Generation of surrogate models of Pareto-optimal performance trade-offs of planar inductors

Abstract. Competition for water between key economic sectors and the environment means agreeing allocations is challenging. Managing releases from the three major dams in Kenya's Tana River basin with its 4.4 million inhabitants, 567 MW of installed hydropower capacity, 33 000 ha of irrigation and ecologically important wetlands and forests is a pertinent example. This research seeks firstly to identify and help decision-makers visualise reservoir management strategies which result in the best possible (Pareto-optimal) allocation of benefits between sectors. Secondly, it seeks to show how trade-offs between achievable benefits shift with the implementation of proposed new rice, cotton and biofuel irrigation projects. To approximate the Pareto-optimal trade-offs we link a water resources management simulation model to a multi-criteria search algorithm. The decisions or "levers" of the management problem are volume-dependent release rules for the three major dams and extent of investment in new irrigation schemes. These decisions are optimised for eight objectives covering the provision of water supply and irrigation, energy generation and maintenance of ecosystem services. Trade-off plots allow decision-makers to assess multi-reservoir rule-sets and irrigation investment options by visualising their impacts on different beneficiaries. Results quantify how economic gains from proposed irrigation schemes trade-off against the disturbance of ecosystems and local livelihoods that depend on them. Full implementation of the proposed schemes is shown to come at a high environmental and social cost. The clarity and comprehensiveness of "best-case" trade-off analysis is a useful vantage point from which to tackle the interdependence and complexity of "water-energy-food nexus" resource security issues.

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The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)

Energies ◽

10.3390/en13092271 ◽

2020 ◽

Vol 13 (9) ◽

pp. 2271 ◽

Cited By ~ 2

Author(s):

Stephen Ntiri Asomani ◽

Jianping Yuan ◽

Longyan Wang ◽

Desmond Appiah ◽

Kofi Asamoah Adu-Poku

Keyword(s):

Neural Network ◽

Objective Function ◽

Surrogate Model ◽

Surrogate Models ◽

Operating Conditions ◽

Pareto Optimal ◽

Objective Functions ◽

Operating Range ◽

Design Variables ◽

Pareto Optimal Points

Pump-as-turbine (PAT) technology permits two operating states—as a pump or turbine, depending on the demand. Nevertheless, designing the geometrical components to suit these operating states has been an unending design issue, because of the multi-conditions for the PAT technology that must be attained to enhance the hydraulic performance. Also, PAT has been known to have a narrow operating range and operates poorly at off-design conditions, due to the lack of flow control device and poor geometrical designs. Therefore, for the PAT to have a wider operating range and operate effectively at off-design conditions, the geometric parameters need to be optimized. Since it is practically impossible to optimize more than one objective function at the same time, a suitable surrogate model is needed to mimic the objective functions for it to be solvable. In this study, the Latin hypercube sampling method was used to obtain the objective function values, the Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Generalized Regression Neural Network (GRNN) were used as surrogate models to approximate the objective functions in the design space. Then, a suitable surrogate model was chosen for the optimization. The Pareto-optimal solutions were obtained by using the Pareto-based genetic algorithm (PBGA). To evaluate the results of the optimization, three representative Pareto-optimal points were selected and analyzed. Compared to the baseline model, the Pareto-optimal points showed a great improvement in the objective functions. After optimization, the geometry of the impeller was redesigned to suit the operating conditions of PAT. The findings show that the efficiencies of the optimized design variables of PAT were enhanced by 23.7%, 11.5%, and 10.4% at part load, design point, and under overload flow conditions, respectively. Moreover, the results also indicated that the chosen design variables (b2, β2, β1, and z) had a substantial impact on the objective functions, justifying the feasibility of the optimization method employed in this study.

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Pareto-optimal formulations for cost versus colorimetric accuracy trade-offs in printer color management

ACM Transactions on Graphics ◽

10.1145/508357.508361 ◽

2002 ◽

Vol 21 (2) ◽

pp. 132-175 ◽

Cited By ~ 5

Author(s):

D. J. Littlewood ◽

P. A. Drakopoulos ◽

G. Subbarayan

Keyword(s):

Pareto Optimal ◽

Color Management ◽

Trade Offs

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Computational Intelligence Techniques for Determining Optimal Performance Trade‐Offs for RF Inductors

Computational Intelligence in Analog and Mixed-Signal (AMS) and Radio-Frequency (RF) Circuit Design ◽

10.1007/978-3-319-19872-9_10 ◽

2015 ◽

pp. 277-296

Author(s):

Elisenda Roca ◽

Rafael Castro‐López ◽

Francisco V. Fernández ◽

Reinier González-Echevarría ◽

Javier Sieiro ◽

...

Keyword(s):

Computational Intelligence ◽

Optimal Performance ◽

Trade Offs ◽

Rf Inductors

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Quieter and Greener rotorcraft: concurrent aerodynamic and acoustic optimization

CEAS Aeronautical Journal ◽

10.1007/s13272-021-00513-x ◽

2021 ◽

Author(s):

Gunther Wilke

Keyword(s):

Pareto Front ◽

Goal Function ◽

Rotor Blades ◽

Helicopter Rotor ◽

Pareto Optimal ◽

Cfd Simulations ◽

Pareto Optimal Set ◽

Trade Offs ◽

Helicopter Rotor Blades ◽

Optimal Set

AbstractWithin the DLR project VicToria an aerodynamic and aero-acoustic optimization of helicopter rotor blades is performed. During the optimization, three independent flight conditions are considered: hover, cruise and descent flight. The first two flight conditions drive the power requirements of the helicopter rotor, while the descent flight is the loudest flight condition for current helicopter generations. To drive down the required power and the emitted noise, a multi-objective design approach coupled with surrogate models is utilized to find a Pareto optimal set of rotors. This approach allows to identify the trade-offs to be made when laying emphasis on either goal function. The underlying CFD simulations utilize fourth-order accurate spatial schemes to capture the vortex dominated flow of helicopter rotor blades. The paper presents the validation of the setups, the optimization results and the off-design analysis of a chosen set of blades from the Pareto front. The conclusion is that the utilization of the Pareto front approach is necessary to find good rotor designs, while the utilization of high-order methods allows for efficient CFD setups.

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