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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 559 records · Page 31

Reliable modeling and prediction of precipitation & radiation for mountainous hydrology

This White Paper focuses on data-driven atmospheric process model emulation and atmospheric process surrogate model development. It proposes leveraging recent AI advances in these approaches to fill in unavoidable observational gaps and enable high-fidelity modeling/predictability of the atmosphere and land-surface interactions in mountainous watersheds. This approach will support studies and predictability of water cycle extremes.

54 ENVIRONMENTAL SCIENCES↗

Combined bending-torsion fatigue reliability. III

Results generated by three, unique fatigue reliability research machines which can apply reversed bending loads combined with steady torque are presented. AISI 4340 steel, grooved specimens with a stress concentration factor of 1.42 and 2.34, and Rockwell C hardness of 35/40 were subjected to various combinations of these loads and cycled to failure. The generated cycles-to-failure and stress-to-failure data are statistically analyzed to develop distributional S-N and Goodman diagrams. Various failure theories are investigated to determine which one represents the data best. The effects of the groove, and of the various combined bending-torsion loads, on the S-N and Goodman diagrams are determined. Two design applications are presented which illustrate the direct useability and value of the distributional failure governing strength and cycles-to-failure data in designing for specified levels of reliability and in predicting the reliability of given designs.

Kececioglu, D.↗

Illustrated structural application of universal first-order reliability method

The general application of the proposed first-order reliability method was achieved through the universal normalization of engineering probability distribution data. The method superimposes prevailing deterministic techniques and practices on the first-order reliability method to surmount deficiencies of the deterministic method and provide benefits of reliability techniques and predictions. A reliability design factor is derived from the reliability criterion to satisfy a specified reliability and is analogous to the deterministic safety factor. Its application is numerically illustrated on several practical structural design and verification cases with interesting results and insights. Two concepts of reliability selection criteria are suggested. Though the method was developed to support affordable structures for access to space, the method should also be applicable for most high-performance air and surface transportation systems.

Verderaime, V.↗

Development of Reliability Based Life Prediction Methods for Thermal and Environmental Barrier Coatings in Ceramic Matrix Composites

Literature survey related to the EBC/TBC (environmental barrier coating/thermal barrier coating) fife models, failure mechanisms in EBC/TBC and the initial work plan for the proposed EBC/TBC life prediction methods development was developed as well as the finite element model for the thermal/stress analysis of the GRC-developed EBC system was prepared. Technical report for these activities is given in the subsequent sections.

Shah, Ashwin↗

DuraMAT Technology Scouting Report: Assessing Module Reliability Risks Associated with Projected Technological Changes

Maintaining the reliability of photovoltaic (PV) modules in the face of rapidly changing technology is critical to maximizing solar energy's contribution to global decarbonization. Our presentation describes expected changes in PV technology and their impacts on performance and reliability. We leverage PV market reports, interviews with PV researchers and other industry stakeholders, and peer-reviewed literature to narrow the multitude of possible changes into a manageable set of 11 impactful trends likely to be incorporated in near-term crystalline-silicon module designs. We group the trends into four categories (module architecture, interconnect technologies, bifacial modules, and cell technology) and explore the drivers behind the changes, their interactions, and associated reliability risks and benefits. Our analysis identifies specific areas that would benefit from accelerating the PV reliability learning cycle, to assess emerging module products and designs more accurately. We recommend that researchers continue tracking module technologies and their reliability implications so efforts can be focused on the most impactful trends. As the rapid technological turnover continues, it is also critical to incorporate fundamental knowledge into models that can predict module reliability. Predictive capabilities complete the PV reliability learning cycle-reducing the time required to assess new designs and mitigating the risks associated with large-scale deployment of new products.

bifacial↗

Sequential cumulative fatigue reliability

A component is assumed to be subjected to a sequence of several groups of sinusoidal stresses. Each group consists of a specific number of cycles having the same maximum alternating stress level and the same mean stress level, the maximum alternating stress level being different from group to group. A method for predicting the reliability of components subjected to such loads is proposed, given their distributional alternating stress versus cycles-to-failure (S-N) diagram. It is called the 'conditional reliability-equivalent life' method. It is applied to four-cases using distributional fatigue data generated in the Reliability Research Laboratory of The University of Arizona, and the predicted reliabilities are compared and discussed.

Kececioglu, D.↗

A causal perspective on reliability assessment

Causality in an engineered system pertains to how a system output changes due to a controlled change or intervention on the system or system environment. Engineered systems designs reflect a causal theory regarding how a system will work, and predicting the reliability of such systems typically requires knowledge of this underlying causal structure. The aim of this work is to introduce causal modeling tools that inform reliability predictions based on biased data sources. We present a novel application of the popular structural causal modeling (SCM) framework to reliability estimation in an engineering application, illustrating how this framework can inform whether reliability is estimable and how to estimate reliability given a set of data and assumptions about the subject matter and data generating mechanism. When data are insufficient for estimation, sensitivity studies based on problem-specific knowledge can inform how much reliability estimates can change due to biases in the data and what information should be collected next to provide the most additional information. We apply the approach to a pedagogical example related to a real, but proprietary, engineering application, considering how two types of biases in data can influence a reliability calculation.

