Accuracy of eigenvalue derivatives from reduced-order structural models
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Sandridge, Chris A.↗
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Results for “Reduced Order Model”
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A Method for Generating Reduced Order Linear Models of Supersonic Inlets
For the modeling of high speed propulsion systems, there are at least two major categories of models. One is based on computational fluid dynamics (CFD), and the other is based on design and analysis of control systems. CFD is accurate and gives a complete view of the internal flow field, but it typically has many states and runs much slower dm real-time. Models based on control design typically run near real-time but do not always capture the fundamental dynamics. To provide improved control models, methods are needed that are based on CFD techniques but yield models that are small enough for control analysis and design.
Chicatelli, Amy↗
A Method for Generating Reduced-Order Linear Models of Multidimensional Supersonic Inlets
Simulation of high speed propulsion systems may be divided into two categories, nonlinear and linear. The nonlinear simulations are usually based on multidimensional computational fluid dynamics (CFD) methodologies and tend to provide high resolution results that show the fine detail of the flow. Consequently, these simulations are large, numerically intensive, and run much slower than real-time. ne linear simulations are usually based on large lumping techniques that are linearized about a steady-state operating condition. These simplistic models often run at or near real-time but do not always capture the detailed dynamics of the plant. Under a grant sponsored by the NASA Lewis Research Center, Cleveland, Ohio, a new method has been developed that can be used to generate improved linear models for control design from multidimensional steady-state CFD results. This CFD-based linear modeling technique provides a small perturbation model that can be used for control applications and real-time simulations. It is important to note the utility of the modeling procedure; all that is needed to obtain a linear model of the propulsion system is the geometry and steady-state operating conditions from a multidimensional CFD simulation or experiment. This research represents a beginning step in establishing a bridge between the controls discipline and the CFD discipline so that the control engineer is able to effectively use multidimensional CFD results in control system design and analysis.
Chicatelli, Amy↗
Investigations in Reduced-Order Aerodynamic Modeling of Aircraft Approaching Stall
No abstract available
Frink, N. T.↗
Flow field Reconstruction for Inhomogeneous Turbulence using Data and Physics Driven Models
A methodology combining Large Eddy Simulation (LES) trained data and a physics driven wave packet model to obtain a reduced order reconstruction for broadband, three-dimensional, temporally stationary but spatially inhomogeneous, incompressible turbulence. Wake turbulence generated by an axisymmetric dragging disk with a turbulent co-flow serves as the benchmark test case. We begin by studying the proper-orthogonal decomposition of the turbulent fluctuations taken from a high-resolution LES to first identify whether the fields demonstrate a low-rank character. It is argued that the presence of the turbulent co-flow results in a largely broadband character lacking any tonal properties. This is especially true for Strouhal numbers greater than 1 and only a small fraction of energy is contained in the leading order Kelvin-Helmholtz modes. As such reconstructions and reduced order modeling purely relying on data from LES does not appear to be a lucrative solution - contrary to problems with strongly tonal character. To supplement the missing energy from a low order truncated mode expansion, we utilize a physics based super-resolution (enrichment) algorithm that relies on spatio-temporally localized Gabor wave packets whose time evolution is described using a set of ordinary differential equations. The reconstructed flow has single- and two-point correlations that are consistent with the reference high resolution simulation data.
SLS↗
Uncertainty Quantification for Reduced-Order Surrogate Models Applied to Cloud Microphysics
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Environmental sciences↗
Regional surrogates for predictive control of digital twins
Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.
Data-driven model↗
Leveraging Optimal Sparse Sensor Placement to Aggregate a Network of Digital Twins for Nuclear Subsystems
Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP.
