Search NASA⌕ Search

SEARCH · Search NASA

Results for “degradation predictions”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Degradation of Adsorbed Bisphenol A by Soluble Mn(III)

Bisphenol A (BPA), a high production volume chemical and potential endocrine disruptor, is found to be associated with sediments and soils due to its hydrophobicity (log K OW of 3.42). Here, we used superfine powdered activated carbon (SPAC) with a particle size of 1.38 ± 0.03 μm as a BPA sorbent and assessed degradation of BPA by oxidized manganese (Mn) species. SPAC strongly sorbed BPA, and desorption required organic solvents. No degradation of adsorbed BPA (278.7 ± 0.6 mg BPA g –1 SPAC) was observed with synthetic, solid α-MnO 2 with a particle size of 15.41 ± 1.35 μm; however, 89% mass reduction occurred following the addition of 0.5 mM soluble Mn(III). Small-angle neutron scattering data suggested that both adsorption and degradation of BPA occurred in SPAC pores. The findings demonstrate that Mn(III) mediates oxidative transformation of dissolved and adsorbed BPA, the latter observation challenging the paradigm that contaminant desorption and diffusion out of pore structures are required steps for degradation. Soluble Mn(III) is abundant near oxic-anoxic interfaces, and the observation that adsorbed BPA is susceptible to degradation has implications for predicting, and possibly managing, the fate and longevity of BPA in environmental systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Distinguishing photo-induced oxygen attack on alkyl chain versus conjugated backbone for alkylthienyl-benzodithiophene (BDTT)-based push–pull polymers

Synthetic design has enabled increasing power conversion efficiency advances in organic photovoltaics (OPV). One continuing knowledge gap is detailed understanding of single-material chemical (photo)stability. Many considerations are based on prior OPV-relevant donor homopolymer systems rather than the next-generation push–pull architectures. Generally, energetic offsets between the lowest occupied molecular orbital of the donor and molecular oxygen are assumed to dictate kinetics of photo-induced charge transfer to a super oxide radical. Herein we determine the ambient-induced photo-degradation pathways, presented as proposed site-specific arrow-pushing mechanisms, for five donor polymers all containing the same push unit – alkylthienyl-substituted-benzodithiophene (BDTT) – but with chemically distinct pull units. The donor-only polymer films were subject to controlled photobleaching in air as an accelerated degradation approach, coupled with simultaneous monitoring of absorptance. Sample subsets were periodically removed and analyzed with X-ray photoelectron spectroscopy, to evaluate near-surface chemical composition and oxygen additions to hetero reporter atoms on either the BDTT push unit or distinct pull unit (i.e., sulphur and nitrogen). Here, this methodology allows us to distinguish between different mechanisms of bond cleavage and formation. Overall, we find that neither polymer redox properties nor individual push or pull unit stability are sufficient to predict photo-oxidative degradation of these polymers. Rather, there is a greater dependence on the susceptibility of unique structural groups within a polymeric system. Alkyl chain oxygen addition is generally the first attack site and direct sulfur oxidation on the conjugated backbone occurs after saturation of the alkyl chain initiation sites. This work provides a standard that could be used to evaluate relative photo-oxidative (in)stability for new OPV materials quickly – prior to time-consuming device optimization – and demonstrates an effective methodology for correlating optical degradation with chemical structure alterations via spectroscopic signatures to guide synthetic design.