42 ENGINEERING↗

Determining Functional Reliability of Pyrotechnic Mechanical Devices

This paper describes a new approach for evaluating mechanical performance and predicting the mechanical functional reliability of pyrotechnic devices. Not included are other possible failure modes, such as the initiation of the pyrotechnic energy source. The requirement of hundreds or thousands of consecutive, successful tests on identical components for reliability predictions, using the generally accepted go/no-go statistical approach routinely ignores physics of failure. The approach described in this paper begins with measuring, understanding and controlling mechanical performance variables. Then, the energy required to accomplish the function is compared to that delivered by the pyrotechnic energy source to determine mechanical functional margin. Finally, the data collected in establishing functional margin is analyzed to predict mechanical functional reliability, using small-sample statistics. A careful application of this approach can provide considerable cost improvements and understanding over that of go/no-go statistics. Performance and the effects of variables can be defined, and reliability predictions can be made by evaluating 20 or fewer units. The application of this approach to a pin puller used on a successful NASA mission is provided as an example.

Bement, Laurence J.↗

Verification of MOOSE/Bison's Heat Conduction Solver Using Combined Spatiotemporal Convergence Analysis

Bison is a computational physics code that uses the finite element method to model the thermo-mechanical response of nuclear fuel. Since Bison is used to inform high-consequence decisions, it is important that its computational results are reliable and predictive. One important step in assessing the reliability and predictive capabilities of a simulation tool is the verification process, which quantifies numerical errors in a discrete solution relative to the exact solution of the mathematical model. One step in the verification process—called code verification—ensures that the implemented numerical algorithm is a faithful representation of the underlying mathematical model, including partial differential or integral equations, initial and boundary conditions, and auxiliary relationships. In this paper, the code verification process is applied to spatiotemporal heat conduction problems in Bison. Simultaneous refinement of the discretization in space and time is employed to reveal any potential mistakes in the numerical algorithms for the interactions between the spatial and temporal components of the solution. For each verification problem, the correct spatial and temporal order of accuracy is demonstrated for both first- and second-order accurate finite elements and a variety of time-integration schemes. Furthermore, these results provide strong evidence that the Bison numerical algorithm for solving spatiotemporal problems reliably represents the underlying mathematical model in MOOSE. The selected test problems can also be used in other simulation tools that numerically solve for conduction or diffusion.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Uncertainty Quantification Enabled by Automatic Differentiation for Hydrodynamic Simulation of Shock‐to‐Detonation Transition in High Explosives

Quantifying the effects of uncertainty in a reactive burn model on the run-to-detonation time in high explosives (HEs) provides a robust methodology for assessing the probability of an HE failing the IHE qualification standard. Moreover, uncertainty quantification helps evaluate whether the model calibration accurately represents data outside the calibration set. This study uses a specialized hydrodynamic simulation code for modeling detonation to determine the run-to-detonation time of the HE PBX 9502 for various impact velocities. To quickly approximate uncertainties in the model, a surrogate was constructed using a Taylor series expansion centered at the mean of the input parameters. To obtain the sensitivities required for constructing the Taylor series, HYP-percomplex Automatic Differentiation (HYPAD) was implemented. HYPAD is a methodology for infusing existing codes with automatic differentiation capabilities by augmenting variables with one or more imaginary units to compute step-size independent partial derivatives. These derivatives are accurate to machine precision with respect to the implemented numerical algorithm, meaning their accuracy reflects that of the underlying method (e.g., integration or discretization schemes). Using reduced order modeling techniques, the mean and standard deviation of the run-to-detonation time of a shock within PBX 9502 were computed for a number of initial impact velocities. A weighted least squares regression was then performed to obtain a best fit curve and prediction interval for the computed statistics. Historical data points from explosively driven wedge tests were utilized to validate the prediction interval, ensuring its reliability in predicting future outcomes. With this prediction interval and a known safety constraint curve, the most probable point of failure and the probability of failure for the HE PBX 9502 were determined.

97 MATHEMATICS AND COMPUTING↗

Reliability growth models for NASA applications

The objective of any reliability growth study is prediction of reliability at some future instant. Another objective is statistical inference, estimation of reliability for reliability demonstration. A cause of concern for the development engineer and management is that reliability demands an excessive number of tests for reliability demonstration. For example, the Space Transportation Main Engine (STME) program requirements call for .99 reliability at 90 pct. confidence for demonstration. This requires running 230 tests with zero failure if a classical binomial model is used. It is therefore also an objective to explore the reliability growth models for reliability demonstration and tracking and their applicability to NASA programs. A reliability growth model is an analytical tool used to monitor the reliability progress during the development program and to establish a test plan to demonstrate an acceptable system reliability.

