A Criterion on Existence and Uniqueness of Behavior in Electric Circuit

Behavior of electric circuits can be observed by solving circuit equations symbolically as well as numerically. In general, symbolic computation for circuits with certain number of circuit elements needs much more time than numerical computation. It is reasonable to check the existence and uniqueness of the solution to circuit equations beforehand in order to avoid computation for the case of no solution. Indeed, some circuits have no solution; in that case, one should notice it and avoid to wait meaningless computation. This paper proposes a new theorem to check whether given circuit equations have a solution and their voltages and currents of all circuit elements are uniquely determined or not. The theorem is suitable for developing a computer algorithm and helps quick symbolic computation for electric circuits

Toward a formal theory for computing machines made out of whatever physics offers: extended version

Approaching limitations of digital computing technologies have spurred research in neuromorphic and other unconventional approaches to computing. Here we argue that if we want to systematically engineer computing systems that are based on unconventional physical effects, we need guidance from a formal theory that is different from the symbolic-algorithmic theory of today's computer science textbooks. We propose a general strategy for developing such a theory, and within that general view, a specific approach that we call "fluent computing". In contrast to Turing, who modeled computing processes from a top-down perspective as symbolic reasoning, we adopt the scientific paradigm of physics and model physical computing systems bottom-up by formalizing what can ultimately be measured in any physical substrate. This leads to an understanding of computing as the structuring of processes, while classical models of computing systems describe the processing of structures.Comment: 76 pages. This is an extended version of a perspective article with the same title that will appear in Nature Communications soon after this manuscript goes public on arxi

A New Framework for the Simulation of Equation-Based Models with Variable Structure

Many modern models contain changes that affect the structure of their underlying equation system, e.g. the breaking of mechanical devices or the switching of ideal diodes. The modeling and simulation of such systems in current equation-based languages frequently poses serious difficulties. In order to improve the handling of variable-structure systems, a new modeling language has been designed for research purposes. It is called Sol and it caters to the special demands of variable-structure systems while still representing a general modeling language. This language is processed by a new translation scheme that handles the differential-algebraic equations in a highly dynamic fashion. In this way, almost arbitrary structural changes can be processed. In order to minimize the computational effort, each change is processed as locally as possible, preserving the existing computational structure as much as possible. Given this methodology, truly object-oriented modeling and simulation of variable-structure systems is made possible. The corresponding process of modeling and simulation is illustrated by two examples from different domains

Investigating the role of model-based reasoning while troubleshooting an electric circuit

We explore the overlap of two nationally-recognized learning outcomes for physics lab courses, namely, the ability to model experimental systems and the ability to troubleshoot a malfunctioning apparatus. Modeling and troubleshooting are both nonlinear, recursive processes that involve using models to inform revisions to an apparatus. To probe the overlap of modeling and troubleshooting, we collected audiovisual data from think-aloud activities in which eight pairs of students from two institutions attempted to diagnose and repair a malfunctioning electrical circuit. We characterize the cognitive tasks and model-based reasoning that students employed during this activity. In doing so, we demonstrate that troubleshooting engages students in the core scientific practice of modeling.Comment: 20 pages, 6 figures, 4 tables; Submitted to Physical Review PE

PowerSimulationsDynamics.jl -- An Open Source Modeling Package for Modern Power Systems with Inverter-Based Resources

The inclusion of inverter-based resources from renewable energy creates new challenges for the stability and transient behavior of power systems which are best understood by studying their dynamic responses through simulation. In this paper, we develop an open source simulation toolbox, PowerSimulationDynamics.jl, to study the dynamic response of a balanced system with high penetration of inverter-based resources. PowerSimulationDynamics.jl is implemented in the Julia language and features a rich library of synchronous generator components and inverter models. In addition, it allows the study of both quasi-static phasors that employ an admittance matrix representation for the network and electromagnetic dq models that use a dynamic representation of the network. Case studies and validation exercises show that PowerSimulationDynamics.jl results closely match Quasi-Static Phasor (QSP) tools like Siemens PSSe, ANDES, and wave-form Electro-magnetic Transient (EMT) simulations like PSCA

