On Using Adaptive Surrogate Modeling in Design for Efficient Fluid Power

Rao, Lakshmi Gururaja; Schuh, Jonathon K.; Ewoldt, Randy H.; Allison, James T.

doi:10.1115/detc2015-46832

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…The DFSM method presented here is based on adaptive SM [12][13][14]40,44,45] as a means to ensure accuracy between the surrogate and original derivative function. After solving an optimal codesign problem using an approximate derivative function, additional samples of the high-fidelity derivative function are taken in areas of strategic interest (e.g., near the optimum) [15,16].…”

Section: Derivative Function Surrogate Modeling Methodologymentioning

confidence: 99%

Design of Dynamic Systems Using Surrogate Models of Derivative Functions

Deshmukh

Allison

2017

Journal of Mechanical Design

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Optimization of dynamic systems often requires system simulation. Several important classes of dynamic system models have computationally expensive time derivative functions, resulting in simulations that are significantly slower than real time. This makes design optimization based on these models impractical. An efficient two-loop method, based on surrogate modeling, is presented here for solving dynamic system design problems with computationally expensive derivative functions. A surrogate model is constructed for only the derivative function instead of the simulation response. Simulation is performed based on the computationally inexpensive surrogate derivative function; this strategy preserves the nature of the dynamic system, and improves computational efficiency and accuracy compared to conventional surrogate modeling. The inner-loop optimization problem is solved for a given derivative function surrogate model (DFSM), and the outer loop updates the surrogate model based on optimization results. One unique challenge of this strategy is to ensure surrogate model accuracy in two regions: near the optimal point in the design space, and near the state trajectory in the state space corresponding to the optimal design. The initial evidence of method effectiveness is demonstrated first using two simple design examples, followed by a more detailed wind turbine codesign problem that accounts for aeroelastic effects and simultaneously optimizes physical and control system design. In the last example, a linear state-dependent model is used that requires computationally expensive matrix updates when either state or design variables change. Results indicate an order-of-magnitude reduction in function evaluations when compared to conventional surrogate modeling. The DFSM method is expected to be beneficial only for problems where derivative function evaluation expense, and not large problem dimension, is the primary contributor to solution expense (a restricted but important problem class). The initial studies presented here revealed opportunities for potential further method improvement and deeper investigation.

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Section: Derivative Function Surrogate Modeling Methodologymentioning

confidence: 99%

Design of Dynamic Systems Using Surrogate Models of Derivative Functions

Deshmukh

Allison

2017

Journal of Mechanical Design

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Enhancing Full-Film Lubrication Performance Via Arbitrary Surface Texture Design

Lee

Schuh

Ewoldt

et al. 2017

Journal of Mechanical Design

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Minimizing energy loss and improving system load capacity and compactness are important objectives for fluid power systems. Recent studies reveal that microtextured surfaces can reduce friction in full-film lubrication, and that asymmetric textures can reduce friction and increase normal force simultaneously. As an extension of these previous discoveries, we explore how enhanced texture design can maximize these objectives together. We design surface texture using a set of distinct parameterizations, ranging from simple to complex, to improve performance beyond what is possible for previously investigated texture geometries. Here, we consider a rotational tribo-rheometer configuration with a fixed textured bottom disk and a rotating top flat disk with controlled separation gap. To model Newtonian fluid flow, the Reynolds equation is formulated in cylindrical coordinates and solved using a pseudospectral method. Model assumptions include incompressibility, steady flow, constant viscosity, and a small gap height to disk radius ratio. Multi-objective optimization problems are solved using the epsilon-constraint method along with an interior-point (IP) nonlinear programming algorithm. The trade-off between competing objectives is quantified, revealing mechanisms of performance enhancement. Various geometries are explored and optimized, including symmetric and asymmetric circular dimples, and novel arbitrary continuous texture geometries represented using two-dimensional cubic spline interpolation. Shifting from simple dimpled textures to more general texture geometries resulted in significant simultaneous improvement in both performance metrics for full-film lubrication texture design. An important qualitative result is that textures resembling a spiral blade tend to improve performance for rotating contacts.

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On Using Adaptive Surrogate Modeling in Design for Efficient Fluid Power

Cited by 2 publications

References 45 publications

Design of Dynamic Systems Using Surrogate Models of Derivative Functions

Design of Dynamic Systems Using Surrogate Models of Derivative Functions

Enhancing Full-Film Lubrication Performance Via Arbitrary Surface Texture Design

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