42 ENGINEERING↗
Lower length scale model for palladium attack of silicon carbide in TRISO fuel
TRistructural ISOtropic (TRISO) particle fuels rely on silicon carbide (SiC) as the primary barrier for metallic fission product (FP) release. Palladium (Pd) generated by fission degrades the SiC layer, resulting in the formation of lamellar layers of palladium silicides (PdxSi) and carbon (C) perpendicular to the direction of attack. The Pd attack has been hypothesized to be responsible for failure of the SiC layer and enhance FP release. To better understand and quantify Pd attack of SiC in TRISO particles, a multiscale, mechanistic model of Pd transport is being developed by the NEAMS program. Previous work provided an initial hypothesis for modeling lamellar microstructure formation in SiC due to Pd attack using a phase-field model. The work described in this report builds on the previous model by using molecular dynamics (MD) simulations to parameterize the phase-field model kinetics, and build a reduced order model in BISON using the improved mesoscale Pd penetration model.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Development of a Techno-Economic Analysis Framework for a Solar Thermochemical Fuel Production Process
Synthetic liquid fuels can provide a drop-in substitute for fossil-based fuels in sectors such as aviation and maritime, where electrification is not a viable option due to the need for high specific energy density. However, for these alternative fuels to be adopted at a commercial scale, their price must be competitive compared to their fossil-based counterparts. The reverse water-gas shift (RWGS) reaction offers a promising pathway, using hydrogen (sourced from electrolysis) and carbon dioxide as the feed and reacting to produce syngas - a mixture of H2 and CO at a specific ratio. Syngas is a useful precursor that can be converted into fuels and chemicals via known downstream processes, such as liquid transportation fuels via Fischer-Tropsch (FT) synthesis. The RWGS reaction is currently not applied in commercial scale, unlike the rest of the components in the process chain (electrolyzers and syngas-to-fuel synthesis units). The RWGS reaction poses several challenges due to its restrictive thermodynamics. Being an equimolar reaction, high temperatures and a large excess of H2 are needed to achieve reasonable CO2 conversion at equilibrium. This has detrimental effects on practical process implementation and the quality of syngas that can be produced, with direct effect on the energy and capital requirements, as well as the need for expensive downstream separation. In this work, we are proposing to develop a new concentrating solar thermal (CST) compatible RWGS reactor, performing the reaction in a 2-step chemical looping process using metal oxide at a temperature range of 600-800 degrees Celsius. By decoupling the reactor from the solar receiver, the Generation 3 (Gen3) CST technology could be utilized, together with its proposed thermal energy storage (TES) technology, benefitting from a good match to the required temperatures. CST technology is a viable option for supplying the heat that could be rapidly deployed in scale, thus being a good match to the gas-to-liquid (GTL) process which requires a large minimal scale to be commercially viable. The integration of TES with CST also allows operating the plant at large annual capacity factors and avoids multiple shutdown/startup cycles, thus fitting into the steady-state operation mode that most GTL processes require. The main innovation in the proposed design hinges on a countercurrent reaction design using a packed bed reactor. In 2019 Metcalfe et al. showed the benefits of countercurrent species exchange could be realized in a redox chemical-looping processes, by storing the favorable countercurrent chemical potential profiles in a packed bed of non-stoichiometric oxide. Metcalfe et al. applied this breakthrough concept to the WGS reaction, which is conventionally a co-feed catalytic process, showing a dramatic improvement. Bulfin et al. (2023) performed a similar proof-of-concept demonstration for the RWGS reaction using CeO2, achieving cumulative and peak CO2 conversions of 88% and 95%, respectively, compared to a thermodynamic limit of 58% for the co-feed catalytic process at the same conditions. In our new REGENLOOP project, we are developing a reactor prototype from the heat-exchange packed bed reactor-type, a commonly used reactor in the chemical industry. The endothermic heat of reduction will be supplied to the reactor using CST, while the same heat transfer fluid (HTF) mechanism will be used to extract the exothermic heat of oxidation. An array of multiple reactors is used to supply constant high-purity CO stream, that is then mixed with H2 from electrolysis to produce a high-purity syngas at the required composition. By removing the CO-CO2 separation after the RWGS process, significant energy and cost reduction can be achieved. A physics-based TEA framework is currently being developed, covering all the major plant processes, from the solar collection through storage, chemical looping RWGS, GTL, and auxiliary unit operations, up to the liquid hydrocarbon product. This modeling framework will utilize reduced-order models for the chemical looping RWGS and TES, CST modeling using SolarPILOT, and Aspen Plus for the GTL. By using this combined physics-based approach, the effects of design/operating parameters on the performance and cost can be elucidated. In our presentation, the modeling framework will be presented in detail, including preliminary cost predictions of using this plant configuration under a few selected relevant case studies. This study will be used to identify the major cost drivers, informing further system design and optimization needed to chart the way for a commercially viable pathway.
