Design of a New Medium-temperature Stirling Engine for Distributed Cogeneration Applications☆

Abstract This paper presents and discusses the design and first prototype realization for a brand new generation of Stirling engines. This unit is realized within the DiGeSPo Project, in which it is coupled with a small-size parabolic trough concentration solar field. The engine is conceived for working with low-temperature heat sources (200-300 C), in order to match the typical temperatures for the solar field itself. The first part presents the thermodynamic design, which is realized by using models and simulations, and give the specifications for each component, including pistons dimensions; the number, length, diameter for the heat exchangers tubes; regenerators porosity, length and diameter. Four independent and equally working spaces were chosen, as a compromise between the compactness of the overall system, limits imposed by the maximum charging pressure, and the target electrical power (3 kW). The parameters of the overall system were optimized during this phase with an iterative procedure, taking into account different concurrent constraints, such as the heat exchange requirements, mechanical friction power losses, and small dead spaces. The engine has been subsequently arranged in a double-acting mechanical configuration, in which the cylinders are opposed as in a boxer engine. This configuration gives the advantages of reducing leaking losses and can work with four pistons. The heat exchangers, which are the most crucial and complex components, have been realized by the Selective Laser Melting (SLM) manufacturing technique. The specific scientific and technical details related to a low-temperature Stirling engine, and the solutions adopted, are discussed and presented trough the paper, and finals recommendations are provided

Measurement of the Self-Sensing Capability of Synchronous Machines for High Frequency Signal Injection Sensorless Drives

Signal injection sensor-less control for synchronous machines is known to be afflicted by an estimation error dependent on the load current. The estimation error is related to the cross- saturarion and the saliency of the adopted synchronous machine. A motor can be more or less suitable for signal injection sensorless control compared to other motors with different designs or sizes. A sensorless drive can even be afflicted by the control divergence when the machine is highly saturated, resulting in a useless drive. Moreover, even when the control converges, the actual current control trajectory is different from the given reference. In this paper, a measurement procedure of the convergence region, i.e. the operating points where the motor can be successfully controlled without a position sensor is presented and validated. In particular, two different synchronous motors are considered, a permanent magnet assisted synchronous reluctance motor (PMA-SynRM) and a synchronous reluctance motor (SynRM

Novel system for distributed energy generation from a small scale concentrated solar power

The present work describes the realization of a modular 1-3 kWe, 3-9 kWth micro Combined Heat and Power (m-CHP) system based on innovative Concentrated Solar Power (CSP) and Stirling engine technology. The cogeneration of energy at distributed level is one of leading argument in large part of energy policies related to renewable energy resources and systems. This CSP m-CHP will provide electrical power, heating and cooling for single and multiple domestic dwellings and other small buildings. The developed system integrates small-scale concentrator optics with moving and tracking components, solar absorbers in the form of evacuated tube collectors, a heat transfer fluid, a Stirling engine with generator, and heating and/or cooling systems; it incorporates them into buildings in an architecturally acceptable manner, with low visual impact. Some good results have already been achieved, while developments on several technology subcomponents will be finalized through first part of 2013. Two Cer.Met. have been modelled, realized and tested. The up scaled receiver, in form of Cer.Met. coating based on TiO2 - Nb, has been confirmed an absorptance of 0.94 and emittance of 0.1 (@350°C). A second Cer.Met. coating based on SiO2 - W has demonstrated an absorptance of 0.93 and emittance of 0.09 (@350°C). A full-evacuated solar tube has been designed and realized, with absorber of 12 mm in diameter and length in 2 meters. The system is provided of a concentration ratio 12:1, and a single module is 200 cm long, 40 cm wide and 20-25 cm high. Two or more modules can be combined. The evacuated solar tube, located on the focus, has the selective absorber on a tube of 12 mm in diameter. A very thin glass mirror has been developed (< 1 mm). The overall mirror reflectivity has been measured, the verified value is 0,954. Research has proposed a high energy density, double acting Stirling engine, provided of innovative heat exchangers realized through Selective Laser Melting process. The engine is a low speed (250 RPM), high pressure (130 Bars) and compact solution able to be run at 300°C and generate 3,5 kW nominal power. The solar technology has actually entered the proof-of-concept stage. A solar plant has been installed in Malta, by Arrow Pharm company, supplying the industrial process of generated steam at 180°C and 3.5 absolute pressure. The solar collector's efficiency is close to 47% in presence of 900 W/m2 of direct solar radiation. During 2013, solar evacuated tubes with innovative Cer.Met. coating, together with new thin glass mirrors will upgrade the demonstration site, together with a new and innovative low temperature difference and high energy density Stirling. By end-2013, the system will be demonstrated, with the overall objective to achieve a minimum of 65% in solar collectors' efficiency at 300°C, and 12 - 15% of overall electrical efficiency by the Stirling cycle.peer-reviewe

