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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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A privacy-preserving average consensus algorithm is designed based on the Beaver triple technique against passive adversaries. The Beaver triple technique is integrated into a restructure of the discrete-time average consensus algorithm to preserve the privacy of initial values of agents in a multiagent system. The performance of the algorithm is theoretically analyzed.
Accurate modeling of photovoltaic (PV) performance requires the precise calculation of module temperature. Currently, most temperature models rely on steady-state assumptions that do not account for the transient climatic conditions and thermal mass of the module. On the other hand, complex physics-based transient models are computationally expensive and difficult to parameterize. In order to address this, a new approach to transient thermal modeling was developed, in which the steady-state predictions from previous timesteps are weighted and averaged to accurately predict the module temperature at finer time scales. This model is informed by 3-D finite-element analyses, which are used to calculate the effect of wind speed and module unit mass on module temperature. The model, in application, serves as an added filter over existing steady-state models that smooths out erroneous values that are a result of intermittency in solar resource. Validation of this moving-average model has shown that it can improve the overall PV energy performance model accuracy by as much as 0.58% over steady-state models based on mean absolute error improvements and can significantly reduce the variability between the model predictions and measured temperature times series data.
A privacy-preserving average consensus algorithm is proposed that synergizes the Beaver triple in secret sharing theory and noise obfuscation. The algorithm safeguards the initial values of agents against passive adversaries in a multiagent system. It is proved that the proposed algorithm can concurrently ensure average consensus and privacy, while also reducing the online computation and communication overhead compared to encryption-based ones. In addition, it imposes a less stringent condition for privacy preservation compared to certain noise-obfuscation techniques.
The Method of Finite Averages (MoFA) is a rigorous multiscale modeling methodology for efficiently modeling multi-physical phenomena in heterogeneous porous media. The code developed in this project aims to perform the numerical calculations required to formulate, implement, and verify MoFA models for Earth and Energy systems (i.e., model verification refers to performing fully-resolved simulations of the systems and comparing their results to those of the models). In general, MoFA transforms partial differential equations (PDEs) describing the fine-scale physics of a system into coupled ordinary differential equations (ODEs)---in time---that describe the coarse-scale---or "average"---physical behaviors of the system. This transformation significantly expedites system simulation, as the coarse-scale ODEs involve vastly fewer degrees of freedom than the fine-scale PDEs. The code developed under this project will allow users to 1.) generate system geometries and numerical meshes, 2.) solve the PDE and ODE systems required for MoFA model formulation and implementation, 3.) solve the PDE systems required to obtain fully-resolved simulation results for model verification, and 4.) compare and plot results (e.g., the model and fully-resolved simulation solutions, the error between the solutions, etc.).
We used COMSOL Multiphysics to design a prototype actively cooled “flow-cell” substrate targeted at high-average-power applications, fabricated the prototype from cordierite ceramic, and tested the substrate under load in our thermal loading test stand. Sub-aperture testing revealed average-power handling up to 3.88-W/cm 2 absorbed power density, in excellent agreement with model predictions. Gratings fabricated on 2-in.-diam cordierite coupons were subjected to laser-damage testing and showed a damage threshold of 250 mJ/cm 2 .
Focal Area(s): We will use unsupervised machine learning methods to identify and quantify the influence of large scale natural modes of climate variability to gain insight into the observed and simulated seasonal average and extreme precipitation changes. Science Challenge: A recent paper, led by co-PI Mark Risser, finds that although much of the variability in seasonal average and extreme precipitation over CONUS is unforced, the effect of large-scale modes of circulation variability (such as ENSO, AMO, PNA, etc.) can be detected and attributed. However, it is unclear whether or not unsupervised learning methods can (a) replicate this finding or (b) yield insight into possible nonlinear behavior that was not captured in the initial statistical analysis. Further work would entail extending this framework to other global land areas.
This report documents the quantitative results of the current industry-average performance for components and initiating events (IEs) at U.S. commercial nuclear power plants (NPPs). It represents the third update of the original analysis in NUREG/CR-6928 with data through 2020. Continuous characterization and updating of current industry-average performance with the latest industry data available are important steps in maintaining up-to-date risk models. Typically, data from 1998–2002 were used in NUREG/CR-6928, data from 1998–2010 in the first update, data from 1998–2015 in the second update, and data from 2006–2020 in this update, although many IEs required longer periods for adequate characterization of frequencies in all these analyses. As with NUREG/CR-6928 and previous updates, four types of events are covered in this report: component unreliability (e.g., a pump that fails to start or fails to run), component or train unavailability resulting from test or maintenance outages, special event probabilities covering operational issues (e.g., pump restarts and injection valve re-openings during unplanned demands), and IE frequencies. Results (in the form of beta distributions for failure probabilities upon demand and gamma distributions for rates) are used as inputs to the U.S. Nuclear Regulatory Commission standardized plant analysis risk models covering U.S. commercial NPPs.