A Method for Comparing Multivariate Time Series with Different Dimensions

In many situations it is desirable to compare dynamical systems based on their behavior. Similarity of behavior often implies similarity of internal mechanisms or dependency on common extrinsic factors. While there are widely used methods for comparing univariate time series, most dynamical systems are characterized by multivariate time series. Yet, comparison of multivariate time series has been limited to cases where they share a common dimensionality. A semi-metric is a distance function that has the properties of non-negativity, symmetry and reflexivity, but not sub-additivity. Here we develop a semi-metric – SMETS – that can be used for comparing groups of time series that may have different dimensions. To demonstrate its utility, the method is applied to dynamic models of biochemical networks and to portfolios of shares. The former is an example of a case where the dependencies between system variables are known, while in the latter the system is treated (and behaves) as a black box

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Domain Specific Language for Magnetic Measurements at CERN

CERN, the European Organization for Nuclear Research, is one of the world’s largest and most respected centres for scientific research. Founded in 1954, the CERN Laboratory sits astride the Franco–Swiss border near Geneva. It was one of Europe’s first joint ventures and now has 20 Member States. Its main purpose is fundamental research in partcle physics, namely investigating what the Universe is made of and how it works. At CERN, the design and realization of the new particle accelerator, the Large Hadron Collider (LHC), has required a remarkable technological effort in many areas of engineering. In particular, the tests of LHC superconducting magnets disclosed new horizons to magnetic measurements. At CERN, the objectively large R&D effort of the Technolgy Department/Magnets, Superconductors and Cryostats (TE/MSC) group identified areas where further work is required in order to assist the LHC commissioning and start-up, to provide continuity in the instrumentation for the LHC magnets maintenance, and to achieve more accurate magnet models for the LHC exploitation. In view of future projects, a wide range of software requirements has been recently satisfied by the Flexible Framework for Magnetic Measurements (FFMM), designed also for integrating more performing flexible hardware. FFMM software applications control several devices, such as encoder boards, digital integrators, motor controllers, transducers. In addition, they synchronize and coordinate different measurement tasks and actions

Multi-domain Modeling and Simulation

One starting point for the analysis and design of a control system is the block diagram representation of a plant. Since it is nontrivial to convert a physical model of a plant into a blockk diagram, this can be performed manually only for small models. Based on reseach from the last 40 years, more andmore mature tools are available to achieve this transformation fully automatically. As a result, multi-domain plants, for example, systems with electrical, mechanical, thermal, and fluid parts, can be modled in a unified way and can be used directly as input-output blocks for control system design. An overview of the basic principles of this approach is given, and it is shown how to utilize nonlinear, multidomain plant models directly in a controller. Finally, the low-level "Functional Mockup Interface" standard is sketched to exchang multi-domain models between many different modeling and simulation environments

Steady-state Modelling of a Vapor Compression Refrigeration Cycle

In this work a steady-state model of a simple vapor compression refrigeration cycle is presented. All the fundamental components of this system are modeled separately in order to consider them as black boxes that take inputs and convert them into output variables. The heat exchangers are treated as a set of multiple zones, identified by the refrigerant's state, connected in series, in which the heat transfer coefficient (HTC) is constant. A non-linear system of equations is obtained applying the energy balances and the ε-NTU method for each zone in the heat exchangers. A study on the HTC correlations used to connect the length of the zones with the value of the respective HTC is developed. The compressor is modeled using a polynomial function. Some iterative methods for the resolution in Matlab of the models of the components and the machine are presented, focusing on the strategy to decrease the execution time and to increase the accuracy of the results. Finally, all the models are validated through a set of experimental data and the global model is used to make some considerations about the efficiency and the exergy destruction in the plant