Modelon AB
  • Lund, Sweden
Recent publications
The parallel hybrid (or boosted) turbofan engine alleviates the system complexity of radical electrified powertrain architectures, while also demonstrates substantial benefits in reducing specific fuel consumption. This conservative, yet promising, electrified configuration incorporates an electrical drive coupled with the engine low-pressure or gearbox fan spool. Sophisticated models for the gas turbine and the electrical drive system are developed. The former deploys a multi-point design matching scheme coupled with an installed engine performance approach, as well as an engine sizing and weight estimation tool. The latter incorporates an analytical electrical machine sizing and performance methodology. The objective of this paper is to shed light on the optimal parallel hybrid engine design, considering installed cycle performance and tight coupling of engine and electrical drive systems. The impact of installation drag components on the integrated powertrain system performance is analyzed and design trade-offs are explored. Electrical machine efficiency, propulsion system weight and installed specific fuel consumption demonstrate opposing trends with varying specific thrust for different electrical drive installation positions and mechanical connections. It is shown that fan spinner-mounted electrical machine which is mechanically coupled to the low-pressure spool presents the greatest potential in terms of electrical machine efficiency and propulsion system installed performance. A 11.23% and 15.11% increase in installed specific fuel consumption at Top of Climb and Cruise, respectively, is observed for the Cruise-based optimal specific thrust variant, rendering installation effects and electrical drive considerations critical for future low-specific thrust hybrid-electric aero-engine concepts.
The design optimization of multirotor drones is a key enabler for improving existing and future vehicle architectures performance. For many multirotor applications, the design is performed in order to respect the maximum speed requirements and maximizing the endurance in hover of the drone. For specific applications, the requirements enable to propose different alternatives for the trajectory which can thus be considered as an additional degree of freedom in the design process. In this paper, different approaches are proposed for achieving the vehicle design optimization, the trajectory optimization and both simultaneously. The approaches taken explore the possible use of multidisciplinary analysis and design optimization with OpenMDAO and dynamic optimization with Modelon Impact and Optimica. The specific multirotor drone application considered is a lifting and handling vehicle which could replace cranes in dense urban areas. The most significant finding is that vehicle operations that involve transient trajectories considerably affect the overall drone design especially the choice of the propeller pitch.
Aligned with international trends, fluctuating renewable energy generation is planned to be continuously increased in the Chinese electricity market. For existing power plants, representing integral parts of the electricity infrastructure as well as large financial investments, this results in a demand for optimization and potential re-design. Transient power plant models based on first-principle physical equations have proven to be an efficient tool to investigate process and control system adaptions to prepare field testing and implementations. This paper will give insights into first-principle, component-based modeling using the open standard programming language Modelica. A comprehensive system model of a Chinese reference plant will be presented. The model will be used to simulate off-design transient scenarios for fast load response activation such as primary frequency controls to illustrate how the model-based approach can be used to develop control strategies. In addition, the same model will be used to demonstrate scenarios of component failures that can be used for the development of risk mitigation strategies.
A parallel hybrid configuration is a feasible means to reduce fuel consumption of gas turbines propelling aircraft. It introduces an electric drive on one of the spools of the gas turbine, typically the low pressure spool. The electric drive is supplied by a battery, which can also be charged when excess power is available (for instance during conditions requiring handling bleed in conventional designs). It also requires a thermal management system to dissipate heat away from electric components. While the scientific literature describes parallel hybrid studies and anticipated benefits assuming various future entry into service dates, there is limited information on the design of the gas turbine component of such a system. For conventional gas turbines, multi-point design schemes are used. This paper describes, in a consistent fashion and based on a formalized notation, how such multi-point design schemes are applied to parallel hybrid aero engines. It interprets published approaches, fills gaps in methodology descriptions with meaningful assumptions and summarizes design intent. It also discusses cycle designs generated by different methodologies based on the same cycle model. Results show that closure equations prescribing boost power can be preferable over closure equations prescribing temperature ratios for uniqueness and engineering intuitiveness while the latter can be beneficial in a second step for design space exploration.