Next-generation lasers that combine high-peak-power ultrashort pulses with high repetition rates have the potential to efficiently deliver the high-average-power outputs required for inertial fusion energy generation, laser-based particle acceleration, and to generate compact secondary x- and 𝛾-ray radiation sources, among others. The combination of long gain length with high surface-to-volume ratio to remove residual heat makes gas-cooled multi-slab amplifier heads an inherently aperture- and average power scalable technology that enables this new class of solid-state lasers, and their thermal management becomes the most pressing technical challenge. As the index-of refraction depends on the gas density, heat removal from the slab surfaces unavoidably entails scattering of propagated light from regions of high temperature fluctuations. The requirement to maximize convective heat extraction from the gain medium under the constraint of minimum optical aberration of the beam renders amplifier slab cooling a technological challenge at the intersection of aero-optics, fluid-mechanics and heat transfer. The goal of this research is to leverage computational fluid dynamics and aero optics simulations of different levels of fidelity to devise a numerical modeling and optimization framework for gas-cooled multi-slab amplifiers. The project will be conducted in close collaboration with the Advanced Photon Technologies Group at Lawrence Livermore National Laboratory, where a gas-cooled test stand for laser materials and thermal management structures will be developed and installed. The final goal of the collaborative effort is to manufacture and benchmark a physical prototype of an numerically optimized amplifier head design. Measurement data on solid-state and thermo-optical distortions will be used for the validation of the numerical models. The key technical challenge in this multi-objective design problem is that of balancing optimal heat removal with minimal optical distortion. To facilitate computationally efficient design optimization and, at the same time, deepen our basic understanding of the interaction of light with turbulence, a complementary study that combines high and low-fidelity flow simulations and aero-optical models is proposed. Along the lines of these two objectives and solution strategies, the project is organized into two thrusts.
We propose the average spectrum norm to study the minimum number of measurements required to approximate a multidimensional array (i.e., sample complexity) via low-rank tensor recovery. Our focus is on the tensor completion problem, where the aim is to estimate a multiway array using a subset of tensor entries corrupted by noise. Our average spectrum norm-based analysis provides near-optimal sample complexities, exhibiting dependence on the ambient dimensions and rank that do not suffer from exponential scaling as the order increases.
We present calculations of neutron thermal cross sections, Westcott factors, resonance integrals, Maxwellian-averaged cross sections, astrophysical reaction rates, and solar system r-process abundances using the latest data from the major evaluated nuclear libraries for 849 ENDF target materials. The recent release of ENDF/B-VIII.1 library, progress in 252 Cf(SF) evaluation, extensive analysis of newly-evaluated neutron reaction cross sections, neutron covariances, and improvements in data processing techniques motivated us to calculate the nuclear industry and neutron physics parameters, produce s-process Maxwellian-averaged cross sections and astrophysical reaction rates, extract r-process abundances, systematically calculate uncertainties, and provide additional insights on currently available neutron-induced reaction data.
A critical R&D initiative for the Electron Ion Collider (EIC) involves the design of an electron gun capable of gen erating a high average current and high-brightness electron beam for the hadron cooler. This is essential to preserve hadron beam quality and achieve the collider’s luminosity target of 1 × 10 34 s −1 cm −2 . For an energy recovery linac (ERL)-based hadron cooler, the gun must deliver a high average current of 98.5 mA, a normalized transverse emittance of less than 2 mm−mrad, and a bunch charge of up to 2.5 nC. This proceeding outlines the high-voltage design of a DC gun operating at 500 kV, with conditioning capability up to 600 kV. The design incorporates several unique fea tures, including the use of inverted ceramic at this voltage level, active cooling for the cathode, and large single-crystal multi-alkali cathodes grown on a silicon carbide substrate. High-brightness electron sources are also pivotal for other ad vanced applications, such as high-intensity gamma sources, future 𝑒 + 𝑒 − colliders, and ultra-deep UV sources for the semiconductor industry. Additionally, this paper provides an overview of the hadron cooling approaches planned or proposed for the EIC.
