946 resultados para Electric circuits.
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The 1941 edition has title: Electrical circuits and machines.
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Includes bibliographical references.
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Correspondència referida a l'article de R. Giannetti, publicat ibid. vol.49 p.87-88
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In this work the numerical coupling of thermal and electric network models with model equations for optoelectronic semiconductor devices is presented. Modified nodal analysis (MNA) is applied to model electric networks. Thermal effects are modeled by an accompanying thermal network. Semiconductor devices are modeled by the energy-transport model, that allows for thermal effects. The energy-transport model is expandend to a model for optoelectronic semiconductor devices. The temperature of the crystal lattice of the semiconductor devices is modeled by the heat flow eqaution. The corresponding heat source term is derived under thermodynamical and phenomenological considerations of energy fluxes. The energy-transport model is coupled directly into the network equations and the heat flow equation for the lattice temperature is coupled directly into the accompanying thermal network. The coupled thermal-electric network-device model results in a system of partial differential-algebraic equations (PDAE). Numerical examples are presented for the coupling of network- and one-dimensional semiconductor equations. Hybridized mixed finite elements are applied for the space discretization of the semiconductor equations. Backward difference formluas are applied for time discretization. Thus, positivity of charge carrier densities and continuity of the current density is guaranteed even for the coupled model.
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Teachers are deeply concerned on how to be more effective in our task of teaching. We must organize the contents of our specific area providing them with a logical configuration, for which we must know the mental structure of the students that we have in the classroom. We must shape this mental structure, in a progressive manner, so that they can assimilate the contents that we are trying to transfer, to make the learning as meaningful as possible. In the generative learning model, the links before the stimulus delivered by the teacher and the information stored in the mind of the learner requires an important effort by the student, who should build new conceptual meanings. That effort, which is extremely necessary for a good learning, sometimes is the missing ingredient so that the teaching-learning process can be properly assimilated. In electrical circuits, which we know are perfectly controlled and described by Ohm's law and Kirchhoff's two rules, there are two concepts that correspond to the following physical quantities: voltage and electrical resistance. These two concepts are integrated and linked when the concept of current is presented. This concept is not subordinated to the previous ones, it has the same degree of inclusiveness and gives rise to substantial relations between the three concepts, materializing it into a law: The Ohm, which allows us to relate and to calculate any of the three physical magnitudes, two of them known. The alternate current, in which both the voltage and the current are reversed dozens of times per second, plays an important role in many aspects of our modern life, because it is universally used. Its main feature is that its maximum voltage is easily modifiable through the use of transformers, which greatly facilitates its transfer with very few losses. In this paper, we present a conceptual map so that it is used as a new tool to analyze in a logical manner the underlying structure in the alternate current circuits, with the objective of providing the students from Sciences and Engineering majors with another option to try, amongst all, to achieve a significant learning of this important part of physics.
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"October 21, 1958"
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v.2. Unbalances and system disturbances.
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v. 1. Circuits.--v. 2. Machines.
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Remote laboratories are an emergent technological and pedagogical tool at all education levels, and their widespread use is an important part of their own improvement and evolution. This paper describes several issues encountered on laboratorial classes, on higher education courses, when using remote laboratories based on PXI systems, either using the VISIR system or an alternate in-house solution. Three main issues are presented and explained, all reported by teachers, that gave support to students' use of remote laboratories. The first issue deals with the need to allow students to select the actual place where an ammeter is to be inserted on electric circuits, even incorrectly, therefore emulating real-world difficulties. The second one deals with problems with timing when several measurements are required at short intervals, as in the discharge cycle of a capacitor. In addition, the last issue deals with the use of a multimeter in dc mode when reading ac values, a use that collides with the lab settings. All scenarios are presented and discussed, including the solution found for each case. The conclusion derived from the described work is that the remote laboratories area is an expanding field, where practical use leads to improvement and evolution of the available solutions, requiring a strict cooperation and information-sharing between all actors, i.e., developers, teachers, and students.
