Chalmers Open Digital Repository
Not a member yet
26247 research outputs found
Sort by
CFD on GPUs in Aerospace Applications. Benchmarking the Fluent native GPU solver on aerospace applications, and how to approach purchasing GPUs for CFD as a business case.
No full textRunning CFD simulations on GPUs is becoming commercially viable, and one major early adopter is the automotive industry, where external aerodynamics cases can be run at a fraction of the time compared to simulations on CPUs. The aerospace industry has not yet adopted GPUs to the same extent, as aerospace cases often require support for more complex physics such as compressible flows and combustion.
This study compares the performance of the Ansys Fluent GPU solver with the CPU solver in aerospace applications and is carried out with the support of GKN Aerospace and EDR & Medeso, a reseller of Ansys software. It uses a novel approach to evaluate the attractiveness of purchasing GPUs for a local cluster, compared to purchasing CPUs, from a cost, power consumption, and strategic perspective. A case-based methodology is used to compare the solvers with 3 simulation setups that are representative of typical aerospace applications. The current version of the GPU solver supports all the necessary features to run 2/3 cases, although it requires minor simplifications to the case setups. For the cases it does support, key results include GPU simulations providing a time reduction of 41-98% per iteration, an energy consumption reduction of 88-93% per iteration, a 27-73% reduction in iterations to reach convergence, a cloud computing cost reduction of 83-91% and a total cost of ownership reduction of 48-67% for systems with equivalent simulation capacity on a local cluster. If the simulation capacity demand for simulation setups that the GPU solver supports is sufficient, purchasing GPUs for CFD simulation is a cost-effective and energy-efficient solution to meet simulation capacity demands in comparison to purchasing CPUs. The speedups provided by the Ansys Fluent GPU solver can be leveraged to generate significant value in an engineering process by
enabling more design iterations, improved simulation fidelity, and faster simulation turnaround, compared to the CPU solver
Modeling Handball-Induced Head Injuries: Developing a Model of a Handball for Evaluating Concussion Risk and the Effectiveness of Protective Gear for Handball Players
No full textConcussions are a major problem in many sports, and handball is no exception.
While some efforts have been made to reduce the prevalence of concussions, the
risks remain high, with several cases of players ending their careers in their early
twenties due to repeated concussions. Despite this, players are not allowed any form
of head gear or other protection by the International Handball Federation rule set.
This thesis developed an explicit Finite Element handball model for LS Dyna based
on coupon material test data and dynamic impact test data. The risk of concussion
for a handball player was then estimated by modeling ball-to-head contacts using the
new ball model and a state-of-the-art Human Body Model (HBM), the VIVA+, and
the Injury Risk Function (IRF) DAMAGE. The use of HBM and IRF in quantifying
concussion risks is a well-established methodology, particularly in the automotive
industry.
The standing average male VIVA+ model was used, modified to contain only head
and neck, constrained at the lower neck. Four impact scenarios at 110km/h were
simulated, where results were used to quantify mild Traumatic Brain Injury (mTBI)
risk for each scenario. Two of these impact scenarios were tested under varying
coefficients of friction between ball and head, with and without a foam sliding layer,
representing an abstraction of head protective gear. The risk of mTBI was estimated
to 10 - 35% without headgear. With headgear, the results indicate that a substantial
reduction of up to 25% for head-on impacts and 50% for lateral impacts in mTBI
risk can be achieved with the appropriate headgear design
Tendon Cells from Younger and Older Patients Exhibit Similar Mechanical and Biological Properties in a 3D In Vitro Model
No full textDeveloping an Evaluation Framework for Early Stage Technologies A Case Study in the Aerospace Industry
No full textIn today’s rapidly evolving business landscape, innovation and R&D activities are
crucial for competitive advantage. Many firms struggle with their innovation performance and especially at the early decisions, which often decide the outcome. This
thesis delves into the evaluation of early stage technologies at a tier 1 supplier in
the aerospace industry, focusing on the Research & Technology division. Today’s
evaluation process is generally effective, but fragmented and lacks a structured approach, clear tools, and a decision-making process.
Through a case study approach, the thesis utilized a literature review, internal in terviews, and workshops. First, with data obtained from the literature review and
interviews, the thesis investigated what criteria are necessary when evaluating early
stage aerospace engine technologies. Secondly, drawing from the fields of New Prod uct Development, tools and methods were assessed and tested within Research &
Technology through workshops. Lastly, based on the findings a systematic process
was developed when evaluating early stage technologies.