14 SOLAR ENERGY↗
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↗
NASA NTRS2010

Accelerated Aging System for Prognostics of Power Semiconductor Devices

Prognostics is an engineering discipline that focuses on estimation of the health state of a component and the prediction of its remaining useful life (RUL) before failure. Health state estimation is based on actual conditions and it is fundamental for the prediction of RUL under anticipated future usage. Failure of electronic devices is of great concern as future aircraft will see an increase of electronics to drive and control safety-critical equipment throughout the aircraft. Therefore, development of prognostics solutions for electronics is of key importance. This paper presents an accelerated aging system for gate-controlled power transistors. This system allows for the understanding of the effects of failure mechanisms, and the identification of leading indicators of failure which are essential in the development of physics-based degradation models and RUL prediction. In particular, this system isolates electrical overstress from thermal overstress. Also, this system allows for a precise control of internal temperatures, enabling the exploration of intrinsic failure mechanisms not related to the device packaging. By controlling the temperature within safe operation levels of the device, accelerated aging is induced by electrical overstress only, avoiding the generation of thermal cycles. The temperature is controlled by active thermal-electric units. Several electrical and thermal signals are measured in-situ and recorded for further analysis in the identification of leading indicators of failures. This system, therefore, provides a unique capability in the exploration of different failure mechanisms and the identification of precursors of failure that can be used to provide a health management solution for electronic devices.

Celaya, Jose R.↗
NASA NTRS2016

Parameter Uncertainty for Aircraft Aerodynamic Modeling using Recursive Least Squares

A real-time method was demonstrated for determining accurate uncertainty levels of stability and control derivatives estimated using recursive least squares and time-domain data. The method uses a recursive formulation of the residual autocorrelation to account for colored residuals, which are routinely encountered in aircraft parameter estimation and change the predicted uncertainties. Simulation data and flight test data for a subscale jet transport aircraft were used to demonstrate the approach. Results showed that the corrected uncertainties matched the observed scatter in the parameter estimates, and did so more accurately than conventional uncertainty estimates that assume white residuals. Only small differences were observed between batch estimates and recursive estimates at the end of the maneuver. It was also demonstrated that the autocorrelation could be reduced to a small number of lags to minimize computation and memory storage requirements without significantly degrading the accuracy of predicted uncertainty levels.

Grauer, Jared A.↗
DOE OSTI2025

Adhesively Bonded Copper and Polyimide for Flexible Printed Circuit Boards: A Data-Driven Degradation Study Under Accelerated Aging

The long-term durability of polyimide laminated adhesive based flexible printed circuit boards (PCBs) is critical to the microelectronics industry. The interface of the adhesive layer binding the polyimide and copper layers is particularly vulnerable since decreased bonding strength can lead to deadhesion, performance loss, and premature device failure. Here, in this study, adhesive-bonded polyimide and copper samples are subjected to 1000 h of damp heat exposure (85 °C/85%RH) to evaluate the impact of moisture and copper inclusion on the adhesive bonding performance. Significant copper diffusion through the adhesive layer is observed, which, combined with moisture uptake, is found to substantially weaken interfacial adhesion in copper-containing samples, thereby reducing device lifetime. Observed surface and bulk material changes are correlated to adhesion strength using the network structural equation modeling (netSEM) approach, resulting in a robust predictive model of performance degradation based on measurable materials properties.

Chemistry↗
NASA NTRS1984

Thermal barrier coating life prediction model development

In order to fully exploit thermal barrier coatings (TBCs) on turbine components and achieve the maximum performance benefit, the knowledge and understanding of TBC failure mechanisms must be increased and the means to predict coating life developed. The proposed program will determine the predominant modes of TBC system degradation and then develop and verify life prediction models accounting for those degradation modes. The successful completion of the program will have dual benefits: the ability to take advantage of the performance benefits offered by TBCs, and a sounder basis for making future improvements in coating behavior.

Hillery, R. V.↗
DOE OSTI2022

Physics-informed neural networks for identification of material properties using standing waves

A metallic structure in its initial stage of failure involves plastic deformation or environmental degradation that changes the elastic modulus and density. This work presents the detection of change in wave velocity (a function of elastic modulus and density) as a system identification problem. A physics-informed neural network (PINN) is proposed to solve the system identification problem. The PINN takes the spatial coordinates of scanning locations and time as inputs and provides the displacement and wave velocity as outputs. The governing partial differential equation of standing waves in a rod is incorporated into the neural network as physics in the form of a loss function. The wave velocity vector is randomly initiated. During the training of the network, physics is used to determine and update the wave velocity target vector from the network’s displacement predictions. The measured data, comprising sparse displacement response on the rod structure, are used to train the PINN. The wave velocity at the sparse locations on the rod is learned from the predicted displacements during the training. Using the predictions of the trained network, the response of free vibration or material property variation can be reconstructed at unscanned locations on the structure to obtain high-resolution maps for full-field imaging to detect and localize the changes caused by plastic deformation. The PINN’s sparse scanning and simultaneous prediction capability during training can lead to high scanning and data-processing speeds. This capability yields a nondestructive evaluation system that can predict the presence of degraded material locations as the structural vibrations are scanned and processed in real time.

