1,110 research outputs found
Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics
It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been
emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations
Modern meat: the next generation of meat from cells
Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community.
The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World.
The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia
A reduced order modeling methodology for the parametric estimation and optimization of aviation noise
The successful mitigation of aviation noise is one of the key enablers of sustainable aviation growth. Technological improvements for noise reduction at the source have been countered by increasing number of operations at most airports. There are several consequences of aviation noise including direct health effects, effects on human and non-human environments, and economic costs. Several mitigation strategies exist including reduction of noise at source, land-use planning and management, noise abatement operational procedures, and operating restrictions. Most noise management programs at airports use a combination of such mitigation measures. To assess the efficacy of noise mitigation measures, a robust modeling and simulation capability is required. Due to the large number of factors which can influence aviation noise metrics, current state-of-the-art tools rely on physics-based and semi-empirical models. These models help in accurately predicting noise metrics in a wide range of scenarios; however, they are computationally expensive to evaluate. Therefore, current noise mitigation studies are limited to singular applications such as annual average day noise quantification. Many-query applications such as parametric trade-off analyses and optimization remain elusive with the current generation of tools and methods.
There are several efforts documented in literature which attempt to speed up the process using surrogate models. Techniques include the use of pre-computed noise grids with calibration models for non-standard conditions. These techniques are typically predicated on simplifying assumptions which greatly limit the applicability of such models. Simplifying assumptions are needed to downsize the number influencing factors to be modeled and make the problem tractable. Existing efforts also suffer due to the inclusion of categorical variables for operational profiles which are not conducive to surrogate modeling.
In this research, a methodology is developed to address the inherent complexities of the noise quantification process, and thus enable rapid noise modeling capabilities which can facilitate parametric trade-off analysis and optimization efforts. To achieve this objective, a research plan is developed and executed to address two major gaps in literature. First, a parametric representation of operational profiles is proposed to replace existing categorical descriptions. A technique is developed to allow real-world flight data to be efficiently mapped onto this parametric definition. A trajectory clustering method is used to group similar flights and representative flights are parametrized using an inverse-map of an aircraft performance model. Next, a field surrogate modeling method is developed based on Model Order Reduction techniques to reduce the high dimensionality of computed noise metric results. This greatly reduces the complexity of data to be modeled, and thus enables rapid noise quantification. With these two gaps addressed, the overall methodology is developed for rapid noise quantification and optimization. This methodology is demonstrated on a case study where a large number of real-world flight trajectories are efficiently modeled for their noise results. As each such flight trajectory has a unique representation, and typically lacks thrust information, such noise modeling is not computationally feasible with existing methods and tools. The developed parametric representations and field surrogate modeling capabilities enable such an application.Ph.D
Transfer Learning of Deep Learning Models for Cloud Masking in Optical Satellite Images
Los satĂ©lites de observaciĂłn de la Tierra proporcionan una oportunidad sin precedentes para monitorizar nuestro planeta a alta resoluciĂłn tanto espacial como temporal. Sin embargo, para procesar toda esta cantidad creciente de datos, necesitamos desarrollar modelos rápidos y precisos adaptados a las caracterĂsticas especĂficas de los datos de cada sensor. Para los sensores Ăłpticos, detectar las nubes en la imagen es un primer paso inevitable en la mayorĂa de aplicaciones tanto terrestres como oceánicas. Aunque detectar nubes brillantes y opacas es relativamente fácil, identificar automáticamente nubes delgadas semitransparentes o diferenciar nubes de nieve o superficies brillantes es mucho más difĂcil. Además, en el escenario actual, donde el nĂşmero de sensores en el espacio crece constantemente, desarrollar metodologĂas para transferir modelos que funcionen con datos de nuevos satĂ©lites es una necesidad urgente. Por tanto, los objetivos de esta tesis son desarrollar modelos precisos de detecciĂłn de nubes que exploten las diferentes propiedades de las imágenes de satĂ©lite y desarrollar metodologĂas para transferir esos modelos a otros sensores. La tesis está basada