Glenn Research Center

This paper presents the RaDICAL sensing framework, a monostatic passive radar concept that combines a Sparse Uniform Circular Array (SUCA), deterministic multi-frequency dither, and dictionary-based waveform recognition for target detection and classification. Rather than forming conventional spatial images or relying primarily on Doppler processing, RaDICAL encodes target geometry directly into a composite receiver waveform and performs hypothesis testing by matching measured signals to a library of predicted responses. The paper develops the SUCA-based signal model for point and extended targets and formulates recognition as a waveform-domain dictio-nary matching problem using normalized complex correlation and QR-domain process-ing. A reproducible MATLAB-based study evaluates waveform separability, probability of detection versus dictionary SNR, physical power balance, receiver operating char-acteristic (ROC) behavior, and detection performance versus illuminator EIRP. The results show that deterministic frequency dither produces distinctive composite wave-forms with strong hypothesis separability. ROC simulations demonstrate reliable detec-tion at physical SNR levels below those typical of classical single-pulse matched-filter detection, while EIRP-based analysis indicates feasible detection for targets ranging from large aircraft to small drones and pedestrians. These results support the feasibil-ity of waveform-domain passive sensing using deterministic spatial–frequency encoding and dictionary-based recognition.

Digital engineering (DE) and integrated computational materials engineering (ICME) are widely recognized as critical enablers of faster, more affordable, and more reliable aerospace systems. However, many organizations have struggled to realize the promised return on investment (ROI) from digital initiatives. A primary reason is the absence of a shared, decision-focused framework that distinguishes simple digitization of existing workflows from true digital transformation that fundamentally changes how engineering decisions are made. This paper introduces an ICME capability maturity framework that fills this gap. The framework defines six cumulative ICME capability maturity levels (CMLs), explicitly tied to decision authority, engineering integration, optimization, and uncertainty management across material, process, structure, and performance scales. It is designed to complement established readiness metrics such as technology readiness levels (TRLs), manufacturing readiness levels (MRLs), and integration readiness levels (IRLs), by addressing a missing dimension: the conditions required for model-informed decision authority across scales. A unifying figure and capability table illustrate the six-level ICME Capability Maturity Framework, showing how organizations progress from digitization—with limited or negative ROI—to true digital transformation, where ICME-enabled workflows deliver measurable improvements in decision quality, cycle time, risk reduction, and reuse. The framework is intended for both technical practitioners and executive leadership, providing a common language to assess current state, guide roadmaps, align software ecosystem investments, and set realistic expectations for digital transformation outcomes. A regulatory-relevant statement clarifying the relationship between ICME capability and existing certification frameworks is provided.

Bayesian optimization (BO) can accelerate materials discovery by guiding expensive experiments toward the most promising processing conditions. We systematically compare five BO surrogate and framework combinations (Gaussian processes in Ax, Gaussian processes and Monte-Carlo neural networks in BayBE, random forests in Lolopy, and tree-structured Parzen (TPE) estimators in Hyperopt) on three benchmarks that mimic common materials design tasks (a discrete solid-electrolyte composition space, a hybrid discrete/continuous laminate-composite design problem solved with micromechanics modeling, and the continuous Ishigami analytic function which is a standard optimization benchmark). Each BO surrogate is paired with posterior mean, probability of improvement, and expected improvement acquisition functions and run for 100 trials from randomized initial samples with uniform random search providing a control. Across five random seeds per setting, BayBE’s Gaussian-process surrogate with expected improvement consistently reached ≥95 % of the known optimum in the fewest evaluations, while Lolopy’s random forest matched or exceeded GP performance on purely categorical or mixed spaces at a higher computational cost. Posterior mean alone often stagnated at local optima, underscoring the need for exploration, whereas probability and expected improvement balanced exploration and exploitation leading to better optimization in fewer trials. Execution times ranged from milliseconds for TPE to minutes for neural-network and random-forest surrogates. These results establish baseline expectations for BO in automated materials laboratories and highlight expected improvement with Gaussian processes as a reliable first choice, with random forests offering a strong alternative when categorical variables dominate. The benchmark suite and code are released to facilitate future surrogate, acquisition, and constraint-handling research in data-driven materials optimization.

