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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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DOE OSTI2020

Accomplishments and Mid-Year Performance Report: Wind Energy Program Fiscal Year 2020

Through transformative science and innovation, the U.S. Department of Energy's (DOE's) National Wind Technology Center (NWTC) at the National Renewable Energy Laboratory's (NREL's) Flatirons Campus helps lead the way to a sustainable energy future that powers America with significant levels of reliable, low-cost, and accessible wind energy. Since 1976, NREL has provided an ideal environment for the research and development (R&D) of advanced energy technologies through: Wind energy resource assessments, World-class research facilities, High-performance computing and modeling, Data and technology analysis, Manufacturing breakthroughs, Environmental analysis and wildlife conservation efforts, Education and training programs for a future workforce. These activities enable the innovations needed to advance U.S. wind systems, address market and deployment barriers, and drive down the cost of wind energy with more efficient, more reliable, and more predictable wind energy systems. This report provides an overview of the achievements NREL delivered on behalf of DOE's Wind Energy Technologies Office (WETO) and other partners during the first and second quarters of Fiscal Year (FY) 2020.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2016

Two-phase Flow Regime Transition Study using Level-Set Method

In Boiling Water Reactors (BWRs) the coolant exiting the core often has void fractions as high as 50%, resulting in high gas volume fraction two-phase flow regimes, such as annular or churn-turbulent flow. As the coolant boils along the subchannels it will experience flow regime transitions from bubbly to slug, slug to churn, and, finally, churn to annular. While these transition points are known to occur as a higher void develops along the flow path, the driving physics of the transition phenomenon is not well understood. Currently, experiment based correlations are the most reliable method for predicting where in the core the flow regime transitions will occur. However, experimentally, it's ever difficult to observe the details of the two-phase flow at prototypical pressures/temperatures, or, as with high fidelity experiments, the conditions differ from those in a reactor. Being able to predict these flow regime transitions is critical to safe and efficient reactor operations as well as maintaining fuel rod integrity by avoiding dryout. The characterization of flow regimes has been extensively studied, producing a wide set of flow regime maps based on characteristic parameters such as vapor and liquid superficial velocity, time dependent pressure drop, interfacial area density, etc. While these studies are useful in predicting the flow regime for certain geometry and flow conditions, they do not provide a universal predictive capability for any geometry under any conditions. Additionally, it is difficult to achieve reactor realistic pressure and temperature conditions in experiments. Direct Numerical Simulation (DNS) coupled with Interface Tracking Methods (ITMs) becomes advantageous in this case because the simulations are not limited by the material properties or to the real world conditions. DNS allows for analysis of flows that would be impossible to achieve, or observe in detail, experimentally. The finite element based DNS code, PHASTA (parallel, hierarchic, higher-order accurate, adaptive, stabilized transient analysis flow solver), used in this work utilizes the level set ITM and has been previously shown to properly resolve bubbly, slug, and annular flows, but not transitions between the two-phase flow regimes. The present work uses PHASTA to analyze flow in a pipe for various void fractions and superficial velocities. Flow regime transitions were induced to demonstrate the transient behavior of this phenomenon. With the unique advanced analysis capabilities developed in PHASTA that allow for individual bubble tracking, it becomes possible to extract new localized information about the flow behavior that is currently not attainable from experiments. The potential of this capability is to uncover the underlying physics of the flow regime transition phenomenon. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Polymorphism and melt in high-pressure tantalum. II. Orthorhombic phases