Taneja, Vidya S.↗

Concept of Operations for Management by Trajectory

This document describes Management by Trajectory (MBT), a concept for future air traffic management (ATM) in which every flight operates in accordance with a four-dimensional trajectory (4DT) that is negotiated between the airspace user and the Federal Aviation Administration (FAA) to respect the airspace user's goals while complying with National Airspace System (NAS) constraints. In the present-day NAS, the ATM system attempts to predict the trajectory for each flight based on the approved flight plan and scheduled or controlled departure time. However, once the aircraft starts to move, controllers tactically manage the aircraft to implement traffic management restrictions, separate otherwise conflicting aircraft, and address arising NAS constraints. Tactical controller actions are not directly communicated to the automation systems or other stakeholders. Furthermore, the initial trajectory prediction does not anticipate these disruptions or how they will impact the flight. Consequently, and compounded by gaps in required data and models, trajectory predictions are less accurate than possible, which affects Traffic Flow Management (TFM) performance. A cornerstone of the MBT concept is that all air vehicles have, at all times, an assigned 4DT from their current state to their destination. These assigned trajectories consist of trajectory constraints and descriptions. Pilots and air traffic controllers, with the aid of automation, operate the aircraft to comply with the assigned trajectory, unless first negotiating a revision. Equipped aircraft have substantial responsibility for complying with the assigned trajectory without controller intervention. To maximize the operational flexibility available to the airspace user, the assigned trajectory only imposes trajectory constraints as required to achieve the ATM goals of NAS constraint compliance and aircraft separation. Trajectory descriptions are added to the assigned trajectory to ensure sufficient predictability. To further improve trajectory prediction accuracy, airspace users supplement the assigned trajectory by broadcasting intent information and updating it as necessary. Air vehicle intent is a more detailed description of the airspace user's plan for how the flight will fly the assigned trajectory. Air vehicle intent can change freely, without negotiation, as long as it remains in compliance with the assigned trajectory. Aircraft assigned trajectories, air vehicle intent, and predicted trajectories are shared, creating a common view among stakeholders. A NAS Constraint Service gathers and publishes information about all known NAS constraints, enabling airspace users to be informed participants in trajectory negotiation. Trajectory constraints in the assigned trajectory are mapped to NAS constraints to facilitate identifying which aircraft are affected when NAS constraints change. To support efficient trajectory negotiation, all aircraft provide current information about air vehicle capabilities. Assigned trajectories are constructed to satisfy all known NAS constraints, improving trajectory stability and predictability. Uncertainty and disruptions are handled by modifying the assigned trajectory as far in advance as possible. By proactively negotiating changes to the assigned trajectory, rather than relying on controller-selected tactical actions such as vectors to resolve traffic conflicts or implement miles-in-trail restrictions, MBT keeps aircraft on closed trajectories that are fully known to all stakeholders. Since reactive air traffic control actions cannot be predicted in advance, the downstream trajectory cannot be accurately predicted until they happen. Reliable trajectory predictions allow the system to identify needed modifications to trajectories further in advance, where they can be negotiated and communicated as amendments (i.e., additional or altered trajectory constraints) to the assigned trajectory. Decision Support Tools (DSTs) aid controllers in rapidly defining and communicating closed trajectories to the aircraft and support all stakeholders in trajectory negotiation. Anticipated MBT benefit mechanisms include more accurate trajectory predictions, improved ATM performance and robustness to off-nominal conditions, increased flexibility and operational efficiency, reduced impediments to emerging classes of airspace users accessing NAS resources, reduced environmental impacts, and enhanced safety.

ConOps↗

Reliability Analysis of Complex NASA Systems with Model Based Engineering

The emergence of model-based engineering, with Model- Based Systems Engineering (MBSE) leading the way, is transforming design and analysis methodologies. The recognized benefits to systems development include moving from document-centric information systems and document-centric project communication to a model-centric environment in which control of design changes in the life cycles is facilitated. In addition, a “single source of truth” about the system, that is up-to-date in all respects of the design, becomes the authoritative source of data and information about the system. This promotes consistency and efficiency in regard to integration of the system elements as the design emerges and thereby may further optimize the design. Therefore Reliability Engineers (REs) supporting NASA missions must be integrated into model-based engineering to ensure the outputs of their analyses are relevant and value-needed to the design, development, and operational processes for failure risks assessment and communication.

