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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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The severe HELIOS mission environment asked for an appropriate material selection for optical surfaces based on a good approach to predict degradation and contamination effects by analysis and an adequate vertification by test. Analysis results are compared with flight data of the HELIOS-A mission.
The paper describes the coloring induced by 85-MeV protons in three Schott glasses representative of glass types used as focusing elements in the International Ultraviolet Explorer Fine Error Sensor. It is shown that the absorption (i.e., coloring) induced in each glass by the radiation can be resolved into three optical absorption bands in the near UV-visible range. It is also discussed how that absorption grows with increasing dosages of protons, thus providing the means to predict degradation in any of the three glasses for a particular fluence of proton radiation.
Flying qualities problems of modern, full-authority electronic flight control systems are most often related to the introduction of additional time delay in aircraft response to a pilot input. These delays can have a significant effect on the flying qualities of the aircraft. Time delay effects are reexamined in light of recent flight test experience with aircraft incorporating new technology. Data from the X-29A forward-swept-wing demonstrator, a related preliminary in-flight experiment, and other flight observations are presented. These data suggest that the present MIL-F-8785C allowable-control system time delay specifications are inadequate or, at least, incomplete. Allowable time delay appears to be a function of the shape of the aircraft response following the initial delay. The cockpit feel system is discussed as a dynamic element in the flight control system. Data presented indicate that the time delay associated with a significant low-frequency feel system does not result in the predicted degradation in aircraft flying qualities. The impact of the feel system is discussed from two viewpoints: as a filter in the control system which can alter the initial response shape and, therefore, the allowable time delay, and as a unique dynamic element whose delay contribution can potentially be discounted by special pilot loop closures.
Flying qualities problems of modern, full-authority electronic flight control systems are most often related to the introduction of additional time delay in aircraft response to a pilot input. These delays can have a significant effect on the flying qualities of the aircraft. This paper reexamines time delay effects in light of recent flight test experience with aircraft incorporating new technology. Data from the X-29A forward-swept-wing demonstrator, a related preliminary in-flight experiment, and other flight observations are presented. These data suggest that the present MIL-F-8785C allowable-control system time delay specifications are inadequate or, at least, incomplete. Allowable time delay appears to be a function of the shape of the aircraft response following the initial delay. The cockpit feel system is discussed as a dynamic element in the flight control system. Data presented indicate that the time delay associated with a significant low-frequency feel system does not result in the predicted degradation in aircraft flying qualities. The impact of the feel system is discussed from two viewpoints: as a filter in the control system which can alter the initial response shape and, therefore, the allowable time delay, and as a unique dynamic element whose delay contribution can potentially be discounted by special pilot loop closures.
EOL power estimates for solar array designs are significantly influenced by the predicted degradation due to charged particle radiation. New radiation-induced power degradation data for GaAs/Ge solar arrays applicable to missions ranging from low earth orbit (LEO) to geosynchronous earth orbit (GEO) and compares these results to silicon BSF/R arrays. These results are based on recently published radiation damage coefficients for GaAs/Ge cells. The power density ratio (GaAs/Ge to Si BSF/R) was found to be as high as 1.83 for the proton-dominated worst-case altitude of 7408 km medium Earth orbit (MEO). Based on the EOL GaAs/Ge solar array power density results for MEO, missions which were previously considered infeasible may be reviewed based on these more favorable results. The additional life afforded by using GaAs/Ge cells is an important factor in system-level trade studies when selecting a solar cell technology for a mission and needs to be considered. The data presented supports this decision since the selected orbits have characteristics similar to most orbits of interest.
