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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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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

A transient plasticity study and low cycle fatigue analysis of the Space Station Freedom photovoltaic solar array blanket

The Space Station Freedom photovoltaic solar array blanket assembly is comprised of several layers of materials having dissimilar elastic, thermal, and mechanical properties. The operating temperature of the solar array, which ranges from -75 to +60 C, along with the material incompatibility of the blanket assembly components combine to cause an elastic-plastic stress in the weld points of the assembly. The weld points are secondary structures in nature, merely serving as electrical junctions for gathering the current. The thermal mechanical loading of the blanket assembly operating in low earth orbit continually changes throughout each 90 min orbit, which raises the possibility of fatigue induced failure. A series of structural analyses were performed in an attempt to predict the fatigue life of the solar cell in the Space Station Freedom photovoltaic array blanket. A nonlinear elastic-plastic MSC/NASTRAN analysis followed by a fatigue calculation indicated a fatigue life of 92,000 to 160,000 cycles for the solar cell weld tabs. Additional analyses predict a permanent buckling phenomenon in the copper interconnect after the first loading cycle. This should reduce or eliminate the pulling of the copper interconnect on the joint where it is welded to the silicon solar cell. It is concluded that the actual fatigue life of the solar array blanket assembly should be significantly higher than the calculated 92,000 cycles, and thus the program requirement of 87,500 cycles (orbits) will be met. Another important conclusion that can be drawn from the overall analysis is that, the strain results obtained from the MSC/NASTRAN nonlinear module are accurate to use for low-cycle fatigue analysis, since both thermal cycle testing of solar cells and analysis have shown higher fatigue life than the minimum program requirement of 87,500 cycles.

Armand, Sasan C.↗
DOE OSTI2020

BACKFLIP Project: Determination of BACKsheet Material Properties: A Comparison of Market-Benchmark Technologies to Novel Non-Fluoro-Based Co-Extruded Backsheet Materials and Their Correlation and Impact on PV Module Degradation Rates

Quantifying degradation rate of backsheets will help procuring materials for 30+ years. Today most backsheets on the market have a laminated PET core; polyolefin materials may provide better properties for backsheets (barrier to water, mechanical properties through UV and hydrolytic environments, etc.). End-of-life regulations may require fluorine-free backsheet products in future global markets. Parametrized equation predicting the service life of backsheets, and validated by both lab and outdoor extensive testing. Technical and commercial confidence to the entire value chain: from backsheet and module manufacturers to the downstream community (IPP, banks, developers) that materials of higher quality can help reduce finance costs, therefore driving down LCOE. A fast track PV module market adaptation for novel high-quality materials, such as co-extruded polyolefin-core backsheets, by obtaining unbiased durability data.

14 SOLAR ENERGY↗
NASA NTRS1994

Fatigue analysis of multiple site damage at a row of holes in a wide panel

This paper is concerned with predicting the fatigue life of unstiffened panels which contain multiple site damage (MSD). The initial damage consists of through-the-thickness cracks emanating from a row of holes in the center of a finite width panel. A fracture mechanics analysis has been developed to predict the growth, interaction, and coalescence of the various cracks which propagate in the panel. A strain-life analysis incorporating Neuber's rule for notches, and Miner's rule for cumulative damage, is also employed to predict crack initiation for holes without initial cracking. This analysis is compared with the results of a series of fatigue tests on 2024-T3 aluminum panels, and is shown to do an excellent job of predicting the influence of MSD on the fatigue life of nine inch wide specimens. Having established confidence in the ability to analyze the influence of MSD on fatigue life, a parametric study is conducted to examine the influence of various MSD scenarios in an unstiffened panel. The numerical study considered 135 cases in all, with the parametric variables being the applied cyclic stress level, the lead crack geometry, and the number and location of MSD cracks. The numerical analysis provides details for the manner in which lead cracks and MSD cracks grow and coalesce leading to final failure. The results indicate that MSD located adjacent to lead cracks is the most damaging configuration, while for cases without lead cracks, MSD clusters which are not separated by uncracked holes are most damaging.

Buhler, Kimberley↗
NASA NTRS1980

Predicting lifetime of cast parts

Life expectancy of cast aluminum machine parts can be predicted accurately from fatigue tests at 78 K on notched specimens of aluminum alloy. Method was developed for rocket engine turbopump parts made of high strength, heat treatable alloy with high silicon content; however, technique is applicable to other aluminum casting alloys.

