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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Performance, design, test, and qualification requirements of computer programs for support system for on-board checkout and data management system of Saturn/Apollo application program
The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to fabricate nuclear grade components to be assembled into a fully functional microreactor core. Compared to traditional manufacturing technologies, additive manufacturing technologies allow (1) observation of the manufacturing process at a much higher resolution in real time using in situ monitoring technologies to capture the sensor signature that scientifically describes each event occurring over time and space, and (2) validation of the manufacturing process quality using domain-informed data analytics techniques as a potential qualification and certification methodology for the final component. This report provides an update on the program work on laser powder bed fusion in-situ process monitoring and associated data analytics results. Examples are provided to illustrate the progress. Elements of the Digital Thread and data management are discussed in the main document, and an extensive supplemental material section is provided detailing the Digital Platform, as well as its implementation and components. In conclusion the path forward for the next fiscal year is discussed.
The long-term thermal stability of structural alloys is essential for ensuring the safe and reliable operation of nuclear reactors and other power plants. While extensive research has explored the effects of thermal aging on conventional stainless steels, the behavior of additively manufactured (AM) alloys remains less understood. This study examines the thermal aging response of laser powder bed fusion (LPBF) 316H stainless steel (SS) at temperatures ranging from 550 °C to 750 °C over durations of up to 10,000 h (approximately 1.14 years). Advanced characterization techniques, including electron microscopy and synchrotron X-ray diffraction, were used to investigate dislocation recovery and phase evolution. Based on these findings, a time-temperature-precipitation (TTP) diagram was developed for LPBF 316H SS, revealing a 10- to100-fold acceleration in precipitation kinetics compared to wrought 316H SS. A physics-informed model was calibrated using the short-term experimental data, enabling predictions of average precipitate sizes, volume fractions of M 23 C 6 and Laves phases, and changes in molybdenum solute concentration for aging up to 1 × 10⁶ h (114 years). These microstructural insights were further utilized to estimate yield strength and extrapolate strength reduction factors over the extended aging period. Despite the accelerated aging kinetics, LPBF 316H SS demonstrated superior yield strength retention compared to its wrought counterpart. In conclusion, this study establishes a framework for evaluating long-term performance using short-term experimental data and supports the accelerated qualification of AM materials for high-temperature structural applications.
To impact physical mechanical system design decisions and realize the full promise of high-fidelity computational tools, simulation results must be integrated at the earliest stages of the design process. This is particularly challenging when dealing with uncertainty and optimizing for system-level performance metrics, as full-system models (often notoriously expensive and time-consuming to develop) are generally required to propagate uncertainties to system-level quantities of interest. Methods for propagating parameter and boundary condition uncertainty in networks of interconnected components hold promise for enabling design under uncertainty in real-world applications. These methods avoid the need for time consuming mesh generation of full-system geometries when changes are made to components or subassemblies. Additionally, they explicitly tie full-system model predictions to component/subassembly validation data which is valuable for qualification. These methods work by leveraging the fact that many engineered systems are inherently modular, being comprised of a hierarchy of components and subassemblies that are individually modified or replaced to define new system designs. By doing so, these methods enable rapid model development and the incorporation of uncertainty quantification earlier in the design process. The resulting formulation of the uncertainty propagation problem is iterative. We express the system model as a network of interconnected component models, which exchange solution information at component boundaries. We present a pair of approaches for propagating uncertainty in this type of decomposed system and provide implementations in the form of an open-source software library. We demonstrate these tools on a variety of applications and demonstrate the impact of problem-specific details on the performance and accuracy of the resulting UQ analysis. This work represents the most comprehensive investigation of these network uncertainty propagation methods to date.
Apollo X ray astronomy experiment qualification test program with test data
The results of comprehensive investigation of interconnect fatigue that has led to the definition of useful reliability-design and life-prediction algorithms are presented. Experimental data indicate that the classical strain-cycle (fatigue) curve for the interconnect material is a good model of mean interconnect fatigue performance, but it fails to account for the broad statistical scatter, which is critical to reliability prediction. To fill this shortcoming the classical fatigue curve is combined with experimental cumulative interconnect failure rate data to yield statistical fatigue curves (having failure probability as a parameter) which enable (1) the prediction of cumulative interconnect failures during the design life of an array field, and (2) the unambiguous--ie., quantitative--interpretation of data from field-service qualification (accelerated thermal cycling) tests. Optimal interconnect cost-reliability design algorithms are derived based on minimizing the cost of energy over the design life of the array field.