Future NASA missions demand increased data rates in satellite communications for near real-time transmission of large volumes of remote data. Increased data rates necessitate higher order digital modulation schemes and larger system bandwidth, which place stricter requirements on the allowable distortion caused by the high-power amplifier, or the traveling-wave-tube amplifier (TWTA). In particular, intersymbol interference caused by the TWTA becomes a major consideration for accurate data detection at the receiver. Experimentally investigating the effects of the physical TWTA on intersymbol interference would be prohibitively expensive, as it would require manufacturing numerous amplifiers in addition to acquiring the required digital hardware. Thus, an accurate computational model is essential to predict the effects of the TWTA on system-level performance when a communication system is being designed with adequate digital integrity for high data rates. A fully three-dimensional, time-dependent, TWT interaction model has been developed using the electromagnetic particle-in-cell code MAFIA (Solution of Maxwell's equations by the Finite-Integration-Algorithm). It comprehensively takes into account the effects of frequency-dependent AM (amplitude modulation)/AM and AM/PM (phase modulation) conversion, gain and phase ripple due to reflections, drive-induced oscillations, harmonic generation, intermodulation products, and backward waves. This physics-based TWT model can be used to give a direct description of the effects of the nonlinear TWT on the operational signal as a function of the physical device. Users can define arbitrary excitation functions so that higher order modulated digital signals can be used as input and that computations can directly correlate intersymbol interference with TWT parameters. Standard practice involves using communication-system-level software packages, such as SPW, to predict if adequate signal detection will be achieved. These models use a nonlinear, black-box model to represent the TWTA. The models vary in complexity, but most make several assumptions regarding the operation of the high-power amplifier. When the MAFIA TWT interaction model was used, these assumptions were found to be in significant error. In addition, digital signal performance, including intersymbol interference, was compared using direct data input into the MAFIA model and using the system-level analysis tool SPW for several higher order modulation schemes. Results show significant differences in predicted degradation between SPW and MAFIA simulations, demonstrating the significance of the TWTA approximations made in the SPW model on digital signal performance. For example, a comparison of the SPW and MAFIA output constellation diagrams for a 16-ary quadrature amplitude modulation (16-QAM) signal (data shown only for second and fourth quadrants) is shown. The upper-bound degradation was calculated from the corresponding eye diagrams. In comparison to SPW simulations, the MAFIA data resulted in a 3.6-dB larger degradation.
A prediction model of signal degradation in a Land Mobile Satellite Service (LMSS) for urban areas is proposed. This model treats shadowing effects caused by buildings statistically and can predict a Cumulative Distribution Function (CDF) of signal diffraction losses in urban areas as a function of system parameters such as frequency and elevation angle and environmental parameters such as number of building stories and so on. In order to examine the validity of the model, we compared the percentage of locations where diffraction losses were smaller than 6 dB obtained by the CDF with satellite visibility measured by a radiometer. As a result, it was found that this proposed model is useful for estimating the feasibility of providing LMSS in urban areas.
This project successfully developed a multiphysics, multiscale computational framework to enhance the design and development of CMCs, with a focus on modeling highly nonlinear, time-dependent damage mechanisms and material degradation under extreme conditions, such as those experienced in turbine service environments. The project made significant advances in improving our understanding of progressive damage, oxidative degradation, and time-dependent inelastic deformation in CMCs, with particular attention to the role of uncertainties in predictions. Key outcomes include the integration of advanced material characterization, uncertainty quantification, and multiphysics constitutive models to predict the behavior of CMCs over their service life. A novel multiscale methodology was employed, which integrated microscale constituent behaviors with structural-scale responses, enabling the manufacturing defects in the microstructure that are prone to damage nucleation. Through the development of DL algorithms, the project advanced the prediction of damage initiation and crack propagation, taking into account the defect morphology and statistical variations across multiple scales. The framework was rigorously validated using thermomechanical experiments, which tested CMCs under various mechanical loadings at elevated temperatures, further enhancing the model's predictive capability. Overall, the research outcomes have provided a more accurate, reliable method for predicting CMC component life, significantly advancing material design, and improving component reliability in extreme environments. This work has strong implications for the optimization of turbine components and other high-performance applications where CMCs are used.
Swelling associated with the formation and growth of cavities is among the most damaging of radiation-induced degradation modes for structural materials in advanced nuclear reactor concepts. Ion irradiation has emerged as the only practical option to rapidly assess swelling in candidate materials. For decades, researchers have tried to simulate the harsh environment in a nuclear reactor in the laboratory at an accelerated rate. Here we present the first case in which swelling in a candidate alloy irradiated ~ 2 years in a nuclear reactor was replicated using dual ion irradiation in ~ 1 day with precise control over damage rate, helium injection rate, and temperature and utilize physical models to predict the effects of radiation in reactors. The capability to predict and replicate the complex processes surrounding cavity nucleation and growth across many decades of radiation dose rate highlights the potential of accelerated radiation damage experiments. More importantly, it demonstrates the capability to predict the swelling evolution and the possibility to predict other features of the irradiated microstructure evolution that control material property degradation required to accelerate the development of new, radiation-tolerant materials.