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Remote Laboratories are an emergent technological and pedagogical tool at all education levels, and their widespread use is an important part of their own improvement and evolution. This paper describes several issues encountered on laboratorial classes, on higher education courses, when using remote laboratories based on PXI systems, either using the VISIR system or an alternate in-house solution. Three main issues are presented and explained, all reported by teachers that gave support to students use of remote laboratories. The first issue deals with the need to allow students to select the actual place where an ammeter is to be inserted on electric circuits, even incorrectly, therefore emulating real world difficulties. The second one deals with problems with timing when several measurements are required at short intervals, as in the discharge cycle of a capacitor. And the last issue deals with the use of a multimeter in DC mode when reading AC values, a use that collides with the lab settings. All scenarios are presented and discussed including the solution found for each case. The conclusion derived from the described work is that the remote laboratories area is an expanding field, where practical use leads to improvement and evolution of the available solutions, requiring a strict cooperation and information sharing between all actors, i.e. developers, teachers and students.
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In recent years, significant research in the field of electrochemistry was developed. The performance of electrical devices, depending on the processes of the electrolytes, was described and the physical origin of each parameter was established. However, the influence of the irregularity of the electrodes was not a subject of study and only recently this problem became relevant in the viewpoint of fractional calculus. This paper describes an electrolytic process in the perspective of fractional order capacitors. In this line of thought, are developed several experiments for measuring the electrical impedance of the devices. The results are analyzed through the frequency response, revealing capacitances of fractional order that can constitute an alternative to the classical integer order elements. Fractional order electric circuits are used to model and study the performance of the electrolyte processes.
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Comentari sobre l'article publicat anteriorment pels mateixos autors: Vargas, M; Pallás, R; The seemingly paradoxical noise behaviour some active circuits. IEEE Transactions on Instrumentation and Measurement. 1994, vol. 43, núm. 5, p. 764-767
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The focus of the present work was on 10- to 12-year-old elementary school students’ conceptual learning outcomes in science in two specific inquiry-learning environments, laboratory and simulation. The main aim was to examine if it would be more beneficial to combine than contrast simulation and laboratory activities in science teaching. It was argued that the status quo where laboratories and simulations are seen as alternative or competing methods in science teaching is hardly an optimal solution to promote students’ learning and understanding in various science domains. It was hypothesized that it would make more sense and be more productive to combine laboratories and simulations. Several explanations and examples were provided to back up the hypothesis. In order to test whether learning with the combination of laboratory and simulation activities can result in better conceptual understanding in science than learning with laboratory or simulation activities alone, two experiments were conducted in the domain of electricity. In these experiments students constructed and studied electrical circuits in three different learning environments: laboratory (real circuits), simulation (virtual circuits), and simulation-laboratory combination (real and virtual circuits were used simultaneously). In order to measure and compare how these environments affected students’ conceptual understanding of circuits, a subject knowledge assessment questionnaire was administered before and after the experimentation. The results of the experiments were presented in four empirical studies. Three of the studies focused on learning outcomes between the conditions and one on learning processes. Study I analyzed learning outcomes from experiment I. The aim of the study was to investigate if it would be more beneficial to combine simulation and laboratory activities than to use them separately in teaching the concepts of simple electricity. Matched-trios were created based on the pre-test results of 66 elementary school students and divided randomly into a laboratory (real circuits), simulation (virtual circuits) and simulation-laboratory combination (real and virtual circuits simultaneously) conditions. In each condition students had 90 minutes to construct and study various circuits. The results showed that studying electrical circuits in the simulation–laboratory combination environment improved students’ conceptual understanding more than studying circuits in simulation and laboratory environments alone. Although there were no statistical differences between simulation and laboratory environments, the learning effect was more pronounced in the simulation condition where the students made clear progress during the intervention, whereas in the laboratory condition students’ conceptual understanding remained at an elementary level after the intervention. Study II analyzed learning outcomes from experiment II. The aim of the study was to investigate if and how learning outcomes in simulation and simulation-laboratory combination environments are mediated by implicit (only procedural guidance) and explicit (more structure and guidance for the discovery process) instruction in the context of simple DC circuits. Matched-quartets were created based on the pre-test results of 50 elementary school students and divided randomly into a simulation implicit (SI), simulation explicit (SE), combination implicit (CI) and combination explicit (CE) conditions. The results showed that when the students were working with the simulation alone, they were able to gain significantly greater amount of subject knowledge when they received metacognitive support (explicit instruction; SE) for the discovery process than when they received only procedural guidance (implicit instruction: SI). However, this additional scaffolding was not enough to reach the level of the students in the combination environment (CI and CE). A surprising