en cuatro trabajos los cuales proponen soluciones a estos problemas. En la primera contribuciĂłn, "Multitemporal cloud masking in the Google Earth Engine", implementamos un modelo de detecciĂłn de nubes multitemporal que se ejecuta en la plataforma Google Earth Engine y que supera los modelos operativos de Landsat-8. La segunda contribuciĂłn, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", es un caso de estudio de transferencia de un algoritmo de detecciĂłn de nubes basado en aprendizaje profundo de Landsat-8 (resoluciĂłn 30m, 12 bandas espectrales y muy buena calidad radiomĂ©trica) a Proba-V, que tiene una resoluciĂłn de 333m, solo cuatro bandas y una calidad radiomĂ©trica peor. El tercer artĂculo, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", propone aprender una transformaciĂłn de adaptaciĂłn de dominios que haga que las imágenes de Proba-V se parezcan a las tomadas por Landsat-8 con el objetivo de transferir productos diseñados con datos de Landsat-8 a Proba-V. Finalmente, la cuarta contribuciĂłn, "Towards global flood mapping onboard low cost satellites with machine learning", aborda simultáneamente la detecciĂłn de inundaciones y nubes con un Ăşnico modelo de aprendizaje profundo, implementado para que pueda ejecutarse a bordo de un CubeSat (Ď•Sat-I) con un chip acelerador de aplicaciones de inteligencia artificial. El modelo está entrenado en imágenes Sentinel-2 y demostramos cĂłmo transferir este modelo a la cámara del Ď•Sat-I. Este modelo se lanzĂł en junio de 2021 a bordo de la misiĂłn WildRide de D-Orbit para probar su funcionamiento en el espacio.Remote sensing sensors onboard Earth observation satellites provide a great opportunity to monitor our planet at high spatial and temporal resolutions. Nevertheless, to process all this ever-growing amount of data, we need to develop fast and accurate models adapted to the specific characteristics of the data acquired by each sensor. For optical sensors, detecting the clouds present in the image is an unavoidable first step for most of the land and ocean applications. Although detecting bright and opaque clouds is relatively easy, automatically identifying thin semi-transparent clouds or distinguishing clouds from snow or bright surfaces is much more challenging. In addition, in the current scenario where the number of sensors in orbit is constantly growing, developing methodologies to transfer models across different satellite data is a pressing need. Henceforth, the overreaching goal of this Thesis is to develop accurate cloud detection models that exploit the different properties of the satellite images, and to develop methodologies to transfer those models across different sensors. The four contributions of this Thesis are stepping stones in that direction. In the first contribution,"Multitemporal cloud masking in the Google Earth Engine", we implemented a lightweight multitemporal cloud detection model that runs on the Google Earth Engine platform and which outperforms the operational models for Landsat-8. The second contribution, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", is a case-study of transferring a deep learning based cloud detection algorithm from Landsat-8 (30m resolution, 12 spectral bands and very good radiometric quality) to Proba-V, which has a lower{333m resolution, only four bands and a less accurate radiometric quality. The third paper, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", proposes a learning-based domain adaptation transformation of Proba-V images to resemble those taken by Landsat-8, with the objective of transferring products designed on Landsat-8 to Proba-V. Finally, the fourth contribution, "Towards global flood mapping onboard low cost satellites with machine learning", tackles simultaneously cloud and flood water detection with a single deep learning model, which was implemented to run onboard a CubeSat (Ď•Sat-I) with an AI accelerator chip. In this case, the model is trained on Sentinel-2 and transferred to theĎ•Sat-I camera. This model was launched in June 2021 onboard the Wild Ride D-Orbit mission in order to test its performance in space
DESIGN AND VERIFICATION OF AUTONOMOUS SYSTEMS IN THE PRESENCE OF UNCERTAINTIES
Autonomous Systems offer hope towards moving away from mechanized, unsafe, manual, often inefficient practices. The last decade has seen several small, but important, steps towards making this dream into reality. These advancements have helped us to achieve limited autonomy in several places, such as, driving, factory floors, surgeries, wearables, and home assistants, etc. Nevertheless, autonomous systems are required to operate in a wide range of environments with uncertainties (viz., sensor errors, timing errors, dynamic nature of the environment, etc.). Such environmental uncertainties, even when present in small amounts, can have drastic impact on the safety of the system—thus hampering the goal of achieving higher degree of autonomy, especially in safety critical domains. To this end, the dissertation shall discuss formaltechniques that are able to verify and design autonomous systems for safety, even under the presence of such uncertainties, allowing for their trustworthy deployment in the real world. Specifically, the dissertation shall discuss monitoring techniques for autonomous systems from available (noisy) logs, and safety-verification techniques of autonomous system controllers under timing uncertainties. Secondly, using heterogeneous learning-based cloud computing models that can balance uncertainty in output and computation cost, the dissertation will present techniques for designing safe and performance-optimal autonomous systems.Doctor of Philosoph
