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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6

This paper documents the methodology and preliminary results from a perturbed parameter ensemble (PPE) technique, where multiple parameters are varied simultaneously and the parameter values are determined with Latin hypercube sampling. This is done with the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community Earth System Model version 2 (CESM2). We apply the PPE method to CESM2–CAM6 to understand climate sensitivity to atmospheric physics parameters. The initial simulations vary 45 parameters in the microphysics, convection, turbulence and aerosol schemes with 263 ensemble members. These atmospheric parameters are typically the most uncertain in many climate models. Control simulations and targeted simulations to understand climate forcing due to aerosols and fast climate feedbacks are analyzed. The use of various emulators is explored in the multi-dimensional space mapping input parameters to output metrics. Parameter impacts on various model outputs, such as radiation, cloud and aerosol properties, are evaluated. Machine learning is also used to probe optimal parameter values against observations. Our findings show that PPE is a valuable tool for climate uncertainty analysis. Furthermore, by varying many parameters simultaneously, we find that many different combinations of parameter values can produce results consistent with observations, and thus careful analysis of tuning is important. The CESM2–CAM6 PPE is publicly available and extensible to other configurations to address questions of other model processes in the atmosphere and other model components (e.g., coupling to the land surface).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Towards Fast and Accurate Predictions of Radio Frequency Power Deposition and Current Profile via Data-driven Modeling

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. The machine learning models use a database of 16,000+ GENRAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods ensure that the database covers the range of 9 input parameters ($n_{e0}$, $T_{e0}$, $I_p$, $B_t$, $R_0$, $n_{||}$, $Z_{eff}$, $V_{loop}$, $P_{LHCD}$) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to ~ms with high accuracy across the input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Design and Optimization of a Gas-Cooled, Airfoil Fin Microchannel Heat Exchanger

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, was analyzed and optimized using a design of experiments with artificial intelligence and machine learning techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin PCHEs has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure was developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. STAR-CCM+ was used to analyze a simplified two-channel configuration where five parameters were varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods were compared for generating surrogate models – a 5D polynomial and a regression neural network. A response surface approximation was created from the surrogate models and input to a genetic algorithm. The genetic algorithm identified a set of optimal points on the Pareto front. The optimal geometry was found across six channel Reynolds numbers ranging from 1000 to 5000 to analyze how varying inlet conditions affects the optimal design. A set of optimal designs that maximizes heat transfer and minimizes pressure drop was identified, and a thermal stress analysis was performed on the optimal design. This work has developed a digital framework for the expedient topology design and evaluation of PCHE designs for gas-cooled microreactor applications. Correlations for the Nusselt number and Darcy friction factor were developed that can be useful for thermal hydraulic analyses using system codes. A thermal stress analysis was conducted and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗
DOE OSTI2020

Probabilistic Power Flow Based on a Gaussian Process Emulator

In this letter, a novel Gaussian process emulator is proposed, for the first time, to conduct the probabilistic power-flow calculation. Based on Bayesian inference, a Gaussian process emulator is trained and served as a nonparametric, reduced-order model of the nonlinear power-flow model. This emulator has allowed us to evaluate the time-consuming power-flow solver at the sampled values with a negligible computational cost. The simulations reveal the excellent performance of this method.

42 ENGINEERING↗
DOE OSTI2020

Optimization of a Cyclone Using Multiphase Flow Computational Fluid Dynamics

The U.S. Department of Energy National Energy Technology Laboratory's (NETL) 50 kW th chemical looping reactor (CLR) has an underperforming cyclone, which was designed using empirical correlations. To improve the performance of this cyclone using computational fluid dynamics (CFD)-based modeling simulations, four critical design parameters including the vortex tube radius and length, barrel radius, and the inlet width and height were optimized. Here, NETL's open source multiphase flow with interphase exchange (MFiX) CFD code has been used to model a series of cyclones by systematically varying the geometric design parameters. To perform the optimization process, the surrogate modeling and sensitivity analysis followed by the optimization capability in nodeworks was used. The basic methodology for the process is to employ a statistical design of experiments (DOE) method to generate sampling simulations that fill the design space. Corresponding CFD models are then created, executed, and postprocessed. A response surface is created to characterize the relationship between input parameters and the quantities of interest (QoI). Finally, the CFD-surrogate is used by an optimization method to find the optimal design condition based on the objective and constraints prescribed. The resulting optimal cyclone has a larger diameter and longer vortex tube, a larger diameter barrel, and a taller and narrower solids inlet. The improved design has a predicted pressure drop 11 times lower than the original design while reducing the mass loss by a factor of 2.3.