In light-water reactors (LWRs), the concrete biological shield (CBS) designates the concrete structure directly facing the reactor pressure vessel (RPV). The primary function of the CBS is to protect equipment and personnel from the neutron and gamma radiation exiting the RPV. Most CBSs in operation in the United States also provide structural support to the RPV. Based on a long-term structural analysis, the structural integrity of the CBS to transfer the in-service passive load to the foundation, dynamic loading during an earthquake, and thermal loading during a loss-of-coolant accident (LOCA) must be maintained. Based on test reactor data for accelerated conditions, the mechanical properties of irradiated concrete are affected when exposed to neutron fluence higher than ≈ 10 19 n.cm -2 (E > 0.1 MeV). At 80 years of operation, the fluence estimates at the surface of the CBS range between 1×10 19 and 7×10 19 n.cm -2 (E > 0.1 MeV): the fluence value depends mostly on the design. Although fluence attenuates through the CBS by virtue of the shielding properties of concrete, about 5% to 10% of the CBS wall depth is affected by high fluence. The main degradation mechanism is the so-called radiation-induced volumetric expansion (RIVE) caused by the amorphization of aggregate-forming minerals. RIVE causes large deformation of up to 18% in pure quartz, and it also reduces the structural properties of concrete. The structural effects of long-term exposure to fast neutron irradiation in LWRs was studied in the report entitled “Assessment of the Effect of the Irradiation-Induced Degradation on the In-Service Structural Performance of the Concrete Biological Shields” (M3LW-23OR0403013). This report illustrates that (1) irradiation-induced damage expands further than the region subject to a cumulative fluence of 10 19 n.cm -2 (E > 0.1 MeV) to reach the steel reinforcement location, and (2) the irradiation-induced damage region shows no substantial residual bearing capacity and should not be accounted for in the integrity assessment of the CBS.
Tests of weathering and aging behavior are being developed to characterize the degradation and predict the lifetimes of low-cost photovoltaic arrays. Environmental factors which affect array performance include UV radiation, thermal energy, water, oxygen (generally involved in synergistic effects with UV radiation or high temperatures), physical stress, pollutants (oxides of nitrogen, sulfur dioxide and ozone), abrasives and dirt. A survey of photovoltaic array testing has shown the need to establish quantitative correlations between certain measurable properties (carbonyl formation, glass transition temperature, and molecular weight change) and modes of degradation and failure.
Volcanic aerosol forcing can affect global climate, but its role in climate prediction remains poorly understood. We isolate the impact of volcanic eruptions on multiyear-to-decadal climate prediction skill by comparing two suites of initialized decadal hindcasts conducted with and without historical volcanic forcing. Unexpectedly, the inclusion of volcanic forcing in the prediction system significantly degrades the forecast skill of detrended multiyear-to-decadal sea surface temperature (SST) variability in the central-eastern tropical Pacific. The ensemble mean hindcasts produce multiyear-to-decadal tropical Pacific SST cooling in response to large tropical volcanic eruptions through thermodynamic and El Niño–Southern Oscillation (ENSO)–like dynamic processes. However, in observations, these eruptions coincided with tropical Pacific warming, which is well predicted by the no-volcano hindcasts and, hence, is likely related to internal climate variability. Improved model representation of volcanic response and its interaction with internal climate variability is required to advance prediction of tropical Pacific decadal variability and associated global impacts.
Not long after launch of the Chandra X-Ray Observatory, it was discovered that the Advanced CCD Imaging Spectrometer (ACIS) detector was rapidly degrading due to radiation. Analysis by Chandra personnel showed that this degradation was due to 10w energy protons (100 - 200 keV) that scattered down the optical path onto the focal plane. In response to this unexpected problem, the Chandra Team developed a radiation-protection program that has been used to manage the radiation damage to the CCDs. This program consists of multiple approaches - scheduled sating of the ACIS detector from the radiation environment during passage through radiation belts, real-time monitoring of space weather conditions, on-board monitoring of radiation environment levels, and the creation of a radiation environment model for use in computing proton flux and fluence at energies that damage the ACIS detector. This radiation mitigation program has been very successful. The initial precipitous increase in the CCDs' charge transfer inefficiency (CTI) resulting from proton damage has been slowed dramatically, with the front-illuminated CCDS having an increase in CTI of only 2.3% per year, allowing the ASIS detector's expected lifetime to exceed requirements. This paper concentrates on one aspect of the Chandra radiation mitigation program, the creation of the Chandra Radiation Model (CRM). Because of Chandra's highly elliptical orbit, the spacecraft spends most of its time outside of the trapped radiation belts that present the severest risks to the ACIS detector. However, there is still a proton flux environment that must be accounted for in all parts of Chandra's orbit. At the time of Chandra's launch there was no engineering model of the radiation environment that could be used in the outer regions of the spacecraft's orbit, so the CRM was developed to provide the flux environment of 100 - 200 keV protons in the outer magnetosphere, magnetosheath, and solar wind regions of geospace. This presentation describes CRM, its role in Chandra operations, and its prediction of the ACIS CTI increase.
