Modeling Corrosion: Efficient Models for Long Term Degradation
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Katona, Ryan Michael↗
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Modeling Corrosion: Efficient Models and Validation for Long Term Degradation
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Katona, Ryan Michael [Sandia National Laboratories↗
Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites: TL-LUE Parameterization and Validation
We present the first extended validation of satellitemicrowave (MW) liquidwater path (LWP) for low nonprecipitating clouds, from four operational sensors, against ship-borne observations from a three-channel MW radiometer collected along ship transects over the northeast Pacific during May–August 2013. Satellite MW retrievals have an overall correlation of 0.84 with ship observations and a bias of 9.3 g/m2. The bias for broken cloud scenes increases linearly with water vapor path and remains below 17.7 g/m2. In contrast, satelliteMWLWP is unbiased in overcast scenes with correlations up to 0.91, demonstrating that the retrievals are accurate and reliable under these conditions. Satellite MW retrievals produce a diurnal cycle amplitude consistent with ship-based observations (33 g/m2). Observations taken aboard extended ship cruises to evaluate not only satellite MW LWP but also LWP derived from visible/infrared sensors offer a new way to validate this important property over vast oceanic regions.
Zhou, Yanlian↗
Computationally efficient models for aqueous organic redox flow batteries
The rising usage of intermittent energy has garnered the need for large scale energy storage systems. Redox flow batteries (RFB) based energy storage system shows promising potential. Numerical simulations and machine learning approaches have been widely used to study RFB performance. The development of autonomous material discovery framework and digital twin of energy storage system usually needs to query cell performance through fast response models. In this study, two computationally efficient models are introduced: a physics-based analytical flow battery model (EZBattery), and a machine learning operator model (Deep Operator Network, denoted by DeepONet). Both models can provide cell performance near instantly, and prediction accuracy was systematically examined on an application of evaluating the performances of a 780 cm 2 aqueous organic redox flow battery (AORFB), using potential anolyte candidates in dihydroxyphenazine (DHP)-based family of organic materials. A validated computationally expansive 3-dimensional multi-physics finite element model by COMSOL was used as the ground truth and provided the training data set for the DeepONet. 1280 samples were generated with 10 properties to mimic the different possible anolyte candidates, and the cell performances were evaluated under 10 different combined operating conditions. The accuracy comparisons for the two computationally efficient models show that both models can provide comparable accuracy in predicting cell charging/discharging voltage curves. DeepONet can provide slightly higher overall accuracy than EZBattery with faster calculation speed, but highly relies on the training dataset. EZBattery does not need a training dataset and can provide interpretable physics-based explanations of the results, while being more flexible to adjust to adapt any different cell designs, flow battery architectures, and electrolyte materials.
Analytical model↗
Efficient transfer learning for neural network language models.
Abstract not provided.
Skryzalin, Jacek↗
Validation of general purpose mathematical efficiency modeling with ISOCS - 15579
Gamma spectroscopy is a commonly used method for radioactive waste characterization and activity determination in in-situ objects. The gamma rays emitted by radionuclides in the waste container matrix are highly penetrating and can be measured using one or more gamma ray detectors, thus allowing for a passive nondestructive assay. One problem is that a source based calibration of full energy peak efficiency can be difficult or impractical because of the large size and/or an unusual shape of the sample geometry. One attractive solution is to use mathematical efficiency modeling to calculate the full energy peak efficiency. The In-Situ Object Calibration Software (ISOCS) has successfully been used for more than a decade for mathematical efficiency modeling of template based geometries. Templates include the most common objects in waste characterization and D and D, including but not limited to: boxes, drums, and pipes with hotspots, beams, tanks and walls. Scenarios exist where these templates cannot model the desired measurement geometry. Canberra has developed a general purpose version of ISOCS where primitive objects can be combined into practically any geometry. The primitive objects are right circular cylinder, right elliptical cylinder, right circular cone, right elliptical cone, sphere, spheroid, ellipsoid, box (rectangular parallelepiped), torus, and beaker (any cylindrical symmetric object). Multiple primitive objects can be combined using the priority scheme and cut by planes into almost any desired shape. In addition, multiple objects can be defined in the same scene, for example cylinders next to each other or a pipe and a box. Collimators can be constructed as easily as in template based ISOCS. The ray tracing algorithm in ISOCS has been extended to support the new primitive objects for attenuation calculation between the radioactive source and the detector end cap. The new version uses the same vacuum efficiency response, or characterization, in the energy range 10 - 7000 keV of individual detectors to be able to calculate the efficiency as accurately as possible. MCNP has been shown to be able to calculate detection efficiencies for any geometry and the new version of ISOCS have been validated to MCNP. The vacuum efficiency is based on an MCNP model and this model was the base for constructing an identical model in MCNP as in ISOCS. The calculated efficiencies from the two codes have been compared and the agreement is within 3% except for the lowest energies for one geometry. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Environment-sensitivity functions for gross primary productivity in light use efficiency models
The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO 2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO 2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products.