Cooper, R. A.↗
NASA NTRS2012

Model-based prognostics for batteries which estimates useful life and uses a probability density function

This invention develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Effects of temperature and load current have also been incorporated into the model. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics for a sample case. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.

Saha, Bhaskar↗
NASA NTRS1978

Owens-Illinois liquid solar collector materials assessment

From the beginning, it was noted that the baseline drawings for the liquid solar collector exhibited a distinct weakness concerning materials specification where elastomers, plastics, and foam insulation materials were utilized. A relatively small effort by a competent design organization would alleviate this deficiency. Based on results obtained from boilout and stagnation tests on the solar simulator, it was concluded that proof testing of the collector tubes prior to use helps to predict their performance for limited service life. Fracture mechanics data are desirable for predicting extended service life and establishing a minimum proof pressure level requirement. The temperature capability of this collector system was increased as the design matured and the coating efficiency improved. This higher temperature demands the use of higher temperature materials at critical locations in the collector.

Nichols, R. L.↗
DOE OSTI2021

PyBLM (Python-based Battery Life Model) [SWR-21-50]

PyBLM is a battery life model to predict the capacity fade of two home battery energy storage systems, manufactured by LG and Tesla. The battery life model is built from experimental test data, and accounts for calendar and cycling aging mechanisms as functions of cell environmental conditions and use. PyBLM is developed in Python and formatted to work in conjunction with another NREL software named TEMPEST (Thermo-Electric Model for Powering Energy Storage Technologies).

Mishra, Partha↗
DOE OSTI2022

Comparing Life Histories of the Shortest-Lived Turtle Known (Chicken Turtles, Deirochelys reticularia ) with Long-Lived Blanding's Turtles ( Emydoidea blandingii )

Evolutionary theories predict major differences in life-history trait values of long- and short-lived organisms. Such comparisons have not been possible for chelonians because no shortlived turtle was known until research revealed that chicken turtles (Deirochelys reticularia; DR) have a maximum longevity of 21 yrs. Life-history trait values of DR females are 1) age at maturity of females = 5–6 yrs; 2) clutches per season = 1.6; 3) annual fecundity = 6–8 female eggs per female; 4) average juvenile survivorship from age 1 to maturity = 0.60; and 5) low average annual adult survivorship = 0.66. We compared DR with the very long-lived Blanding’s turtles (Emydoidea blandingii; EB) in Michigan. Over 14 yrs with no mortality (the minimum age at maturity of EB), the maximum potential fecundity produced by a single female embryo and her mature female offspring was 5 female eggs for EB and 1040 eggs for DR. Comparisons of life table output for approximately stable populations of DR and EB resulted in cohort generation times of 7 and 37 yrs, respectively. The life-history prediction that short-lived organisms should produce smaller offspring was not supported. Average wet mass of eggs is 10 g (8.4–11.3 g) for DR and 12 g (10–14 g) for EB; and average wet mass of hatchlings is 7.3 g (6–9 g) for DR and 9.3 g (6–13 g) for EB. Both differences are smaller than expected based on the difference in longevity. Here, short-lived female DR have an unusual tactic of investing in high fecundity and making substantial body sizespecific investment in large eggs, which may reflect why juvenile survivorship had greater influence on population change rates than did adult survivorship. In contrast, adult survivorship had the greatest influence on population change rates of EB. Comparison of cohorts of 1000 female DR and EB hatchlings highlights the differences in life histories of short- and long-lived turtles: all DR would be dead by the time the last female EB had reached maturity at 21 yrs of age.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2014

Recalibrated Equations for Determining Effect of Oil Filtration on Rolling Bearing Life

In 1991, Needelman and Zaretsky presented a set of empirically derived equations for bearing fatigue life (adjustment) factors (LFs) as a function of oil filter ratings. These equations for life factors were incorporated into the reference book, "STLE Life Factors for Rolling Bearings." These equations were normalized (LF = 1) to a 10-micrometer filter rating at Beta(sub x) = 200 (normal cleanliness) as it was then defined. Over the past 20 years, these life factors based on oil filtration have been used in conjunction with ANSI/ABMA standards and bearing computer codes to predict rolling bearing life. Also, additional experimental studies have been made by other investigators into the relationship between rolling bearing life and the size, number, and type of particle contamination. During this time period filter ratings have also been revised and improved, and they now use particle counting calibrated to a new National Institute of Standards and Technology (NIST) reference material, NIST SRM 2806, 1997. This paper reviews the relevant bearing life studies and describes the new filter ratings. New filter ratings, Beta(sub x(c)) = 200 and Beta(sub x(c)) = 1000, are benchmarked to old filter ratings, Beta(sub x) = 200, and vice versa. Two separate sets of filter LF values were derived based on the new filter ratings for roller bearings and ball bearings, respectively. Filter LFs can be calculated for the new filter ratings.

