Linköping Electronic Conference Proceedings
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A Study on Vehicle Suspension Loads Prediction Method Based on Hybrid Road Simulation using Modelica Library and FMI
Full text linkThis study presents a method for predicting vehiclesuspension component loads at the early design stage. Ahybrid road simulation combines road load data acquiredfrom a reference vehicle with the Time Waveform Replication(TWR) technique to generate virtual equivalent roadprofiles. The TWR was implemented in Python, and amultibody dynamics vehicle model developed using Modelon'sVehicle Dynamics Library was used to simulate chassisresponse. Integration and iterative simulation between theTWR system and the vehicle model were conducted viaFunctional Mock-up Units using the Python FMI library,FMPy. These virtual inputs were applied to a virtual testrig. In this study, road load data from a reference vehiclewere used to derive the input signals, which were thenapplied to simulate the suspension loads of a targetvehicle. Simulation results were validated againstmeasurement data to confirm the effectiveness of theproposed method
Towards a Common Standard for Uncertainty Quantification
Full text linkUncertainty Quantification (UQ) studies allow us todetermine whether a model is fit for a particular purpose,as well as the operational domain in which it can be used.Standardising the UQ analysis setup and result summaryenables the iterative composition of UQ information, whichis a crucial step in evaluating model credibility. In thispaper, we present an initial attempt to specify UQinformation as a cross-layer standard for Modelica-, FMI-,and SSP-based workflows subject to two essentialrestrictions: (a) uncertainties can only be described interms of parameters, and (b) analysis is limited to forwarduncertainty propagation and sensitivity analysis ofnonlinear models. More analysis features are planned forthe future. The approach is illustrated using both a simpleexample and an industrial use case
An innovative heterogeneous modeling approach to build a cooling system for battery thermal management with common fluid properties involving FMI terminals
Full text linkIn this paper, a new modeling approach combining nativeSimcenter Amesim submodels and Modelica submodels ispresented. FMI terminals are used to enable the physicalconnection of fluids between Simcenter Amesim and Modelica.The innovation lies in the fluid properties that arecomputed in the causal and acausal worlds using the sametechnology. The architecture of a new Modelica AmeTpfMedialibrary developed to ensure continuity of fluid propertiesis presented, along with its accuracy and performance.Using this new approach, a demonstrator of a closed-loopheterogeneous cooling system for battery thermal managementis built, opening the door to a new way of thinking complexmulti-physics systems
Railway Marketplace for Data, Know-How and Services
Full text linkLike any other industry the railway sector undergoes greattransformations. To maketransportation by rail more sustainable and affordableinfrastructure, maintenance is key.However, this needs a managed and collective effort of manyplayers in order to make this efficientand easily accessible to experts of many different fields:Measurement experts, Simulation experts and Engineers whoperform the maintenance. In this work, we present a marketplace which leverages modern technologies and standardslike GAIA-X, Functional Mock-up Interface (FMI),Distributed Co-Simulation Protocol (DCP) or SystemStructure and Parameterization (SSP) to provide accessibleand easy to use services and retrieve crucial data fromother providers, while protecting sensitive data andknow-how. This market place is developed under the Umbrellaof the ERJU of the European Union. This work is an updateof our publication in Eisenbahn Ingenieur Kompendiumm 2024and contains several additions regarding the implementationand the usage of Modelica standards. A working prototypewill be available in 2026
From Simulation to Reality: Deployment of Reinforcement Learning-Based Neural Network Controllers Trained with Modelica Models
Full text linkTo address the limitations of traditional control methodsin complex systems, reinforcement learning (RL) combinedwith simulation models provides an efficient approach forcontroller development. In this work, we present a completetoolchain for developing and deploying RL-based neuralnetwork controllers using Modelica system models. Servingas a showcase, a real-world double-inverted pendulum isconstructed. The system was modeled in Modelica bycombining physics-based and data-driven modeling approachesfor efficient development. The hybrid model provides thetransition dynamics necessary for RL training. Couplingwith the RL environment is achieved through the FunctionalMock-up Interface (FMI) standard. Successful training andsim-to-real transfer are demonstrated on a single-pendulumsetup, validating the approach for extension to thedouble-inverted pendulum. This paper provides areproducible and extensible framework, well-suited foradvanced control tasks, and highlights the strengths ofModelica in combination with machine learning approaches
