Results for “Modeling” — Page 33

NASA NTRSDate not supplied

Modeling of a Solid Oxide Fuel Cell as Part of a Predictive Functional Model for Aerospace Fuel-Cell Systems

This paper will discuss initial efforts at modeling a solid oxide fuel cell (SOFC) for aerospace applications. Fuel cells historically have been used in spacecraft from the Gemini to the Shuttle era for providing spacecraft power with the added benefit of producing water for crew use. However, there are many potential applications for fuel cells and electrolyzers in spaceflight, including oxygen generation, in-situ resource utilization (ISRU) and propellant production. This proof-of-concept model has been developed using a commercial multiphysics modeling software package. Two-dimensional and one-dimensional isothermal models were created based on a certain SOFC design and results were compared to test data from the real system. Local Butler-Volmer kinetic relations were adjusted, and an effective porous medium approach was taken in order to capture how the many interconnects between cells in the gas channels affected fluid flow. The model was able to reproduce polarization curves derived from test data within around 0.02 Volts for a given current density. This model could be adapted to model a solid oxide electrolyzer, proton exchange membrane fuel cell, or other type of fuel cell technology in order to understand how these types of technologies could fit into broader spacecraft designs and advance the capabilities of spaceflight systems.

Mary Lou Nadeau↗

NASA NTRSDate not supplied

Modeling of a Solid Oxide Fuel Cell as Part of a Predictive Functional Model for Aerospace Fuel-Cell Systems

This paper will discuss initial efforts at modeling a solid oxide fuel cell (SOFC) for aerospace applications. Fuel cells historically have been used in spacecraft from the Gemini to the Shuttle era for providing spacecraft power with the added benefit of producing water for crew use. However, there are many potential applications for fuel cells and electrolyzers in spaceflight, including oxygen generation, in-situ resource utilization (ISRU) and propellant production. This proof-of-concept model has been developed using a commercial multiphysics modeling software package. Two-dimensional and one-dimensional isothermal models were created based on a certain SOFC design and results were compared to test data from the real system. Local Butler-Volmer kinetic relations were adjusted, and an effective porous medium approach was taken in order to capture how the many interconnects between cells in the gas channels affected fluid flow. The model was able to reproduce polarization curves derived from test data within around 0.02 Volts for a given current density. This model could be adapted to model a solid oxide electrolyzer, proton exchange membrane fuel cell, or other type of fuel cell technology in order to understand how these types of technologies could fit into broader spacecraft designs and advance the capabilities of spaceflight systems.

Mary Lou Nadeau↗

NASA NTRSDate not supplied

Modeling of a Solid Oxide Fuel Cell as Part of a Predictive Functional Model for Aerospace Fuel-Cell Systems

This paper will discuss initial efforts at modeling a solid oxide fuel cell (SOFC) for aerospace applications. Fuel cells historically have been used in spacecraft from the Gemini to the Shuttle era for providing spacecraft power with the added benefit of producing water for crew use. However, there are many potential applications for fuel cells and electrolyzers in spaceflight, including oxygen generation, in-situ resource utilization (ISRU) and propellant production. This proof-of-concept model has been developed using a commercial multiphysics modeling software package. Two-dimensional and one-dimensional isothermal models were created based on a certain SOFC design and results were compared to test data from the real system. Local Butler-Volmer kinetic relations were adjusted, and an effective porous medium approach was taken in order to capture how the many interconnects between cells in the gas channels affected fluid flow. The model was able to reproduce polarization curves derived from test data within around 0.02 Volts for a given current density. This model could be adapted to model a solid oxide electrolyzer, proton exchange membrane fuel cell, or other type of fuel cell technology in order to understand how these types of technologies could fit into broader spacecraft designs and advance the capabilities of spaceflight systems.

Mary Lou Nadeau↗

NASA NTRSDate not supplied

Modeling of a Solid Oxide Fuel Cell as Part of a Predictive Functional Model for Aerospace Fuel-Cell Systems

This paper will discuss initial efforts at modeling a solid oxide fuel cell (SOFC) for aerospace applications. Fuel cells historically have been used in spacecraft from the Gemini to the Shuttle era for providing spacecraft power with the added benefit of producing water for crew use. However, there are many potential applications for fuel cells and electrolyzers in spaceflight, including oxygen generation, in-situ resource utilization (ISRU) and propellant production. This proof-of-concept model has been developed using a commercial multiphysics modeling software package. Two-dimensional and one-dimensional isothermal models were created based on a certain SOFC design and results were compared to test data from the real system. Local Butler-Volmer kinetic relations were adjusted, and an effective porous medium approach was taken in order to capture how the many interconnects between cells in the gas channels affected fluid flow. The model was able to reproduce polarization curves derived from test data within around 0.02 Volts for a given current density. This model could be adapted to model a solid oxide electrolyzer, proton exchange membrane fuel cell, or other type of fuel cell technology in order to understand how these types of technologies could fit into broader spacecraft designs and advance the capabilities of spaceflight systems.