Needelman, William M.↗
DOE OSTI2024

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model

Abstract Soil microbiomes are highly diverse, and to improve their representation in biogeochemical models, microbial genome data can be leveraged to infer key functional traits. By integrating genome-inferred traits into a theory-based hierarchical framework, emergent behaviour arising from interactions of individual traits can be predicted. Here we combine theory-driven predictions of substrate uptake kinetics with a genome-informed trait-based dynamic energy budget model to predict emergent life-history traits and trade-offs in soil bacteria. When applied to a plant microbiome system, the model accurately predicted distinct substrate-acquisition strategies that aligned with observations, uncovering resource-dependent trade-offs between microbial growth rate and efficiency. For instance, inherently slower-growing microorganisms, favoured by organic acid exudation at later plant growth stages, exhibited enhanced carbon use efficiency (yield) without sacrificing growth rate (power). This insight has implications for retaining plant root-derived carbon in soils and highlights the power of data-driven, trait-based approaches for improving microbial representation in biogeochemical models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2024

Rapid assessment of the creep rupture life of metals: A model enabling experimental design

Prediction of the creep rupture life of engineering metals is critical for qualification and design of new materials. The use of long-term creep tests and the need to quantify the performance variability in a priori similar systems hinder the rapid creep assessment of a given material. Therefore, it is essential to develop methods that can extrapolate the long-term performance of alloys and the associated variability from short-term experiments. To this end, this study introduces a new model which enables the estimation of the rupture life of a material for a given stress and temperature. This model relies on two components. First, a new relation for the minimum creep rate (MCR) of materials is introduced. It includes a stress dependent stress exponent allowing the model to capture the variation of MCR across a wide range of temperatures and stresses. Second, employing the Monkman-Grant (MG) law, we establish a relation between stress, temperature and creep rupture life. Together, these two elements yield a new closed-form mathematical expression for the Larson Miller parameter as a function of stress and temperature. This expression captures the creep rupture time for many metals (Gr91, Copper, Gr122 and 347H) and compares favorably with alternate empirical approaches. The model is then used to assess the minimum duration of creep rates necessary to qualify the material up to 100000h. Furthermore, it is found that depending on the material system, creep tests as few as five limited to 5000 h for steels (Gr91, Gr122, 347H) and 100 h for copper are sufficient to model creep lifetimes. Finally, using a Bayesian inference-based approach to calibrate the model, we demonstrate that variability in rupture life can be captured via the quantification of the uncertainty in the model parameters and extrapolated from a limited number of short to moderately short creep tests; thereby paving the way for accelerated creep testing.

36 MATERIALS SCIENCE↗
NASA NTRS1973

Cost analysis of life support systems

A methodology was developed to predict realistic relative cost of Life Support Systems (LSS) and to define areas of major cost impacts in the development cycle. Emphasis was given to tailoring the cost data for usage by program planners and designers. The equipment classifications used based on the degree of refinement were as follows: (1) Working model; (2) low-fidelity prototype; (3) high-fidelity prototype; and (4) flight-qualified system. The major advanced LSS evaluated included the following: (1) Carbon dioxide removal; (2) oxygen recovery systems; (3) water recovery systems; (4) atmosphere analysis system.

Yakut, M. M.↗
NASA NTRS2011

Model Adaptation for Prognostics in a Particle Filtering Framework

One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.