Electric aircraft applications require electric motors with increased specific power and efficiency. Composite structural components in motors are a potential solution for reducing motor mass, reducing magnetic losses, and limiting undesired conduction paths for fault, electromagnetic interference, or common-mode currents. In this report, manufacturing trials for a high-speed carbon fiber reinforced polymer composite motor shaft are presented. Four prototype shafts were produced using a hybrid biaxial/triaxial fabric that was circumferentially wrapped onto an additively manufactured high-temperature washout mandrel. An additional traditional overbraid approach was also evaluated and shows promise for high-rate, high-performance parts using automated manufacturing. This paper discusses the shaft design, manufacturing methods explored, material selection, the manufacturing trials, and the lessons learned. The results of this manufacturing investigation show feasibility for manufacturing composite shafts for electric motors.

Urban Air Mobility (UAM) is a rapidly emerging industry targeting delivery of air transportation services around metropolitan areas with the potential for aviation to become a practical part of everyday life while leveraging emerging technologies and operational concepts. This whitepaper, a collaboration between NASA’s Air Mobility Pathfinders (AMP) project and Wisk, explores the interoperability between crewed and uncrewed aircraft in UAM operations, leveraging operational concepts put forth in the Federal Aviation Administration (FAA) UAM Concept of Operations (ConOps) for the Midterm UAM phase. Specifically, UAM aircraft are envisioned to takeoff and land from vertiports and primarily operate within Cooperative Areas (CAs). CAs are designated as airspace volumes with specific requirements for performance, communication, and surveillance in which UAM Operators follow cooperative operating practices (COPs). Additionally, Third-Party Service Providers (TSPs) are envisioned to play a crucial role in conflict management, including both strategic and tactical deconfliction within the CAs.

The Power and Propulsion Element (PPE) is the first piece of the Artemis Lunar Gateway. This spacecraft will host a 50kW-class state of the art electric propulsion subsystem, containing a combination of never-before-flown 6kW and 12kW thrusters, large Carbon Fiber Overwrapped Pressure Vessel xenon tanks, and a refueling interface. The combination of the high degree of new hardware within the propulsion system, the inclusion of PPE within the larger human-rated Artemis Gateway, and the high level of Lanteris Space Systems heritage with the Lanteris 1300 platform required a tailored safety and hazard process for the program. This paper details the system safety and hazard analysis methods applied during the design phase, summarizes key hazard identification and mitigation activities, and discussed how these influenced the final architecture of the electric propulsion subsystem.

Lightweight cryogenic propellant storage solutions with zero or near zero boil-off represent a critical need for NASA in both aeronautics and space technology missions. Applications for composite cryogen storage tanks range from hydrogen powered aircraft to long-duration in-space and on-surface missions. Over the past several decades, NASA and industry partners have supported several notable composite cryotank development programs documenting lessons learned with respect to materials and manufacturing. This paper specifically reports the results of material screening efforts funded by both NASA’s Aeronautics Research Mission Directorate and Space Technology Mission Directorate to evaluate thermoset and thermoplastic composites for cryogen storage. Although the end applications differ, the fundamental material requirements across the two projects are the same. This paper details characterization of candidate thermoplastic and thermoset composite materials for cryogen storage, with test data generated (1) at -196oC and (2) following 100 thermal cycles between room temperature (21oC) and LN2 temperature (-196oC).

Abstract This article demonstrates the complementary value of combining electron backscatter diffraction (EBSD) and x-ray diffraction (XRD) for materials characterization through two case studies. EBSD provides localized crystallographic information from micron to millimeter-scale areas within a scanning electron microscope, while XRD characterizes larger centimeter-sized regions using a diffractometer, with both techniques capable of revealing texture and phase composition. The first case study examined texture measurements in oxide dispersion strengthened (ODS) and non-ODS additively manufactured nickel-based superalloys. The second case study focused on phase identification of desert soil samples serving as lunar analogues for radio frequency property investigations. Together, the studies illustrate how EBSD's site-specific sensitivity and XRD's bulk representativeness create a comprehensive characterization approach where each technique validates and complements the other, ensuring that neither minor phases nor overall composition are overlooked in materials analysis.