14 SOLAR ENERGY↗
Fundamental Mistuning Model for Probabilistic Analysis Studied Experimentally
The Fundamental Mistuning Model (FMM) is a reduced-order model for efficiently calculating the forced response of a mistuned bladed disk. FMM ID is a companion program that determines the mistuning in a particular rotor. Together, these methods provide a way to acquire mistuning data in a population of bladed disks and then simulate the forced response of the fleet. This process was tested experimentally at the NASA Glenn Research Center, and the simulated results were compared with laboratory measurements of a "fleet" of test rotors. The method was shown to work quite well. It was found that the accuracy of the results depends on two factors: (1) the quality of the statistical model used to characterize mistuning and (2) how sensitive the system is to errors in the statistical modeling.
Griffin, Jerry H.↗
Validation of Loci-Stream for Autogenous Pressurization of Cryogenic Propellant Tank
Autogenous pressurization of cryogenic propellant tanks eliminates the need to have an additional pressurant tank on the space vehicle, which is highly advantageous due to reduced vehicle mass and design complexity. Autogenous pressurization therefore is one of the key technologies for deep space exploration and long-term space missions. The complex interaction of thermal gradients, turbulence and phase change near the interface make the problem a challenging one to model. Nodal analysis tools and reduced order models are unable to capture the necessary physics. 3-D CFD analyses are necessary to fully characterize autogenous pressurization. CFD analyses pose their own difficulties. The requisite CFD tool to tackle this problem need to be modular with the ability to incorporate various physics models, efficient, and computationally scalable for simulating flight size tanks. NASA MSFC's Loci-Stream CFD tool along with the VOF module is a great candidate to fit this mold. We demonstrate our modeling approach and validation of Loci-Stream for predicting autogenous pressurization of a flight scale propellant tank in order for the solver to serve as a reliable design and analysis tool for NASA's CFM application needs. Liquid hydrogen tank pressurization tests carried out at the MSFC test stand 300 facilities provide reliable validation data for this purpose. These tests were modeled using the Loci-Stream solver with a newly implemented two-phase sharp interface treatment. We show that our modeling approach and CFD solver predict the autogenous pressurization phenomena satisfactorily, and document challenging aspects of modeling this problem.
cryogenic fluid management↗
Validation of Loci-Stream for Autogenous Pressurization of Cryogenic Propellant Tank
Autogenous pressurization of cryogenic propellant tanks eliminates the need to have an additional pressurant tank on the space vehicle, which is highly advantageous due to reduced vehicle mass and design complexity. Autogenous pressurization therefore is one of the key technologies for deep space exploration and long-term space missions. The complex interaction of thermal gradients, turbulence and phase change near the interface make the problem a challenging one to model. Nodal analysis tools and reduced order models are unable to capture the necessary physics. 3-D CFD analyses are necessary to fully characterize autogenous pressurization. CFD analyses pose their own difficulties. The requisite CFD tool to tackle this problem need to be modular with the ability to incorporate various physics models, efficient, and computationally scalable for simulating flight size tanks. NASA MSFC's Loci-Stream CFD tool along with the VOF module is a great candidate to fit this mold. We demonstrate our modeling approach and validation of Loci-Stream for predicting autogenous pressurization of a flight scale propellant tank in order for the solver to serve as a reliable design and analysis tool for NASA's CFM application needs. Liquid hydrogen tank pressurization tests carried out at the MSFC test stand 300 facilities provide reliable validation data for this purpose. These tests were modeled using the Loci-Stream solver with a newly implemented two-phase sharp interface treatment. We show that our modeling approach and CFD solver predict the autogenous pressurization phenomena satisfactorily, and document challenging aspects of modeling this problem.
