New capabilities in CREATE™-AV Helios Version 11

CREATE™-AV Helios is a high-fidelity coupled CFD/CSD infrastructure developed by the U.S. Dept. of Defense for aeromechanics predictions of rotorcraft. This paper discusses new capabilities added to Helios version 11.0. A new fast-running reduced order aerodynamics option called ROAM has been added to enable faster-turnaround analysis. ROAM is Cartesian-based, employing an actuator line model for the rotor and an immersed boundary model for the fuselage. No near-body grid generation is required and simulations are significantly faster through a combination of larger timesteps and reduced cost per step. ROAM calculations of the JVX tiltrotor configuration give a comparably accurate download prediction to traditional body-fitted calculations with Helios, at 50X less computational cost. The unsteady wake in ROAM is not as well resolved, but wake interactions may be a less critical issue for many design considerations. The second capability discussed is the addition of six-degree-of-freedom capability to model store separation. Helios calculations of a generic wing/store/pylon case with the new 6-DOF capability are found to match identically to calculations with CREATE™-AV Kestrel, a code which has been extensively validated for store separation calculations over the past decade.

Download Full-text

Development of a parameterized reduced-order vertical-axis wind turbine wake model

Wind Engineering ◽

10.1177/0309524x19849864 ◽

2019 ◽

Vol 44 (5) ◽

pp. 494-508 ◽

Cited By ~ 1

Author(s):

Eric B Tingey ◽

Andrew Ning

Keyword(s):

Wind Turbine ◽

Wind Farm ◽

Computational Cost ◽

Vertical Axis ◽

Speed Ratio ◽

Vertical Axis Wind Turbine ◽

Reduced Order ◽

Wake Interactions ◽

Wake Model ◽

Turbine Wake

Analyzing or optimizing wind farm layouts often requires reduced-order wake models to estimate turbine wake interactions and wind velocity. We propose a wake model for vertical-axis wind turbines in streamwise and crosswind directions. Using vorticity data from computational fluid dynamic simulations and cross-validated Gaussian distribution fitting, we produced a wake model that can estimate normalized wake velocity deficits of an isolated vertical-axis wind turbine using normalized downstream and lateral positions, tip-speed ratio, and solidity. Compared with computational fluid dynamics, taking over a day to run one simulation, our wake model predicts a velocity deficit in under a second with an appropriate accuracy and computational cost necessary for wind farm optimization. The model agreed with two experimental studies producing percent differences of the maximum wake deficit of 6.3% and 14.6%. The wake model includes multiple wake interactions and blade aerodynamics to calculate power, allowing its use in wind farm layout analysis and optimization.

Download Full-text

A study of computational methods for wake structure and base pressure prediction of a generic SUV model with fixed and rotating wheels

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407016685496 ◽

2017 ◽

Vol 231 (9) ◽

pp. 1222-1238 ◽

Cited By ~ 5

Author(s):

David Forbes ◽

Gary Page ◽

Martin Passmore ◽

Adrian Gaylard

Keyword(s):

Experimental Data ◽

Computational Methods ◽

Large Scale ◽

Ground Plane ◽

Computational Cost ◽

Rear Wheel ◽

Base Pressure ◽

Detached Eddy Simulation ◽

Wake Structure ◽

Wake Interactions

This study is an evaluation of the computational methods in reproducing experimental data for a generic sports utility vehicle (SUV) geometry and an assessment on the influence of fixed and rotating wheels for this geometry. Initially, comparisons are made in the wake structure and base pressures between several CFD codes and experimental data. It was shown that steady-state RANS methods are unsuitable for this geometry due to a large scale unsteadiness in the wake caused by separation at the sharp trailing edge and rear wheel wake interactions. unsteady RANS (URANS) offered no improvements in wake prediction despite a significant increase in computational cost. The detached-eddy simulation (DES) and Lattice–Boltzmann methods showed the best agreement with the experimental results in both the wake structure and base pressure, with LBM running in approximately a fifth of the time for DES. The study then continues by analysing the influence of rotating wheels and a moving ground plane over a fixed wheel and ground plane arrangement. The introduction of wheel rotation and a moving ground was shown to increase the base pressure and reduce the drag acting on the vehicle when compared to the fixed case. However, when compared to the experimental standoff case, variations in drag and lift coefficients were minimal but misleading, as significant variations to the surface pressures were present.