Rathod, Vivek↗
DOE OSTI2024

Event-Based Energy Impact Tracking and Forecasting with Limited Measurements for Rooftop Units

Packaged air conditioning units and heat pumps, also known as rooftop units (RTUs), are responsible for almost 133 billion kWh of electricity usage annually on site for space cooling U.S. commercial buildings. In addition, the use of heat pumps is a trend we expect to accelerate as buildings transition from fossil fuel-based heating to electricity as a key step for decarbonizing the U.S. commercial buildings sector. However, the operation conditions and energy use of RTUs and heat pumps are usually not well monitored as they are not commonly integrated with building automation systems and lack exposed sensing and control points. To fill this gap, this paper proposes a framework for tracking and forecasting energy impacts resulting from degradation of performance and improved performance for unit servicing using limited data. The proposed framework makes use of a constrained dataset, specifically measurements of the outdoor air temperature and the power demand of individual RTUs, to track and forecast changes in energy use associated with changes in performance over various temporal horizons ranging from days to weeks. Following the detection of an RTU fault, performance degradation, or performance improvement, the framework employs a prediction model to assess the cumulative energy impact. We demonstrate the effectiveness of the method with field-collected data for servicing and degradation examples and compare the predicting accuracy of Gradient Boosting Decision Tree (GBDT) Regression models to Support Vector Regression and Linear Regression models. The results show that GBDT achieved the best accuracy for time-series validation datasets for the servicing and degradation cases, and the prediction model was able to track the cumulative energy impacts of events. The proposed framework can inform building owners of the cumulative change in energy usage of RTUs associated with performance degradation, performance improvement, or a fault.

packaged air conditioners, packaged heat pumps, ro↗
DOE OSTI2021

Assessing Machine Learning as a Tool to Explain Variance in Deployed Photovoltaic (PV) System Degradation

Degradation remains a large uncertainty in forecasting production for PV plants, creating significant risk for developers and financiers. This study aims to quantify the distribution and drivers of degradation across 10,000 PV systems deployed for distributed or utility generation by training a machine learning model to predict year-over-year degradation rates from metadata characteristics. A combination of K-Means clustering and random forest regressor were found to associate multiple metadata features as potential drivers of degradation, including module characteristics, system design, and climate features. From this, it is inferred that if machine learning is able to find complex patterns between metadata features and system performance loss, such methods can be employed to help developers and financiers make data-informed decisions when estimating long-term energy production forecasts in financial models.

Dunn, Jimmy C.↗
DOE OSTI2020

Foam Degradation Model during Thermal Hypothetical Accident Conditions

Current practice in Safety Analysis Report for Packaging (SARP) thermal analysis for hypothetical accident conditions (HAC) of packages is to not explicitly model the consequence to the foam impact limiting material, but instead to make conservative estimates of those consequences and begin calculations from that point. This approach is typically used for the foam regression distance, which is the thickness of the degraded foam layer after the 30-minute fire event. Estimates for regression distance can come directly from package burn test results or from correlations based on one-dimensional testing by the foam manufacturer. In either case, this is a subjective process, and although it can be used to produce a conservative result, it is a significant approximation in the analysis. This paper describes testing and refinement of a simplified foam regression model. Comparisons are made against available data from laboratory experiments. Model refinements were tested until satisfactory agreement was achieved. The final modeling approach was then compared with post-test inspection results from HAC burn test data. A new model with an effective thermal conductivity based on foam to char density variation was shown successful in representing laboratory-scale foam thermal degradation experiments and full-scale burn tests of a current package. This model offers a technically defensible and predictive approach for modeling thermal degradation of rigid polyurethane insulation in transport packages.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Atomic-scale mechanisms of void strengthening in tungsten

