Gulshan Noorsumar

• Mortensen, Dag; Noorsumar, Gulshan; Fjær, Hallvard Gustav; Babaei, Reza & Drønen, Per Erik (2026). Mitigating distortions in cast automotive subframes: A finite element simulation approach. The International Journal of Advanced Manufacturing Technology. ISSN 0268-3768. 142(11-12), s. 5879–5898. doi: 10.1007/s00170-026-17515-w. Fulltekst i vitenarkiv Vis sammendrag

  Abstract Ensuring dimensional accuracy is critical for automotive parts, as they must meet strict tolerances - often within a few hundred microns at key locations to ensure proper assembly within the vehicle. However, the casting and heat treatment of large aluminium structures introduces thermal stresses and distortions that must be controlled to maintain these requirements. To address this challenge, a mathematical modeling based framework has been developed to predict and mitigate the deformations by designing the casting process along with the heat-treatment and quenching process. The simulations conducted, using an in-house developed Finite Element based solver, are validated against physical test data from industrial production. The comparisons show that the the models developed in this work, along with their underlying assumptions, provide a comprehensive representation of the thermo-mechanical behaviour of subframe parts during the casting process. Finally, further refinements to the modelling approach have been suggested to improve the test-simulation correlations.

• Lindholm, Dag; Arbes, Florian Maurits; Meir, Michaela Georgine; Rekstad, John; Sørheim, Einar Arne & Jensen, Øyvind [Vis alle 9 forfattere av denne artikkelen] (2025). Toward a Digital Twin: Extrusion Die for Manufacturing Polyphenylene Sulfide Solar Thermal Collectors. Polymer Engineering and Science. ISSN 0032-3888. s. 1–11. doi: 10.1002/pen.70340. Fulltekst i vitenarkiv Vis sammendrag

  The European Union's commitment to increasing renewable energy use emphasizes the relevance of solar thermal collectors. A key component in flat-plate solar collectors is the absorber plate. Compared to traditional metal-based solar collectors, polyphenylene sulfide (PPS) offers several advantages including lower weight, easier installation, and strong thermal and corrosion resistance. However, extrusion of defect-free PPS plates is challenging. Accurate control of die exit temperatures and process conditions is crucial for producing high-quality PPS absorber plates, as even small deviations can cause slag deposition at the die exit. Detached slag deposits can lead to serious surface defects and costly production interruptions. A digital representation of the die allows estimation of how process-related factors affect PPS at the die exit. In this study, a detailed 3D model predicts temperatures that closely align with thermocouple data from a specific production run, revealing that measured die temperatures are about 10°C lower than the PPS die exit temperature. Melt inlet temperature and heater settings are shown to have significant impact on the die exit temperatures, while the effect of the ambient temperature is small. A non-intrusive POD-based reduced-order model enables real-time prediction of the die exit temperature, combining CFD data, interpolation, and sensor integration to support quality control and waste reduction in the extrusion process.

• Noorsumar, Gulshan; Adhikari, Sayan; Fjær, Hallvard Gustav; Jensen, Øyvind & Meir, Michaela Georgine (2025). Enhancing Extrusion Efficiency: Development of a Digital Twin for Glass Reinforced Polymer Processes Using Machine Learning and Real-Time Data Integration. I Luby, Michael (Red.), TMS 2025 154th Annual Meeting and Exhibition Supplemental Proceedings. Springer. ISSN 9783031807480. s. 620–633. doi: 10.1007/978-3-031-80748-0_55. Fulltekst i vitenarkiv
• Holen, Martin; Rogovchenko, Svitlana; Noorsumar, Gulshan & Goodwin, Morten (2025). Predicting Vehicle Impact Severity With Deep Neural Network for Reinforcement Learning Based Autonomous Vehicle Simulators. IEEE Conference on Emerging Technologies and Factory Automation. ISSN 1946-0740. s. 1553–1558. doi: 10.1109/ICMLA61862.2024.00239. Fulltekst i vitenarkiv Vis sammendrag

