A methodological approach for the optimal design of the toroidal field coils of a Tokamak device using artificial intelligence

2021 ◽  
Author(s):  
Giordano Tomassetti ◽  
Gianluca De Marzi ◽  
Chiarasole Fiamozzi Zignani ◽  
Francesco Giorgetti ◽  
Antonio della Corte
Keyword(s):  
Optimal Design ◽  
Power Plants ◽  
Methodological Approach ◽  
Electrical Power ◽  
Cost Effective ◽  
Toroidal Field ◽  
Double Layers ◽  
Optimized Design ◽  
Design Variables ◽  
Discrete Design Variables

Abstract As prototypes of future commercial Tokamaks, DEMOs nuclear fusion power plants are expected to be able to produce cost-effective electrical power. In this view, an optimized design becomes crucial in the whole engineering workflow. Up to now, the design of one of the most critical components, the cross-section of each of the toroidal field coils inner leg winding pack, was performed using a sequential trial-and-error procedure. In this work, a novel comprehensive approach is proposed to include all the main design aspects into a unified tool taking advantage of Artificial Neural Networks (ANNs) for faster computation in finding optimal design configurations. This procedure overcomes several difficulties including dealing with both real-valued and discrete design variables, the significant CPU-time of magneto-structural analysis and also guarantees the optimality for the winding pack configuration. The proposed methodology was demonstrated for the 2019 ENEA DEMO configuration which includes 16 toroidal field coils, made-up of 6 3 double layers and a Wind & React manufacturing technique.

Optimal Design of Solenoid Actuators Driving Butterfly Valves

2013 ◽  
Vol 135 (9) ◽  
Author(s):  
Peiman N. Mousavi ◽  
C. Nataraj
Keyword(s):  
Optimal Design ◽  
Intelligent Design ◽  
Simulated Annealing Algorithm ◽  
Dynamic Models ◽  
Optimized Design ◽  
Local Power ◽  
Design Variables ◽  
Annealing Algorithm ◽  
Nonlinear Dynamic Models ◽  
Full Consideration

Smart valves are used in cooling applications and are responsible for regulating and supplying the coolant, which is critical for safe and effective operation of many components on naval and commercial ships. In order to be operated under local power (for various mission-critical reasons) they need to consume as little energy as possible in order to ensure continued operability. This paper focuses on optimized design of a typical system using high fidelity nonlinear dynamic models for all the subsystems with full consideration of stability constraints. A simulated annealing algorithm is applied to explore optimal design using two sets of design variables. The results indicate that substantial amount of energy can be saved by an intelligent design that helps select parameters carefully, but also uses hydrodynamic loads to augment the closing effort.

Successive Optimal Design Procedure Applied on Conventional Fuel Injection Equipment

1996 ◽  
Vol 118 (4) ◽  
pp. 490-493 ◽  
Author(s):  
B. Kegl
Keyword(s):  
High Pressure ◽  
Optimal Design ◽  
Design Problem ◽  
Fuel Injection ◽  
Design Parameters ◽  
Optimal Design Problem ◽  
Injection Equipment ◽  
Design Variables ◽  
Discrete Design Variables ◽  
Conventional Fuel

The paper describes a procedure of solving an optimal design problem with continuous/discrete design variables. The procedure is applied to a set of design parameters of a conventional fuel injection equipment for a diesel engine. The design parameters concern the design of the cam, high pressure pump, delivery valve, snubber valve, high pressure tube and injector. By the proposed procedure the continuous/discrete optimal design problem is replaced by a finite sequence of auxiliary problems where all design variables are treated as continuous. After solving each auxiliary problem one of the discrete design variables is set equal to the closest available discrete value and eliminated from the set of design variables. This process does not guarantee that an optimal solution to the continuous/integer programming problem is located; however it does produce improved near optimal designs for conventional fuel injection equipment. The proposed procedure is illustrated with a numerical example.

scholarly journals Objective and Algorithm Considerations When Optimizing the Number and Placement of Turbines in a Wind Power Plant

2021 ◽  
Author(s):  
Andrew P. J. Stanley ◽  
Owen Roberts ◽  
Jennifer King ◽  
Christopher J. Bay
Keyword(s):  
Power Plant ◽  
Objective Function ◽  
Wind Power ◽  
Power Plants ◽  
Difficult Problem ◽  
Optimal Number ◽  
Wind Power Plant ◽  
Cost Of Energy ◽  
Design Variables ◽  
Discrete Design Variables

Abstract. Optimizing turbine layout is a challenging problem that has been extensively researched in literature. However, optimizing the number of turbines within a given boundary has not been studied as extensively and is a difficult problem because it introduces discrete design variables and a discontinuous design space. An essential step in performing wind power plant layout optimization is to define the objective function, or value, that is used to express what is valuable to a wind power plant developer, such as annual energy production, cost of energy, or profit. In this paper, we demonstrate the importance of selecting the appropriate objective function when optimizing a wind power plant. We optimize several different wind power plants with different wind resources and boundary sizes. Results show that the optimal number of turbines varies drastically depending on the objective function. For a simple, one-dimensional, land-based scenario, we found that a wind power plant optimized for minimal cost of energy produced just 72 % of the profit as the wind power plant optimized for maximum profit, which corresponded to a loss of about $2 million each year. This paper also compares the performance of several different optimization algorithms, including a novel repeated-sweep algorithm that we developed. We found that the performance of each algorithm depended on the number of design variables in the problem as well as the objective function.