Continuing uncertainty in the high-pressure melt curves of bcc transition metals has spawned renewed research interest in the phase diagrams of these materials, with tantalum becoming an important prototype. Here, the present paper extends the quantum-based investigation of high- T, P polymorphism and melt in Ta that was begun in Paper I [Haskins et al., Phys. Rev. B 86, 224104 (2012)] on five candidate cubic and hexagonal structures (bcc, A15, fcc, hcp, and hex-ω) to here treat four promising orthorhombic structures (Pnma, Fddd, Pmma, and α-U). Using DFT-based MGPT multi-ion potentials that allow accurate MD simulations of large systems, we showed in Paper I that the mechanically unstable fcc, hcp, and hex-ω structures can only be stabilized at high- T , P by large anharmonic vibrational effects, requiring systems of ~500 atoms to produce size-independent melt curves and reliable calculations of thermodynamic stability. This reversed a previous small-cell quantum-simulation prediction of a high- T , P hex-ω phase. Subsequent DFT calculations have now suggested a more energetically favorable and mechanically stable Pnma structure, which again small-cell quantum simulations predict could be a high- T , P phase. Our present MGPT total-energy and phonon calculations show that not only Pnma, but all four orthorhombic structures considered here, are similarly energetically favorable, and that Fddd in addition to Pnma is mechanically stable at T = 0 up to 420 GPa. MGPT-MD simulations further reveal spontaneous temperature-induced Pnma → bcc and Fddd → bcc transformations at modest temperatures, peaking at ~1450 K near 100 GPa. At high temperatures near melt, we find T -dependent c / a and b / a axial ratios and large stabilizing anharmonicity present in all four orthorhombic structures. The anharmonicity drives significantly larger melt size effects, requiring systems of ~1000 – 4000 atoms to produce converged melt curves for reliable predictions of relative thermodynamic stability. In the large-cell limit, with ~40000 solid-phase atoms and accurate two-phase MGPT-MD melt simulation, we find that Pnma, Fddd and α-U have melt temperatures that are equal to bcc over small pressure ranges in the vicinity of 100 GPa, but that the orthorhombic melt temperatures never exceed bcc up to 420 GPa. This finding suggests that Pnma, Fddd, and α-U remain highly competitive metastable phases that could coexist with bcc and possibly be observed experimentally. Finally, to add additional insight into our results we have constructed global Helmholtz free energies for the A15, Pnma, and Fddd phases of Ta, complementing previous free energies obtained for the bcc, fcc, and liquid phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Predictive Models and Novel Accelerated Tests for the Reliability of Cell Metallization in Photovoltaic Modules (Final Report)

Studies of metallization corrosion m photovoltaics have mainly been limited to comparisons of modules placed in accelerated chambers to fielded modules [1]. Damp Heat accelerated tests and phenomenological equations [2] are used to assess metallization corrosion without understanding the effect of UV light and temperature and humidity cycles on encapsulant adhesion degradation. The roles of encapsulant in-and out-diffusions of moisture and encapsulant impurities are important. Furthermore, few photovoltaic metallization corrosion studies included the role of bias and leakage currents, which are crucial in the electrochemical reaction of metallization. Leakage currents can highly accelerate the corrosion mechanism and are important to include in the studies of corrosion. In our research plan, we will address the following gaps in the PV community's understanding of metallization corrosion: (1) metallization corrosion with bias, humidity, and impurities in the encapsulant or metallization; (2) humidity diffusion through fresh and degraded encapsulants; and (3) comparison of model predictions with outdoor field modules and SunPower's extensive data for its back-contact and front-contact fleets [2] along with NREL's store of >20 year old modules. Our goal is to build models and accelerated tests to predict long term degradation of metallization corrosion of photovoltaic modules in the field. Our studies will include metals used in c-Si solar cells (Cu, Ag, and Al) and commonly used encapsulants (EV A (ethylene vinyl acetate), TPO (thermoplastic olefin), and silicone).

14 SOLAR ENERGY↗
DOE OSTI2024

Digital Twinning and Predictive Modeling of Traffic for Safe, Efficient, and Reliable Intersections

Over the last decade, the advances in connected and autonomous vehicles (CAVs) have far surpassed the technological realm of transportation infrastructure. There is a growing need to have a technologically commensurate transportation infrastructure to enable safe and reliable movement of goods and people. The recent creation of Advanced Research Projects Agency - Infrastructure (ARPA-I) through the Infrastructure Investment and Jobs Act by the U.S. Department of Transportation (USDOT) has further amplified the need to revolutionize the transportation infrastructure system in the US. This need is perhaps felt most at traffic intersections, as more than 50 percent of the combined total of fatal and injury crashes occur at or near intersections. The proposed concept of Infrastructure Perception and Control (IPC) is aimed at bridging this technological gap by building a real-time digital twin of traffic by fusing detections from sensors installed at the intersection. This digital twin can then empower a wide variety of applications such as smart traffic signals or infrastructure-to-everything (I2X) communications. Smart signaling can help avoid crashes through early-prediction, while I2X can augment the CAV sensors under uncertain driving conditions and provide connected vehicles (CVs) with traffic information they can use to optimize their travel.

connected vehicles↗
DOE OSTI2015

Realising effective theories of tribrid inflation: are there effects from messenger fields?