The Impact of Additive Manufacturing on Supply Chains and Business Models: Qualitative Analyses of Supply Chain Design, Governance Structure, and Business Model Change
Recent global crises like the COVID-19 pandemic challenge traditional global supply chains (SCs). Their disaggregated, “fine-sliced” character comes with a high risk of disruption, and current supply bottlenecks (e.g., the chip shortage in the automotive industry) demonstrate that there is often no quick fix. Firms are increasingly under pressure to react and (re-)design their SCs to increase their resilience. Additive manufacturing (AM) technologies are acclaimed for their potential to foster the shift from global SCs to shorter, decentralized, and more resilient SCs. The key feature of AM technologies lies in their inherently digital and flexible nature. Their specific characteristics are envisioned to enable location-independent manufacturing close to or even at the point of demand and lead to a commoditization of manufacturing infrastructure for flexible outsourcing to local partners. Moreover, AM technologies are expected to revolutionize the way firms do business and put traditional business models at stake.
This doctoral thesis is motivated by the outlined potential of AM and the resulting impact on firms’ supply chain design (SCD) and business model choices. The extant literature raises high expectations for AM. However, concrete and real-world insights from specific application domains are still scarce. This thesis seeks to fill the gap between high-level literature-based visions and currently emerging realistic business models and SCDs for AM. Thereby, AM is understood as a potential intervention emanating from outside firms and requiring them to react by realigning their business models and SC structures to maintain a fit. This thesis aims to build an in-depth understanding of these mechanisms and, hence, of the inner causal processes involved in the AM SCD and business model choices. This concentration on the rationales and underlying behavioral patterns is formalized with primarily exploratory (how and why) research questions that are addressed with qualitative research methodologies, mainly case study research and grounded theory. These methodological practices are applied in the industrial AM context, entailing an embedding of this thesis in challenging industries where AM applications have already started to create value (i.e., in the aerospace, rail, automotive, and machinery and equipment industries). The selected research approaches are mostly inductive and, hence, strongly driven by the data collected from this context (e.g., in interviews, by reviewing documents, and by analyzing websites). Additionally, this thesis relies on grand theories, namely transaction cost economics, the resource-based view, and configuration theory, to discuss the findings in their light and to interpret and distill nuances of these theories for their application in the industrial AM context.
This thesis is cumulative, consisting of four studies that form its main body. These studies are organized in two parts, part A and part B, since two domains of strategic decisions are targeted jointly, the business model development (part A) and AM SCD choice (part B) for industrial AM. Different perspectives are associated with the two parts. Logistics service providers (LSPs) are in a critical position to develop AM business models. Based on the expected shift to decentralized, shorter SCs, the traditional business models of LSPs are at risk, and their inherent customer orientation puts them under pressure to adjust to their customers’ needs in AM. In part A, study A.1 applies a process-based perspective to build a broad understanding of how LSPs currently respond to AM and consumer-oriented polymer 3D printing with specific AM activities. It proposes six profiles of how LSPs leverage AM, both as users for their in-house operations and as developers of AM-specific services for external customers. A key finding is that the initiated AM activities are oftentimes strongly based on LSPs’ traditional resources. Only a few LSPs are found whose AM activities are detached from their traditional business models to focus on digital platform-based services for AM. In contrast to the process-based perspective and focus on business model dynamics in study A.1, study A.2 takes an output perspective to propose six generic business model configurations for industrial AM. Each configuration emerges from the perspective of LSPs and is reflected by their potential partners/competitors and industrial customers. Study A.2 explores how the six generic configurations fit specific types of LSPs and how they are embedded in a literature-based service SC for industrial AM. In combination, studies A.1 and A.2 provide a comprehensive understanding of how LSPs are currently reacting to AM and an empirically grounded perspective on “finished” AM business models to evaluate and refine literature-based visions.