Download Full-text

An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques

Bollettino dell Unione Matematica Italiana ◽

10.1007/s40574-020-00263-4 ◽

2020 ◽

Author(s):

Nicola Demo ◽

Giulio Ortali ◽

Gianluca Gustin ◽

Gianluigi Rozza ◽

Gianpiero Lavini

Keyword(s):

Optimization Problems ◽

Computational Cost ◽

Gaussian Process Regression ◽

Dynamic Mode Decomposition ◽

Data Driven ◽

Free Form ◽

Dynamic Mode ◽

Two Phase ◽

Initial Shape ◽

Reduced Order

Abstract This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. We adopt reduced order models in order to improve the efficiency of the overall optimization, keeping a modular and equation-free nature to target the industrial demand. We applied the above mentioned pipeline to a realistic cruise ship in order to reduce the total drag. We begin by defining the design space, generated by deforming an initial shape in a parametric way using free form deformation. The evaluation of the performance of each new hull is determined by simulating the flux via finite volume discretization of a two-phase (water and air) fluid. Since the fluid dynamics model can result very expensive—especially dealing with complex industrial geometries—we propose also a dynamic mode decomposition enhancement to reduce the computational cost of a single numerical simulation. The real-time computation is finally achieved by means of proper orthogonal decomposition with Gaussian process regression technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape.

Download Full-text

A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation

Geophysical Journal International ◽

10.1093/gji/ggaa415 ◽

2020 ◽

Vol 223 (3) ◽

pp. 1837-1863

Author(s):

M C Manassero ◽

J C Afonso ◽

F Zyserman ◽

S Zlotnik ◽

I Fomin

Keyword(s):

Large Scale ◽

Computational Cost ◽

Forward Problem ◽

Parallel Structure ◽

Data Sets ◽

Magnetotelluric Data ◽

Reduced Order ◽

Mcmc Algorithms ◽

Simulation Based ◽

And Performance

SUMMARY Simulation-based probabilistic inversions of 3-D magnetotelluric (MT) data are arguably the best option to deal with the nonlinearity and non-uniqueness of the MT problem. However, the computational cost associated with the modelling of 3-D MT data has so far precluded the community from adopting and/or pursuing full probabilistic inversions of large MT data sets. In this contribution, we present a novel and general inversion framework, driven by Markov Chain Monte Carlo (MCMC) algorithms, which combines (i) an efficient parallel-in-parallel structure to solve the 3-D forward problem, (ii) a reduced order technique to create fast and accurate surrogate models of the forward problem and (iii) adaptive strategies for both the MCMC algorithm and the surrogate model. In particular, and contrary to traditional implementations, the adaptation of the surrogate is integrated into the MCMC inversion. This circumvents the need of costly offline stages to build the surrogate and further increases the overall efficiency of the method. We demonstrate the feasibility and performance of our approach to invert for large-scale conductivity structures with two numerical examples using different parametrizations and dimensionalities. In both cases, we report staggering gains in computational efficiency compared to traditional MCMC implementations. Our method finally removes the main bottleneck of probabilistic inversions of 3-D MT data and opens up new opportunities for both stand-alone MT inversions and multi-observable joint inversions for the physical state of the Earth’s interior.

Download Full-text

A Proper Generalized Decomposition (PGD) approach to crack propagation in brittle materials: with application to random field material properties

Computational Mechanics ◽

10.1007/s00466-019-01778-0 ◽

2019 ◽

Vol 65 (2) ◽

pp. 451-473 ◽

Cited By ~ 1

Author(s):

Hasini Garikapati ◽

Sergio Zlotnik ◽

Pedro Díez ◽

Clemens V. Verhoosel ◽

E. Harald van Brummelen

Keyword(s):

Crack Propagation ◽

Material Properties ◽

Computational Cost ◽

Brittle Materials ◽

Energy Criterion ◽

Reduced Order Modeling ◽

Proper Generalized Decomposition ◽

Modeling Framework ◽

Global Energy ◽

Reduced Order

Abstract Understanding the failure of brittle heterogeneous materials is essential in many applications. Heterogeneities in material properties are frequently modeled through random fields, which typically induces the need to solve finite element problems for a large number of realizations. In this context, we make use of reduced order modeling to solve these problems at an affordable computational cost. This paper proposes a reduced order modeling framework to predict crack propagation in brittle materials with random heterogeneities. The framework is based on a combination of the Proper Generalized Decomposition (PGD) method with Griffith’s global energy criterion. The PGD framework provides an explicit parametric solution for the physical response of the system. We illustrate that a non-intrusive sampling-based technique can be applied as a post-processing operation on the explicit solution provided by PGD. We first validate the framework using a global energy approach on a deterministic two-dimensional linear elastic fracture mechanics benchmark. Subsequently, we apply the reduced order modeling approach to a stochastic fracture propagation problem.

