Linköping Electronic Conference Proceedings
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Dynamic Simulation of a PEM Electrolysis System
Full text linkElectrolysis systems are the key technology to producehydrogen from renewable energy sources. The modeling andsimulation of proton exchange membrane (PEM) electrolyzersand the connected power grid are a challenge due to highlyfluctuating loads in the available electrical power. Ourstudy focuses on the modeling of the electrolyzer and itsbalance of plant with varying loads resulting fromdifferent energy sources. The balance of plant contains awater supply system, the electrical power supply andprocesses for treating the produced hydrogen, in particulardrying. The electrolyzer model is validated usingsteady-state and dynamic data from the literature. Thereby,it is shown that the developed model accurately representsthe measurement results from the literature. Furthermore,we implement an empirical degradation model to assess theeffects of different use-cases on the efficiency of theelectrolyzer
The FlightControl library for aircraft control design applications
Full text linkController development of aerial vehicles is along-standing task during aircraft design and has a largeimpact on the resulting systems performance. In today'sintegrated design loops, ideally all components of theaircraft must be considered in simulation and testing inorder to develop complex system architectures and meetperformance requirements. One of the tools that are suitedfor modeling and simulation of a multidisciplinary aircraftand systems assembly is DLR's FlightDynamics Library, whichtakes advantage of the capabilities of the Modelicamodeling language. In this work, a new augmentation to thelibrary is discussed, which implements a range of commonaircraft control concepts which can be used for design ofnew controllers, closed-loop simulation and experimentaltesting via code-export. The library is set up in a modularway, so that flight guidance and flight control systems canbe developed for multiple aerial platforms, includingmanned/unmanned fixed- and rotary-wing aircraft. For thispaper, a simulation example is provided by means of anautoland controller that shall be designed for ahigh-fidelity 6-DoF fixed-wing aircraft model in Modelica.The combination of aircraft and controller models issubjected to a three step synthesis process, which yields acontroller that is robust against external and internaldisturbances
FMI Layered Standard for Network Communication: Applications in Networked ECU Development
Full text linkThis paper introduces the FMI 3.0 Layered Standard forNetwork Communication (FMI-LS-BUS), an extension of theFunctional Mock-up Interface 3.0 (FMI 3.0) standarddesigned to address interoperability challenges insimulating distributed, networked systems, particularly inautomotive applications. By leveraging FMI 3.0 featuressuch as clocks, clocked variables, and hierarchicalterminals,the standard defines two complementary abstraction layers:Physical Signal Abstraction (High-Cut): Representingphysical signal values as clocked variables.Network Abstraction (Low-Cut): Emulates hardware-level busprotocols (e.g., CAN, Ethernet) using FMI 3.0’s clockedbinary variables.Aligning with the V-model development process, wedemonstrate how these layers address distinct challenges indifferent design phases: High-Cut supports require-ments engineering and functional testing by simplifyingsignal exchange during Virtual Electronic Control Unit(vECU) integration. Low-Cut enables later phases of thedesign validation by replicating network timing andprotocol specific properties, such as error handling.The standard’s applicability currently focuses onautomotive use cases (e.g., CAN, CAN FD, CAN XL, Ethernet,FlexRay, LIN) but can be extended to industrial au-tomation and IoT, facilitated by its domain-agnosticstructure
A Thermal Digital Twin of Asphalt Pavements: Implementation and Application to an Instrumented Pavement in Costa Rica
Full text linkThis article presents the development of a syntheticthermal digital twin designed to reproduce the temperaturedistribution within the asphalt layers of a pavementsystem. At the core of the digital twin is a numericalmodel that solves Fourier’s heat conduction equation usinga finite difference method, enabling the simulation oftransient heat transfer across multiple material layers.The digital twin architecture is organized into threemodular layers: the physical twin, which includes real-timetemperature measurements from embedded sensors(synthetically simulated in this study); the twinninglayer, which synchronizes physical and virtual data throughfeedback control using proportional-integral (PI)controllers; and the virtual twin, which integratesmaterial properties, heat fluxes, and environmentalconditions to replicate pavement thermal behavior.The numerical model was validated using climate data andsurface temperature measurements collected over a one-yearperiod from a reference instrumented pavement in CostaRica. In addition to radiative, convective, and conductiveheat fluxes, the model accounts for the cooling effects ofrainfall and subsequent evaporation—factors that areparticularly relevant in tropical climates. The resultsdemonstrate the capability of the digital twin to captureboth long-term thermal trends and short-term responses toenvironmental events
Elements of Cycle Design for Parallel Hybrid Mixed Flow Turbofans: Select Trade-Offs in Performance and Energy-Efficiency
Full text linkIn the last decade, parallel and serial hybrid combining gas turbine propulsion with electric power systems have been investigated. For the commercial aviation sector, they provide fuel burn and emission reduction potential and have been explored to some extent. Depending on the chosen concept of operation, propulsion cycle, and reference basis, minimal to mild double digit fuel consumption reductions are anticipated. A key challenge remains the low specific energy of current battery technology, leading to a high penalty on electrical energy use. High-speed civil propulsion has gained interest in recent years due to advances in material capabilities and technology enhancements, especially for what concerns aircraft and sonic boom noise reductions. As a result, there is now a renewed and stronger focus on developing supersonic transport aircraft that are environmentally sustainable, technologically feasible, and economically competitive with the existing civil subsonic aviation market sector. The propulsion system for supersonic civil aircraft must satisfy more stringent requirements and more limiting constraints compared to a subsonic application, due to the more severe operating conditions. In this