Linköping Electronic Conference Proceedings
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Human Factors and HMI for Future Air Domain
Full text linkFuture Air Domain will be defined by emerging technologies, such as the integration of autonomous systems and the application of artificial intelligence to support complex decision-making. A central concept in the Future Air Domain is the “system of systems” approach, in which multiple manned and unmanned platforms operate collaboratively. In this context, effective human-machine collaboration is critical, highlighting the need for investigating the design of human-machine interfaces (HMI) that facilitate interaction. This paper explores some of the challenges related to HMI design for Future Air Domain. It discusses the research currently under development within a joint Swedish–Brazilian collaboration that investigates how cognitive modelling and pilot state monitoring can contribute to HMI development, how adaptive interfaces can support varying levels of autonomy, and how human factors are influenced by the demands of communicating with and controlling multiple unmanned aerial vehicles (UAVs). To support this research, six different HMI concepts are currently being developed and will be tested in different simulation environments
Dynamic Modeling Methodology for Near Isothermal Compressor
Full text linkCompressors are the vital component of the vapor compression systems and account for the majority of energy consumption. Developing appropriate controllers or optimizing compressor design can significantly reduce the carbon emissions. The isothermal compressor combines the compressor chamber and gas cooler, using the liquid piston to compress the working fluid for nearisothermal compression. This methodology can reach up to 30% energy saving compared to the traditional isentropic compression work. This paper leverages the CEEE Modelica Library (CML) to demonstrate a detailed isothermal compressor model that captures the nearisothermal compression process of transcritical carbon dioxide (CO2) cycle. The model uses the real experimental data as the boundary conditions, and the relevant component-level experimental validation was carried out by using a prototype with 1-ton nominal capacity. The results proved the accuracy of the dynamic model (7.5% relative error for chamber pressure and 0.74 K deviation for chamber temperature), and provide a guideline for designing the isothermal compressor chamber. Finally, the modeling for the isothermal compression cycle is ongoing and the field is still in its infancy
FMI-3.0 Export for Models with a Clock in a Signal Flow Diagram Environment
Full text linkThe FMI-3.0 standard, recently released, introduces several promising features, such as clocks and arrays. FMI-3.0 supports various clock types, including time-based clocks, triggered input, and triggered output clocks. Altair Twin Activate (TA), as a modeling and simulation environment, inherently supports hybrid systems combining continuous-time and discrete-time models. The discrete-time part is typically activated by events and clocks. The clock types provided by FMI-3.0, however, may differ from those in TA. In the paper (Najafi and Nikoukhah 2022), we explained how different clocks defined in FMI-3.0 can be successfully imported into TA. Building upon this, our current paper aims to demonstrate how various clocks used in TA can be used in the export of a subsystem in both FMI-3.0 and FMI-2.0 formats. Specifically, we will explain the way input periodic clocks and input triggered clocks are exported
A Modelica Implementation of an Organic Rankine Cycle
Full text linkOrganic Rankine cycle (ORC) systems generate power from low-grade heat sources, such as geothermal sources and industrial waste heat. A key feature is that a working fluid is selected to match the temperature of the source. With the vast pool of candidate working fluids comes the challenge of developing a large number of robust thermodynamic media models. We implemented a subcritical ORC model in Modelica that uses working fluid data records and interpolation schemes in lieu of thermodynamic medium evaluation for energy recovery estimation. This is a component model that can be integrated into a larger energy system model. It does not require detailed thermodynamic, heat transfer, or machine analysis. Our ORC model fills a gap where working fluids are ready to choose or easy to add, and at the same time can be integrated into an energy system
Thermo-Fluid Modeling Framework for Supercomputer Digital Twins: Part 2, Automated Cooling Models
Full text linkThe development of digital twins for the purpose of improving the energy efficiency of supercomputing facilities is a non-trivial endeavor that is complicated by the difficulty of creating physics-based thermo-fluid cooling system models (CSMs). Within ExaDigit—an opensource framework for liquid-cooled supercomputing digital twins—a thermo-fluid modeling framework is being developed. This effort has been segmented into two with two companion papers describing each portion of the overall effort. Part 1 focuses on the development of a cooling system library in Dymola for the Frontier supercomputer at Oak Ridge National Laboratory (Kumar et al. 2024). Part 2, this paper, describes an effort to create a templatebased auto-generation methodology for CSMs, called AutoCSM. In this paper, an overview of the initial AutoCSM architecture and workflow is provided, along with a practical example using the Oak Ridge Leadership Computing Facility’s (OLCF) Frontier supercomputer CSM. AutoCSM will (1) improve ExaDigiT’s user accessibility by providing a flexible workflow for modularizing the creation of the CSM system and control logic, (2) decrease the development time of CSMs, and (3) standardize the method for incorporating CSMs into the ExaDigiT framework
Computationally Efficient Optimization of Long Term Energy Storage Using Machine Learning