Download Full-text

Decoupled Current Controller Based on Reduced Order Generalized Integrator for Three-Phase Grid-Connected VSCs in Distributed System

Energies ◽

10.3390/en12122426 ◽

2019 ◽

Vol 12 (12) ◽

pp. 2426 ◽

Cited By ~ 1

Author(s):

Sen Zhang ◽

Jianfeng Zhao ◽

Zhihong Zhao ◽

Kangli Liu ◽

Pengyu Wang ◽

...

Keyword(s):

Dynamic Response ◽

Design Method ◽

Reference Signal ◽

Response Speed ◽

Critical Issue ◽

Voltage Source ◽

Current Loop ◽

Reduced Order ◽

Distributed Power Generation ◽

Current Controller

Grid-connected voltage source converters (GC-VSCs) are used for interfacing the distributed power generation system (DPGS) to the utility grid. Performance of the current loop is a critical issue for these GC-VSCs. Recently, reduced order generalized integrator (ROGI)-based current controller is proposed, such that AC reference signal of positive or negative sequences can be separately tracked without steady-state error, which has the advantage of less computational burden. However, the cross-coupling within the ROGI-based current controller would deteriorate the transient response of the current loop. In this paper, a ROGI-based decoupled current controller is proposed to eliminate the coupling between α -axis and β -axis. Thus, the faster dynamic response performance can be achieved while maintaining the merits of ROGI-based current controller. An optimal gain parameter design method for the proposed current controller is presented to improve the stability and dynamic response speed of current loop. Simulation and experiments were performed in MATLAB/Simulink and TMS320C28346 DSP-based laboratory prototype respectively, which validated the proposed theoretical approach.

Download Full-text

An Energy Closure Criterion for Model Reduction of a Kicked Euler–Bernoulli Beam

Journal of Vibration and Acoustics ◽

10.1115/1.4048663 ◽

2020 ◽

Vol 143 (4) ◽

Author(s):

Suparno Bhattacharyya ◽

Joseph P. Cusumano

Keyword(s):

Model Reduction ◽

Mode Selection ◽

Computational Cost ◽

Impulsive Loading ◽

Bernoulli Beam ◽

Full System ◽

Fixed Amount ◽

Reduced Order ◽

Proper Orthogonal ◽

Euler Bernoulli Beam

Abstract Reduced order models (ROMs) can be simulated with lower computational cost while being more amenable to theoretical analysis. Here, we examine the performance of the proper orthogonal decomposition (POD), a data-driven model reduction technique. We show that the accuracy of ROMs obtained using POD depends on the type of data used and, more crucially, on the criterion used to select the number of proper orthogonal modes (POMs) used for the model. Simulations of a simply supported Euler–Bernoulli beam subjected to periodic impulsive loads are used to generate ROMs via POD, which are then simulated for comparison with the full system. We assess the accuracy of ROMs obtained using steady-state displacement, velocity, and strain fields, tuning the spatiotemporal localization of applied impulses to control the number of excited modes in, and hence the dimensionality of, the system’s response. We show that conventional variance-based mode selection leads to inaccurate models for sufficiently impulsive loading and that this poor performance is explained by the energy imbalance on the reduced subspace. Specifically, the subspace of POMs capturing a fixed amount (say, 99.9%) of the total variance underestimates the energy input and dissipated in the ROM, yielding inaccurate reduced-order simulations. This problem becomes more acute as the loading becomes more spatio-temporally localized (more impulsive). Thus, energy closure analysis provides an improved method for generating ROMs with energetics that properly reflect that of the full system, resulting in simulations that accurately represent the system’s true behavior.

Download Full-text

Influence of Vehicle Model Complexity in Autonomous Emergency Manoeuvre Planning

Volume 17: Transportation Systems ◽

10.1115/imece2008-68659 ◽

2008 ◽

Author(s):

Daniele Bortoluzzi ◽

Francesco Biral ◽

Enrico Bertolazzi ◽

Paolo Bosetti ◽

Fabrizio Zendri

Keyword(s):