context, the publicly available literature is more oriented towards aircraft rather than propulsion system design. For high-speed propulsion and supersonic transport, and the corresponding mixed-flow turbofan cycles, very limited research propulsion system-centred is available, both for the conventional design and for hybrid electric integration. For these cycles, both emission reductions, noise requirement compliance, and performance improvement potential are of substantial interest, thus widening the metrics of interest. In this work, a hybrid mixed-flow turbofan configuration is investigated and modelled, and cycle design trade-offs are identified and selected. A non-hybridised baseline configuration targeting low bypass ratios based on performance data obtained from the open literature is considered for comparison. A multipoint synthesis scheme for the gas turbine is combined with a hybrid concept of operations through physics-based cycle modelling and previously published polytropic efficiency corrections. Trade studies are conducted introducing hybrid configuration, analysis on key performance and fuel consumption metrics are presented and recommendations on hybrid potential exploitation are provided
Bayesian Networks Applied to Risk Modeling of Depressurized Flight at High Altitude
Full text linkThis study analyses the health risks of high-altitude parachute launch missions, such as hypoxia, decompression sickness, and otological barotrauma, using Bayesian probability networks to assess and mitigate these risks. All of these risk factors arise from the characteristics of the mission, which occurs in an environment hostile to human physiology in terms of pressure and temperature. Bayesian networks are effective tools for modelling uncertainties and diagnosing complex systems, aiding in event planning and decision-making. In the study, three Bayesian networks were constructed using Netica software, with steps for defining causes, effects, and causal relationships, in addition to determining probabilities. For the networks assembled, the results showed that hypoxia presents a risk of serious damage of 1 in 10,000 flights, while decompression sickness has a lesser impact, with a risk of moderate damage of 1 in 50,000 flights. Otological barotrauma presents a risk of moderate damage of 1 in 250 flights. In some of the analyses, the human factor proved to be an essential element in mitigating risks. Finally, the effectiveness of Bayesian networks in risk assessment is highlighted, suggesting the acquisition of more data to consolidate the probabilitiesemployed, and proposing studies that include logistical and operational aspects in the planning of missions of this profile
Event Support for Simulation and Sensitivity Analysis in CasADi for use with Modelica and FMI
Full text linkCasADi is an open-source framework that can be used to efficiently solve optimization problems involving user-defined ODE/DAE models. Supported solution methods include so-called shooting methods, where solvers for initial-value problems in ODEs or DAEs are referenced inside nonlinear programming (NLP) formulations. In order to solve such NLP formulations with gradient-based algorithms, CasADi implements a fully automatic sensitivity analysis. This analysis includes forward sensitivity analysis, adjoint sensitivity analysis as well as the calculation of higher-order sensitivities for the ODE/DAE models. Because of the variational (differentiate-then-integrate) approach used, the numerical solution can be performed with variable-step size, variable-order integrators such as those from the SUNDIALS suite.
In this work, we present a generalization of the sensitivity analysis support in CasADi to systems with events, as are common in real-world cyber-physical models. In particular, the event extension enables us to formulate and solve optimization problems with such event systems, without a priori knowledge of the number and ordering of events. Ultimately, we expect the proposed approach to be compatible with general cyber-physical models formulated in Modelica or available as model-exchange FMUs.
We demonstrate the proposed approach for two proof-of-concept examples; the classical bouncing ball written in CasADi directly and a simple hybrid DAE describing a breaking spring formulated in Modelica and imported symbolically into CasADi. In the examples, we show that the forward sensitivities calculated to high precision using the proposed approach are consistent with a cruder finite-difference approximation and provide an example of how they can be embedded into optimization formulations. We discuss how the approach can be extended to handle standard FMUs, adhering to FMI 2 or FMI 3, as well as non-trivial Modelica models imported via a symbolic interface based on the emerging Base Modelica standard
Development and Validation of a Water-to-Air Heat Pump Model using Modelica
Full text linkWater-to-air heat pumps are widely used Heating, Ventilation, and Air Conditioning (HVAC) devices due to their versatility and energy efficiency. However, there is a scarcity of readily available Modelica models that support reversible operation (heating and cooling modes), use compressor speed as the control signal, and accurately predict the system performance. To address this gap, this paper presents a speed-input water-to-air heat pump model developed using Modelica. Performance curves are employed to represent the functionality and predict the system’s capacity and power usage. To validate the proposed model’s effectiveness, manufacturer-provided data are used to generate the performance curves. The model, based on these curves, is then used to simulate testing conditions, which are implemented in a real heat pump testbed. The comparison between simulated and measured values shows that the errors during normal operation stages are within an acceptable range, demonstrating the effectiveness of the developed water-to-air heat pump model
Integrating Generative Machine Learning Models and Physics-Based Models for Building Energy Simulation
Full text linkThis paper describes the integration of generative deep learning models for data-driven building energy simulation. The generative models (GMs) are trained to learn distributions of building input signals from data using Python and PyTorch and interfaced with physics-based Modelica models. The developed integration requirements provide background on typical needs that focus on building energy simulation performance. Simulation examples using models from the Buildings library, refactored to receive GM inputs, are presented to illustrate the benefits of the proposed integration approach and how GMs can be used for building energy performance analysis