A new seal test facility for measuring high-pressure seal rotor-dynamic characteristics has recently been made operational at Case Western Reserve University (CWRU). This work is being sponsored by the Electric Power Research Institute (EPRI). The fundamental concept embodied in this test apparatus is a double-spool-shaft spindle which permits independent control over the spin speed and the frequency of an adjustable circular vibration orbit for both forward and backward whirl. Also, the static eccentricity between the rotating and non-rotating test seal parts is easily adjustable to desired values. By accurately measuring both dynamic radial displacement and dynamic radial force signals, over a wide range of circular orbit frequency, one is able to solve for the full linear-anisotropic model's 12 coefficients rather than the 6 coefficients of the more restrictive isotropic linear model. Of course, one may also impose the isotropic assumption in reducing test data, thereby providing a valid qualification of which seal configurations are well represented by the isotropic model and which are not. In fact, as argued in reference (1), the requirement for maintaining a symmetric total system mass matrix means that the resulting isotropic model needs 5 coefficients and the anisotropic model needs 11 coefficients.
Two JAYGO planetary mixers (12 and 42 gallon) are being qualified to mix STW5-3224 liner and STW5-3223 castable inhibitor. These mixers are an integral part of a mix process which allows for safe addition of the asbestos component. An essential part of the engineering evaluation (ETP-0347) of these mixers is the generation of static test fire data. Ultimately, these results will help confirm the adequacy of these mixers for production mixing of liner and inhibitor. (These data are not required for qualification of the Certification Test Plan CTP-0125). The details on the mixing, inhibiting, and sling-lining of JAYGO-mixed castable inhibitor and liner which were applied to the FSM-1 segments are presented. The objectives are the following: (1) to document processing events surrounding the JAYO mixing of castable inhibitor and liner, and the subsequent inhibiting and sling lining onto the FSM-1 segments; and (2) to substantiate the measured properties of these JAYGO-mixed materials (rheological and mechanical) and compare these properties to existing production database.
Energy Dispersive X-ray Spectroscopy (EDS) is an essential technique for determining elemental concentrations and distributions within microstructures, critical for materials discovery, optimization, and qualification. However, most published EDS data is qualitative because current quantitative EDS analysis methods require extensive calibration and post-processing, limiting their practicality and widespread adoption. This work seeks to establish a framework for accelerated EDS characterization and spectrum analysis that can leverage ML to analyze correlations between various elemental compositions and resulting EDS spectra. The complex physics and data result in a high-dimensional problem that grows exponentially with the number of elements in the system and the complexity of the spectrum analysis. ML provides a way to compute and optimize the results of this highly dimensional problem in a flexible way to tailor it to the user’s specific needs and material system. However, the framework emphasizes transparency through a strictly mathematical affine transformation, so the analysis remains understandable and reviewable to facilitate adoption by the scientific community. While currently implemented methods are simplistic and unvalidated, further development and demonstration of this framework could enable high-throughput, accurate, and accessible EDS characterization.
Energy Dispersive X-ray Spectroscopy (EDS) is an essential technique for determining elemental concentrations and distributions within microstructures, critical for materials discovery, optimization, and qualification. However, most published EDS data is qualitative because current quantitative EDS analysis methods require extensive calibration and post-processing, limiting their practicality and widespread adoption. This work seeks to establish a framework for accelerated EDS characterization and spectrum analysis that can leverage ML to analyze correlations between various elemental compositions and resulting EDS spectra. The complex physics and data result in a high-dimensional problem that grows exponentially with the number of elements in the system and the complexity of the spectrum analysis. ML provides a way to compute and optimize the results of this highly dimensional problem in a flexible way to tailor it to the user’s specific needs and material system. However, the framework emphasizes transparency through a strictly mathematical affine transformation, so the analysis remains understandable and reviewable to facilitate adoption by the scientific community. While currently implemented methods are simplistic and unvalidated, further development and demonstration of this framework could enable high-throughput, accurate, and accessible EDS characterization.