scholarly journals Size and Shape Optimization of Space Trusses Considering Geometrical Imperfection-Sensitivity in Buckling Constraints

2018 ◽  
Vol 3 (12) ◽  
pp. 1314 ◽  
Author(s):  
Fardad Haghpanah ◽  
Hamid Foroughi
Keyword(s):  
Optimal Design ◽  
Structural Engineering ◽  
Real Life ◽  
Imperfection Sensitivity ◽  
Cross Sectional ◽  
Design Variables ◽  
Geometrical Imperfection ◽  
Discrete Design Variables ◽  
Teaching Learning ◽  
Space Trusses

Optimal design considering buckling of compressive members is an important subject in structural engineering. The strength of compressive members can be compensated by initial geometrical imperfection due to the manufacturing process; therefore, geometrical imperfection can affect the optimal design of structures. In this study, the metaheuristic teaching-learning-based-optimization (TLBO) algorithm is applied to study the geometrical imperfection-sensitivity of members’ buckling in the optimal design of space trusses. Three benchmark trusses and a real-life bridge with continuous and discrete design variables are considered, and the results of optimization are compared for different degrees of imperfection, namely 0.001, 0.002, and 0.003. The design variables are the cross-sectional areas, and the objective is to minimize the total weight of the structures under the following constraints: tensile and compressive yielding stress, Euler buckling stress considering imperfection, nodal displacement, and available cross-sectional areas. The results reveal that higher geometrical imperfection degrees significantly change the critical buckling load of compressive members, and consequently, increase the weight of the optimal design. This increase varies from 0.4 to 119% for different degrees of imperfection in the studied trusses.

Strategies for Integration of Advanced Gas and Steam Turbines in Power Generation Applications

2007 ◽  
Author(s):  
Justin J. Zachary
Keyword(s):  
Power Generation ◽  
Gas Turbines ◽  
Power Plants ◽  
Electrical Power ◽  
Cost Effective ◽  
Combined Cycle ◽  
Pollutant Removal ◽  
Steam Turbines ◽  
Turbine Cooling ◽  
Removal Technologies

Combined cycle power plants (CCPPs) using fossil fuel generate the cleanest and most efficient form of electrical power. CCPP technologies have evolved significantly in providing better, more cost-effective products: gas turbines (GTs), steam turbines (STs), heat recovery steam generators (HRSGs), heat sinks, pollutant removal technologies, balance of plant (BOP), water treatment and fuel treatment equipment, etc. A major reason for these improvements was the introduction of the G and H technologies for gas turbines, in which an inseparable thermodynamic and physical link was created between the primary and secondary power generation systems by using steam instead of air, in a closed loop to perform most (or all) turbine cooling activities.

Design Optimization of the Flexspline in Harmonic Drive

2012 ◽  
Vol 215-216 ◽  
pp. 59-63 ◽  
Author(s):  
Juan Dai ◽  
Li Zhi Chen ◽  
Xiao Bing Pang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Design Optimization ◽  
Optimization Model ◽  
Design Solution ◽  
Harmonic Drive ◽  
Discrete Variables ◽  
Geometrical Constraint ◽  
Design Variables ◽  
Discrete Design Variables

In order to reduce the weight of harmonic drive (HD), the total volume of flexspline and circular spline was formulated and used as an objection function. Under the constraints including the condition on the strength of flexspline, the condition on averting the tooth top interference, the condition on the transmission ratio of HD and the geometrical constraint conditions of flexspline, a design optimization model with mixed discrete variables was established. For directly applying the optimal design solution of flexspline to manufacture, a manufacture-oriented method for dealing with mixed discrete design variables was used and the established model was solved by using an improved compound genetic algorithm. An optimal design example of flexspline was given and it shows that the proposed method is practical and effective.

The Most Probable Optimal Design Method for Global Optimization

1999 ◽  
Author(s):  
Qinzhong Shi ◽  
Ichiro Hagiwara ◽  
Futoshi Takashima
Keyword(s):  
Neural Network ◽  
Optimal Design ◽  
Design Method ◽  
Global Optimum ◽  
Local Optimum ◽  
Space Design ◽  
Design Variables ◽  
Discrete Design Variables ◽  
Global Optimal ◽  
Optimal Design Method

Abstract In this study, to find the global optimum efficiently, holographic neural network is introduced to be an activate function of response surface methodology. Since the accuracy of approximation function near the global optimal design is merely important, techniques to search the region of containing the global optimal design using conditional random seeds, and techniques for finding more, accurate approximation near the global optimal design, using holographic neural network are exploited. In the study, the proposed approach called the most probable optimal design (MPOD) method to pick up one local optimum design which has the biggest probability in the design space. Design example of crash worthiness for the passenger injury with continuous and discrete design variables are shown the validity of the method.

scholarly journals Application of Air Saturation to Integrated Coal Gasification/CAES Power Plants

1991 ◽  
Author(s):  
M. Nakhamkin ◽  
M. Patel ◽  
E. Swensen ◽  
Arthur Cohn ◽  
Bert Louks
Keyword(s):  
Power Generation ◽  
Electrical Power Generation ◽  
Power Plants ◽  
Coal Gasification ◽  
Electrical Power ◽  
Cost Effective ◽  
Gasification Technology

Several studies have recently been conducted with the objective of finding cost-effective applications of coal gasification technology for intermediate load electrical power generation, e.g. 4000 hours per year.

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