54 ENVIRONMENTAL SCIENCES↗
Performance of Linear and Nonlinear Two-Leaf Light Use Efficiency Models at Different Temporal Scales
The reliable simulation of gross primary productivity (GPP) at various spatial and temporal scales is of significance to quantifying the net exchange of carbon between terrestrial ecosystems and the atmosphere. This study aimed to verify the ability of a nonlinear two-leaf model (TL-LUEn), a linear two-leaf model (TL-LUE), and a big-leaf light use efficiency model (MOD17) to simulate GPP at half-hourly, daily and 8-day scales using GPP derived from 58 eddy-covariance flux sites in Asia, Europe and North America as benchmarks. Model evaluation showed that the overall performance of TL-LUEn was slightly but not significantly better than TL-LUE at half-hourly and daily scale, while the overall performance of both TL-LUEn and TL-LUE were significantly better (p < 0.0001) than MOD17 at the two temporal scales. The improvement of TL-LUEn over TL-LUE was relatively small in comparison with the improvement of TL-LUE over MOD17. However, the differences between TL-LUEn and MOD17, and TL-LUE and MOD17 became less distinct at the 8-day scale. As for different vegetation types, TL-LUEn and TL-LUE performed better than MOD17 for all vegetation types except crops at the half-hourly scale. At the daily and 8-day scales, both TL-LUEn and TL-LUE outperformed MOD17 for forests. However, TL-LUEn had a mixed performance for the three non-forest types while TL-LUE outperformed MOD17 slightly for all these non-forest types at daily and 8-day scales. The better performance of TL-LUEn and TL-LUE for forests was mainly achieved by the correction of the underestimation/overestimation of GPP simulated by MOD17 under low/high solar radiation and sky clearness conditions. TL-LUEn is more applicable at individual sites at the half-hourly scale while TL-LUE could be regionally used at half-hourly, daily and 8-day scales. MOD17 is also an applicable option regionally at the 8-day scale.
54 ENVIRONMENTAL SCIENCES↗
The importance of radiation for semiempirical water-use efficiency models
Water-use efficiency (WUE) is a fundamental property for the coupling of carbon and water cycles in plants and ecosystems. Existing model formulations predicting this variable differ in the type of response of WUE to the atmospheric vapor pressure deficit of water (VPD). We tested a representative WUE model on the ecosystem scale at 110 eddy covariance sites of the FLUXNET initiative by predicting evapotranspiration (ET) based on gross primary productivity (GPP) and VPD. We found that introducing an intercept term in the formulation increases model performance considerably, indicating that an additional factor needs to be considered. We demonstrate that this intercept term varies seasonally and we subsequently associate it with radiation. Replacing the constant intercept term with a linear function of global radiation was found to further improve model predictions of ET. Our new semiempirical ecosystem WUE formulation indicates that, averaged over all sites, this radiation term accounts for up to half (39–47 %) of transpiration. These empirical findings challenge the current understanding of water-use efficiency on the ecosystem scale.
58 GEOSCIENCES↗
BOREAS TE-17 Production Efficiency Model Images
A Boreal Ecosystem-Atmospheric Study (BOREAS) version of the Global Production Efficiency Model (http://www.inform.umd.edu/glopem/) was developed by TE-17 (Terrestrial Ecology) to generate maps of gross and net primary production, autotrophic respiration, and light use efficiency for the BOREAS region. This document provides basic information on the model and how the maps were generated. The data generated by the model are stored in binary image-format files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
Hall, Forrest G.↗
P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production
Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C 3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C 3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R 2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R 2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R 2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr –1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).
54 ENVIRONMENTAL SCIENCES↗
A Solution Methodology and Computer Program to Efficiently Model Thermodynamic and Transport Coefficients of Mixtures
A solution methodology has been developed to efficiently model multi-specie, chemically frozen, thermally perfect gas mixtures. The method relies on the ability to generate a single (composite) set of thermodynamic and transport coefficients prior to beginning a CFD solution. While not fundamentally a new concept, many applied CFD users are not aware of this capability nor have a mechanism to easily and confidently generate new coefficients. A database of individual specie property coefficients has been created for 48 species. The seven coefficient form of the thermodynamic functions is currently used rather then the ten coefficient form due to the similarity of the calculated properties, low temperature behavior and reduced CPU requirements. Sutherland laminar viscosity and thermal conductivity coefficients were computed in a consistent manner from available reference curves. A computer program has been written to provide CFD users with a convenient method to generate composite specie coefficients for any mixture. Mach 7 forebody/inlet calculations demonstrated nearly equivalent results and significant CPU time savings compared to a multi-specie solution approach. Results from high-speed combustor analysis also illustrate the ability to model inert test gas contaminants without additional computational expense.
Ferlemann, Paul G.↗
High-Efficiency Model Predictive Control for Star-Connected Cascaded H-Bridge STATCOM Under Unbalanced Conditions
This paper presents a high-efficiency model predictive control (HMPC), which is enabled by a sorting approach based on dc capacitor voltages to significantly reduce the exponentially-increasing switching states in cascaded Hbridge (CHB) converters. In addition, a generic discrete-time prediction model for star-connected CHB static synchronous compensator is proposed to address the issues due to either grid and/or load unbalances. Both simulation and hardware-in-loop experimental results are presented to validate the feasibility and effectiveness of the proposed HMPC strategy.