Tribrid inflation is a variant of supersymmetric hybrid inflation in which the inflaton is a matter field (which can be charged under gauge symmetries) and inflation ends by a GUT-scale phase transition of a waterfall field. These features make tribrid inflation a promising framework for realising inflation with particularly close connections to particle physics. Superpotentials of tribrid inflation involve effective operators suppressed by some cutoff scale, which is often taken as the Planck scale. However, these operators may also be generated by integrating out messenger superfields with masses below the Planck scale, which is in fact quite common in GUT and/or flavour models. The values of the inflaton field during inflation can then lie above this mass scale, which means that for reliably calculating the model predictions one has to go beyond the effective theory description. We therefore discuss realisations of effective theories of tribrid inflation and specify in which cases effects from the messenger fields are expected, and under which conditions they can safely be neglected. In particular, we point out how to construct realisations where, despite the fact that the inflaton field values are above the messenger mass scale, the predictions for the observables are (to a good approximation) identical to the ones calculated in the effective theory treatment where the messenger mass scale is identified with the (apparent) cutoff scale.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

SOLAR CYCLE 25: ANOTHER MODERATE CYCLE?

Surface flux transport simulations for the descending phase of Cycle 24 using random sources (emerging bipolar magnetic regions) with empirically determined scatter of their properties provide a prediction of the axial dipole moment during the upcoming activity minimum together with a realistic uncertainty range. The expectation value for the dipole moment around 2020 (2.5 ± 1.1 G) is comparable to that observed at the end of Cycle 23 (about 2 G). The empirical correlation between the dipole moment during solar minimum and the strength of the subsequent cycle thus suggests that Cycle 25 will be of moderate amplitude, not much higher than that of the current cycle. However, the intrinsic uncertainty of such predictions resulting from the random scatter of the source properties is considerable and fundamentally limits the reliability with which such predictions can be made before activity minimum is reached.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

On the applicability of surrogate-based Markov chain Monte Carlo-Bayesian inversion to the Community Land Model: Case studies at flux tower sites: SURROGATE-BASED MCMC FOR CLM

The Community Land Model (CLM) has been widely used in climate and Earth system modeling. Accurate estimation of model parameters is needed for reliable model simulations and predictions under current and future conditions, respectively. In our previous work, a subset of hydrological parameters has been identified to have significant impact on surface energy fluxes at selected flux tower sites based on parameter screening and sensitivity analysis, which indicate that the parameters could potentially be estimated from surface flux observations at the towers. To date, such estimates do not exist. In this paper, we assess the feasibility of applying a Bayesian model calibration technique to estimate CLM parameters at selected flux tower sites under various site conditions. The parameters are estimated as a joint probability density function (PDF) that provides estimates of uncertainty of the parameters being inverted, conditional on climatologically-average latent heat fluxes derived from observations. We find that the simulated mean latent heat fluxes from CLM using the calibrated parameters are generally improved at all sites when compared to those obtained with CLM simulations using default parameter sets. Further, our calibration method also results in credibility bounds around the simulated mean fluxes which bracket the measured data. The modes (or maximum a posteriori values) and 95% credibility intervals of the site-specific posterior PDFs are tabulated as suggested parameter values for each site. Analysis of relationships between the posterior PDFs and site conditions suggests that the parameter values are likely correlated with the plant functional type, which needs to be confirmed in future studies by extending the approach to more sites.

Huang, Maoyi↗
DOE OSTI2023

A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation

Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. Here high-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.

58 GEOSCIENCES↗
DOE OSTI2012

Determination of peripheral underdosage at the lung-tumor interface using Monte Carlo radiation transport calculations

Prediction of dose distributions in close proximity to interfaces is difficult. In the context of radiotherapy of lung tumors, this may affect the minimum dose received by lesions and is particularly important when prescribing dose to covering isodoses. The objective of this work is to quantify underdosage in key regions around a hypothetical target using Monte Carlo dose calculation methods, and to develop a factor for clinical estimation of such underdosage. A systematic set of calculations are undertaken using 2 Monte Carlo radiation transport codes (EGSnrc and GEANT4). Discrepancies in dose are determined for a number of parameters, including beam energy, tumor size, field size, and distance from chest wall. Calculations were performed for 1-mm{sup 3} regions at proximal, distal, and lateral aspects of a spherical tumor, determined for a 6-MV and a 15-MV photon beam. The simulations indicate regions of tumor underdose at the tumor-lung interface. Results are presented as ratios of the dose at key peripheral regions to the dose at the center of the tumor, a point at which the treatment planning system (TPS) predicts the dose more reliably. Comparison with TPS data (pencil-beam convolution) indicates such underdosage would not have been predicted accurately in the clinic. We define a dose reduction factor (DRF) as the average of the dose in the periphery in the 6 cardinal directions divided by the central dose in the target, the mean of which is 0.97 and 0.95 for a 6-MV and 15-MV beam, respectively. The DRF can assist clinicians in the estimation of the magnitude of potential discrepancies between prescribed and delivered dose distributions as a function of tumor size and location. Calculation for a systematic set of 'generic' tumors allows application to many classes of patient case, and is particularly useful for interpreting clinical trial data.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2012