Background At age 5, while only embarking on their educational journey, substantial differences in children’s cognitive ability will already exist. The aim of this study was to examine the causal association between below average cognitive ability at age 5 years and child-reported experience of school and self-concept, and teacher-reported class engagement and emotional-behavioural function at age 9 years. Methods This longitudinal cohort study used data from 7,392 children in the Growing Up in Ireland Infant Cohort, who had completed the Picture Similarities and Naming Vocabulary subtests of the British Abilities Scales at age 5. Principal components analysis was used to produce a composite general cognitive ability score for each child. Children with a general cognitive ability score more than 1 standard deviation (SD) below the mean at age 5 were categorised as ‘Below Average Cognitive Ability’ (BACA), and those scoring above this as ‘Typical Cognitive Development’ (TCD). The outcomes of interest, measured at age 9, were child-reported experience of school, child’s self-concept, teacher-reported class engagement, and teacher-reported emotional behavioural function. Binary and multinomial logistic regression models were used to examine the association between BACA and these outcomes. Results Compared to those with TCD, those with BACA had significantly higher odds of never liking school [Adjusted odds ratio (AOR) 1.82, 95% CI 1.37–2.43, p < 0.001], of being picked on (AOR 1.27, 95% CI 1.09–1.48) and of picking on others (AOR 1.53, 95% CI 1.27–1.84). They had significantly higher odds of experiencing low self-concept (AOR 1.20, 95% CI 1.02–1.42) and emotional-behavioural difficulties (AOR 1.34, 95% CI 1.10–1.63, p = 0.003). Compared to those with TCD, children with BACA had significantly higher odds of hardly ever or never being interested, motivated and excited to learn (AOR 2.29, 95% CI 1.70–3.10). Conclusion Children with BACA at school-entry had significantly higher odds of reporting a negative school experience and low self-concept at age 9. They had significantly higher odds of having teacher-reported poor class engagement and problematic emotional-behavioural function at age 9. The findings of this study suggest BACA has a causal role in these adverse outcomes. Early childhood policy and intervention design should be cognisant of the important role of cognitive ability in school and childhood outcomes.
Techniques are provided for scaling the average power of high-energy solid-state lasers to high values of average output power while maintaining high efficiency. An exemplary technique combines a gas-cooled-slab amplifier architecture with a pattern of amplifier pumping and extraction in which pumping is continuous and in which only a small fraction of the energy stored in the amplifier is extracted on any one pulse. Efficient operation is achieved by propagating many pulses through the amplifier during each period equal to the fluorescence decay time of the gain medium, so that the preponderance of the energy cycled through the upper laser level decays through extraction by the amplified pulses rather than through fluorescence decay.
Vibration-based condition monitoring techniques are widely used for diagnosing faults in rotating machines. These techniques are implemented in the time domain, the frequency domain, or both. However, the composite and noisy nature of the raw data collected requires a preprocessing stage such as filtering and decomposition using in-depth processing techniques. Moreover, these methods require good frequency resolution and involve examining a broad frequency range to discern both healthy and faulty cases. In this work, we introduce a simple and fast diagnostic scheme for wind turbine gear teeth wear based on time domain analysis. The proposed method is based on the local minima interpolation of a filtered version of the vibration signal following time synchronous averaging (TSA) technique. Given tachometer signal, the TSA of the vibration data is performed using MTALAB software. Then, local minima of the filtered signal are interpolated using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) function. The variance of the interpolated curve built a gear fault index. The derived fault index resulting of the proposed technique allows a substantial distinction between the healthy and faulty cases. Its efficiency is validated using 10 real-world datasets of vibration stemmed from a wind turbine planetary gearbox. The proposed method boasts a low computation time and ease of interpretation, specifically beneficial for gearbox fault diagnosis purposes.
RANSBox is a zero-dimensional software package for Reynolds-averaged Navier-Stokes (RANS) modeling developed at Lawrence Livermore National Laboratory to support common implementation of RANS models across a variety of host codes with different numerical schemes and code bases. Herein this work describes the key features of RANSBox including “model-agnostic integration,” which allows new models to be implemented in RANSBox and quickly deployed to host codes without additional changes to the host code base. Three one-dimensional test problems with analytical asymptotic properties are described which can be used to verify correctness of RANSBox integration. Results for these problems are then compared across different RANS models in a single code and across different codes with a common RANS model. By using a common model implementation in RANSBox, host codes with different numerical schemes and formal orders of accuracy are demonstrated to predict the expected behavior for the three test problems.
