The implementation of a three-dimensional viscous inverse design used for an axial compressor is introduced in this paper. The derivation process of the inverse design algorithm is also described in detail. Moreover, an improved blade update method and a modified relaxation factor are included to enhance the inverse design algorithm. The inverse design is built on an in-house inverse design module coupled with commercial Computational Fluid Dynamic (CFD) software NUMECATM. In contrast to analysis design, the pressure loading and the normal thickness distribution along the blade surfaces are prescribed during the process of inverse design. The numerical methods used to solve the flow field are verified using the experimental data of the transonic fan rotor NASA Rotor 67. A recovery test for the Rotor 67 is carried out to validate the developed three-dimensional inverse design tool. To explore the potential application of the inverse design system, it is then used to improve the aerodyna... [more]

In order to research the influence mechanism of bow blades on the flow field of supersonic tandem rotors of compressors, the supersonic rotor rotor37 is taken as the prototype and redesigned as a supersonic tandem rotor. Compared with the prototype rotor37, the efficiency of the design tandem rotor is increased by 0.24% and the stall margin is increased by about 5.6%. Although the negative bow blade deteriorates the flow field in the tip region and the hub region, it significantly increases the efficiency and total pressure ratio of the tandem rotor from 20% span to 85% span. The efficiency of the design point of the tandem rotor with a negative bow angle of 10° is improved by 0.37%, and the stall margin is also increased to 20.71%. Positive bow blades improve the efficiency of the hub region and casing region of tandem rotors, but they significantly reduce the efficiency of the tandem rotor from 10% to 50%. The positive bow blade reduces the pressure ratio and efficiency at the design... [more]

Economic expedience of waste heat recovery systems (WHRS), especially for low temperature difference applications, is often questionable due to high capital investments and long pay-back periods. With a simple design, isobaric expansion (IE) machines could provide a viable pathway to utilizing otherwise unprofitable waste heat streams for power generation and particularly for pumping liquids and compression of gases. Different engine configurations are presented and discussed. A new method of modeling and calculation of the IE process and efficiency is used on IE cycles with various pure and mixed working fluids. Some interesting cases are presented. It is shown in this paper that the simplest non-regenerative IE engines are efficient at low temperature differences between a heat source and heat sink. The efficiency of the non-regenerative IE process with pure working fluid can be very high, approaching Carnot efficiency at low pressure and heat source/heat sink temperature differences... [more]

A common denominator in the vast majority of processes in the food industry is refrigeration. Such systems guarantee the quality and the requisites of the final product at the expense of high amounts of energy. In this regard, the new Industry 4.0 framework provides the required data to develop new data-based methodologies to reduce such energy expenditure concern. Focusing in this issue, this paper proposes a data-driven methodology which improves the efficiency of the refrigeration systems acting on the load side. The solution approaches the problem with a novel load management methodology that considers the estimation of the individual load consumption and the necessary robustness to be applicable in highly variable industrial environments. Thus, the refrigeration system efficiency can be enhanced while maintaining the product in the desired conditions. The experimental results of the methodology demonstrate the ability to reduce the electrical consumption of the compressors by 17%... [more]

One of the main concerns of industry is energy efficiency, in which the paradigm of Industry 4.0 opens new possibilities by facing optimization approaches using data-driven methodologies. In this regard, increasing the efficiency of industrial refrigeration systems is an important challenge, since this type of process consume a huge amount of electricity that can be reduced with an optimal compressor configuration. In this paper, a novel data-driven methodology is presented, which employs self-organizing maps (SOM) and multi-layer perceptron (MLP) to deal with the (PLR) issue of refrigeration systems. The proposed methodology takes into account the variables that influence the system performance to develop a discrete model of the operating conditions. The aforementioned model is used to find the best PLR of the compressors for each operating condition of the system. Furthermore, to overcome the limitations of the historical performance, various scenarios are artificially created to fin... [more]

Source code for the case study presented in the paper "Approximating Nonlinear Relationships for Optimal Operation of Natural Gas Transport Networks". The case study involves solving the compressor fuel cost minimization problem (FCMP) on three simple natural gas networks. For each gas network three different formulations of the FCMP are tested: a common simplified FCMP model (FCMP_S), the novel approximation FCMP model (FCMP_N) that is developed in the paper, and a partially rigorous FCMP model (FCMP_PR) that models components of the model using their most rigorous calculations where feasible. The FCMP for each of these tests was optimized using GAMS, for which the code is provided. The accuracy of each of the three models was then assessed by comparing them to a rigorous simulation. The rigorous simulation was coded in Matlab and is provided, where separate files are used to calculate the rigorous gas pressure drop along a pipeline, and the energy input required for gas compression... [more]

Artificial neural networks (ANNs) are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of ANN embedded optimization problems. The proposed method is based on relaxations of algorithms using McCormick relaxations in a reduced-space [\textit{SIOPT}, 20 (2009), pp. 573-601] including the convex and concave envelopes of the nonlinear activation function of ANNs. The optimization problem is solved using our in-house global deterministic solver MAiNGO. The performance of the proposed method is shown in four optimization examples: an illustrative function, a fermentation process, a compressor plant and a chemical process optimization. The results show that computational solution time is favorable compared to the global general-purpose optimization solver BARON.