Modern composite seat structures can be designed to integrate multiple functions, including structural support, comfort features, and safety systems, all while maintaining the lightweight characteristics essential for aircraft performance.
We present the scientific highlights and status from years 9-12 of NASA’s Mars Science Laboratory mission, with its Curiosity rover. A thermochemolysis-based sample analysis revealed a variety of organic molecules preserved in clay mineral-rich lake/lake margin deposits of the Glen Torridon region. The stratigraphically higher clay-sulfate mineral transition on Aeolis Mons was found to be associated with a lithological change from lacustrine to dry aeolian depositional environments. Water intermittently returned to the surface after the increase in aridity, including during the deposition of wave ripple strata which indicate a climate capable of sustaining an ice-free lake in the early Hesperian. The overlying Mg sulfate-bearing unit has both poly- and monohydrated Mg sulfates and is also enriched in iron carbonate of a purity consistent with chemical sedimentation and therefore atmospheric sequestration. A channel-incised canyon within Gediz Vallis and distal alluvial deposits recorded numerous fluvial and debris flow events after the deposition, lithification, and erosion of the clay- and sulfate-bearing units. Bright clasts found in Gediz Vallis canyon are composed of native sulfur. The investigation of the modern atmosphere and environment focused on the influence of the crater and mountain topography and captured high-altitude noctilucent and iridescent clouds. Radiation monitoring now spans an 11-year solar cycle. Rover systems and science instruments remain capable of addressing mission scientific objectives. All ten scientific instruments continue to return high-value data despite degradation of the Chemistry and Camera instrument’s high-voltage system, loss of multispectral imaging capability, and loss of active neutron spectroscopy. The mission has mitigated the loss of redundant brakes on two arm joints, additional wheel wear, and highly reduced capability of the redundant flight computer. Efficiency improvements in rover energy utilization and operations staffing have allowed the mission team to maintain a high level of productivity despite declining rover power generation and funding.
Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency. Based on optimal solutions obtained from the graph-based trajectory planning algorithm (GTPA), a deep neural network (DNN) is developed to learn the optimal vehicle speed for the next time step given its current state. It is demonstrated that the trained DNN can provide energy-efficient trajectories with high computational efficiency and high flexibility adopting to dynamic changes in the surrounding environment. To address the impact of downstream traffic, a queue prediction model is further developed using data from radars and connected vehicles (CVs), as well as signal timing data from SPaT messages. A comprehensive simulation study in the microscopic traffic modeling software PTV VISSIM shows that the proposed DLQ-EAD can achieve 18.7%-24.0% energy efficiency improvements for a single PHEB on various traffic congestion levels. The proposed queue prediction model can be of practical significance even at low penetration rates of CVs. Specifically, additional energy savings of 2.0%-8.2% can be further achieved with 20% vehicles in the network.
A host machine uses a range-based address translation system rather than a conventional page-based system. This enables address translation to be performed with improved efficiency, particularly when nest virtual machines are used. A data processing system utilizes range-based address translation to provide fast address translation for virtual machines that use virtual address space.
A fault detection and diagnostics (FDD) tool is a type of energy management and information system designed to continuously identify the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and application of automated analytics. Building owners and operators at the leading edge of technology adoption are using FDD tools to enable average whole-building portfolio savings of 8 percent. Although FDD tools can inform building operators of operational faults, currently a manual action is always required to correct faults and generate the associated energy savings. A subset of faults, however, such as biased sensors and manual override, can be addressed automatically, removing the need for operations and maintenance staff intervention. Automating this fault “correction” can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. Doing so is expected to advance the usability, as well as the technical and economic performance, of FDD technologies. In this paper, we present the development of 10 innovative fault auto-correction algorithms for HVAC and lighting systems. When the auto-correction routine is triggered, it will overwrite the control setpoints or other variables (via BACnet or other protocol) to implement the intended changes. These algorithms are able to automatically correct the faults or improve the operation associated with an incorrectly programmed schedule, override manual control, sensor bias, control hunting, rogue zone, and less aggressive setpoints/setpoints setback. The paper will also discuss the implementation of the auto-correction algorithms in FDD software products.
Nearly every major U.S. city has committed itself to ambitious climate action goals – for Washington, DC this means a 50 percent reduction in greenhouse gases by 2032 and carbon neutrality by 2050. In support of these goals, Washington, DC has passed one of the most aggressive and practical climate action bills in the nation—with the Clean Energy DC Omnibus Act, DC became the first city in the U.S. to adopt energy performance standards for existing buildings. DC’s Building Energy Performance Standards (BEPS) require energy efficiency improvements for all commercial and multifamily buildings that do not meet a sector-specific minimum ENERGY STAR score or equivalent metric, with iterative compliance cycles every five years that will accelerate the pace of whole building retrofits. This paper explores this revolutionary policy framework and uses two data analysis projects that DC conducted to evaluate the potential impact of the BEPS and move towards carbon neutrality. First, we analyze the potential energy savings and greenhouse gas reductions, as well as potential cost impacts, from the implementation of a BEPS policy in DC We then examine the role of BEPS in a carbon neutrality strategy, how BEPS savings iterate over time, and what additional existing building improvements will be driven by the gravitational pull of new building codes on median performance. The paper highlights the benefits and limitations of such data-driven approaches to support policy decisions. Finally, we will review ongoing BEPS implementation, including expected policy directions, critical supportive programs, and lessons learned to date.