  Road accidents leads to a significant number of deaths in the world, with human error being attributed as one of the roots of the problem. One of the possible alternatives to address the issue is the development of autonomous vehi-cles. Of the many technological advancements in the field of autonomous vehicles, Reinforcement Learning (RL) has shown progress because its methodology is not reliant on human data. However, one of its challenges is the definition of the reward function, which is critical to RL algorithms. In this context, determining the severity of a crash allows for an improvement in the reward function, especially in simulators. In this study, we present a Deep Neural Network (DNN) based model that predicts the crash impact severity in terms of vehicle deformation during the event, along with the de-formation we create a reward strategy for RL- based vehicle simulators. The model is developed based on data generated from finite element simulations replicating a full-frontal vehicle impact against a rigid barrier. We compare the trained model with a variety of NN methods, and different input data. The Neural Network model performs well, supporting the development of a reward system to improve RL-based autonomous vehicle simulators.

• Noorsumar, Gulshan; Rogovchenko, Svitlana; Robbersmyr, Kjell Gunnar; Vysochinskiy, Dmitry & Klausen, Andreas (2023). Development and Extended Validation of a Lumped Parameter Prediction Model for Analysing Injury Parameters in a Vehicle Crash. I Wagner, Gerd; Werner, Frank; Tuncer, Oren & Rango, Floriano De (Red.), Simulation and Modeling Methodologies, Technologies and Applications. SIMULTECH 2021. Springer. ISSN 9783031231490. doi: 10.1007/978-3-031-23149-0_1. Fulltekst i vitenarkiv
• Noorsumar, Gulshan; Rogovchenko, Svitlana; Vysochinskiy, Dmitry & Robbersmyr, Kjell Gunnar (2023). A Numerical Comparison of Lagrange and Kane’s Method for Modeling and Crashworthiness Assessment of a Modified Vehicle. I Wagner, Gerd; Werner, Frank & Rango, Floriano De (Red.), Simulation and Modeling Methodologies, Technologies and Applications. SIMULTECH 2022. Springer. ISSN 9783031438233. s. 156–176. doi: 10.1007/978-3-031-43824-0_9. Fulltekst i vitenarkiv
• Noorsumar, Gulshan; Rogovchenko, Svitlana; Robbersmyr, Kjell Gunnar & Vysochinskiy, Dmitry (2022). Vehicle crashworthiness performance in frontal impact: Mathematical model using elastic pendulum. Mechanics research communications. ISSN 0093-6413. 124. doi: 10.1016/j.mechrescom.2022.103954. Fulltekst i vitenarkiv Vis sammendrag

  Vehicle occupant injuries due to collisions cause many fatalities every year. Safe vehicle design plays a critical role in averting serious injuries to occupants and vulnerable road users in the event of a crash. In this paper we study a full frontal vehicle crash against a rigid barrier introducing a Lumped Parameter Model (LPM) inspired by the elastic pendulum motion. The model uses polar coordinates to simplify the problem and the governing equations have been defined using Lagrangian formulation. The Simulink model has been validated against Finite Element (FE) data demonstrating good correlation with pitching angle and maximum crush of the vehicle. These parameters are crucial for designing vehicles which efficiently protect occupants.

• Noorsumar, Gulshan; Rogovchenko, Svitlana; Vysochinskiy, Dmitry & Robbersmyr, Kjell Gunnar (2022). Modeling of Modified Vehicle Crashworthiness using a Double Compound Pendulum. I Wagner, Gerd; Werner, Frank & Rango, Floriano De (Red.), Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SciTePress. ISSN 9789897585784. s. 102–111. doi: 10.5220/0011306100003274. Fulltekst i vitenarkiv Vis sammendrag

  Vehicle crash modeling has been a challenge for researchers for several decades. Occupant injury prevention and prediction is a critical area within vehicle safety design. The modeling of material failure in structural members during a full frontal crash has been presented in this paper. This study presents a Lumped Parameter Model (LPM) with an elastic double compound pendulum replicating the impact kinematics. The model defined using Lagrangian formulation; presents a novel methodology to represent material fracture caused due to heat affected zones or welding in Ultra High Strength Steels (UHSS) in a non-linear crash event. The material fracture leads to rotation of the vehicle; presented in the form of torsional springs in the LPM developed in this study. The Simulink model has been validated with a finite element simulation and shows good correlation to predict parameters crucial to design for occupant protection in a vehicle crash.