The research identified 22 criteria distributed within 4 overarching areas for a comprehensive evaluation. A modified scoring model was deemed the most relevant
utilizing rating scales and which stimulates discussion. Evaluators conduct individual evaluations of the technology before being merged with all evaluations. If
large deviations between the evaluators occur, discussions are launched. Further,
to enhance flexibility the ability to assign weights were introduced as well as visual
representations of technologies currently in the portfolio to maintain a balanced and
strategically aligned portfolio. By utilizing the framework proposed in this thesis
the company can incorporate a systematic way to assess early stage technologies
individually and across the portfolio
Risk assessment for autonomous vehicles in occluded areas: Identifying and mapping potential risks originating from occluded areas
No full textSimilar to an experienced human driver, an automated vehicle needs to take the unknown into account while driving. This work proposes a way to perform risk assessment for autonomous vehicles when approaching areas that are occluded from the vehicle’s sensor. The approach uses an occupancy grid and map data to describe risks associated with occluded areas. A risk assessment algorithm evaluates how occluded road users may pose a risk to safe driving, using the reachability of occluded road users through the use of phantom objects (e.g. objects that may be in an occluded area, with associated speeds, directions, etc.). The phantom object’s predicted movement is modelled with an implementation of the A* algorithm, and this work incorporates a cost point in the A*’s heuristic function, to create realistic turning trajectories. The reachability is expressed in a grid as a probability of reach given a time horizon. The method may help automated vehicles evaluate the potential existence and movement of phantom objects to heighten their perception when navigating through an environment with occluded areas. Evaluation of the method was made on data collected from real traffic scenarios and self-created synthetic data depicting occluded scenarios in urban environments. Qualitative analysis of the results show how the method reliably finds occluded phantom objects and predicts reasonable trajectories of their movement. Successfully assigning the probability of a reach for each cell in the grid that a phantom object’s predicted trajectory traverses.
Ke
Hall E-Smart palm sensor for autonomous home-based hand rehabilitation
No full textHand rehabilitation is an important process for restoring functionality and quality of life, targeting injuries such as "avocado hand", with tendon or nerve damage. In such applications, estimation of finger forces plays a vital role in understanding tactile interactions and hand function. This thesis presents the design and development of a tactile sensor plate based on Hall-effect sensors, specifically the MLX90393, for detecting and analyzing finger forces and gestures. The primary goal is to design a sensor plate able to perform precise measurement of magnitude and directions of the forces exerted by the fingers, providing real-time feedback to users on 3D force distribution, contact areas, and tactile gestures to enhance interaction analysis and control.
The tactile sensor plate design includes a matrix of 48 Hall-effect sensors, and one Inertial Measurement Unit, embedded within a compact, silicone-coated plate designed for comfort, durability, and ease of cleaning. By collecting sensor data at a sampling rate of 100 Hz, the plate is capable of estimating both normal and shear forces, important for understanding and guiding finger movements. A machinelearning Random Forest model was developed to process sensor data and predict applied force parameters. The results demonstrate the sensor plate’s ability to measure force magnitudes and directions with high accuracy, achieving an R2 value of approximately 96% − 99% for force prediction in specific test cases.
The research addresses challenges such as hysteresis caused by the silicone layer, calibration for diverse hand sizes, and environmental conditions like magnetic field variations. The system architecture, built on a Raspberry Pi 5 as a main processing unit, opens possibilities for future enhancements, including the integration of additional sensors and improved calibration methods.