Mary Lou Nadeau↗

NASA NTRS2023

Unifying Radiative Transfer Models in Computer Graphics and Remote Sensing, Part II: A Differentiable, Polarimetric Forward Model and Validation

The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 2 of this work, we demonstrate that Monte Carlo forward models in computer graphics are capable of sufficient accuracy for remote sensing by extending Mitsuba 3, a forward and inverse modeling framework recently developed in the computer graphics community, to simulate simple atmosphere-ocean systems and show that our framework is capable of achieving error on par with codes currently used by the remote sensing community on benchmark results.

Radiative transfer↗

NASA NTRS2025

Assessment of Model Outcomes Between the Integrated Medical Model (IMM) and the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT)

The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is a computational model that provides human health and medical risk predictions for crewed spaceflight missions. MEDPRAT utilizes discrete event modeling and dynamic probabilistic simulation to predict critical mission outcomes (total medical events, crew health index, quality time lost, loss of crew life, removal to definitive care), condition occurrences, and resource consumption. Input parameters for MEDPRAT include crew attributes (e.g., sex), types of mission activities (e.g., whether and where crew members perform an extravehicular activity (EVA)), available resources, treatment information, and probability distributions for medical conditions. As an evolution of the Integrated Medical Model (IMM), MEDPRAT provides enhanced capabilities and higher fidelity, and incorporates more appropriate assumptions for long-duration spaceflight. IMM is the currently accepted standard for quantifying spaceflight mission medical risk in NASA operations that uses a probabilistic risk assessment (PRA) approach. MEDPRAT builds on the same logical foundation as IMM but implements the model architecture through highly optimized Monte Carlo sampling methods. An analysis is performed comparing the outputs from IMM with those from MEDPRAT V1.0 and V2.0 for the same reference missions in order to quantify similarities and differences in the model outcomes. The juxtaposition between IMM and MEDPRAT V1.0 and 2.0 shown in this report demonstrates that these two models generate very similar results; where differences in outcomes are shown, these are in accordance with the underlying assumptions and differences in the model architectures. This validation effort further establishes the credibility and reliability of the MEDPRAT software.

Matthew T Prelich↗

DOE OSTI2020

Combining model-based and model-free methods for stochastic control of distributed energy resources

Modern distribution systems are experiencing a fast transformation with the growing penetration of distributed energy resources (DERs). Along with the economic and environmental benefits of DERs, challenges arise to address the uncertainties caused by their inherent volatility. If properly coordinated, however, DERs have the potential to provide the controllability that grid operators need. Here, we propose a hierarchical control framework that combines the model-based and model-free methods for stochastic DER control in distribution systems. The upper-level scheduler considers a chance-constrained optimal power flow problem (model-based) that schedules DER setpoints to minimize the operational cost and maintain the operating reserve. The lower-level distributed DER controllers absorb real-time disturbances and uncertainties using the extremum seeking control (model-free) to achieve grid objectives. The combination of model-based and model-free methods allows us to take the advantages of both methods to effectively manage the uncertainty in distribution systems. The proposed work is demonstrated on the IEEE 13-node feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Isomeric effects on the reactivity of branched alkenes: An experimental and kinetic modeling study of methylbutenes

Here, a detailed experimental study of the low-to-intermediate temperature combustion of methylbutene isomers, i.e., branched C 5 alkenes, has been undertaken with multiple experimental facilities. Ignition delay times were measured at equivalence ratios 0.5–2.0, 685–1020 K and up to 45 bar condition from two rapid compression machines and showed slight deviation from an Arrhenius behavior for all three isomers, while their reactivity order differs as temperature changes. Sampled intermediates formed during the oxidation process of mixtures at 900–1150 K and 0.82 bar from a flow reactor and at 730 K and 20 bar from a rapid compression machine were analyzed using gas chromatography techniques. Trends in the formation and consumption of sampled intermediates were modeled using a kinetic model developed in this work for all three isomers. Rate of production and sensitivity analyses emphasize the role of double bond-specific reactions governing the global reactivity of these fuels. Additional studies of the addition reactions of HO 2 radicals to the double bond and to allylic radicals may improve the model performance.