• Noorsumar, Gulshan; Robbersmyr, Kjell Gunnar; Rogovchenko, Svitlana & Vysochinskiy, Dmitry (2022). An Overview of Data Based Predictive Modeling Techniques Used in Analysis of Vehicle Crash Severity. I Sanfilippo, Filippo; Granmo, Ole-Christoffer; Yayilgan, Sule Yildirim & Bajwa, Imran Sarwar (Red.), Intelligent Technologies and Applications: 4th International Conference, INTAP 2021, Grimstad, Norway, October 11–13, 2021, Revised Selected Papers. Springer. ISSN 9783031105241. s. 355–379. doi: 10.1007/978-3-031-10525-8_28. Fulltekst i vitenarkiv Vis sammendrag

  Accident injury prediction is a crucial constituent to reducing fatalities linked to vehicle crashes. The vehicle development process and road safety planning includes also the injury prediction for occupants and Vulnerable Road Users (VRUs) in a vehicle crash and the identification of the factors responsible for increased traffic collision injuries. This paper reviews the different data-based prediction techniques to modeling a vehicle crash event, crash frequency and crash severity. Machine learning (ML) is a research field which has gained impetus in the recent years and is widely used in different engineering applications; including injury prediction in vehicle collisions. The paper is divided into two major sections; the first section presents an overview of the existing predictive models for estimating injury severity in a crash event to occupants and VRUs and the second section describes the applications of data-based modeling techniques to predict crash frequency in different traffic scenarios. We also discuss possible future applications of data-based modeling techniques in this domain.

• Noorsumar, Gulshan; Vysochinskiy, Dmitry; Englund, Even; Robbersmyr, Kjell Gunnar & Rogovchenko, Svitlana (2021). Effect of welding and heat treatment on the properties of UHSS used in automotive industry. The European Physical Journal Conferences. ISSN 2101-6275. 250. doi: 10.1051/epjconf/202125005015. Fulltekst i vitenarkiv Vis sammendrag

  This paper deals with the undesired effects of the heat treatments on the mechanical properties of (UHSS) Ultra High Strength Steel used nowadays in automotive industry to improve crashworthiness performance of vehicles. The UHSS specimens were extracted from certain parts of the car body and subjected to different heat treatments. Four types of specimens were tested: untreated, welded with metal inert gas welding, heat treated at 800 °C, and heat treated at 1250 °C. All heat-treated specimens showed dramatically reduced values of strength. The results suggest that it is important to follow the official repair manuals avoiding unnecessary welding and improper heat treatments of UHSS. The experiments provide the data necessary for constructing a constitutive model and performing a finite-element analysis of improperly repaired UHSS parts.

• Noorsumar, Gulshan; Rogovchenko, Svitlana; Robbersmyr, Kjell Gunnar & Vysochinskiy, Dmitry (2021). Mathematical models for assessment of vehicle crashworthiness: a review. International Journal of Crashworthiness. ISSN 1358-8265. 27(5). doi: 10.1080/13588265.2021.1929760. Fulltekst i vitenarkiv Vis sammendrag

  This article reviews approaches to mathematical modeling of a vehicle crash. The growing focus on vehicle and occupant safety in car crashes has triggered the need to study vehicle crashworthiness in the initial stages of vehicle development. The major motivation for this work is to support vehicle crashworthiness design during the product development process.The article is divided into two parts; the first one overviews existing mathematical models used to solve engineering problems. The second part describes modeling strategies applied for replicating non-linear vehicle crash event and occupant kinematics in an occupant protection loadcase. We also highlight alternative modeling strategies using hybrid modeling techniques aimed at the improvement of the vehicle development process.