Understanding and Predicting Metallic Whisker Growth and its Effects on Reliability (LDRD Final Report)

Tin (Sn) whiskers are conductive Sn filaments that grow from Sn-plated surfaces, such as surface finishes on electronic packages. The phenomenon of Sn whiskering has become a concern in recent years due to requirements for lead (Pb)-free soldering and surface finishes in commercial electronics. Pure Sn finishes are more prone to whisker growth than their Sn-Pb counterparts and high profile failures due to whisker formation (causing short circuits) in space applications have been documented. At Sandia, Sn whiskers are of interest due to increased use of Pb-free commercial off-the-shelf (COTS) parts and possible future requirements for Pb-free solders and surface finishes in high-reliability microelectronics. Lead-free solders and surface finishes are currently being used or considered for several Sandia applications. Despite the long history of Sn whisker research and the recently renewed interest in this topic, a comprehensive understanding of whisker growth remains elusive. This report describes recent research on characterization of Sn whiskers with the aim of understanding the underlying whisker growth mechanism(s). The report is divided into four sections and an Appendix. In Section 1, the Sn plating process is summarized. Specifically, the Sn plating parameters that were successful in producing samples with whiskers will be reviewed. In Section 2, the scanning electron microscopy (SEM) of Sn whiskers and time-lapse SEM studies of whisker growth will be discussed. This discussion includes the characterization of straight as well as kinked whiskers. In Section 3, a detailed discussion is given of SEM/EBSD (electron backscatter diffraction) techniques developed to determine the crystallography of Sn whiskers. In Section 4, these SEM/EBSD methods are employed to determine the crystallography of Sn whiskers, with a statistically significant number of whiskers analyzed. This is the largest study of Sn whisker crystallography ever reported. This section includes a review of previous literature on Sn whisker crystallography. The overall texture of the Sn films was also analyzed by EBSD. Finally, a short Appendix is included at the end of this report, in which the X-Ray diffraction (XRD) results are discussed and compared to the EBSD analyses of the overall textures of the Sn films. Sections 2, 3, and 4 have been or will be submitted as stand-alone papers in peer-reviewed technical journals. A bibliography of recent Sandia Sn whisker publications and presentations is included at the end of the report.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predictive Models and Novel Accelerated Tests for the Reliability of Cell Metallization and Solder Joints in Photovoltaic Modules

Interconnect, solder, and metallization-related failures are the primary reason for premature Si-based photovoltaic panel failure [1, 2] and can lead to a cascade of other failures, including thermal events. This combination of high severity and high occurrence means that understanding interconnect-related failures is key to the long-term health of the photovoltaic industry, and it is crucial that the industry adopt efficient and effective tests that aid in design-for-reliability and manufacturing quality control.

14 SOLAR ENERGY↗
DOE OSTI2016

On the applicability of probabilistic analyses to assess the structural reliability of materials and components for solid-oxide fuel cells

The applicability of probabilistic analyses to assess the structural reliability of materials and components for solid-oxide fuel cells (SOFC) is investigated by measuring the failure rate of Ni-YSZ when subjected to a temperature gradient and comparing it with that predicted using the Ceramics Analysis and Reliability Evaluation of Structures (CARES) code. The use of a temperature gradient to induce stresses was chosen because temperature gradients resulting from gas flow patterns generate stresses during SOFC operation that are the likely to control the structural reliability of cell components The magnitude of the predicted failure rate was found to be comparable to that determined experimentally, which suggests that such probabilistic analyses are appropriate for predicting the structural reliability of materials and components for SOFCs. Considerations for performing more comprehensive studies are discussed.