Improved Sensorless Control of Multiphase Synchronous Reluctance Machine Under Position Sensor Fault

This article presents an investigation on the self- sensing capability of a dual three-phase synchronous reluctance motor. Self-sensing capability refers to the ability of the motor to properly operate in a sensorless drive. The multiphase machine is decomposed into two different three-phase systems according to the multistator approach. Several supply scenarios are studied where the two three-phase windings are controlled at different operating points along a reference trajectory. The analysis is carried out both with finite element analysis simulations and experimental tests. In the first part of this article, the rotor is locked to derive the observer trajectories and find the regions in which the motor can operate without position sensor. A comparison between simulated and experimental results is given. Finally, a sensorless control strategy that allows exploiting the motor self-sensing capability under position sensor fault is developed and validated through experimental tests

Development of a pellet boiler with Stirling engine for m-CHP domestic application

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On a long-standing conjecture of E. De Giorgi: old and recent results

This paper studies a conjecture made by E. De Giorgi in 1978 and concerning the one-dimensional character (or symmetry) of the solutions of semilinear elliptic equation Du = f(u) which are defined on the entire n-dimensional Euclidean space and are increasing in one direction. We extend to all nonlinearities f of class C2 the symmetry result in dimension n=3 previously established by the second and the third authors for a special class of nonlinearities f. The extension of the present paper is based on a new energy estimates which follow from a local minimality property of u. In addition, we establish a symmetry result for semilinear equations in the 4-dimensional halfspace. Finally, we prove that an asymptotic version of the conjecture of De Giorgi is true when the dimension does not exceed 8, namely that the level sets of u are flat at infinity

A new targeted CFTR mutation panel based on next-generation sequencing technology

Searching for mutations in the cystic fibrosis transmembrane conductance regulator gene (CFTR) is a key step in the diagnosis of and neonatal and carrier screening for cystic fibrosis (CF), and it has implications for prognosis and personalized therapy. The large number of mutations and genetic and phenotypic variability make this search a complex task. Herein, we developed, validated, and tested a laboratory assay for an extended search for mutations in CFTR using a next-generation sequencing based method, with a panel of 188 CFTR mutations customized for the Italian population. Overall, 1426 dried blood spots from neonatal screening, 402 genomic DNA samples from various origins, and 1138 genomic DNA samples from patients with CF were analyzed. The assay showed excellent analytical and diagnostic operative characteristics. We identified and experimentally validated 159 (of 188) CFTR mutations. The assay achieved detection rates of 95.0% and 95.6% in two large-scale case series of CF patients from central and northern Italy, respectively. These detection rates are among the highest reported so far with a genetic test for CF based on a mutation panel. This assay appears to be well suited for diagnostics, neonatal and carrier screening, and assisted reproduction, and it represents a considerable advantage in CF genetic counseling

Machine learning from real data: A mental health registry case study

Imbalanced datasets can impair the learning performance of many Machine Learning techniques. Nevertheless, many real-world datasets, especially in the healthcare field, are inherently imbalanced. For instance, in the medical domain, the classes representing a specific disease are typically the minority of the total cases. This challenge justifies the substantial research effort spent in the past decades to tackle data imbalance at the data and algorithm levels. In this paper, we describe the strategies we used to deal with an imbalanced classification task on data extracted from a database generated from the Electronic Health Records of the Mental Health Service of the Ferrara Province, Italy. In particular, we applied balancing techniques to the original data, such as random undersampling and oversampling, and Synthetic Minority Oversampling Technique for Nominal and Continuous (SMOTE-NC). In order to assess the effectiveness of the balancing techniques on the classification task at hand, we applied different Machine Learning algorithms. We employed cost-sensitive learning as well and compared its results with those of the balancing methods. Furthermore, a feature selection analysis was conducted to investigate the relevance of each feature. Results show that balancing can help find the best setting to accomplish classification tasks. Since real-world imbalanced datasets are increasingly becoming the core of scientific research, further studies are needed to improve already existing techniqu