The multi zone fluidized bed reactor instantaneously creates several chemical/physical environments in a single reactor vessel. Effective solid circulation across zones can be achieved by tuning the reactor geometries, solid properties, and operating conditions. However, there is limited research for this innovative reactor concept beyond the laboratory scale, among which a better understanding of the complex fluid dynamics, dominating the solid circulation in different zones, is a basis. This work aims to propose a new method to capture the fluid dynamics of a pilot MZFBR by laboratory measurements with validation from theoretical analysis and simulation. Toward this goal, we first performed particle characterizations, and fluidization testing experiments in a laboratory scale fluidized bed reactor and a pilot scale multi zone fluidized bed reactor at ambient conditions to study the development of fluidization regimes. Then we compared the minimum fluidization velocity with argon and air between the experimental measurements and theoretical calculation results and proposed a modified Ergun equation, which better fits our system. Finally, we conducted computational particle fluid dynamics simulations for the pilot multi zone fluidized bed reactor with the Ergun equation and our modified equation and compared the results against previous experimental observations. Simulations display that the prediction of pressure drop in the pilot scale multi zone fluidized bed reactor with the proposed Ergun equation is similar to that of the original equation, with a relative deviation of around 3%. However, the modified equation captured the bubbling fluidization behavior as the experiment, while the Ergun equation predicted a smooth fluidization without any bubbles. The better agreements validated both our workflow of estimating the fluidization behavior in a pilot multi zone fluidized bed reactor from laboratory measurements and the simulation strategy.

NANDRAD is a dynamic building energy simulation program. It calulates heating/cooling requirements and electric power consumption with respect to realistic climatic conditions and dynamic room usage. The model includes one-dimensional spatially resolved heat transport through multi-layered walls and thermal storage of solid components (room furniture/building walls). Consequently, massive constructions forms in the European area are very well represented. Further, NANDRAD calculates geometrical long radiation heat exchange inside the room. Heating systems may be modeled with a high level of geometrical detail, i.e. surface heating systems as part of the wall constructions and radiant heaters inside the room. NANDRAD can be applied for passive building simulation, energy optimization and thermal comfort analysis with respect to a very detailed building representation. In this terms, the model supports the simulation of a large number of zones and walls without need for subgrouping or other model reduction strategies.

NANDRAD is a dynamic building energy simulation program. It calulates heating/cooling requirements and electric power consumption with respect to realistic climatic conditions and dynamic room usage. The model includes one-dimensional spatially resolved heat transport through multi-layered walls and thermal storage of solid components (room furniture/building walls). Consequently, massive constructions forms in the European area are very well represented. Further, NANDRAD calculates geometrical long radiation heat exchange inside the room. Heating systems may be modeled with a high level of geometrical detail, i.e. surface heating systems as part of the wall constructions and radiant heaters inside the room. NANDRAD can be applied for passive building simulation, energy optimization and thermal comfort analysis with respect to a very detailed building representation. In this terms, the model supports the simulation of a large number of zones and walls without need for subgrouping or other model reduction strategies.:1 Introduction 2 NANDRAD multi-zone building model 2.1 Fundamentals 2.2 Building component models 2.3 Building services and usage 2.4 Climatic model 3 Model equations 3.1 Balance equations 3.2 Construction balance boundary conditions 3.3 Construction energy sources/sinks 3.4 Windows 3.5 Ambient environment 3.6 Zone internal loads 3.7 Construction internal heat sources 3.8 Loads on inside interfaces 3.9 Evaluation of thermal comfort

As wind energy technology continues to mature, farm sizes grow and wind farm layout design becomes more difficult, in part due to the number of design variables and constraints. Wind farm layout optimization is typically approached using gradient-free methods because of the highly multi-modal shape of the wind farm layout design space. Gradient-free method performance generally degrades with increasing problem size, making it difficult to find optimal layouts for larger wind farms. However, gradient-based optimization methods can effectively and efficiently solve large-scale problems with many variables and constraints. To pave the way for effective and efficient wind farm layout optimization for large-scale wind farms, we have worked to overcome the primary barriers to applying gradient-based optimization to wind farm layout optimization. To improve model/algorithm compatibility, we adjusted wake and wind farm models, adding more realistic curvature and smoothness to enable optimization algorithms to travel through areas in the design space where they had previously gotten stuck. We reduced the number of function calls required for gradient-based wind farm layout optimization by over three orders of magnitude for large farms by using algorithmic differentiation to compute derivatives. We reduced the multi-modality of the wind farm layout design space using wake expansion continuation (WEC). We developed WEC to work with existing optimization algorithms, enabling them to get out of local optima while remaining fully gradient-based. Across four case studies, WEC found results with lower wake loss, on average, than the other methods we tested. To resolve concerns about optimization algorithms exploiting model inaccuracies, we compared the initial and optimized layouts to large-eddy simulation (LES) results. The simple models predicted an AEP improvement of 7.7% for a low-TI case, and LES predicted 9.3%. For a high-TI case, the simple models predicted a 10.0% improvement in AEP and LES predicted 10.7%. To resolve uncertainty regarding relative solution quality for gradient-based and gradient-free methods, we collaborated with seven organizations to compare eight optimization methods. Each method was managed by researchers experienced with them. All methods found solutions of similar quality, with optimized wake loss between 15.48 % and 15.70 %. WEC with SNOPT was the only purely gradient-based method included and found the third-to-best solution.