Reentry flow and aerothermal characteristics of a retro-propulsive booster
Full text linkRetro-propulsion of a rocket booster is a topic of rising interest where companies are striving to develop reusable launchers in order to reduce cost, environmental impact and turnover time. Understanding the loads on the nozzles during reentry is key to be able to design and produce nozzles capable to reliably be used multiple times. During the project a tool was developed based on CAD and flight data of a Falcon 9 based rocket. A case was set up and simulated with the help of computational fluid dynamics (CFD) and chemical models in order to understand the flow behaviour and thermal loading on and near the nozzles during two flight altitudes with- and without retro-propulsion. The results concluded that without retro-propulsion, the most exposed area, with highest heat transfer coefficient (HTC) and heat flux, are the throats of the nozzles due to a recirculation within the nozzle cluster stagnating the flow at that region. While with retro-propulsion, the thermal loads were similar in magnitude for start and end burn with local high values at the exit of the nozzles. The major thermal loads during retro-propulsion where due to expansion of the exhaust hitting the nozzle walls due to plume-plume interaction
Improving System Safety in Aviation: Supporting STPA with AI Models
Full text linkBackground: System safety in aeronautics is critical, as it directly affects aircraft reliability, efficiency, safety, and security. Given the complexity of modern aviation systems and the potential consequences of failures, a structured and proactive safety approach is essential. System-Theoretic Process Analysis (STPA) is a modern hazard analysis method designed to identify and mitigate risks. Unlike traditional methods that focus primarily on component failures, STPA accounts for both failures and unsafe interactions among system elements, including human operators, software, and organizational factors. Problem: Despite its effectiveness, STPA poses challenges in practical application. The process is time-consuming and requires extensive expertise in system safety, control theory, and system dynamics. Analysts must heavily rely on expert judgment to define losses, hazards, safety constraints, and unsafe control actions. Additionally, training in STPA is resource-intensive, making automation an appealing solution to streamline the process. Goal: To address these challenges, we developed two AI-driven pipelines to automate the initial steps of STPA, reducing reliance on expert knowledge and enhancing efficiency. Method: The first pipeline leverages a fine-tuned Llama3.1-8B model to extract losses, hazards, and constraints from ConOps documents. The second pipeline, BERT Error Detection for STPA (BEDS), improves accuracy by classifying, verifying, detecting errors, and suggesting potential corrections for the extracted elements. Results: The first pipeline was trained using 134 ConOps documents paired with corresponding STPA safety analysis elements. The dataset comprised 35 authentic documents from the CORDIS repository and 99 AI-generated examples. The model achieved a mean precision of 79.73%, recall of 81.09%, and an F1-score of 80.22%. For the second pipeline, 1,084 sentences were extracted from values identified during the first step of STPA. Three classifiers were developed: the sentence identifier achieved a mean accuracy of 95.20%, the incorrect sentence detector 88.61%, and the sentence error identifier 83.44%. While the pipelines were designed to work together, they can also be used independently. Conclusion: This study tackles the challenges of applying STPA in aeronautics by introducing two automated pipelines to streamline the initial process steps. The first pipeline, powered by a fine-tuned Llama3.1-8B model, extracts losses, hazards, and constraints from ConOps documents. The second pipeline, BEDS, verifies and corrects these elements with high accuracy. The results demonstrate strong precision and recall scores, highlighting the potential to reduce both the time and expertise required for STPA analysis in complex aviation systems
On the coupled integration of ducted heat-exchanger systems for aviation