2-Methyl-1-butene↗

DOE OSTI2020

A quantitative phase-field model for gas bubble evolution in UO2

Due to the large formation energy of vacancies and noble gas atoms in the form of interstitials or substitutional atoms in nuclear fuel (UO2), the thermodynamic equilibrium concentrations of these species are very low in the nuclear fuel matrix even at very high temperatures, which imposes difficulties upon the quantitative study of bubble evolution via the phase-field method. In this study, a quantitative phase-field model is proposed to deal with this problem. The system’s free energy density is derived according to the principles of thermodynamics, with consideration of the elastic effect and with the use of real material parameters from experiments. The model is useful for the study of the kinetics of gas bubble growth with very dilute concentrations of vacancy and gas atoms in the matrix. This model is applied to study single bubble growth and multiple bubble growth under various concentrations of vacancy and gas atoms and at various temperatures. The elastic effect and the effects of the generation rate of vacancies and gas atoms on bubble growth are analyzed.

low defect concentration, nuclear fuel, gas bubble↗

DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part I: Implementation and multi-fidelity hierarchies for the single-particle model

To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapidly. Here, this study seeks to reduce the computational resources needed to determine a battery's internal states by replacing physics-based Li-ion battery models - such as the single-particle model (SPM) and the pseudo-2D (P2D) model - with a physics-informed neural network (PINN) surrogate. The surrogate model makes high-throughput techniques, such as Bayesian calibration, tractable to determine battery internal parameters from voltage responses. This manuscript is the first of a two-part series that introduces PINN surrogates of Li-ion battery models for parameter inference (i.e., state-of-health diagnostics). In this first part, a method is presented for constructing a PINN surrogate of the SPM. A multi-fidelity hierarchical training, where several neural nets are trained with multiple physics-loss fidelities is shown to significantly improve the surrogate accuracy when only training on the governing equation residuals. The implementation is made available in a companion repository (https://github.com/NREL/PINNSTRIPES). The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.

25 ENERGY STORAGE↗

DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part II: Regularization and application of the pseudo-2D model

Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help formulate battery aging models. However, it is computationally intensive and cannot be easily repeated for multiple cycles, multiple operating conditions, or multiple replicate cells. To reduce the computational cost of Bayesian calibration, numerical solvers for physics-based models can be replaced with faster surrogates. A physics-informed neural network (PINN) is developed as a surrogate for the pseudo-2D (P2D) battery model calibration. For the P2D surrogate, additional training regularization was needed as compared to the PINN single-particle model (SPM) developed in Part I. Both the PINN SPM and P2D surrogate models are exercised for parameter inference and compared to data obtained from a direct numerical solution of the governing equations. A parameter inference study highlights the ability to use these PINNs to calibrate scaling parameters for the cathode Li diffusion and the anode exchange current density. By realizing computational speed-ups of ~2250x for the P2D model, as compared to using standard integrating methods, the PINN surrogates enable rapid state-of-health diagnostics. Finally, in the low-data availability scenario, the testing error was estimated to ~2 mV for the SPM surrogate and ~10 mV for the P2D surrogate which could be mitigated with additional data.

25 ENERGY STORAGE↗

DOE OSTI2023

Bayesian Parameter Estimation of the k-ω Shear Stress Transport Model for Accurate Simulations of Impinging-Jet Heat Transfer

Given the lack of fusion-relevant component test facilities, current estimates of the thermo-fluid performance of plasma-facing components are based for the most part on numerical simulations. A major source of uncertainty in these simulations is the semiempirical turbulence (closure) models for the Reynolds stresses appearing in the governing Reynolds-averaged Navier-Stokes equations, which involve a set of constants that depend upon the flow. The objective of this study is to evaluate Bayesian parameter estimation of turbulence closure constants in ANSYS Fluent to model heat transfer in impinging jets. The Bayesian statistical calibration produces a probability distribution for these constants from experimental data; the maximum a posteriori estimates are then taken to be the calibrated constants, or parameters. The turbulence model constants are calibrated using an experimental study of a submerged jet of air impinging on a flat heated surface at Reynolds numbers Re = O(10 4 ) and impingement distance in jet diameters H/d = 2. Numerical predictions using the calibrated model parameters are then compared with those generated using the default constants. Predictions obtained with model parameters calibrated on datasets of two different sizes are compared to evaluate the effect of the number of calibration samples. Lastly, the extrapolative ability of the calibrated model is examined by predictions at a Re beyond the calibration values.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Internal Model-Based Active Damping Strategy for a Back-to-Back Modular Multilevel Converter System for Advanced Grid Support: Preprint