• Noorsumar, Gulshan; Rogovchenko, Svitlana; Robbersmyr, Kjell Gunnar; Vysochinskiy, Dmitry & Klausen, Andreas (2021). A Novel Technique for Modeling Vehicle Crash using Lumped Parameter Models, 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2021). SciTePress. ISSN 9789897585289. s. 62–70. doi: 10.5220/0010529200620070. Fulltekst i vitenarkiv Vis sammendrag

  This paper presents a novel technique for modeling a full frontal vehicle crash. The crash event is divided into two phases; the first until maximum crush and the second part when the vehicle starts pitching forward. This novel technique will help develop a three degrees of freedom (DOF) lumped parameter model (LPM) for crash and support in the vehicle development process. The paper also highlights the design process for reducing vehicle pitching in occupant protection load cases. The model has been validated against a finite element (FE) simulation of a full frontal crash of a Chevrolet Silverado developed by the National Highway Traffic Safety Administration (NHTSA), and the LPM shows good correlation with the FE test data.

• Noorsumar, Gulshan; Robbersmyr, Kjell Gunnar; Rogovchenko, Svitlana & Vysochinskiy, Dmitry (2020). Crash Response of a Repaired Vehicle-Influence of Welding UHSS Members. SAE technical paper series. ISSN 0148-7191. 1- 2020(April), s. 1–5. doi: 10.4271/2020-01-0197. Fulltekst i vitenarkiv Vis sammendrag

  Automakers generally recommend not to weld structural parts after a vehicle crash, and these should be replaced as a whole part in case of a crash event. Sectioning of these members is also not recommended and use of the repair manual is mandatory in case of fracture of such parts. However, repair shops may not adhere to these instructions and use incorrect repair procedures on these members which would modify their strength properties. This study analyses the impact of welding structural members in a vehicle like the A-pillar which use Ultra-High Strength Steels (UHSS) for reducing the weight of the vehicle and improving the crashworthiness of the structure. The research conducted in this paper highlights the differences in the crash performance of a repaired vehicle as opposed to baseline injury values for the vehicle. The performance of the modified vehicle when tested for different loadcases shows reduced crash performance as compared to the baseline performance and it can be concluded that welding or sectioning the UHSS parts would influence the crashworthiness of a vehicle. This paper only focuses on structural integrity of the repaired vehicle in a crash event. The performance of the vehicle in occupant injury is kept out of scope for this study.

• Swamy, Santosh; Noorsumar, Gulshan & Chidanandappa, Shivakumar (2019). Mass Optimized Hood Design for Conflicting Performances. SAE technical paper series. ISSN 0148-7191. doi: 10.4271/2019-28-2546. Fulltekst i vitenarkiv

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• Adhikari, Sayan; Noorsumar, Gulshan; Mortensen, Dag; Celik, Turgay & Babaei, Reza (2026). Predicting Casting-Induced Shape Distortion Using Hybrid Physics-Informed Neural Networks in Low-Pressure Die Casting. doi: https:/congressarchive.cimne.com/wccm_eccomas_2026/abstracts/611fc32805ec11f1919d000c29ddfc0c.pdf. Fulltekst i vitenarkiv Vis sammendrag