When Solstice began working in the community solar industry, it quickly became clear that there was a wide disparity along race and class lines in terms of what kind of household traditional solar offerings benefited. Solstice is an organization that aligns itself to the principles of a Just Transition, or the concept that a transition to a green economy should benefit and prioritize historically marginalized groups of people. Given that low-income communities are disproportionately burdened by our current polluting fossil fuel economy, Solstice set out to investigate the root causes of the exclusion of low to moderate income (LMI) households in the community solar market. As we worked more and more with financiers, developers, and LMI households, we recognized that significant barriers to entry in a developer-owned project were credit threshold requirements. A FICO score of 750 and higher discriminates against LMI households that are not financially stable enough to take on multiple lines of credit (or who may not have been deemed credit worthy enough to access a line of credit) yet who may have been reliable utility customers. As Solstice began looking further into preliminary data and speaking to community-based organizations (CBOs) that serve LMI households, we found our hypothesis to be worth investigating; there was indeed a subset of LMI households with poor FICO scores (or no FICO score at all) that had perfect bill payment history. Though initially Solstice was intent on gathering a body of data to prove or disprove this hypothesis, we were also concerned that all this proof of a financially stable and reliable customer was not being incorporated into FICO, and wondered if such a score would be possible. After preliminary research into the existence of alternative scores, Solstice realized that many mission-driven lending institutions rely on alternative metrics that are formulated for their specific industry. However, we also realized that community solar did not have an alternative credit underwriting mechanism, though there was great need from solar financiers for one. We anticipated that the creation of an EnergyScore would contribute to financiers’ need to qualify more people, as these entities are desperately looking for ways to lower the cost of customer acquisition. FICO turns away nearly 50% of potential customers (according to our own acquisition experience), and much of the community solar industry is realizing that FICO is not the perfect qualifying mechanism. Additionally, with a grounding in energy justice, just transitions, and climate justice, Solstice recognizes the need for a solution that addresses a more urgent need for LMI households to access renewable energy savings and relieve energy burdens. With funding from the Department of Energy, we gathered the data necessary to build the EnergyScore, reached out to mission-driven developers willing to test the metric, and secured several demonstration projects to pilot the EnergyScore. After acquiring customers for these demo projects using the EnergyScore, we will be continuously collecting customer payment behavior data. Though it goes beyond the scope of this project, we plan to disseminate this de-identified data with the wider community solar industry, which includes not only financiers and developers, but other mission-driven nonprofits, solar cooperatives, community-based organizations, environmental justice activists, and academics. While we intend to disprove the notion that LMI households cannot be included in projects without acutely increasing the risk to project finances, we also hope the data can be used to negotiate better pricing of systems and terms of ownership for community groups seeking to build inclusive projects.
The modal response of a liquid-filled tank to external acoustic excitation can be used to infer with high resolutionthe mass of contained liquid, the mass flow rate of liquids into and out of the tank, and changes in tank pressure.Both contained liquid mass and internal ullage pressure affect the modal response of the tank walls through fluidmass-loading of the tank walls and pressure-induced wall stiffening, respectively. Modal Propellant Gauging refersto the technology that exploits these shifts in modal frequencies to infer the mass of propellant in a tank. MPG is anon-invasive gauging technology that has demonstrated gauging resolutions of 1% for settled propellants and 2-3%for unsettled, sloshing propellants. Extensive parabolic flight testing of the MPG system on model tanks has beenconducted to validate the technology in reduced gravity. MPG testing on a qualification tank for the Orion Program'sEuropean Service Module has also been conducted and is reported here. Finite element modeling of the OrionESM "upper" tank is discussed and compared with measurement data. Three computational approaches to massdetermination, Peak Tracking, Point Sensor, and Spectral Density methods, are described here. Use cases are definedand analyzed in the context of the Orion ESM Qualification tank data, and an implementation scheme for continuousmass gauging on the Orion ESM is discussed.Keywords: Propellant Mass Gauging, Modal Analysis, Orion ESM
The U.S. DOE-NE’s Advanced Reactor Technologies (ART) Fast Reactor Program (FRP) and the NE-4 Advanced Fuels Campaign (AFC) have jointly undertaken the qualification of the legacy post-irradiation examination (PIE) data held in the Fuels Irradiation & Physics Database (FIPD), covering metallic fuel experiments conducted in the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). In FY26 this effort reached a milestone: six major types of PIE data—contact profilometry, isotopic gamma scan, fission gas chemistry, fission gas release, laser profilometry, and neutron radiography—have been qualified for all available experiments in FIPD, and the U.S. nuclear industry can now draw on them with confidence in licensing activities for metallic fuel-based advanced fast reactors. This report documents the qualification process and the resulting status of the PIE data.