42 ENGINEERING↗
DOE OSTI2022

Nuclear data covariances are critical input to determine upper sub-critical limits and to design experiments to increase it [Slides]

This presentation discusses how Upper Subcritical Limits (USL) are key parameters to determine operational limits in nuclear criticality safety evaluations. It also discusses an example of plutonium casting operation using tantalum at LANL PF-4. The Whisper tool at Los Alamos relies on many inputs, including covariance data, leading the presentation to ask if an existing benchmark data be used in Whisper to adjust nuclear data and covariances to justify a higher USL. If not, Whisper can be used to help design an optimal new benchmark experiment. The presentation also seeks to determine what the possible impacts are on USL and operational limits for plutonium casting. In conclusion, nuclear data covariances are used for by Whisper for: GSSL adjustment of nuclear data and covariances, identification of most similar existing benchmark experiments to application, simulation of Upper Subcritical Limit of application, and input to optimization techniques for designing most appropriate new benchmark experiment(s) to meet requirements. This requires a complete set of nuclear data covariances, benchmarks and k-effective sensitivity profiles (for both benchmarks and applications). The presentation concludes by asking if end users should trust results that depend on current covariance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Use of Remote Sensing and In-Situ Observations to Develop and Evaluate Improved Representations of Convection and Clouds for the ACME Model

The overachieving goal of the whole CMDV-MCS project is to improve understanding of warm season continental convection and to develop treatments of convection and microphysics capable of representing mesoscale convective systems (MCSs) features in large-scale models. Our tasks for this project contributing to the overachieving goal include: (1) Improve the ice nucleation formulation for MG2 and P3 cloud microphysics schemes; (2) Improve the treatment of subgrid dynamics and thermodynamics driving the ice nucleation in E3SM; and (3) Test the performance of improved ice microphysics in E3SM with observation data. In this project, we have (1) Improved the ice nucleation parameterization for MG2 and P3 in E3SM by implementing two advanced empirical parameterizations with connection to aerosols. The two deterministic heterogeneous ice nucleation parameterizations (i.e., DeMott et al., 2015; Niemand et al., 2012) were merged with the MG2 and P3 cloud microphysics schemes in E3SM. Long-term simulations were conducted to examine the impacts of these new parameterizations on simulated cloud properties; (2) Improved the treatment of subgrid dynamics and thermodynamics driving the ice nucleation in E3SM. We evaluated the double Gaussian PDF of vertical velocity simulated by the Cloud Layers Unified By Binormals (CLUBB) and the sub-column vertical velocity sampled from the Subgrid Importance Latin Hypercube Sampler (SILHS) in E3SM. We introduced the vertical velocity variance induced by topographic gravity waves for ice nucleation and droplet activation; and (3) Tested the performance of improved ice microphysics in E3SM with observation data. We tested the new treatments of ice nucleation in the single column model (SCM) mode for the stratiform mixed-phase clouds observed during 9-10 October 2004 in the DOE ARM Mixed-Phase Arctic Cloud Experiment (M-PACE) and for the convective clouds observed on 20 May 2011 in the Midlatitude Continental Convective Clouds Experiment (MC3E). Modeled ice nucleating particles (INPs) concentrations were compared against observations collected around the globe.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

RAVEN Theory Manual

RAVEN is a software framework able to perform parametric and stochastic analysis based on the response of complex system codes. The initial development was aimed at providing dynamic risk analysis capabilities to the thermohydraulic code RELAP-7, currently under development at Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose stochastic and uncertainty quantification platform, capable of communicating with any system code. In fact, the provided Application Programming Interfaces (APIs) allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating system response and explore input space using various sampling schemes such as Monte Carlo, grid, or Latin hypercube. However, RAVEN strength lies in its system feature discovery capabilities such as: constructing limit surfaces, separating regions of the input space leading to system failure, and using dynamic supervised learning techniques. The development of RAVEN started in 2012 when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework arose. RAVEN’s principal assignment is to provide the necessary software and algorithms in order to employ the concepts developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just to identify the frequency of an event potentially leading to a system failure, but the proximity (or lack thereof) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. peak pressure in a pipe) is exceeded under certain conditions. Most of the capabilities, implemented having RELAP-7 as a principal focus, are easily deployable to other system codes. For this reason, several side activates have been employed (e.g. RELAP5-3D, any MOOSE-based App, etc.) or are currently ongoing for coupling RAVEN with several different software. The aim of this document is to provide a set of commented examples that can help the user to become familiar with the RAVEN code usage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Sensitivity Analysis of Particle-In-Cell Modeling Parameters in Settling Bed, Bubbling Fluidized Bed and Circulating Fluidized Bed

The objective of the work presented is to perform a preliminary sensitivity analysis of particle-in-cell (PIC) model parameters when applied to settling bed, bubbling fluidized bed, and circulating fluidized bed simulations. These examples correspond to widely different flow conditions commonly seen in chemical engineering applications. Simulations were performed using the PIC method in the open-source software Multiphase Flow with Interphase eXchanges (MFiX) developed by the National Energy Technology Laboratory (NETL). As part of the non-intrusive uncertainty quantification (UQ) analysis, simulation campaigns were generated using Nodeworks. Sampling locations or settings for PIC model parameters were determined using the Latin Hypercube method. Response surfaces were created using radial basis functions (RBF), and Sobol’ indices were estimated to quantify the influence of model parameters on the quantities of interest (QoI). This study marks a first step towards systematically determining optimal ranges for model parameters used in MFiX-PIC. Based on limited experience, it is expected that these values would depend strongly on flow conditions. Given the complexity of the multiphase flow systems under analysis, a non-intrusive UQ based approach is used to identify the most influential parameters in each case. This prior knowledge will help in proposing an effective design of experiments (DoE) and determine optimal parameters through techniques such as deterministic or Bayesian calibration, which will be pursued in the future.