finding in Study II was that instructional support had a different effect in the combination environment than in the simulation environment. In the combination environment explicit instruction (CE) did not seem to elicit much additional gain for students’ understanding of electric circuits compared to implicit instruction (CI). Instead, explicit instruction slowed down the inquiry process substantially in the combination environment. Study III analyzed from video data learning processes of those 50 students that participated in experiment II (cf. Study II above). The focus was on three specific learning processes: cognitive conflicts, self-explanations, and analogical encodings. The aim of the study was to find out possible explanations for the success of the combination condition in Experiments I and II. The video data provided clear evidence about the benefits of studying with the real and virtual circuits simultaneously (the combination conditions). Mostly the representations complemented each other, that is, one representation helped students to interpret and understand the outcomes they received from the other representation. However, there were also instances in which analogical encoding took place, that is, situations in which the slightly discrepant results between the representations ‘forced’ students to focus on those features that could be generalised across the two representations. No statistical differences were found in the amount of experienced cognitive conflicts and self-explanations between simulation and combination conditions, though in self-explanations there was a nascent trend in favour of the combination. There was also a clear tendency suggesting that explicit guidance increased the amount of self-explanations. Overall, the amount of cognitive conflicts and self-explanations was very low. The aim of the Study IV was twofold: the main aim was to provide an aggregated overview of the learning outcomes of experiments I and II; the secondary aim was to explore the relationship between the learning environments and students’ prior domain knowledge (low and high) in the experiments. Aggregated results of experiments I & II showed that on average, 91% of the students in the combination environment scored above the average of the laboratory environment, and 76% of them scored also above the average of the simulation environment. Seventy percent of the students in the simulation environment scored above the average of the laboratory environment. The results further showed that overall students seemed to benefit from combining simulations and laboratories regardless of their level of prior knowledge, that is, students with either low or high prior knowledge who studied circuits in the combination environment outperformed their counterparts who studied in the laboratory or simulation environment alone. The effect seemed to be slightly bigger among the students with low prior knowledge. However, more detailed inspection of the results showed that there were considerable differences between the experiments regarding how students with low and high prior knowledge benefitted from the combination: in Experiment I, especially students with low prior knowledge benefitted from the combination as compared to those students that used only the simulation, whereas in Experiment II, only students with high prior knowledge seemed to benefit from the combination relative to the simulation group. Regarding the differences between simulation and laboratory groups, the benefits of using a simulation seemed to be slightly higher among students with high prior knowledge. The results of the four empirical studies support the hypothesis concerning the benefits of using simulation along with laboratory activities to promote students’ conceptual understanding of electricity. It can be concluded that when teaching students about electricity, the students can gain better understanding when they have an opportunity to use the simulation and the real circuits in parallel than if they have only the real circuits or only a computer simulation available, even when the use of the simulation is supported with the explicit instruction. The outcomes of the empirical studies can be considered as the first unambiguous evidence on the (additional) benefits of combining laboratory and simulation activities in science education as compared to learning with laboratories and simulations alone.
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We investigate the dielectric dispersion of water, specially in the low-frequency range, by using the impedance spectroscopy technique. The frequency dependencies of the real R and imaginary Z parts of the impedance Could not be explained by means of the Usual description of the dielectric properties of the water as all insulating liquid containing ions. This is due to the incomplete knowledge of the parameters entering in the fundamental equations describing the evolution of the system, and oil the mechanisms regulating the exchange of charge of the cell with the external circuit. We propose a simple description of our experimental data based on the model of Debye, by invoking a dc conductivity of the cell, related to the nonblocking character of the electrodes. A discussion on the electric Circuits able to simulate the cell under investigation, based oil bulk and Surface elements, is also reported. We find that the simple circuit formed by a series of two parallels of resistance and capacitance is able to reproduce the experimental data concerning the real and imaginary part of the electrical impedance of the cell for frequency larger than 1 Hz. According to this description, one of the parallels takes into account the electrical properties of interface between the electrode and water, and the other of the bulk. For frequency lower than 1 Hz, a good agreement with the experimental data is obtained by simulating the electrical properties of the interface by means of the constant phase element.
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This work describes a methodology for power factor control and correction of the unbalanced currents in four-wire electric circuits. The methodology is based on the insertion of two compensation networks, one wye-grounded neutral and another in delta, in parallel to the load. The mathematical development has been proposed in previous work [3]. In this paper, however, the methodology was adapted to accept different power factors for the system to be compensated. on the other hand, the determination of the compensation susceptances is based on the instantaneous values of the load currents. The results are obtained using the MatLab - Simulink environment.