In today's electric vehicle arena, range, performance, drivability, handling safety and ride comfort are few important attributes an OEM would like to improve on a vehicle. However, they are almost mutually exclusive like greater range & extreme handling safety would need to compromise on drivability & ride comfort and vice versa respectively. Automotive engineers constantly focus on to find the right balance among these attributes by optimum design of the vehicle components. Often these components are multi‐disciplinary involving different engineering domains like mechanical, thermal, electrical, hydraulic & electronics, etc. These complexities demand for model based and systems engineering approach from the start of the development. This emphasizes the need for high fidelity, dynamic vehicle system models suitable for co‐development of different components and controls. In this study, detailed vehicle model representing an electric pickup truck including battery, traction motors, driveline, chassis (suspension, brakes, wheels & mounts) is created in Modelica platform using which multiple vehicle level attributes like range, performance, drivability, handling and ride comfort are studied. Further, it is shown that the simulation speed can be increased by varying fidelity of the subsystems based on study intended within the same model framework.
This paper introduces two physical modeling standards in the gas turbine and cycle analysis context. Modelica is the defacto standard for physical system modeling and simulation. The Functional Mock-Up Interface is a domain-independent standard for model exchange (“engine decks”). The paper summarizes key language concepts and discusses important design patterns in the application of gas turbine simulation concepts to the acausal modeling language. To substantiate how open standards are applicable to gas turbine simulation, the paper closes with two application examples, a conventional unmixed turbofan thermodynamic cycle and weight analysis as well as an electrically boosted geared turbofan.
To achieve the goals of substantial improvements in efficiency and emissions set by Flightpath 2050, fundamentally different concepts are required. As one of the most promising solutions, electrification of the aircraft primary propulsion is currently a prime focus of research and development. Unconventional propulsion sub-systems, mainly the electrical power system, associated thermal management system and transmission system, provide a variety of options for integration in the existing propulsion systems. Different combinations of the gas turbine and the unconventional propulsion sub-systems introduce different configurations and operation control strategies. The trade-off between the use of the two energy sources, jet fuel and electrical energy, is primarily a result of the trade-offs between efficiencies and sizing characteristics of these sub-systems. The aircraft structure and performance are the final carrier of these trade-offs. Hence, full design space exploration of various hybrid derivatives requires global investigation of the entire aircraft considering these key propulsion sub-systems and the aircraft structure and performance, as well as their interactions. This paper presents a recent contribution of the development for a physics-based simulation and optimization platform for hybrid electric aircraft conceptual design. Modeling of each subsystem and the aircraft structure are described as well as the aircraft performance modeling and integration technique. With a focus on the key propulsion sub-systems, aircraft structure and performance that interfaces with existing conceptual design frameworks, this platform aims at full design space exploration of various hybrid concepts at a low TRL level.
Future district heating systems (so called 4th generation district heating (4GDH) systems) have to address challenges such as integration of (de)centralized renewable energy sources and storage, low system temperatures and high fluctuation of the supply temperature. This paper presents a novel framework for representing and simplifying on-grid energy systems as well as for dynamic thermo-hydraulic simulation and optimization of district heating systems. We describe physically precise and numerically robust models for simulation and continuous optimization. Furthermore, we propose a novel method to decompose a mixed-integer-optimal control problem into two sub-problems, separating the discrete part from the continuous. Two use cases show the applicability of the framework. An existing district heating system with more than 100 consumers is adapted to test the framework based on simulation requirements of 4GDH systems. The second case presents the continuous optimization of a district heating system in a virtual city district. A main advantage of combining equation-based modelling and nonlinear optimization is the possibility of including model coherences based on physical laws into the optimization formulation. Results show that the framework is well-suited for simulating larger scale 4GDH systems and that the solution time of the continuous optimization problem is sufficiently low for real-time applications.
Concentrating solar power (CSP) technology with thermal energy storage is a renewable and emerging technology. In this work, dynamic models for analyzing and evaluating energy storage concepts and its interaction with the solar field and the power block have been developed. A physical model of a 50 MW CSP plant has been implemented in the modeling language Modelica. The models are developed in a modular, flexible structure with a well-defined interface to easily replace and test modules of various detail and complexity. Models include turbine island, steam generator, solar field and thermal energy storage system. In addition, a decentralized control configuration has been developed. Results have been successfully validated against the reference plant key steady-state data. Dynamic response of the power block has shown expected behavior, and transient durations were comparable with settling times predicted in literature. Furthermore, the performance of the plant has been evaluated during a typical summer day including effects such as variation of solar irradiance, charging and discharging the heat storage system, and dumping excess heat in the solar field. The summer day scenario results agreed with published performance of the plant.