Abstract not provided.
In system prognostics and health management, multivariable degradation models have been widely developed to predict the life of complex systems using degradation data of multiple Performance Characteristics (PCs). Recent studies have detected a Long-Term Memory (LTM) effect among the degradation process of various PCs, implying a strong coupling phenomenon between the future degradation behavior and historical degradation trajectory. Although the LTM has been widely integrated into single-PC-based degradation modeling, it has not been considered in multi-PC-based scenarios. To capture LTM among multiple PCs, this article proposes a novel LTM-integrated Multivariate Degradation Model (MDM) for system life prediction based on multivariate fractional Brownian motion, which simultaneously incorporates the cross-correlation among different PCs. To estimate parameters of the LTM-integrated MDM, a maximum likelihood method is developed. Here, two likelihood-ratio hypothesis tests are developed to test the existence of the overall and individual LTM effect among multiple PCs. Both simulation studies and physical experiments on the performance degradation of solar energy conversion and storage devices are conducted to validate the proposed model. Results reveal that the proposed LTM-integrated MDM significantly outperforms existing MDMs in life prediction, while the lifetime uncertainty is heavily underestimated by those traditional approaches that neglect the LTM.
The DEGRA computer code that is based on a mathematical model which predicts performance and time-temperature dependent degradation of a radioisotope thermoelectric generator is discussed. The computer code has been used to predict performance and generator degradation for the selenide Ground Demonstration Unit (GDS-1) and the generator used in the Galileo Project. Results of parametric studies of load voltage vs generator output are examined as well as the I-V curve and the resulting predicted power vs voltage. The paper also discusses the increased capability features contained in DEGRA2 and future plans for expanding the computer code performance.
Battery life modeling often involves a lot of manual parameter fitting and is not easy for users to adopt the model and use it. To reduce the difficulties for users to adopt battery lifetime models, an automatic battery lifetime modeling analysis and simulation tool suite, Auto-BLAST, has been developed. Auto-BLAST includes a lithium-loss-base life model, a battery electric model, and an algorithm which automatically fits all key parameters in the electric and life models using user provided data. The models and algorithm are packaged into two user-friendly GUIs, Auto-LifeMod, for easy battery life prognostic model fitting and Auto-LifeSim, for easy battery lifetime simulation. The lithium-loss-based life model adopts a similar model framework that models degradations using aging rate models, using battery cycling data to predict battery degradation and expected lifetime. The electric model is an equivalent circuit model which simulates battery voltage responses based on current/power demand profiles. The auto-fitting algorithm uses the user-input data to generate custom battery life model(s). Two GUIs, wrapping around the life model and the auto-fitting algorithm, provide friendly interfaces for users to generate a life model and use it for case study. One GUI requires summary data from accelerated battery life degradation tests as an input and produces battery life models predicting (a) capacity degradation, and (b) resistance growth of the battery. The GUI displays the electrical model response and the input experimental data against the life model predictions with fitted model parameters and fitting error. The GUI also generates an output file to save the fitted model parameters. The second GUI uses the life model from the first GUI and a user-defined battery cycling profile to predict battery lifetime degradation and expected lifetime. Predicted capacity and resistance vs. time are plotted in the GUI and saved to output file. There is a user guide for both GUIs
Lithium-ion batteries degrade due to usage and exposure to environmental conditions, which affects their capability to store energy and supply power. Accurately predicting the capacity and power fade of lithium-ion battery cells is challenging due to intrinsic manufacturing variances and coupled nonlinear ageing mechanisms. In this paper, we propose a data-driven prognostics framework to predict both capacity and power fade simultaneously with multi-task learning. The model is able to predict the degradation trajectory of both capacity and internal resistance together with knee-points and end-of-life points accurately at early-life stage. The validation shows an average percentage error of 2.37% and 1.24% for the prediction of capacity fade and resistance rise, respectively. The model's ability to accurately predict the degradation, facing capacity and resistance estimation errors, further demonstrates the model's robustness and generalizability. Compared with single-task learning models for forecasting capacity and power degradation, the model shows a significant prediction accuracy improvement and computational cost reduction. This work presents the highlights of multi-task learning in the degradation prognostics for lithium-ion batteries.