Applications of High-fidelity Reduced-order Modeling for an Efficient Numerical Analysis of the Gearbox-housing Analysis Model with Rigid Multipoint Constraints Applied

Author(s):  
Sung-Eun Kim ◽  
Jae-Chul Lee ◽  
Jun-Geol Ahn ◽  
Hyun-Ik Yang
Author(s):  
Allen Mathis ◽  
D. Dane Quinn

Almost every modern engineering structure incorporates some form of mechanical interface, a connection between two otherwise separate mechanical structures. Complex machines and structures such as automobiles, bridges, aircraft, rockets, etc. rely heavily on these interfaces; however, high-fidelity numerical analysis of such connected structures is currently extremely difficult and computationally expensive due to the disparate length and time scales of the interface as compared to those characterizing the overall structure. This paper utilizes recent work in modal analysis of joints using reduced-order models to study the nonlinear effects of these systems while remaining computationally tractable.


SPE Journal ◽  
2009 ◽  
Vol 15 (02) ◽  
pp. 426-435 ◽  
Author(s):  
M.A.. A. cardoso ◽  
L.J.. J. Durlofsky

Summary The determination of optimal well settings is very demanding computationally because the simulation model must be run many times during the course of the optimization. For this reason, reduced-order modeling procedures, which are a family of techniques that enable highly efficient simulations, may be very useful for optimization problems. In this paper, we describe a recently developed reduced-order modeling (ROM) technique that has been used in other application areas, the trajectory piecewise linearization (TPWL) procedure, and incorporate it in production-optimization computations. The TPWL methodology represents solutions encountered during the optimization runs in terms of Taylor-series expansions around previously simulated states. This requires a small number of preprocessing (training) simulations using the full (high-fidelity) model, during which pressure and saturation states and Jacobian matrices are saved. These states and matrices are then projected into a low-dimensional space using proper orthogonal decomposition (POD). Simulations in this reduced space can be performed very efficiently; in this work, we observe runtime speedups of a factor of 450. Overall speedups are, however, less because of the preprocessing overhead. We assess the TPWL representation for simulations of waterflood in a heterogeneous 3D model containing more than 20,000 gridblocks and six wells. The high degree of accuracy of the TPWL model is first demonstrated for several testing simulations in which producer- and injector-well settings differ from those used in the training runs. The TPWL representations are then used in optimizations involving the determination of optimal bottomhole pressures (BHPs) for a reservoir model with four production wells and two injection wells. A gradient-based algorithm is applied for the optimizations. In the first case, the BHPs of the producers and injectors are optimized at six different times (36 control variables) and in the second case at 15 different times (90 control variables). Results for optimized net present value (NPV) using TPWL are shown to be in consistently close agreement with those computed using high-fidelity simulations. Most significantly, when the optimal well settings obtained using the TPWL procedure are applied in high-fidelity models, the resulting NPVs are within approximately 0.5% of the values determined using the high-fidelity simulations. Our overall conclusion is that the TPWL representation may be quite useful in production-optimization problems.


Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1271 ◽  
Author(s):  
M. Siddiqui ◽  
Eivind Fonn ◽  
Trond Kvamsdal ◽  
Adil Rasheed

We present a nonintrusive approach for combining high-fidelity simulations using Finite-Volume (FV) methods with Proper Orthogonal Decomposition (POD) and Galerkin Reduced-Order Modeling (ROM) methodology. By nonintrusive we here imply an approach that does not need specific knowledge about the high-fidelity Computational Fluid Dynamics (CFD) solver other than the velocity and pressure results given on an element mesh representing the related discrete interpolation spaces. The key step in the presented approach is the projection of the FV results onto suitable finite-element (FE) spaces and then use of classical POD Galerkin ROM framework. We do a numerical investigation of aerodynamic flow around an airfoil cross-section (NACA64) at low Reynolds number and compare the ROM results obtained with high-fidelity FV-generated snapshots against similar high-fidelity results obtained with FE using Taylor–Hood velocity and pressure spaces. Our results show that we achieve relative errors in the range of 1–10% in both H 1 -seminorm of the computed velocities and in the L 2 -norm of the computed pressure with reasonably few ROM modes. Similar accuracy was obtained for lift and drag.


AIAA Journal ◽  
2011 ◽  
Vol 49 (8) ◽  
pp. 1665-1682 ◽  
Author(s):  
Kyunghoon Lee ◽  
Taewoo Nam ◽  
Christopher Perullo ◽  
Dimitri N. Mavris

Author(s):  
John P. Mersch ◽  
Jeffrey A. Smith ◽  
George E. Orient ◽  
Peter W. Grimmer ◽  
Jhana Gearhart

Abstract Multiple fastener reduced-order models and fitting strategies are used on a multiaxial dataset and these models are further evaluated using a high-fidelity analysis model to demonstrate how well these strategies predict load-displacement behavior and failure. Two common reduced-order modeling approaches, the plug and spot weld, are calibrated, assessed, and compared to a more intensive approach — a “two-block” plug calibrated to multiple datasets. An optimization analysis workflow leveraging a genetic algorithm was exercised on a set of quasistatic test data where fasteners were pulled at angles from 0° to 90° in 15° increments to obtain material parameters for a fastener model that best capture the load-displacement behavior of the chosen datasets. The one-block plug is calibrated just to the tension data, the spot weld is calibrated to the tension (0°) and shear (90°), and the two-block plug is calibrated to all data available (0°–90°). These calibrations are further assessed by incorporating these models and modeling approaches into a high-fidelity analysis model of the test setup and comparing the load-displacement predictions to the raw test data.


AIAA Journal ◽  
2018 ◽  
Vol 56 (10) ◽  
pp. 4087-4111 ◽  
Author(s):  
S. Ashwin Renganathan ◽  
Yingjie Liu ◽  
Dimitri N. Mavris

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