CFM↗
Validation of Two Phase Flow Modeling Techniques in Loci-Stream using Axial Jet Pressure Control and Autogenous Pressurization Experiment
Autogenous pressurization of cryogenic propellant tanks eliminates the need to have an additional pressurant tank on the space vehicle, which is highly advantageous due to reduced vehicle mass and design complexity. Autogenous pressurization therefore is one of the key technologies for deep space exploration and long-term space missions. The complex interaction of thermal gradients, turbulence and phase change near the interface make the problem a challenging one to model. Nodal analysis tools and reduced order models are unable to capture the necessary physics. 3-D CFD analyses are necessary to fully characterize autogenous pressurization. CFD analyses pose their own difficulties. The requisite CFD tool to tackle this problem need to be modular with the ability to incorporate various physics models, efficient, and computationally scalable for simulating flight size tanks. NASA MSFC's Loci-Stream CFD tool along with the VOF module is a great candidate to fit this mold. We demonstrate our modeling approach and validation of Loci-Stream for predicting autogenous pressurization of a flight scale propellant tank in order for the solver to serve as a reliable design and analysis tool for NASA's CFM application needs. Liquid hydrogen tank pressurization tests carried out at the MSFC test stand 300 facilities provide reliable validation data for this purpose. These tests were modeled using the Loci-Stream solver with a newly implemented two-phase sharp interface treatment. We show that our modeling approach and CFD solver predict the autogenous pressurization phenomena satisfactorily, and document challenging aspects of modeling this problem.
cryogenic fluid management↗
Aeroelastic System Development Using Proper Orthogonal Decomposition and Volterra Theory
This research combines Volterra theory and proper orthogonal decomposition (POD) into a hybrid methodology for reduced-order modeling of aeroelastic systems. The out-come of the method is a set of linear ordinary differential equations (ODEs) describing the modal amplitudes associated with both the structural modes and the POD basis functions for the uid. For this research, the structural modes are sine waves of varying frequency, and the Volterra-POD approach is applied to the fluid dynamics equations. The structural modes are treated as forcing terms which are impulsed as part of the uid model realization. Using this approach, structural and uid operators are coupled into a single aeroelastic operator. This coupling converts a free boundary uid problem into an initial value problem, while preserving the parameter (or parameters) of interest for sensitivity analysis. The approach is applied to an elastic panel in supersonic cross ow. The hybrid Volterra-POD approach provides a low-order uid model in state-space form. The linear uid model is tightly coupled with a nonlinear panel model using an implicit integration scheme. The resulting aeroelastic model provides correct limit-cycle oscillation prediction over a wide range of panel dynamic pressure values. Time integration of the reduced-order aeroelastic model is four orders of magnitude faster than the high-order solution procedure developed for this research using traditional uid and structural solvers.