Part B of this thesis is devoted to the mechanism of (re-)designing SCs for AM, which is investigated from the perspective of focal manufacturing firms based on their dominant position in SCs. Two dimensions are used to characterize AM SCDs, their horizontal scope (geographic dispersion) and vertical scope (governance structure). The combination of both dimensions is ideally suited to capture the literature-based vision of shorter, decentralized AM SCs (horizontal scope) with eased outsourcing to local partners (vertical scope). Study B.1 takes a firm-centric perspective to develop an in-depth understanding for AM make-or-buy decisions of manufacturing firms, the outcomes of which determine the SC governance structure. This study elaborates how the specific (digital and emerging) traits of industrial AM technologies modify arguments of grand theories that explain make-or-buy decisions in the “analog” age. In comparison, study B.2 shifts from a firm-centric to a network perspective to rely on both dimensions for investigating cohesive AM SCD configurations. More specifically, study B.2 explores four polar AM SCD configurations and reveals manufacturing firms’ rationales for selecting them. Thereby, it builds an understanding for why manufacturing firms currently have valid reasons to implement industrial AM in-house or distributed in a secure, firm-owned network. As a result, combining both studies provides an understanding of why manufacturing firms currently select specific governance structures for industrial AM and opt for SCDs that differ from the literature-based vision of decentralized, outsourced AM.
Overall, this thesis positions itself as theory-oriented research that also aims at supporting managers of manufacturing firms and LSPs in making informed decisions when implementing AM in their SCs and developing AM-based business models. The three studies A.1, A.2, and B.2 contribute to initial theory building on how and why specific AM business models and SCDs emerge. With their focus on developing an understanding for the causal processes (how and why) and by assuming a process-based and output perspective, they can draw a line from firms’ current reactions to sound reflections on future-oriented, high-level expectations for AM. As a result, the studies significantly enrich and refine the current body of knowledge in the AM business model literature on LSPs and the operations and supply chain management literature on AM SCDs, focusing on their geographic dispersion and governance structure. This thesis further contributes with its context-specificity to building domain knowledge for industrial AM, which can serve as one “puzzle piece” for theorizing on how AM and other digitally dominated (manufacturing) technologies will shape the era of digital business models and SCs. In particular, study B.1 stands out by its focus on theory elaboration and the objective of developing contextual middle-range theory. It reveals that emerging digital AM is a setting where the argumentation of grand theories provides contradicting guidance on whether to develop AM in-house or outsource the manufacturing process. Such findings for industrial AM raise multiple opportunities for future research, among them are the comparison with other industry contexts with similar characteristics and the operationalization of the propositions developed in this thesis in follow-up quantitative decision-support models
Quantum chemistry meets astrobiology: Approximate vibrational spectral data for potential biosignatures
The chemical characterisation of exoplanet atmospheres plays a crucial role in profoundly enriching our comprehension of exoplanets. By deciphering the array of molecular species shaping the chemical composition of a given exoplanet atmosphere, we unlock invaluable insights into its chemical evolution, climate, physical dynamics, and even its potential for harbouring life.
High-resolution molecular spectroscopy provides the fundamental data needed for robustly identifying molecules in exoplanet atmospheric spectra, as recorded from ground- and/or space-based telescopes. However, the availability of high-resolution molecular spectroscopic data is limited, given its intensive and time-consuming generation process, involving costly quantum chemistry calculations and exhaustive experimental measurements. By the time this thesis was submitted, the repository of high-resolution infrared molecular spectroscopic data encompasses around 100 molecular species. This constrain considerably hinders the scope of new molecular detections in exoplanet atmospheres; if there is no spectroscopic data for a given molecule, we simply cannot find it.
To address this challenge, this thesis introduces a pioneering approach that complements the traditional method for generating high-resolution molecular spectroscopic data. By leveraging routine quantum chemistry calculations, specifically harmonic frequency calculations, this research provides a high-throughput method to rapidly generate approximate vibrational spectral data for thousands of potential biosignature molecules.
