A Theory of Fuel Management via Backward Diffusion Calculation

The State of Mathematical Modeling for Power Plant Training Simulators

“…However, three-dimensional phenomena may be treated more directly using the coupled kinetics methods. The vector processing capability of the next generation of super minicomputers, coupled with improved methods (Chao et al,11,12,13), is expected to permit coupled kinetics models to be run with nodalizations approaching that used with present expansion methods.…”

“…Future developments in core models will be (a) further refinement of mechanistic fuel failure models to represent fuel failure, (b) further refinement of the effect of core voids on in-core and ex-core detector re-sponses, and (c) more detailed flux shape behavior resulting from faster and more powerful digital computers (vector machines), or more advanced core modeling and computation methods (11,12,13), with the attendant better representation of reactivity phenomena and spatial detail.…”

Pressurized water reactor in-core nuclear fuel management by tabu search

“…Kim et al (1987) developed a two-stage procedure for maximizing cycle length, subject to power peaking constraints, by decoupling the fuel and BP placement problems. Stillman et al (1989) used the backward diffusion calculation (Chao et al, 1986) and successive linear programming to determine theoretically optimal fuel and two-dimensional (2D) power distributions for a PWR, minimizing fissile material and BP inventories. Kropaczek and Turinsky (1991) combined the simulated annealing (SA) stochastic optimization technique with a core physics model based on second-order accurate generalized perturbation theory (GPT) to find near-optimal LPs for a variety of different objectives and constraints.…”

Pattern optimization of PWR reactor using hybrid parallel Artificial Bee Colony

“…Several algorithms have been developed and successfully applied to optimize reactor core loading problem such as Dynamic Programming (Wall and Fenech, 1965), direct search (Stout, 1973), Variational Techniques (Terney and Williamson, 1982), Backward Diffusion Calculation (Chao et al, 1986), Reverse Depletion (Downar and Kim, 1986;Kim et al, 1987), Linear Programming (Stillman et al, 1989), Simulated Annealing (Stevens, 1995), Ant Colony algorithm (Schirru et al, 2006), Safarzadeh et al (2011) applied ABC algorithm to power flattening of PWR reactor, continuous Genetic Algorithm (GA) introduced for flatting power distribution (Zolfaghari et al, 2009;Norouzi et al, 2011), discrete PSO (Babazadeh et al, 2009), continuous PSO (Khoshahval et al, 2010), Mohseni et al used GA in multi-objective optimization of lowering power peaking factor, maximization of the effective multiplication factor (Mohseni et al, 2008), Cellular Automata for maximizing initial excess reactivity and minimizing power peaking factor , Perturbation Theory (Stacey, 1974;Hosseini and Vosoughi, 2012), ArtificialIntelligence techniques like Artificial Neural Networks (ANNs) (Sadighi et al, 2002), and combination of fuzzy logic and ANN (Kim et al, 1993) are the ones most commonly used in core fuel management. A further study based on hybrid algorithms was performed (Stevens, 1995;Erdog and Geçkinli, 2003;).…”