42 ENGINEERING↗
DOE OSTI2023

Simulation of Mechanical Fractionation of Chopped Whole-Plant Corn (WPC) Using Discrete Element Method (DEM)

Fractionating whole-plant corn (WPC) in a single-pass harvesting system requires studies on the WPC-to-equipment interaction for improved property control, as well as mechanical and air-driven separation processes compared to the traditional multi-pass grain and stover harvesting system. The discrete element method (DEM) technique has the potential to simulate WPC mechanical fractionation and support simulation-based design of WPC separation processes. In this study, methods to develop DEM particle models of WPC (kernel, cob, stalk, and husk) and their material properties for simulating mass fractionation using the ASABE standard mechanical shaker were proposed. Measurement was done on the axial dimensions (major, intermediate, and minor) and mass of each WPC type (mean sample size is 56), sampled from single-pass harvesting. Applying gaussian multivariate regression and bootstrapping re-sampling techniques, a DEM particle approximate to each WPC was developed. Sensitivity analysis of the DEM Young‘s modulus, Poisson‘s ratio, and interaction parameters of coefficient of restitution, coefficient of rolling friction, and coefficient of static friction on mass fraction was performed after 156 ASABE sieve-shaking DEM simulation runs, generated using Latin Hypercube Design (LHD) design of experiment (DOE) from 19 DEM material parameters. DEM simulation using Hertz-Mindlin with flexible bond contact laws and DOE optimized material properties successfully reproduced the mass fractions retained in ASABE sieves at 9.8% mean relative error and a coefficient of determination of R2 = 0.87. Here, the DEM methodology developed for mechanical WPC mass fractionation could be deployed to perform virtual design of feedstock handling equipment and performance analysis of mechanical fraction systems.

09 BIOMASS FUELS↗
DOE OSTI2024

Methods to Evaluate Subcolumn Profiles Based on Two-Point Diagnostics

In atmospheric models, stochastic generation of subgrid-scale profiles or “subcolumns” has been used for a variety of purposes. Such subcolumns can be generated from subgrid probability density functions (PDFs) at different vertical levels, when such PDFs are available. To do so, the generator needs to decide how strongly points should be correlated in the vertical, that is, how much the values should be overlapped. This is sometimes called “PDF overlap.” To assess vertical correlation in a simplified, observable setting, here the vertical correlation of vertical velocity in subcloud layers is examined. Doppler lidar is used to evaluate the vertical profiles of vertical velocity produced by a large-eddy simulation (LES) model and the Subgrid Importance Latin Hypercube Sampler (SILHS) subcolumn generator. In order to diagnose unrealistic features in subcolumn profiles, various statistical diagnostics are examined here, including the bivariate PDF of vertical velocity at two separated points (i.e., altitudes), the two-point velocity correlation, the integral correlation length, the PDF of two-point velocity differences, and the skewness and kurtosis of two-point velocity differences. The profiles produced by LES match lidar well, except that they are too smooth at small scales. The profiles produced by SILHS exhibit sharp jumps from updraft to downdraft that are not observed in the lidar data. To reduce the generation of these unrealistically sharp jumps, the SILHS sampling method is revised. The diagnostics confirm that the revised sampling method reduces the overprediction of sharp jumps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An initial exploration of Bayesian model calibration for estimating the composition of rocks and soils on Mars

The Mars Curiosity rover carries an instrument, ChemCam, designed to measure the composition of surface rocks and soil using laser-induced breakdown spectroscopy (LIBS). The measured spectra from this instrument must be analyzed to identify the component elements in the target sample, as well as their relative proportions. This process, which we call disaggregation, is complicated by so-called matrix effects, which describe nonlinear changes in the relative heights of emission lines as an unknown function of composition due to atomic interactions within the LIBS plasma. In this work, we explore the use of the plasma physics code ATOMIC, developed at Los Alamos National Laboratory, for the disaggregation task. ATOMIC has recently been used to model LIBS spectra and can robustly reproduce matrix effects from first principles. The ability of ATOMIC to predict LIBS spectra presents an exciting opportunity to perform disaggregation in a manner not yet tried in the LIBS community, namely via Bayesian model calibration. However, using it directly to solve our inverse problem is computationally intractable due to the large parameter space and the computation time required to produce a single output. Therefore, we also explore the use of emulators as a fast solution for this analysis. We discuss a proof of concept Gaussian process emulator for disaggregating two-element compounds of sodium and copper. The training and test datasets were simulated with ATOMIC using a Latin hypercube design. After testing the performance of the emulator, we successfully recover the composition of 25 test spectra with Bayesian model calibration.

97 MATHEMATICS AND COMPUTING↗