Average leaf inclination angle ($\overline{θ}$ L ) is an important canopy structure variable that influences light regime, photosynthesis, and evapotranspiration of plants. $\overline{θ}$ L can be measured through direct methods (e.g., protractor), which are labor-intensive and time-consuming, or through indirect optical instruments, which are more efficient than the direct methods. However, uncertainties of different indirect optical instruments for quantifying $\overline{θ}$ L remain largely unquantified. In this study, we evaluated and compared the performances of three major indirect optical instruments: (1) LAI-2200, (2) 30°-tilted camera, and (3) digital hemispherical photography (DHP), in different crop fields over a growing season, benchmarked with direct measurements. LAI-2200 and 30°-tilted camera showed higher agreement with direct $\overline{θ}$ measurements (R 2 = 0.54, RMSE = 7.37°; R 2 = 0.58, RMSE = 8.08°) than DHP (R 2 = 0.14, RMSE = 13.96°). Different performances of indirect optical instruments could be attributed to the accuracy of gap fraction measurement and the performance of the $\overline{θ}$ L quantification algorithms. When using the LAI-2200 algorithm, larger gap fraction gradients over view zenith angles led to larger $\overline{θ}$ L values, and smaller gap fraction gradients led to smaller $\overline{θ}$ L values. Such error propagation was larger in sparse canopy than in dense canopy. The Wilson G function of the LAI-2200 algorithm performed better in estimating $\overline{θ}$ L than the G function based on the ellipsoidal LAD function used by the CAN_EYE algorithm. We also proposed a modification of the LAI-2200 algorithm, which further improved the performance of LAI-2200 and 30°-tilted cameras in estimating $\overline{θ}$ L . We envision that the low-cost 30°-tilted cameras provide a promising sensor solution to continuously monitor canopy structure for various ecosystems.
Here, this study examines the application of the daytime-mean and insolation-weighted-mean solar zenith angles to calculate the diurnally-averaged aerosol direct radiative effect (DRE), DRE DA , for observed clear-sky conditions at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (TWP) sites. As compared with the reference calculation, the DRE DA are too strong (i.e., more negative) using daytime-mean solar zenith angles (DTMSZA) and are too weak (i.e., less negative) using insolation-weighted-mean solar zenith angles (IWMSZA). The biases in the DRE DA for DTMSZA (IWMSZA) are about -0.8 (0.7) W m -2 at the TOA and about -1.5 (0.9) W m -2 at the surface. Noting the mean TOA DRE DA of -2.85 W m -2 and the surface DRE DA of -7.09 W m -2 at SGP, the relative biases for DTMSZA (IWMSZA) are 26% (-24%) for the TOA DRE DA , and 17% (-11%) for the surface DRE DA . At TWP, the mean TOA DRE DA is -2.81 W m -2 and the surface DRE DA is -11.84 W m -2 which corresponds to relative biases for DTMSZA (IWMSZA) of 29% (-27%) for the TOA DRE DA and 15% (-9%) for the surface DRE DA . By introducing an adjusted insolation-weighted-mean solar zenith angle, the mean biases in the DRE DA at the TOA becomes -0.09 W m -2 and the biases in the DRE DA at the surface are about -0.41 W m -2 . Also presented are the effects of using the mean solar zenith angles on the shortwave (SW) upward fluxes at the TOA and SW downward fluxes at the surface in both clear-sky and pristine-clear-sky (i.e., gases only) conditions.
As energy system design moves to more complex methods of optimization including machine learning there is a significant need for more weather data than is available. One method to solve this is using synthetic data models such as the auto-regressive moving-average (ARMA) model which has been frequently utilized to create such data. This paper looks at extending the ARMA algorithm to generate solar components through the use of clearsky detrending, maintaining vector relationships and by leveraging physical relationships. The method for the creation of entirely synthetic weather data files including key weather variables for energy system analysis is presented. Furthermore, a detailed comparison of energy system simulations utilizing both real and synthetic data is made using NREL’s System Advisor Model. Whilst good agreement is made for the solar variables, and other weather variables, ARMA methods often fail to capture the standard deviation and skew of annual weather distributions. Vector-ARMA is shown to maintain correlations between variables and thus generate data sets that perform similarly in energy system design. Here, it is finally shown that the ARMA method fails to preserve day-today correlations in weather variables and thus over-predicts optimal energy storage by 21% for a residential solar application.