Lucia, David J.↗
Predicting Maximum Temperatures of a Li-ion Battery on a Simulated Flight Profile using a Model-based Prognostics
One of the challenges in using Li-ion packs in aeronautics is their safety, and thermal runaway (TR) is a major concern. The current engineering solutions to prevent a Li-ion pack from a catastrophic TR require additional mass and volume to isolate cells. The excess mass could be reduced by improving detection and, thus, preventing a TR event. One of the possible early warning indicators of a TR is crossing a threshold temperature. We have developed an approach, based on the Unscented Kalman Filter (UKF), to predict the likelihood of reaching the threshold temperature for simulated flight profiles. The current battery prognostics algorithms for aerospace predict state-of-charge (SOC) and end-of-life (EOL) [1]. We extended this two-level algorithm to predict the maximum temperature during discharge. The amount of heat generated in a cell depends on factors such as cell chemistry, cell packaging, total cycles, operating temperature, and abuse history [2]. Our semi-empirical thermal model depends on three phenomenological parameters which account for those factors. In addition, a two-parameter reduced-order model is developed to predict the temperature rise for short bursts of “random-walk” (RW) discharge current sequence, which simulates a flight's current-loading profile. The performance of these models on different datasets and types of current loading will be presented. To predict the maximal temperatures for future cycles we must estimate the evolution of thermal parameters as the batteries age. It is found that the parameters of the 3-parametric thermal model cannot be estimated only from the RW data. To address the issue, we will present two alternative approaches: i) expanding the datasets to include discharge profiles beyond RWs; ii) model reduction to a two-parametric model. The two approaches will be illustrated by an application to the cycling data from a commercial LG 18650 cell. References: 1. M. Daigle, C.S. Kulkarni, End-of-discharge and End-of-life Prediction in Lithium-ion Batteries with Electrochemistry-based Aging Models, in: AIAA Infotech @ Aerospace, American Institute of Aeronautics and Astronautics, San Diego, California, USA, 2016. 2. M. Börner, et. al, Correlation of aging and thermal stability of commercial 18650-type lithium ion batteries, Journal of Power Sources. 342 (2017) 382–392.
Thermal runaway↗
Nonlinear Dynamic Analysis of Disordered Bladed-Disk Assemblies
In a effort to address current needs for efficient, air propulsion systems, we have developed some new analytical predictive tools for understanding and alleviating aircraft engine instabilities which have led to accelerated high cycle fatigue and catastrophic failures of these machines during flight. A frequent cause of failure in Jets engines is excessive resonant vibrations and stall flutter instabilities. The likelihood of these phenomena is reduced when designers employ the analytical models we have developed. These prediction models will ultimately increase the nation's competitiveness in producing high performance Jets engines with enhanced operability, energy economy, and safety. The objectives of our current threads of research in the final year are directed along two lines. First, we want to improve the current state of blade stress and aeromechanical reduced-ordered modeling of high bypass engine fans, Specifically, a new reduced-order iterative redesign tool for passively controlling the mechanical authority of shroudless, wide chord, laminated composite transonic bypass engine fans has been developed. Second, we aim to advance current understanding of aeromechanical feedback control of dynamic flow instabilities in axial flow compressors. A systematic theoretical evaluation of several approaches to aeromechanical feedback control of rotating stall in axial compressors has been conducted. Attached are abstracts of two .papers under preparation for the 1998 ASME Turbo Expo in Stockholm, Sweden sponsored under Grant No. NAG3-1571. Our goals during the final year under Grant No. NAG3-1571 is to enhance NASA's capabilities of forced response of turbomachines (such as NASA FREPS). We with continue our development of the reduced-ordered, three-dimensional component synthesis models for aeromechanical evaluation of integrated bladeddisk assemblies (i.e., the disk, non-identical bladeing etc.). We will complete our development of component systems design optimization strategies for specified vibratory stresses and increased fatigue life prediction of assembly components, and for specified frequency margins on the Campbell diagrams of turbomachines. Finally, we will integrate the developed codes with NASA's turbomachinery aeromechanics prediction capability (such as NASA FREPS).
McGee, Oliver G., III↗
Genetic programming for the nuclear many-body problem: a guide
Genetic Programming (GP) is an evolutionary algorithm that generates computer programs, or mathematical expressions, to solve complex problems. In this Guide, we demonstrate how to use GP to develop surrogate models to mitigate the computational costs of modeling atomic nuclei with ever increasing complexity. The computational burden escalates when uncertainty quantification is pursued, or when observables must be globally computed for thousands of nuclei. By studying three models in which the mean field depends on the total particle density self-consistently, we show that by constructing reduced order models supported by GP one can speed up many-body computations by several orders of magnitude with a negligible loss in accuracy.
dimensionality reduction↗