During the last three decades, a vast variety of methods to numerically solve ordinary differential equations and differential-algebraic equations has been developed and investigated. The methods are mostly freely available in different programming languages and with different interfaces. Accessing them using a unified interface is a need not only of the research community and for education purposes but also to make them available in industrial contexts. An industrial model of a dynamic system is usually not just a set of differential equations. The models today may contain discrete controllers, impacts or friction resulting in discontinuities that need to be handled by a modern solver in a correct and efficient way. Additionally, the models may produce an enormous amount of data that puts strain on the simulation software. In this paper, Assimulo is presented. It is a unified high-level interface to solvers of ordinary differential equations and is designed to satisfy the needs in research and education together with the requirements for solving industrial models with discontinuities and data handling. It combines original classical and modern solvers independent of their programming language with a well-structured Python/Cython interface. This allows to easily control parameter setting and discontinuity handling for a wide range of problem classes. © 2015 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
As automatic sensing and Information and Communication Technology (ICT) get cheaper, building monitoring data becomes easier to obtain. The availability of data leads to new opportunities in the context of energy efficiency in buildings. This paper describes the development and validation of a data-driven grey-box modelling toolbox for buildings. The Python toolbox is based on a Modelica library with thermal building and Heating, Ventilation and AirConditioning (HVAC) models and the optimi-sation framework in The toolchain facilitates and automates the different steps in the system identification procedure, like data handling, model selection , parameter estimation and validation. To validate the methodology, different grey-box models are identified for a single-family dwelling with detailed monitoring data from two experiments. Validated models for forecasting and control can be identified. However, in one experiment the model performance is reduced, likely due to a poor information content in the identification dataset.
A dynamic model of the amine-based CO2-capture process is presented and applied to investigate the transient behavior of the absorption system during and after load changes in Nordjyllandsværket, a state-of-the-art coal-fired power plant with a thermal efficiency of 47.5%. Two scenarios of flexible operation in the power plant are investigated: part-load and peak load operation. Simulations of the load-variation scenarios show that implementation of active control strategies improves capture system performance with respect to capture efficiency and the heat requirement. The reboiler duty can be decreased considerably during part load operation compared to a case where no control strategy is applied. Integration of the capture process with the power plant results in an efficiency decrease of around 9 percentage points at full load and in the range of 8–12 percentage points during 60% part load operation, depending on if a process controller is used or not. Energy requirement for CO2 compression is not included in these numbers. In addition, the response time of the absorption system is significantly decreased in the cases where a process control strategy is implemented, both for part load and peak load operation.
Zusammenfassung Der Simulation kommt im Rahmen einer durchgehenden digitalen Fabrikplanung immer größere Bedeutung zu. Eine Möglichkeit, die Qualität sowie Termintreue bei der Erstellung von automatisierten fertigungstechnischen Anlagen sicherzustellen, stellt die virtuelle Inbetriebnahme (VIBN) dar. Der dafür notwendige Modellbildungsprozess ist jedoch oftmals mit hohen Aufwänden verbunden. Der Beitrag zeigt, wie sich dieser Aufwand durch eine automatische Simulationsmodellgenerierung minimieren lässt.
This paper presents a decomposition strategy applicable to DAE constrained optimization problems. A common solution method for such problems is to apply a direct transcription method and solve the resulting nonlinear program using an interior-point algorithm. For this approach, the time to solve the linearized KKT system at each iteration typically dominates the total solution time. In our proposed method, we exploit the structure of the KKT system resulting from a direct collocation scheme for approximating the DAE constraints in order to compute the necessary linear algebra operations on multiple processors. This approach is applied to find the optimal control profile of a combined cycle power plant with promising results on both distributed memory and shared memory computing architectures with speedups of over 50 times possible.