Flight vibration data are analyzed for the NASA F-15B/Flight Test Fixture II test bed. Understanding the in-flight vibration environment benefits design and integration of experiments on the test bed. The power spectral density (PSD) of accelerometer flight data is analyzed to quantify the in-flight vibration environment from a frequency of 15 Hz to 1325 Hz. These accelerometer data are analyzed for typical flight conditions and maneuvers. The vibration data are compared to flight-qualification random vibration test standards. The PSD levels in the lateral axis generally are greater than in the longitudinal and vertical axes and decrease with increasing frequency. At frequencies less than approximately 40 Hz, the highest PSD levels occur during takeoff and landing. Peaks in the PSD data for the test fixture occur at approximately 65, 85, 105-110, 200, 500, and 1000 Hz. The pitch-pulse and 2-g turn maneuvers produce PSD peaks at 115 Hz. For cruise conditions, the PSD level of the 85-Hz peak is greatest for transonic flight at Mach 0.9. From 400 Hz to 1325 Hz, the takeoff phase has the highest random vibration levels. The flight-measured vibration levels generally are substantially lower than the random vibration test curve.
The standard random vibration qualification test used for Expendable Launch Vehicle components is Maximum Predicted Environment (MPE) + 6dB for a duration of 4 times the service life of the part. This can be a severe qualification test for these fatigue-sensitive structures. This paper uses flight data from several launch vehicles to establish that reducing the qualification approach to MPE+3dB for the duration of the peak environment (1x life) is valid for fatigue-sensitive structural components. Items that can be classified as fatigue-sensitive are probes, ducts, tubing, bellows, hoses, and any non-functional structure. Non-functional structure may be flight critical or carry fluid, but it cannot include any moving parts or electronics. This reduced qualification approach does not include primary or secondary structure which would be exclusively designed by peak loads, either transient or quasi-static, that are so large and of so few cycles as to make fatigue a moot point.
Shock response spectra data from flight certification tests were analyzed to determine envelope variation with respect to mean values in each axis. An overall variation of + or - 8.61 dB or 169 percent exists for the data. This large variation may be attributed to one or more of the following: (1) Instrumentation problems may exist. (2) Variations in the source charge (blasting caps) such as shape or explosive load may exist. (3) Two blasting caps were used to excite the pyrotechnic plate tester. Delay time between charge firings may have varied. The cause or causes of the variations need to be identified and researched to prevent future pyroshock problems.
The current flight qualification program lags technology insertion by 6 to 10 years. The objective is to develop an integrated software engineering and development environment assisted by an expert system technology. An operating system needs to be developed which is portable to the on-board computers of the year 2000. The use of ADA verses a High-Order Language; fault tolerance; fiber optics networks; communication protocols; and security are also examined and outlined.
The Department of Energy’s (DOE) Office of Materials Management and Minimization has been tasked with converting the five remaining United States High Performance Research Reactors (U.S. HPRR) from highly enriched uranium to low-enriched uranium. The Nuclear Regulatory Commission (NRC) regulates Massachusetts Institute of Technology Reactor (MITR), Missouri University Research Reactor (MURR), and National Bureau of Standards Reactor (NBSR); and DOE regulates the High Flux Isotope Reactor and Advanced Test Reactor (ATR). To meet the high demands of these reactors, the U.S. HPRR program has chosen to use 90% uranium - 10% molybdenum (U-10Mo) monolithic fuel. This plate-type fuel will undergo multiple irradiation experiment campaigns in ATR, from mini-plates to full element tests, over a large range of operating conditions. This will provide data in support of the fuel qualification of each reactor. This summary focuses on the as-run neutronic analysis of the first series of mini-plate (MP-1) experiments, which have been irradiated in the ATR. MP-1 experiment’s main goal is to demonstrate the fabrication process and meet the fuel irradiation performance requirements with primary focus on the fuel plates associated with the three NRC reactors, with a small focus on the low power requirements of the ATR fuel. The MP-1 experiments are planned to be the basis of the monolithic U-10Mo fuel for qualification through the NRC.