Neutron and heavy-ion irradiation of tungsten produces nanometer-size vacancy voids, gas-filled bubbles and dislocation loops. These defect features can affect mechanical properties and the impact can be quite significant because of their high density. Understanding the basic mechanisms of mechanical properties degradation is necessary for predicting radiation effects. Predictions can be made using discrete dislocation dynamics or/and finite element approaches which, however, need local interaction mechanisms as inputs. Such knowledge can be provided only by atomic-scale modeling. This paper reports the results of an extensive atomic-scale modeling study of the interactions between moving edge dislocations and voids in tungsten. Furthermore, the main focus is on the effects of the void size and ambient temperature. Critical resolved shear stress was calculated for voids up to 9 nm in diameter. Atomistic results are compared with the theoretical approach and with those obtained earlier for voids in body centered cubic (bcc) iron. An important role of the void surface has been revealed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Corrosion interactions between stainless steel and lead vanado-iodoapatite nuclear waste form part II

This paper studied the release of iodine from lead vanado-iodoapatite (I-APT, Pb 9.85 (VO 4 ) 6 I 1.7 ), a potential nuclear waste form for the radioactive waste element of I-129, which can be enhanced when crevice corrosion of stainless steel (SS) occurring nearby. Reference corrosion studies of I-APT were performed in different bulk solutions including DI water, 0.6 M and 6 M NaCl, and 0.1 M HNO 3 without metal crevice corrosion interactions. The localized enrichment of Cl – , one of the major consequences of SS crevice corrosion, was found to be the decisive factor that led to the enhanced release of iodine. A surface alteration layer consisting of a mixture of nanocrystalline I-APT and Cl-rich apatite (Cl-APT) formed on I-APT surface. Meanwhile, large Cl-APT crystals formed at the crevice mouth on the I-APT surface. This study reveals a new near-field corrosion mechanism for ceramic waste forms when they are exposed to aggressive local corrosive conditions created by the electrochemical reactions of nearby metals. The insight gained in this study could be beneficial for a more accurate prediction of waste form degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Toward the performance assessment of advanced nuclear waste forms: temperature dependence of lanthanide borosilicate glass dissolution

Lanthanide borosilicate (LaBS) glasses are among the most promising waste forms for the immobilization of high-level radioactive waste generated from advanced nuclear fuel cycles. However, the temperature dependence of their dissolution kinetics remains poorly understood and constrained, limiting the integration of these materials into established performance assessment models. Here, we investigate the dissolution behavior of the legacy AmCm2-19 LaBS glass and the benchmark alkali aluminoborosilicate ISG-1 in deionized water between 50 °C and 250 °C using ASTM C1285 (Product Consistency Test-B) protocols. For AmCm2-19 LaBS glass, normalized elemental release rates for boron and silicon increase with temperature before plateauing near 150 °C, consistent with solubility-limited behavior. From data obtained at 50 °C and 100 °C, Arrhenius analysis yields activation energies of E a (B) = 24.8 ± 0.3 kJ mol⁻¹ and E a (Si) = 14.4 ± 0.2 kJ mol⁻¹, similar or slightly lower than those previously reported for two other compositions of LaBS glasses. No secondary phases or alteration layers were detected by SEM-EDX or pXRD. These results establish one of the first temperature-dependent kinetic datasets for LaBS glass dissolution, providing quantitative parameters to inform mechanistic corrosion models and predictive simulations of glass degradation in geological disposal environments.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

A Computational Procedure for Assessing I$_{c}(\varepsilon$) in Nb$_{3}$Sn/Bi-2212 Hybrid Magnets