Li, Yufei↗
A New BRDF Hotspot Model and its Use in Improving Radiative Transfer Model Efficiency and Application to VLIDORT-based PCRTM Model
The term “hotspot” refers to the sharp increase of reflectance occurring when incident (solar) and reflected (viewing) directions almost coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications. The RossThick-LiSparse-Reciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model is widely used in radiative transfer simulations, and the hotspot model mostly used is from Maignan- Bréon but it typically requires large values of numerical quadrature and Fourier expansion terms in order to represent the hotspot accurately. To improve its use in atmospheric radiative transfer (RT) model simulations, we have developed a modified version based on the Maignan-Bréon’s hotspot BRDF model that converge much faster numerically, making it more practical for use in RT models that require Fourier expansion of BRDF to simulate the top-of-atmosphere (TOA) hotspot signatures. On basis of the vector linearized discrete ordinate radiative transfer model (VLIDORT), we have built a line-by-line based simulation system that can simulate the TOA radiance for atmospheres with 26 gases, different aerosols, dust and clouds, and surface with different BRDF. Using simulated results under diverse atmospheric conditions, we have developed VLIDORT-PCRTM model.
Xiaozhen Xiong↗
Adaptive inverse ray-tracing for accurate and efficient modeling of cross beam energy transfer in hydrodynamics simulations
Integrated hydrodynamics simulations of inertial confinement fusion rely on reduced physics models. To reproduce experimental trends, these models often feature tuning parameters, but this comes with a risk: the over-tuning of one model can hide physics inadequacies in another. The ray-based models of cross-beam-energy transfer (CBET) represent this risk. In this paper, we present an accurate and efficient model of CBET suitable for inline implementation in 3D hydrodynamics simulations. Inverse Ray Tracing (IRT) is used to compute the ray field in a 3D permittivity profile described on an unstructured tetrahedral mesh using the Inline Field Reconstruction and Interaction using Inverse Tracing framework. CBET is accounted for through perturbations to the permittivity associated with ion acoustic waves driven by the overlapped fields. Large gradients in the permittivity are resolved by coupling the IRT to a recursive Adaptive Mesh Refinement (AMR) algorithm. The use of AMR also allows for the resolution of caustics, with accurate field reconstruction performed using the Etalon integral method. Comparisons of the model with wave-based solutions from the Laser Plasma Simulation Environment demonstrate its ability to control energy conservation and gain convergence through the AMR depth only, without the use of ad hoc physical models or artificial tuning parameters.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Efficiently modeling neural networks on massively parallel computers
Neural networks are a very useful tool for analyzing and modeling complex real world systems. Applying neural network simulations to real world problems generally involves large amounts of data and massive amounts of computation. To efficiently handle the computational requirements of large problems, we have implemented at Los Alamos a highly efficient neural network compiler for serial computers, vector computers, vector parallel computers, and fine grain SIMD computers such as the CM-2 connection machine. This paper describes the mapping used by the compiler to implement feed-forward backpropagation neural networks for a SIMD (Single Instruction Multiple Data) architecture parallel computer. Thinking Machines Corporation has benchmarked our code at 1.3 billion interconnects per second (approximately 3 gigaflops) on a 64,000 processor CM-2 connection machine (Singer 1990). This mapping is applicable to other SIMD computers and can be implemented on MIMD computers such as the CM-5 connection machine. Our mapping has virtually no communications overhead with the exception of the communications required for a global summation across the processors (which has a sub-linear runtime growth on the order of O(log(number of processors)). We can efficiently model very large neural networks which have many neurons and interconnects and our mapping can extend to arbitrarily large networks (within memory limitations) by merging the memory space of separate processors with fast adjacent processor interprocessor communications. This paper will consider the simulation of only feed forward neural network although this method is extendable to recurrent networks.
Farber, Robert M.↗
A computational efficient modelling of laminar separation bubbles
In predicting the aerodynamic characteristics of airfoils operating at low Reynolds numbers, it is often important to account for the effects of laminar (transitional) separation bubbles. Previous approaches to the modelling of this viscous phenomenon range from fast but sometimes unreliable empirical correlations for the length of the bubble and the associated increase in momentum thickness, to more accurate but significantly slower displacement-thickness iteration methods employing inverse boundary-layer formulations in the separated regions. Since the penalty in computational time associated with the more general methods is unacceptable for airfoil design applications, use of an accurate yet computationally efficient model is highly desirable. To this end, a semi-empirical bubble model was developed and incorporated into the Eppler and Somers airfoil design and analysis program. The generality and the efficiency was achieved by successfully approximating the local viscous/inviscid interaction, the transition location, and the turbulent reattachment process within the framework of an integral boundary-layer method. Comparisons of the predicted aerodynamic characteristics with experimental measurements for several airfoils show excellent and consistent agreement for Reynolds numbers from 2,000,000 down to 100,000.
Dini, Paolo↗