The proposed work focuses on the possibility of achieving the reduction in the size of the interfacing filter for the grid connection for a back-to-back modular multilevel converter system with the use of third order LCL filters. To achieve the same attenuation, it is possible to reduce the size of the interfacing filters by the usage of third order filters. However, this kind of filtering comes with the limitation of having sustained oscillations due to lack of damping especially during transient changes. Therefore, in this work, a methodology has been proposed based on the principle of internal model to cater for this unwanted oscillations. The third order system is modeled inside the microcontroller with the damping enabled. The error between the output from the model and the actual are compared and the error is utilized to accomplish the active damping strategy. The proposed architecture is verified via computer simulations based on MATLAB/Simulink domain and various case study results along with their discussion is presented in this paper.

dq control↗

DOE OSTI2020

BEPAM-E Model Code and CABBI Simulation Results for "Repeal of the Clean Power Plan: Social Cost and Distributional Implications"

The dataset consists of results and various input data that are used in the GAMS model for the publication "Repeal of the Clean Power Plan: Social Cost and Distributional Implications". All the data are either excel files or in the .inc format which can be read within GAMS or Notepad. Main data sources include: agriculture, transportation and electricity data. Model details can be found in the paper and the GAMS model package.

carbon abatement↗

DOE OSTI2024

To Exascale and Beyond—The Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), a Performance Portable Global Atmosphere Model for Cloud-Resolving Scales

The new generation of heterogeneous CPU/GPU computer systems offer much greater computational performance but are not yet widely used for climate modeling. One reason for this is that traditional climate models were written before GPUs were available and would require an extensive overhaul to run on these new machines. In addition, even conventional “high–resolution” simulations don't currently provide enough parallel work to keep GPUs busy, so the benefits of such overhaul would be limited for the types of simulations climate scientists are accustomed to. The vision of the Simple Cloud-Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) project is to create a global atmospheric model with the architecture to efficiently use GPUs and horizontal resolution sufficient to fully take advantage of GPU parallelism. After 5 years of model development, SCREAM is finally ready for use. In this paper, we describe the design of this new code, its performance on both CPU and heterogeneous machines, and its ability to simulate real-world climate via a set of four 40 day simulations covering all 4 seasons of the year.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

The IDAES process modeling framework and model library—Flexibility for process simulation and optimization

Abstract Energy systems and manufacturing processes of the 21st century are becoming increasingly dynamic and interconnected, which require new capabilities to effectively model and optimize their design and operations. Such next generation computational tools must leverage state‐of‐the‐art techniques in optimization and be able to rapidly incorporate new advances. To address these requirements, we have developed the Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform, which builds on the strengths of both process simulators (model libraries) and algebraic modeling languages (advanced solvers). This paper specifically presents the IDAES Core Modeling Framework (IDAES‐CMF), along with a case study demonstrating the application of the framework to solve process optimization problems. Capabilities provided by this framework include a flexible, modifiable, open‐source platform for optimization of process flowsheets utilizing state‐of‐the‐art solvers and solution techniques, fully open and extensible libraries of dynamic unit operations models and thermophysical property models, and integrated support for superstructure‐based conceptual design and optimization under uncertainty.

42 ENGINEERING↗

DOE OSTI2024

Open‐Source Anaerobic Digestion Modeling Platform, Anaerobic Digestion Model No. 1 Fast (ADM1F)

An open‐source modeling platform, called Anaerobic Digestion Model No. 1 Fast (ADM1F), is introduced to achieve fast and numerically stable simulations of anaerobic digestion processes. ADM1F is compatible with an iPython interface to facilitate model configuration, simulation, data analysis, and visualization. Faster simulations and more stable results are accomplished by implementing an advanced open‐source library of numerical methods called Portable Extensive Toolkit for Scientific Computation (PETSc) to solve the ADM1 system of equations. Leveraging PETSc, ADM1F can consistently complete a steady‐state simulation under 0.2 s, over 99% faster than a benchmark ADM1 model implemented with MATLAB while achieving agreement of model outputs within 1% of those obtained with the benchmark model. For dynamic simulations, however, ADM1F has a computational speed advantage only when the influent characteristics update more frequently than every 4 h. The ability of ADM1F to be useful as a tool to study anaerobic digestion systems is demonstrated through two example implementations of ADM1F: (1) a two‐phase co‐digestion scenario evaluating the impact of the organic loading rate and the substrate composition on reactor performance and stability, and (2) a conventional digester scenario assessing the effectiveness of recovery strategies after disruptions that led to instability. These examples demonstrate how the high simulation speed and the convenience of the iPython interface allow ADM1F to complete complex analyses within minutes, much faster than computational strategies currently reported in the literature.

anaerobic co-digestion↗

DOE OSTI2021

Quantifying sensitivity in numerical weather prediction-modeled offshore wind speeds through an ensemble modeling approach

A decade of research has shown that numerical weather prediction (NWP)-modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high-quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP-modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP-modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. In conclusion, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble-based wind resource data sets.

17 WIND ENERGY↗