The critical current of superconductors is commonly measured by testing unloaded wires under an external magnetic field. While stressed by intense Lorentz forces, the existing HTS/LTS superconductors are prone to a reduction in critical current before reaching their structural mechanical limit. Here, in this work, the magnetic and mechanical analysis of the FNAL 4-layer Bi-2212/Nb$_{3}$Sn hybrid dipole magnet is reported, aimed at predicting the critical current degradation for both the superconductors during powering at 16T. All the Rutherford cables in the coils of the hybrid magnet were modeled at the strand level in Ansys APDL with the heterogeneous cable model. Utilizing this detailed geometry, it was possible to evaluate the effects of strain on the critical current degradation for both the Nb$_{3}$Sn and Bi-2212 superconductors under the intense Lorentz forces. The analysis presented in this paper integrates strain-dependent critical current laws, with parameters derived from experimental data, to simulate the hybrid magnet's performance for all possible current-powering configurations. The proposed methodology enables a detailed assessment of conductor integrity and I$_{C}(\varepsilon$) reduction in existing hybrid magnet designs, providing a versatile and rigorous framework for optimizing future high-field hybrid magnets.

D'Agliano, A. [Lawrence Berkeley National Laborato↗
DOE OSTI2021

Damage Modeling of Solid Oxide Fuel Cells Accounting for Redox Effects

This work applies a multiscale mechanistic damage model developed for brittle ceramics and implemented in commercial finite element (FE) packages via user subroutines to study progressive damage in solid oxide fuel cells (SOFC) subjected to thermomechanical loading under normal operating and shutdown conditions including redox effects. The damage model captures the micromechanics of stiffness reduction due to material porosity change and microcracking and integrates the as-obtained stiffness reduction law into a continuum damage mechanics (CDM) formulation for the evolution of microcracks up to fracture. The volumetric “swelling” that occurs during redox is treated in constitutive modeling similarly to thermal expansion, but swelling strains are irreversible. Furthermore, this damage model was first validated through predictions of strength and stress-strain response for the SOFC electrode materials. Next, it has been applied to predict the potential for degradation in a generic planar SOFC stack with large active area cells. Multicell stack models were simulated in both co-flow and counter-flow configurations. In addition, a constant temperature redox cycle was also simulated to capture overall cell electrode damage due to volumetric swelling of the nickel (Ni)-based anode in the anode-supported cells.

25 ENERGY STORAGE↗
DOE OSTI2026

Induced Magnetic Fields in LBNF Horns and DUNE-PRISM Off-Axis Neutrinos

Deep Underground Neutrino Experiment (DUNE) is a next-generation, long-baseline neutrino oscillation experiment that will utilize an intense neutrino beam from Fermilab to measure neutrino oscillation parameters with unprecedented precision. The DUNE-PRISM near detector concept employs an off-axis measurement strategy to mitigate neutrino-nucleus interaction uncertainties; however, this approach relies critically on the accurate characterization of the neutrino flux. As statistical uncertainties are suppressed in the high-intensity Long Baseline Neutrino Facility (LBNF) beam, beamline focusing uncertainties, specifically those arising from focusing horn geometry, become an important systematic uncertainty source. This work presents Geant4 LBNF beam simulations quantifying one of the most important systematic uncertainties for off-axis fluxes: the impact of manufacturing tolerances in the LBNF horns. The focusing horns consist of coaxial inner and outer conductors. Ideally, the region inside the inner conductor is field-free, while the region between the inner and outer conductors serves as the focusing region, where the magnetic field follows a nominal $1/r$ dependence. However, inner conductor deformations such as eccentricity and ellipticity introduce asymmetry, inducing unintended magnetic fields inside the field-free region. Crucially, while the on-axis neutrino flux remains largely unaffected by these induced fields, the resulting flux fractional shifts become pronounced over a particular range of off-axis positions where the DUNE-PRISM program will perform measurements. Consequently, these inner conductor deformations can impact the precision of flux predictions, which could potentially degrade the sensitivity to oscillation parameters. Possible mitigation strategies for these effects will also be presented.

Bae, Yuri [U. Minnesota, Minneapolis (main)] (ORCI↗