  Thermo-mechanical simulations are central to predicting solidification behaviour, hot‑spot formation, defect evolution and distortions in shape‑casting processes. However, finite‑element–based modelling remains computationally demanding due to mesh‑generation effort, stiff non-linear heat‑transfer equations, and the need to solve PDE residuals at a very high spatial resolution. These challenges limit the use of FEM solvers in rapid design iteration, optimization loops, and real‑time process control. Physics‑informed machine learning (PIML) provides a promising route to alleviate these constraints by embedding governing thermal physics directly into neural‑network training [1]. In this work, we develop a hybrid physics‑informed neural network (PINN) framework tailored to simulating low‑pressure die casting (LPDC) processes used in the manufacture of structural automotive components at Aludyne in Norway. Traditionally run with Finite Element Methods (FEM) the main objective of the simulations is to accurately predict the final shape of the automotive component after casting and compare it to the intended CAD geometry. During each LPDC production cycle, the mould is repeatedly heated, causing it to deform under thermal loading. Consequently, the liquid aluminium solidifies inside a mould that is already distorted, producing a part in an unintended shape. Predicting and correcting these thermally driven shape changes requires a detailed numerical model, and the complexity involved makes vanilla PINNs inadequate [2]. The hybrid PINN formulation incorporates the heat‑transfer Partial Differential Equations, relevant boundary conditions, and temperature‑dependent material behavior along with data from a simplified FEM model into a unified loss function. This enables the model to approximate transient temperature fields and solidification‑front motion while predicting distortions and stresses in the mould and the cast geometry. Predictions from the hybrid PINN framework are validated against FEM results to assess the performance of the proposed approach, and future work will extend this validation to simulations of an actual production part.

• Noorsumar, Gulshan; Rogovchenko, Svitlana; Robbersmyr, Kjell Gunnar; Vysochinskiy, Dmitry & Klausen, Andreas (2021). A Novel Technique for Modeling Vehicle Crash using Lumped Parameter Models. Fulltekst i vitenarkiv
• Noorsumar, Gulshan; Robbersmyr, Kjell Gunnar; Rogovchenko, Svitlana & Vysochinskiy, Dmitry (2021). An overview of Data Based Predictive Modeling Techniques used in Analysis of Vehicle Crash Severity. Fulltekst i vitenarkiv
• Noorsumar, Gulshan; Rogovchenko, Svitlana; Robbersmyr, Kjell Gunnar & Vysochinskiy, Dmitry (2023). Modeling, Simulation and Prediction of Vehicle Crashworthiness in Full Frontal Impact. Universitetet i Agder. ISSN 9788284271132. Fulltekst i vitenarkiv Vis sammendrag

  Vehicle crashworthiness assessment is critical to help reduce road accident fatalities and ensure safer vehicles for road users. Techniques to assess crashworthiness include physical tests and mathematical modeling and simulation of crash events, the latter is preferred as mathematical modeling is generally cheaper to perform in comparison with physical testing. The most common mathematical modeling technique used for crashworthiness assessment is nonlinear Finite Element (FE) modeling. However, a problem with the use of Finite Element Model (FEM) for crashworthiness assessment is inaccessibility to individual researchers, public bodies, small universities and engineering companies due to need for detailed CAD data, software licence costs along with high computational demands. This thesis investigates modeling strategies which are affordable, computationally and labour inexpensive, and could be used by the above-mentioned groups. Use of Lumped Parameter Models (LPM) capable of capturing vehicle parameters contributing to vehicle crashworthiness has been proposed as an alternative to adopting FEM, while the later have been used to validate LPMs developed in this thesis. The main crash scenario analysed is a full frontal impact against a rigid barrier. Front-end deformation which can be used to measure crash energy absorption and pitching which could lead to occupant injuries in a frontal crash event are parameters focused on. The thesis investigates two types of vehicles; vehicle with initial structure intact is defined as baseline vehicle, while a vehicle that underwent unprofessional repairs on its structural members made of Ultra High Strength Steel (UHSS) is defined as a modified vehicle. The proposed novel LPM for a baseline vehicle impact is inspired by pendulum motion and expresses the system using Lagrangian formulation to predict the two phases of impact: front-end deformation and vehicle pitching. Changes in crashworthiness performance of a modified vehicle were investigated with a FEM; tensile tests on UHSS coupons were conducted to generate material inputs for this FEM. Further, a full scale crash test was conducted to validate the FE simulations. An LPM to conduct crashworthiness assessment of a modified vehicle has been proposed, it is based on a double pendulum with a torsional spring representing the vehicle undergoing a full frontal impact.

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