Saha, Bhaskar↗
NASA NTRS1985

A review of fracture mechanics life technology

Current lifetime prediction technology for structural components subjected to cyclic loads was reviewed. The central objectives of the project were to report the current state of and recommend future development of fracture mechanics-based analytical tools for modeling and forecasting subcritical fatigue crack growth in structures. Of special interest to NASA was the ability to apply these tools to practical engineering problems and the developmental steps necessary to bring vital technologies to this stage. A survey of published literature and numerous discussions with experts in the field of fracture mechanics life technology were conducted. One of the key points made is that fracture mechanics analyses of crack growth often involve consideration of fatigue and fracture under extreme conditions. Therefore, inaccuracies in predicting component lifetime will be dominated by inaccuracies in environment and fatigue crack growth relations, stress intensity factor solutions, and methods used to model given loads and stresses. Suggestions made for reducing these inaccuracies include: development of improved models of subcritical crack growth, research efforts aimed at better characterizing residual and assembly stresses that can be introduced during fabrication, and more widespread and uniform use of the best existing methods.

Thomas, J. M.↗
NASA NTRS1994

Large-displacement structural durability analyses of simple bend specimen emulating rocket nozzle liners

Large-displacement elastic and elastic-plastic, finite-element stress-strain analyses of an oxygen-tree high-conductivity (OFHC) copper plate specimen were performed using an updated Lagrangian formulation. The plate specimen is intended for low-cost experiments that emulate the most important thermomechanical loading and failure modes of a more complex rocket nozzle. The plate, which is loaded in bending at 593 C, contains a centrally located and internally pressurized channel. The cyclic crack initiation lives were estimated using the results from the analyses and isothermal strain-controlled low-cycle fatigue data for OFHC copper. A comparison of the predicted and experimental cyclic lives showed that an elastic analysis predicts a longer cyclic life than that observed in experiments by a factor greater than 4. The results from elastic-plastic analysis for the plate bend specimen, however, predicted a cyclic life in close agreement with experiment, thus justifying the need for the more rigorous stress-strain analysis.

Arya, Vinod K.↗
DOE OSTI2021

Soil microbiome predictability increases with spatial and taxonomic scale

Soil microorganisms shape ecosystem function, yet it remains an open question whether we can predict the composition of the soil microbiome in places before observing it. Furthermore, it is unclear whether the predictability of microbial life exhibits taxonomic- and spatial-scale dependence, as it does for macrobiological communities. Here, we leverage multiple large-scale soil microbiome surveys to develop predictive models of bacterial and fungal community composition in soil, then test these models against independent soil microbial community surveys from across the continental United States. We find remark- able scale dependence in community predictability. The predictability of bacterial and fungal communities increases with the spatial scale of observation, and fungal predictability increases with taxonomic scale. These patterns suggest that there is an increasing importance of deterministic versus stochastic processes with scale, consistent with findings in plant and animal communities, suggesting a general scaling relationship across biology. Biogeochemical functional groups and high-level taxonomic groups of microorganisms were equally predictable, indicating that traits and taxonomy are both powerful lenses for understanding soil communities. Here, by focusing on out-of-sample prediction, these findings suggest an emerging generality in our understanding of the soil microbiome, and that this understanding is fundamentally scale dependent

Biogeography↗
NASA NTRS1992

Design prediction for long term stress rupture service of composite pressure vessels

Extensive stress rupture studies on glass composites and Kevlar composites were conducted by the Lawrence Radiation Laboratory beginning in the late 1960's and extending to about 8 years in some cases. Some of the data from these studies published over the years were incomplete or were tainted by spurious failures, such as grip slippage. Updated data sets were defined for both fiberglass and Kevlar composite stand test specimens. These updated data are analyzed in this report by a convenient form of the bivariate Weibull distribution, to establish a consistent set of design prediction charts that may be used as a conservative basis for predicting the stress rupture life of composite pressure vessels. The updated glass composite data exhibit an invariant Weibull modulus with lifetime. The data are analyzed in terms of homologous service load (referenced to the observed median strength). The equations relating life, homologous load, and probability are given, and corresponding design prediction charts are presented. A similar approach is taken for Kevlar composites, where the updated stand data do show a turndown tendency at long life accompanied by a corresponding change (increase) of the Weibull modulus. The turndown characteristic is not present in stress rupture test data of Kevlar pressure vessels. A modification of the stress rupture equations is presented to incorporate a latent, but limited, strength drop, and design prediction charts are presented that incorporate such behavior. The methods presented utilize Cartesian plots of the probability distributions (which are a more natural display for the design engineer), based on median normalized data that are independent of statistical parameters and are readily defined for any set of test data.

Robinson, Ernest Y.↗