Full text linkEnergy storage can be charged when energy is cheap and discharged when it is expensive to make an energy system more profitable or used to make the plant operation more efficient to reduce CO2 emissions. To optimize long term energy storage with conventional methods a long time horizon must be used. When the long term energy storage is combined with a complex energy system the computational cost becomes large when using conventional methods. To reduce the time horizon, an algorithm will be used to decide the state of charge of the long term energy storage at the end of the day. This algorithm is trained using machine learning with data of the optimal state of charge obtained by running computationally heavy long time mixed integer linear programming ahead of time. Then a one-day or week mixed integer linear programming optimization will be done for the production planning. The seasonal patterns of the long term energy storage can then be captured while giving the plant operator a simple one-day or week production plan. A case study will be done with a combined heat and power plant system with 4 boilers, a long-term thermal storage, and a hydrogen storage system. Using this method the complexities of a multi energy system with long term energy storage can be captured while doing day ahead production plannin
Green infrastructure for resilient urban design: the mapping and management of green roofs in Oslo
Full text linkAchieving “Climate-Neutral and Smart Cities” is now high on the agenda and the city of Oslo has set an even more ambitious goal of becoming a zero-emission city. However, the promotion of more compact development may lead to some negative effects such as the entrapment of polluted air, wind tunnel effects or urban heat islands. Green infrastructure (GI) can be used as a mitigation measure, bringing many benefits such as improving air quality, regulating thermal environment, reducing energy consumption, managing storm water, or promoting urban biodiversity. In this work, we aim to map the existing green roof infrastructure in Oslo and develop an evidence-base strategy for its further development. Interviews with stakeholders revealed the practical challenges such as structural limitations, high installation and maintenance costs, and regulatory compliance issues. However, they also recognized the significant environmental advantages that highlight the importance of green roofs in urban sustainability strategies. Geographical information system (GIS) tools are used to identify the potential areas for further green roof implementation, considering the spatial, morphological and environmental conditions. 91 Priority green roof areas (PRIOGRAs) and 13 Potential green roof areas (PGRAs) are identified as the most suitable after applying filters like roof surface area, and dominating roof area and slope criteria, exclusion of cultural heritage buildings and existing green roofs, tree density per person deficit, and building age. 2044 roofs can be considered suitable without the building age criteria. These findings will potentially help providing actionable insights for policymakers, urban planners, and the research community
Alternative fuels for the maritime industry and its impact on flue gas composition
No full textThe maritime industry contributes to 80-90% of global trade and is on an increasing trend. However, it is also responsible for substantial amounts of greenhouse gas (GHG) emissions such as carbon dioxide (CO2), nitrogen oxides (NOx), sulfur oxides (SOx), carbon monoxide (CO), and hydrocarbons (HC). Therefore, industries are searching for alternative solutions to reduce GHG emissions by using alternative fuels. This study presents a novel investigation exploring the performance of various alternative marine fuels such as liquefied natural gas (LNG), methanol (MeOH), ammonia (NH3), and hydrogen(H2) in terms of combustion and emissions. Such comprehensive evaluation is limited in literature, making this study uniquely valuable in contributing to the field. The study assesses the impact of different equivalence ratios on emissions for the studied fuel profiles using Cantera and Aspen HYSYS simulations. Results show that CO2 peaks at the stoichiometric ratio, with CO rising from 0.8 to 1.1. Non-carbon fuels like NH3 and H2 emit fewer GHGs than carbonaceous fuels such as LNG and MeOH. H2 has the highest energy release at 87.21 MJ per kg, while NH3 shows lower emission levels, suggesting its potential as a sustainable maritime fuel. This research emphasizes the significance of choosing the right fuel to mitigate maritime emissions, highlighting NH3 and H2 as promising alternatives
New Chemical Kinetics Mechanism for Simulation of Natural Gas/Hydrogen/Diesel multi-fuel combustion in Engines
Full text linkReactivity Controlled Compression Ignition (RCCI) stands out as a promising combustion method for the next wave of internal combustion engines, offering cleaner and more efficient operation, particularly in heavy-duty engines. A key approach within this strategy involves pairing diesel as the high reactivity fuel with natural gas (NG) as the low reactivity counterpart. Further optimization can be achieved by introducing hydrogen to replace portions of NG, thereby enhancing combustion quality while reducing greenhouse gas emissions. For accurate numerical simulation of engines employing this strategy, specialized chemical kinetics reaction mechanism tailored for internal combustion engines becomes essential. To facilitate computationally efficient 3-D Computational Fluid Dynamics (CFD) simulations, the mechanism has been reduced to include 60 species and 372 reactions, with N-heptane acting as a diesel fuel surrogate. This compact mechanism is optimized to align with experimental ignition delay time (IDT) data for N-heptane. The accuracy of the mechanism's predictions for IDT and laminar burning velocity (LBV) is validated using available experimental data. Furthermore, 3-D CFD and quasi-dimensional multi-zone engine simulations are performed with the new mechanism to validate engine operating parameters against experimental data
Identifiability and Kalman Filter Parameter Estimation Applied to Biomolecular Controller Motifs
Full text linkIn this paper we apply Augmented Extended Kalman filters (AEKFs) to performparameter estimation in two different biological controller motifs under both noise-free andnoisy conditions. Based on measurements of the two states of the controller motifs, we showthat under both noise conditions it is possible to estimate all 5 and 6 parameters, respectively,which is in accordance with previously published results that investigated the theoretical conceptof structural identifiability. We further investigate how the level of process/measurement noiseand the initial estimates of both the parameters and states in the AEKFs affect the estimationperformance, and the results indicate that the degree of non-linearity affects filter performance