Lara-Curzio, Edgar↗
DOE OSTI2024

Detecting defects that reduce breakdown voltage using machine learning and optical profilometry

Abstract Semiconductor wafer manufacturing relies on the precise control of various performance metrics to ensure the quality and reliability of integrated circuits. In particular, GaN has properties that are advantageous for high voltage and high frequency power devices; however, defects in the substrate growth and manufacturing are preventing vertical devices from performing optimally. This paper explores the application of machine learning techniques utilizing data obtained from optical profilometry as input variables to predict the probability of a wafer meeting performance metrics, specifically the breakdown voltage (V bk ). By incorporating machine learning techniques, it is possible to reliably predict performance metrics that cause devices to fail at low voltage. For diodes that fail at a higher (but still below theoretical) breakdown voltage, alternative inspection methods or a combination of several experimental techniques may be necessary.

42 ENGINEERING↗
DOE OSTI2019

Maximizing ENSO as a source of western US hydroclimate predictability

Abstract Until recently, the El Niño–Southern Oscillation (ENSO) was considered a reliable source of winter precipitation predictability in the western US, with a historically strong link between extreme El Niño events and extremely wet seasons. However, the 2015–2016 El Niño challenged our understanding of the ENSO-precipitation relationship. California precipitation was near-average during the 2015–2016 El Niño, which was characterized by warm sea surface temperature (SST) anomalies of similar magnitude compared to the extreme 1997–1998 and 1982–1983 El Niño events. We demonstrate that this precipitation response can be explained by El Niño’s spatial pattern, rather than internal atmospheric variability. In addition, observations and large-ensembles of regional and global climate model simulations indicate that extremes in seasonal and daily precipitation during strong El Niño events are better explained using the ENSO Longitude Index (ELI), which captures the diversity of ENSO’s spatial patterns in a single metric, compared to the traditional Niño3.4 index, which measures SST anomalies in a fixed region and therefore fails to capture ENSO diversity. The physically-based ELI better explains western US precipitation variability because it tracks the zonal shifts in tropical Pacific deep convection that drive teleconnections through the response in the extratropical wave-train, integrated vapor transport, and atmospheric rivers. This research provides evidence that ELI improves the value of ENSO as a predictor of California’s seasonal hydroclimate extremes compared to traditional ENSO indices, especially during strong El Niño events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Discussion of Strength Reduction Factor Development for Thermal Aging Effect on Nuclear Structural Alloys

In consideration of structural alloy property deterioration during long-term exposure to elevated temperatures, the yield and ultimate tensile strength reduction factors are provided in the Boiler and Pressure Vessel Code Section III for nuclear reactor component design and operation analysis. Because the Gen IV reactor requirement of 40 ~ 60 years of service life makes it difficult to acquire such long exposure test data for developing the reduction factors, they must be derived from test data with relatively short exposure by predictive methods considered to be reasonably reliable. A novel approach with a physically-based model has recently been proposed for application to development of reduction factors for 9Cr-1Mo-V. In the model, contributors to the tensile strength are first identified and related to definite microstructural features of the alloy, then some physically-based methods are employed to simulate the microstructural evolution, and finally the model is assembled with test-data-calibrated parameters to generate the yield and ultimate tensile strength reduction factors covering elevated temperature exposure for up to 57 years. The approach is undoubtedly a trailblazing development that will, if proven reliable, lead to a paradigm shift in predicting thermal aging behavior of many other alloys. Its debut application to Section III, however, concerns nuclear safety and naturally warrants objective, impartial, and thorough technical scrutiny. In the present paper, the novel and conventional approaches are discussed. Necessary improvements to the novel approach are recommended for its application to nuclear structural component design and analysis, and for its potential expanded use to other alloys.

Ren, Weiju↗
DOE OSTI2017

Qualification Testing Versus Quantitative Reliability Testing of PV - Gaining Confidence in a Rapidly Changing Technology: Preprint

Continued growth of PV system deployment would be enhanced by quantitative, low-uncertainty predictions of the degradation and failure rates of PV modules and systems. The intended product lifetime (decades) far exceeds the product development cycle (months), limiting our ability to reduce the uncertainty of the predictions for this rapidly changing technology. Yet, business decisions (setting insurance rates, analyzing return on investment, etc.) require quantitative risk assessment. Moving toward more quantitative assessments requires consideration of many factors, including the intended application, consequence of a possible failure, variability in the manufacturing, installation, and operation, as well as uncertainty in the measured acceleration factors, which provide the basis for predictions based on accelerated tests. As the industry matures, it is useful to periodically assess the overall strategy for standards development and prioritization of research to provide a technical basis both for the standards and the analysis related to the application of those. To this end, this paper suggests a tiered approach to creating risk assessments. Recent and planned potential improvements in international standards are also summarized.

14 SOLAR ENERGY↗