In order to comply with increasing consumer and regulatory demand for improved fuel economy and lower emissions, the engines and engine aftertreatment systems must be improved continuously. Since the complete system is very complex, models are useful in order to be able to cost efficiently develop new control strategies and selection of hardware. In this article, a model library for dynamic engine modeling in Dymola is presented. The library consists of models of the standard engine components such as manifolds, pipe, turbines, compressors, valves and mechanics. The combustion model is a mean value model and the focus has been on air path management and exhaust modeling with real-time-like simulation times, useful for engine optimization and for evaluation of control strategies. Engine simulation results from a 13-L Volvo engine demonstrates that the models capture the dynamics and have sufficient accuracy to be useful in engine optimization.
Dynamic optimization problems involving differential-algebraic equation (DAE) systems are traditionally solved while retaining the semi-explicit or implicit form of the DAE. We instead consider symbolically transforming the DAE into an ordinary differential equation (ODE) before solving the optimization problem using a collocation method. We present a method for achieving this, which handles DAE-constrained optimization problems. The method is based on techniques commonly used in Modelica tools for simulation of DAE systems. The method is evaluated on two industrially relevant benchmark problems. The first is about vehicletrajectory generation and the second involves startup of power plants. The problems are solved using both the DAE formulation and the ODE formulation and the performance of the two approaches is compared. The ODE formulation is shown to have roughly three times shorter execution time. We also discuss benefits and drawbacks of the two approaches.
Representing a physical system with a mathematical model requires knowledge not only about the physical laws governing the dynamics but also about the param-eter values of the system. The parameters can some-times be measured or calculated, but some of them are often difficult or impossible to obtain directly. Never the less, finding accurate parameter values is crucial for the accuracy of the mathematical model. Estimating the parameters using optimization algo-rithms which attempt to minimize the error between the response from the mathematical model and the real physical system is a common approach for improving the accuracy of the model. Optimization algorithms usually require informa-tion about the derivatives which may not always be easily available or which may be difficult to com-pute due to, e.g., hybrid dynamics. In such cases, derivative-free optimization algorithms offer an alter-native for design and parameter optimization. In this paper, we present an implementation of derivative-free optimization algorithms for parameter estimation in the platform. The imple-mentation allows the underlying dynamic system to be represented as a Functional Mock-up Unit (FMU), and thus enables parameter optimization of models ex-ported from modeling tools compliant with the Func-tional Mock-up Interface (FMI).
The Functional Mockup Interface (FMI) is a tool independent standard for the exchange of dynamic models and for Co-Simulation. The first version, FMI 1.0, was published in 2010. Already more than 30 tools support FMI 1.0. In this paper an overview about the upcoming version 2.0 of FMI is given that combines the formerly separated interfaces for Mod-el Exchange and Co-Simulation in one standard. Based on the experience on using FMI 1.0, many small details have been improved and new features introduced to ease the use and increase the perfor-mance especially for larger models. Additionally, a free FMI compliance checker is available and FMI models from different tools are made available on the web to simplify testing.
A dynamic model of a chemical CO2 absorption process with aqueous monoethanolamine (MEA) is presented, validated against experimental data. Based on the validated model, a reduced-order model is developed, suitable for an online optimization control strategy. The objective of the optimization is to enable fast adaptations to changes in operating conditions of the power plant, while minimizing the energy consumption in the operation of the CO2 separation plant. The results indicate that model-based online optimization is a feasible technology for control of CO2 separation systems.
The equation-based Modelica language allows the modeller to specify custom functions. The body of a function is an algorithm that contains procedural code to be executed when the function is called. This language feature is useful for many applications; however, the insertion of a function often prevent model optimizations that require the model to be formulated in purely declarative form by equations only. This paper discusses several non-trivial cases in which the function call and the corresponding algorithmic code can be transformed into an equivalent purely equation-based model, thus allowing further optimization. The inlining algorithms presented in the paper go well beyond the state of the art in commercial and open-source Modelica tools.
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19 members
Fredrik Magnusson
  • Simulation and Optimization Research and Development
Johan Andreasson
  • Physical Modeling R&D
Toivo Henningsson
  • Simulation and Optimization R&D
Lund, Sweden