Full text linkThis paper investigates the design and aerothermal optimization of duct geometries with integrated finned heat exchangers. The primary focus of the paper is to investigate how the performance varies with heat exchanger inlet area and total duct length. The results provide new insights into the underlying trade-offs. Heat exchangers with a larger area result in lower losses over the heat exchanger matrix but incur increased losses in the ducts. Additionally, shorter ducts lead to higher losses over the heat exchanger due to the reduced diffusive capacity, while duct losses remain largely unchanged. The study also investigates the effects of the matrix configuration in the heat exchanger overall aerodynamic performance. A fixed and optimized geometry is therefore selected and the impact of decreased flow restriction in the heat-exchanger transversal direction is investigated. The results show that removing the fins, leads to a negligible increase in the normalized losses from 1.146 to 1.159, indicating that the pressure drop across the heat exchanger matrix is the primary driver of diffusion, rather than by the finned structure itself
Experimental Evaluation of Classical Washout Filter Configurations for Fighter Jet Motion Cueing
Full text linkThis research presents an exploratory investigation on the performance of classical washout filter configurations in replicating the motion dynamics of a fighter aircraft on the SIVOR platform, which is a flight simulator with a 7 dof robotic arm. Using the ADMIRE model to simulate flight dynamics, two washout configurations (baseline and tuned) were evaluated under smooth and aggressive commands for the same set of maneuvers. The simulator’s end-effector motion was compared to the aircraft’s original dynamics using a vestibular system model, incorporating human perception thresholds to quantify perceptual mismatch. Root Mean Square Error (RMSE) and normalized cross-correlation were computed to assess cue fidelity across flight segments between the expected aircraft flight and the simulated flights. Additionally, CoppeliaSim is employed to simulate and visualize SIVOR’s behavior during each test case to evaluate collision occurrences in advance. Although the tuned MCA demonstrated marginal improvement over the baseline, both algorithms failed to consistently represent the fighter motion accurately. Results revealed that fixed-parameter filters underperformed not only across different maneuver types, but also for variations within the same maneuver due to small changes in control inputs. These initial findings are in agreement with literature, which highlights the limitations of classical washout filters and emphasize the need for adaptive or model-predictive cueing strategies, especially for high-gain flight scenarios
Cognition and Computation in Decision-Making: Applying the Critical Decision Method to Artificial Intelligence for Aviation Event Analysis
Full text linkThis paper examining how local Large Language Models (LLMs) can partially automate the Critical Decision Method (CDM) in aviation safety investigations. The CDM, while widely respected for its ability to elucidate human factors and decision-making processes in rare or complex scenarios, often requires labor-intensive qualitative coding. To address this challenge, we developed a pipeline employing two specialised models: \emph{Phi-3-Mini-Instruct} for generating structured responses and \emph{Zephyr-7B-Beta} as a “judge” to evaluate confidence, completeness, and groundedness. A single anonymised incident served as our pilot case. Seventy-two participants (36 aviation professionals (pilots) and 36 novices) responded to a 53-item CDM-inspired questionnaire, creating a human reference dataset. The pipeline’s performance was benchmarked against both this human data and a classical NLP baseline (TF-IDF + SVM). Results revealed that the LLM matched 78\% of the majority-human multiple-choice answers and achieved a mean absolute error (MAE) of 0.38 on Likert-scale questions. Its open-ended responses, although moderately accurate, occasionally exhibited factual hallucinations (e.g.\ referencing non-existent systems) and role misattributions. Further stratification showed that the LLM outperformed novices but did not match pilots’ domain expertise, underscoring the importance of operational familiarity for nuanced decision analyses. Despite the single-incident scope limiting statistical generalisation, these findings suggest that LLM-based tools can substantially expedite repetitive data processing and facilitate consistent categorisation tasks that often consume investigators’ bandwidth. Future work will expand to multiple incidents, integrate flight data recorder (FDR) and cockpit voice recorder (CVR) information to reduce speculation, and refine both self-evaluation mechanisms and ethical safeguards