This study contributes to the field of tactile sensing and force measurement by offering a precise detection of finger movements. The proposed system can estimate the applied force making a foundation for supporting guided exercises, allowing a better recovery process, and reducing dependence on physiotherapists. It also aligns with sustainable design principles. Future work will focus on enhancing sensor accuracy, refining the graphical user interface, and exploring potential applications in various tactile sensing and human-machine interaction scenarios
Diagnosing Brachycephalic Obstructive Airway Syndrome in Dogs Using Computer Vision and Machine Learning
No full textBrachycephalic Obstructive Airway Syndrome (BOAS) is a breathing disorder that
is common among dogs of certain breeds, impairing their quality of life. Diagnosing
BOAS requires licensed veterinarians to undergo specialized training, which limits
accessibility. There is demand for a more accessible solution, which machine learning
could provide. This thesis evaluates two different approaches of machine learning to
explore the possibility of classifying BOAS based on audio recorded from Android
mobile phones, using Python. The first approach extracts signal features from the
audio, which are used to train a simple machine learning model. The second approach
relies on computer vision, using spectrograms; visual representations of the
audio signals, to train a Convolutional Neural Network (CNN). Due to limited data,
I employed augmentation techniques to artificially expand the dataset for training
the models. My findings suggest that the spectrogram-based model is better suited
for the problem, with a perfect prediction accuracy when the dogs were recorded
after a short exercise test, suggesting strong performance under that condition. For
dogs at rest, this model achieved an 81.7% accuracy, indicating somewhat promising
results even under less favourable conditions. However, due to the limited dataset,
the predictive performance was evaluated on few samples, and therefore additional
data is needed for a more robust conclusion. Furthermore, one particular augmentation
technique designed to account for differences in the recording devices’ frequency
response enhanced the model’s general performance and could be further refined to
improve accuracy, especially for dogs at rest
Flow: an RE & UX analysis on a baggage handling SCADA system
No full textThe thesis presents a redesign of a Supervisory Control and Data Acquisition (SCADA) interface used for baggage handling at a Swedish airport company, with the aim of improving usability and system efficiency. It was found that the current system meets most of the users’ needs but some adaptation was needed for a better user experience. The previous developers have used patterns for no particular reason other than that they should. Among the methods used to evaluate the UX, interviews were found to be the most effective and should therefore be used in future evaluations.
Applying user experience (UX) and requirements engineering (RE) together, a prototype was created using Figma and a set of functional and non-functional requirements (FRs and NFRs) was formulated as an additive to existing requirements. Data on the functionality as well as user feedback was collected using a static UX analysis, a user experience questionnaire (UEQ), participant observations, and contextual inquiry as well as phenomenological interviews. Seven users answered the questionnaire, and seven users were observed and interviewed in total. A qualitative data analysis method was performed to categorise data into themes, called thematic analysis, to identify user pain points and key areas to improve. Coding and conclusions drawn from the data were validated through peer reviews. To ensure that the proposed designs and requirements were ethically sound, they were reviewed against the Association for Computing Machinery (ACM) code of ethics. To ensure relevance, validation interviews were conducted with seven users. Following the feedback gained from this, the proposed design and requirements were revised and adjusted accordingly.
The project was based on an insight into usability issues in the current system experienced by baggage handling employees. The study aimed to evaluate the current system and suggest design improvements that aim to improve both operator satisfaction and system efficiency
Motstöd för granitkantstöd i infrastrukturprojekt - en studie av ekologiska och ekonomiska hållbarhetsaspekter
No full textNär granitkantstöd sätts krävs ett motstöd för att stenen skall stå stabilt och motstå
eventuella belastningar, till exempel från fordon eller väder. Idag används olika
metoder med olika material för att fästa ett kantstöd. De tre traditionella och mest
använda materialen för motstöd till granitkantstöd är krossmaterial, asfaltsmassa och
betong. En ny metod där stål används som motstödsmaterial har utvecklats. Med nu ett
flertal olika metoder att välja mellan för att stödja granitkantstöd och på så sätt krävs
det en analys för att ta reda på hur valet av motstöd kan påverka hållbarheten i ett
infrastrukturprojekt.
Hållbarhet är en viktig del inom byggsektorn vilket har varit den drivande faktorn för
att i denna rapport analysera hur ett motstöd för granitkantstöd kan bidra till mer
hållbara infrastrukturprojekt i framtiden. Metoden som har använts är att jämföra de
olika motstödsmaterialen utifrån de två olika hållbarhetsdimensioner som fokuserar på
ekologi och ekonomi. Med intervjuer av platschefer, projektörer och beställare har en
grund skapats för hur motstöd traditionellt väljs samt hur de olika motstödsmetoderna
ofta utförs. Med hjälp av detta har ett teoretiskt värde enligt principritningar i AMA
tagits fram och jämförts med ett verkligare praktiskt värde.
Resultaten av de analyser som gjorts visar på att betong släpper ut mycket
koldioxidekvivalenter som påverkar klimatet negativt i jämförelse med de andra
materialen. Stål kan påverka klimatet i en mycket lägre grad men då kostanden kan bli
högre så måste de två hållbarhetsaspekterna värderas mot varandra