This thesis is organised into two main themes. Firstly, it focuses on refining harmonic frequency calculations for large-scale spectral data generation. Previous research lacked comprehensive benchmarking, making it difficult for users to choose appropriate levels of theory and basis set pairs (aka model chemistries) for these calculations. This work addresses such limitation by performing an extensive evaluation of over 600 model chemistries using a newly developed vibrational frequency benchmark data set. The findings from this work highlight the B97-1/def2-TZVPD model chemistry for its exceptional balance between accuracy and computational cost. Indeed, a median error of 10 cm-1 is expected in the calculated harmonic frequencies after scaling, along with good transition intensity predictions due to its accurate dipole moment calculations.
With the optimised harmonic frequency calculations in place, the second theme of the thesis focuses on generating approximate spectral data for thousands of astrochemistry-relevant molecules, specifically potential biosignatures. Employing an automated high-throughput approach, this thesis produces approximate vibrational spectra for 2743 molecules, most of which had limited, or completely absent, spectroscopic data available in the literature. While these approximate spectral data are not accurate enough to enable definitive molecular detections in exoplanet atmospheres, and cannot replace the generation of high-resolution spectroscopic data, they have powerful applications in identifying potential molecular candidates responsible for unknown spectral features. This application is firstly explored using the SO2 detection in the atmospheric spectrum of WASP-39b as a proof-of-concept, and then applied to shortlist potential molecular candidates to the 4.25 microns (2352 cm-1) spectral feature in the same spectrum, which by the time this thesis was submitted had not been assigned to any molecular species yet.
Beyond screening potential molecular candidates to unknown spectral features, this large-scale approximate spectral data generation offers broader applications, such as identifying molecules with strong absorption features that may be detectable at low abundances, and serving as a training set for machine learning predictions of vibrational frequencies.
The approximate spectral data generated in this thesis will play a crucial role in supporting our understanding of the chemical composition of exoplanet atmospheres. By highlighting potential candidates to unknown spectral features, this approach seeks to complement the generation of high-resolution molecular spectroscopic data, directing attention towards prioritised molecules warranting meticulous data acquisition. This synergy between approximate and high-resolution spectroscopic data will certainly amplify our potential to unveil the chemical composition of exoplanet atmospheres, providing directions into possible initial identifications of the more unusual molecules to emerge
3D Design Review Systems in Immersive Environments
Design reviews play a crucial role in the development process, ensuring the quality and effectiveness of designs in various industries. However, traditional design review methods face challenges in effectively understanding and communicating complex 3D models. Immersive technologies, particularly Head-Mounted Displays (HMDs), offer new opportunities to enhance the design review process. In this thesis, we investigate using immersive environments, specifically HMDs, for 3D design reviews. We begin with a systematic literature review to understand the current state of employing HMDs in industry for design reviews. As part of this review, we utilize a detailed taxonomy from the literature to categorize and analyze existing approaches. Additionally, we present four iterations of an immersive design review system developed during my industry experience. Two of these iterations are evaluated through case studies involving domain experts, including engineers, designers, and clients. A formal semi-structured focus group is conducted to gain further insights into traditional design review practices. The outcomes of these evaluations and the focus group discussions are thoroughly discussed. Based on the literature review and the focus group findings, we uncover a new challenge associated with using HMDs in immersive design reviews—asynchronous and remote collaboration. Unlike traditional design reviews, where participants view the same section on a shared screen, HMDs allow independent exploration of areas of interest, leading to a shift from synchronous to asynchronous communication. Consequently, important feedback may be missed as the lead designer disconnects from the users' perspectives. To address this challenge, we collaborate with a domain expert to develop a prototype that utilizes heatmap visualization to display 3D gaze data distribution. This prototype enables lead designers to quickly identify areas of review and missed regions. The study incorporates the Design Critique approach and provides valuable insights into different heatmap visualization variants (top view projection, object-based, and volume-based). Furthermore, a list of well-defined requirements is outlined for future spatio-temporal visualization applications aimed at integrating into existing workflows. Overall, this thesis contributes to the understanding and improvement of immersive design review systems, particularly in the context of utilizing HMDs. It offers insights into the current state of employing HMDs for design reviews, utilizes a taxonomy from the literature to analyze existing approaches, highlights challenges associated with asynchronous collaboration, and proposes a prototype solution with heatmap visualization to address the identified challenge
- …