Control Algorithm ◽

Computational Cost ◽

Model Complexity ◽

Critical Conditions ◽

Vehicle Model ◽

Reduced Order ◽

Evasive Manoeuvre ◽

Horizon Planning ◽

The Mathematical Model

In this paper the effectiveness of an optimal reference manoeuvre is analysed w.r.t. the complexity of the vehicle model used within the optimal control algorithm. The optimal reference manoeuvre is computed by means of a Nonlinear Receding Horizon planning (NRHP) strategy which is based on a simplified vehicle model. The reference manoeuvre is tracked by a controller implemented on a low level faster loop. The system is able to perform autonomously lane change and obstacle avoidance manoeuvres by tracking the computed reference one. The quality of the performed manoeuvres depends on the reference manoeuvre and consequently on the vehicle model used by the NRHP. For manoeuvres with low or mild lateral accelerations reduced order models might yield realistic and reliable reference manoeuvres. However, critical conditions (e.g. evasive manoeuvre) require a manoeuvre planner able to catch highly non-linear vehicle dynamics that characterizes such situations. On the other hand, being the NRHP computational cost generally high and related to the number of equations of the mathematical model, a trade-off between computational efficiency and model complexity is required. The work analyses the reference manoeuvres produced by two vehicle models of increasing complexity used as reference within the NRHP. Optimal planner performance evaluation on evasive manoeuvre in critical conditions will be presented with simulations results.

Download Full-text

Immersed Boundary Method Application as a Way to Deal with the Three-Dimensional Sudden Contraction

Computation ◽

10.3390/computation6030050 ◽

2018 ◽

Vol 6 (3) ◽

pp. 50

Author(s):

Jonatas Borges ◽

Marcos Lourenço ◽

Elie Padilla ◽

Christopher Micallef

Keyword(s):

Immersed Boundary Method ◽

Stokes Equations ◽

Computational Cost ◽

Three Dimensional ◽

Immersed Boundary ◽

Fractional Step Method ◽

Central Difference ◽

Boundary Method ◽

Contraction Ratio ◽

Sudden Contraction

The immersed boundary method has attracted considerable interest in the last few years. The method is a computational cheap alternative to represent the boundaries of a geometrically complex body, while using a cartesian mesh, by adding a force term in the momentum equation. The advantage of this is that bodies of any arbitrary shape can be added without grid restructuring, a procedure which is often time-consuming. Furthermore, multiple bodies may be simulated, and relative motion of those bodies may be accomplished at reasonable computational cost. The numerical platform in development has a parallel distributed-memory implementation to solve the Navier-Stokes equations. The Finite Volume Method is used in the spatial discretization where the diffusive terms are approximated by the central difference method. The temporal discretization is accomplished using the Adams-Bashforth method. Both temporal and spatial discretizations are second-order accurate. The Velocity-pressure coupling is done using the fractional-step method of two steps. The present work applies the immersed boundary method to simulate a Newtonian laminar flow through a three-dimensional sudden contraction. Results are compared to published literature. Flow patterns upstream and downstream of the contraction region are analysed at various Reynolds number in the range 44 ≤ R e D ≤ 993 for the large tube and 87 ≤ R e D ≤ 1956 for the small tube, considerating a contraction ratio of β = 1 . 97 . Comparison between numerical and experimental velocity profiles has shown good agreement.

Download Full-text

Wind Farm Yaw Optimization via Random Search Algorithm

Energies ◽

10.3390/en13040865 ◽

2020 ◽

Vol 13 (4) ◽

pp. 865

Author(s):

Jim Kuo ◽

Kevin Pan ◽

Ni Li ◽

He Shen

Keyword(s):

Real Time ◽

Wind Farm ◽

Search Algorithm ◽

Random Search ◽

Computational Cost ◽

Solution Quality ◽

Farm Production ◽

Wake Interactions ◽

Random Search Algorithm ◽

Significant Attention

One direction in optimizing wind farm production is reducing wake interactions from upstream turbines. This can be done by optimizing turbine layout as well as optimizing turbine yaw and pitch angles. In particular, wake steering by optimizing yaw angles of wind turbines in farms has received significant attention in recent years. One of the challenges in yaw optimization is developing fast optimization algorithms which can find good solutions in real-time. In this work, we developed a random search algorithm to optimize yaw angles. Optimization was performed on a layout of 39 turbines in a 2 km by 2 km domain. Algorithm specific parameters were tuned for highest solution quality and lowest computational cost. Testing showed that this algorithm can find near-optimal (<1% of best known solutions) solutions consistently over multiple runs, and that quality solutions can be found under 200 iterations. Empirical results show that as wind farm density increases, the potential for yaw optimization increases significantly, and that quality solutions are likely to be plentiful and not unique.

Download Full-text