Recent Advancements in the Helios Rotorcraft Simulation Code

Wissink, Andrew M.; Sitaraman, Jayanarayanan; Jayaraman, Buvana; Roget, Beatrice; Lakshminarayan, Vinod K.; Potsdam, Mark; Jain, Rohit; Leell, Joshua; James, Rob; Bauer, Andrew; River, Pax

doi:10.2514/6.2016-0563

Cited by 47 publications

(11 citation statements)

References 26 publications

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“…In this international effort, two organisations, the U.S. Army and ONERA, have each applied their best current practices to analyse model-scale wind-tunnel data in an attempt to advance the state of the art in aeroelastic rotor predictions (46) . The U.S. Army applied the Helios framework (47) with OVERFLOW as the near-body solver and SAMCart as the off-body Cartesian solver, coupled with the Rotorcraft Comprehensive Analysis System (RCAS) comprehensive code (48) , while ONERA applied elsA (49) as the CFD solver and Helicopter Overall Simulation Tool (HOST) (50) as the structural dynamics solver.…”

Section: Quantitative Analysis Of Cfd/csd Predictionsmentioning

confidence: 99%

Towards certification of computational fluid dynamics as numerical experiments for rotorcraft applications

2017

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Virtual Engineering (VE), also known as Model-Based Systems Engineering (MBSE), is necessary in both current operational engineering qualifications and to help reduce the costs of future vertical lift design and analysis. As computational power continues to provide increasing capability to the rotorcraft engineering community to perform simulations in both real time and off line, it is imperative that the community develop verification and validation protocols and processes to certify these methods so that they can be reliably used to help reduce engineering cost and schedule. Computational Fluid Dynamics (CFD) has become a major Computational Science and Engineering (CSE) tool in the fixed wing and vertical lift communities, but it has not been developed to the point where it is accepted as a replacement for testing in certification of new or existing systems or vehicles. Since the rise of modern CFD in the 1980s, the promise of CFD’s capabilities has been met or exceeded, but its role in certification arguably remains less prominent than projected. The ability to implement transformative technologies further drives the need for CFD in design. To meet CFD’s role in certification, several goals must be met to provide a true “numerical experiment” from which accuracies (error estimates), sensitivities, and consistent application results can be extracted. This paper discusses the progress and direction towards developing CFD strategies for certification.

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Section: Quantitative Analysis Of Cfd/csd Predictionsmentioning

confidence: 99%

Towards certification of computational fluid dynamics as numerical experiments for rotorcraft applications

2017

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“…There are examples where grid adaptivity has found its way into production usage, but these are primarily for simplified geometries or for applications where isotropic adaptation can be utilized. 34,35 There are several reasons for this slow adoption, but some of the main impediments are stringent geometry requirements, poor robustness (particularly for high Reynolds number turbulent flows on complex geometry), e ciency relative to fixed grid approaches, limitations of error estimation methods, and inadequate demonstration on verification and validation databases. The past decade has seen a gradual maturation in several critical technologies that are necessary for a robust and e cient adaptive capability.…”

Section: Current Status and Limitations Of Unstructured Grid Adapmentioning

confidence: 99%

“…There is a class of hierarchical subdivision techniques that have been extremely successful for isotropic resolution, 35,109 especially when coupled to error estimation techniques that target functional errors.…”

Section: Iva Grid Mechanicsmentioning

confidence: 99%

“…34 Even anisotropic refinement in Cartesian directions typically requires coupling to a di↵erent technique for anisotropic resolution of boundary layer flows (e.g., hybrid grids, 110 strand solver, 35 near-body mixed-element unstructured solver, 35 or near wall model 111,112 ). The subdivision of block structured grids by Hartman 113 and Ceze and Fidkowski 114 provides arbitrary alignment, but this alignment must be specified during initial grid generation because it is restricted to the local curvilinear grid orientation.…”

Section: Iva Grid Mechanicsmentioning

confidence: 99%

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Unstructured Grid Adaptation: Status, Potential Impacts, and Recommended Investments Towards CFD 2030

Park

Krakos

Michal

et al. 2016

46th AIAA Fluid Dynamics Conference

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Unstructured grid adaptation is a powerful tool to control Computational Fluid Dynamics (CFD) discretization error. It has enabled key increases in the accuracy, automation, and capacity of some fluid simulation applications. Slotnick et al. provide a number of case studies in the CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences to illustrate the current state of CFD capability and capacity. The study authors forecast the potential impact of emerging High Performance Computing (HPC) environments forecast in the year 2030 and identify that mesh generation and adaptivity will continue to be significant bottlenecks in the CFD workflow. These bottlenecks may persist because very little government investment has been targeted in these areas. To motivate investment, the impacts of improved grid adaptation technologies are identified. The CFD Vision 2030 Study roadmap and anticipated capabilities in complementary disciplines are quoted to provide context for the progress made in grid adaptation in the past fifteen years, current status, and a forecast for the next fifteen years with recommended investments. These investments are specific to mesh adaptation and impact other aspects of the CFD process. Finally, a strategy is identified to di↵use grid adaptation technology into production CFD work flows.

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“…While there are less modules available in the current work, the simplified framework provides an opportunity for rapid development of novel schemes and solution procedures with the ability to easily migrate technology to the production version. The Helios framework [52] includes multiple nearbody solvers (structured, unstructured and semi-structured), an off-body Cartesian solver capable of highlyscalable adaptive mesh refinement, structural coupling, highly-scalable domain connectivity [53] providing efficient handling of moving bodies and well-defined APIs encouraging continued collaboration with industry, academia and other governmental entities. In contrast, the simplified framework consists of a single flow solver using Cartesian grids with fixed refinement, an open-source domain connectivity module [54] and an actuator line model used to introduce rotor wakes without the need to handle moving bodies.…”

Section: Vb Python-based Infrastructurementioning

confidence: 99%

Towards Efficient Parallel-in-Time Simulation of Periodic Flows

Leffell

Sitaraman

Lakshminarayan

et al. 2016

54th AIAA Aerospace Sciences Meeting

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In response to the stagnation of computer microprocessor speeds over the past decade, the design emphasis of novel supercomputing architectures has focused primarily on increasing the overall number of available cores and reducing communication bottlenecks. Typically, flow solvers have been able to achieve parallel efficiency using domain decomposition, but this approach has the natural limitation that saturation will manifest itself on a finite number of cores at which point parallel speedup stalls and eventually deteriorates. In order to improve parallel scalability we seek to leverage the existing knowledge base on spatial decomposition, while attempting to exploit additional parallelism in the temporal dimension. Specifically, we explore the case of time periodic flows using Parallel-in-Time (PinT) variants of the Time-Spectral (TS) method. A framework employing a Pythonbased infrastructure is described including a standalone library that can be coupled to existing flow solvers in order to facilitate PinT calculations. A model problem of a periodic density pulse is used to examine the different discretization options. Implications for application to wind turbines and rotors are addressed.

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Recent Advancements in the Helios Rotorcraft Simulation Code

Cited by 47 publications

References 26 publications

Towards certification of computational fluid dynamics as numerical experiments for rotorcraft applications

Towards certification of computational fluid dynamics as numerical experiments for rotorcraft applications

Unstructured Grid Adaptation: Status, Potential Impacts, and Recommended Investments Towards CFD 2030

Towards Efficient Parallel-in-Time Simulation of Periodic Flows

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