Reliability prediction↗

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗

Future-proofing photovoltaics module reliability through a unifying predictive modeling framework

Solar energy, especially photovoltaics (PV), plays a significant role in the global energy transition required for decarbonization. Technological advancements in increasing efficiencies coupled with cost reductions through economies of scale made PV a cost-competitive energy source set to exceed the milestone of 1 TW globally deployed capacity in 2022. However, ongoing downward price pressure coupled with increasing service life expectations creates tremendous challenges for reliability engineers in ensuring the safe and reliable operation of PV modules and systems over the anticipated service life. Today's reliability research efforts aim for module lifetimes of up to 50 years. Still, our current tools fall short in accurately assessing degradation mechanisms and failure modes over such extended periods. We argue that the well-established PV reliability learning cycle needs to be accelerated to keep up with the rapid technological advancements and high expectations that are put on PV and its role in the global energy transition. In this article, we explore the evolution of the PV reliability learning cycle and highlight the significance that predictive modeling capabilities will have on future PV module reliability. We propose creating a unifying modeling framework - which once established will enable the holistic assessment of PV module reliability, accelerate the PV reliability learning cycle, and bring us closer to quantitative service life predictions of PV modules.

14 SOLAR ENERGY↗

An Acoustic Charge Transport Imager for High Definition Television Applications: Reliability Modeling and Parametric Yield Prediction of GaAs Multiple Quantum Well Avalanche Photodiodes

Reliability modeling and parametric yield prediction of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiodes (APDs), which are of interest as an ultra-low noise image capture mechanism for high definition systems, have been investigated. First, the effect of various doping methods on the reliability of GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiode (APD) structures fabricated by molecular beam epitaxy is investigated. Reliability is examined by accelerated life tests by monitoring dark current and breakdown voltage. Median device lifetime and the activation energy of the degradation mechanism are computed for undoped, doped-barrier, and doped-well APD structures. Lifetimes for each device structure are examined via a statistically designed experiment. Analysis of variance shows that dark-current is affected primarily by device diameter, temperature and stressing time, and breakdown voltage depends on the diameter, stressing time and APD type. It is concluded that the undoped APD has the highest reliability, followed by the doped well and doped barrier devices, respectively. To determine the source of the degradation mechanism for each device structure, failure analysis using the electron-beam induced current method is performed. This analysis reveals some degree of device degradation caused by ionic impurities in the passivation layer, and energy-dispersive spectrometry subsequently verified the presence of ionic sodium as the primary contaminant. However, since all device structures are similarly passivated, sodium contamination alone does not account for the observed variation between the differently doped APDs. This effect is explained by the dopant migration during stressing, which is verified by free carrier concentration measurements using the capacitance-voltage technique.

Hunt, W. D.↗

Photovoltaic Inverter Reliability Assessment

As the price of PV modules drops, the price of power electronics become more important. Power electronics now constitute 8%-12% of the total lifetime PV cost. As of 2010, the inverter and associated power conditioning components accounted for $\$0.25$/W, well above the DOE benchmark of $\$0.12$/W by 2020. As efforts to reduce PV module costs yield diminishing returns, the importance of understanding and reducing inverter costs becomes an increasingly critical and cost-effective investment toward achieving the DOE SunShot goals. With this in view, this report showcases and describes an approach to help assess and predict the reliability of photovoltaic (PV) inverters. To predict the reliability, thermal cycling is considered as a prominent stressor in the inverter system. To evaluate the impacts of thermal cycling, a detailed linearized model of the PV inverter is developed along with controllers. This research also develops models and methods to compute the losses of the power electronics switches and other components in a PV inverter. The losses are then used to estimate the junction and heat sink temperature. The model is verified by developing an in-house inverter. Additionally, to assess the scalability of the research, the hardware inverter is placed inside a thermal chamber to verify the losses for different ambient temperatures. After the verification of the model, a reduced-order model of the inverter is implemented to translate the profile of ambient temperature and solar irradiance into the profile of junction temperatures of the switches. The estimated junction temperature data are used to identify inverter reliability indices and predict the useful lifetime of the inverter system. After developing the models to predict the useful lifetime of the system, the impact of reactive power on the overall reliability of the system will be studied. The first chapter involves the motivation behind the research on assessing the reliability of PV inverters. The inverter power stage and controller design of the power converter used in this research is also explained in detail in this chapter. The second chapter presents the various losses of the power converter system and the translation of power loss into temperature increase with thermal modeling. The results of the loss modeling and the thermal modeling are also explained in detail in this chapter. The third chapter involves implementing the reduced-order model to translate the mission profile of ambient temperature and solar irradiance into profile of the junction temperatures of the switches. The final chapter involves identifying reliability indices, which are of interest for this system to predict the remaining useful life of the system, and concludes with the impact of reactive power on the overall reliability of the inverter system.

14 SOLAR ENERGY↗