Open Access

Dieses erfolgreiche, didaktisch ausgereifte Lehrbuch wurde zuletzt fachlich in der Gasdynamik, der Behandlung des Verdichtungsstoßes und bei den verlustbehafteten Strömungen ergänzt. Außerdem wurde der allgemeine Energiesatz aufgenommen. Das Buch zeichnet sich durch eine prägnant kurze, mathematisch verständliche und anwendbare Einführung in die Grundlagen aus und gibt dem Leser Gelegenheit, sein Verständnis durch Übungen zu vertiefen. Die 7. Auflage enthält einige Verbesserungen bei Abbildungen und die Berichtigung von Druckfehlern in Text- und Formelsatz. Das Literaturverzeichnis wurde ergänzt und aktualisiert.

Dieses ausgereifte Lehrbuch bringt in prägnant kurzer und mathematisch verständlicher Darstellung die strömungstechnischen Grundlagen. In der aktuellen Auflage wurde ein Abschnitt zu Dissipation und Viskosen Potentialströmungen ergänzt. Aufgaben mit Lösungen helfen den Lernstoff richtig anzuwenden und fördern das Verständnis.

Die Navier-Stokes Gleichungen bilden mit der Energiegleichung die Basis zur Beschreibung reibungsbehafteter Strömungen. Kennzahlen bilden die Grundlage der Ähnlichkeitsbetrachtungen und Modellgesetze. Lösungen werden für laminare und turbulente Strömungen ermittelt. Der Impulssatz dient zur Berechnung von Kraftwirkungen. Druckverluste bei Durchströmungen und Strömungswiderstände bei Umströmungen werden an Beispielen ermittelt. Die Grenzschichttheorie findet bei hohen Reynoldszahlen Anwendung.

Ausgehend von eindimensionalen reibungsfreien Strömungen wird die Bernoulli-Gleichung und die Energiebilanz hergeleitet. Mit der Eulerschen Betrachtungsweise wird der Unterschied zwischen stationären und zeitabhängigen Strömungen verdeutlicht und an zahlreichen Beispielen angewandt. Die zweidimensionalen reibungsfreien und inkompressiblen Strömungen werden mit der Potenzialtheorie behandelt. Die Lösungseigenschaften werden am Beispiel der Zylinderumströmung ohne und mit Zirkulation aufgezeigt.

Modern society is more than ever striving for digital connectivity -- everywhere and at any time, giving rise to megatrends such as the Internet of Things (IoT). Already today, 'things' communicate and interact autonomously with each other and are managed in networks. In the future, people, data, and things will be interlinked, which is also referred to as the Internet of Everything (IoE). Billions of devices will be ubiquitously present in our everyday environment and are being connected over the Internet. As an emerging technology, printed electronics (PE) is a key enabler for the IoE offering novel device types with free form factors, new materials, and a wide range of substrates that can be flexible, transparent, as well as biodegradable. Furthermore, PE enables new degrees of freedom in circuit customizability, cost-efficiency as well as large-area fabrication at the point of use. These unique features of PE complement conventional silicon-based technologies. Additive manufacturing processes enable the realization of many envisioned applications such as smart objects, flexible displays, wearables in health care, green electronics, to name but a few. From the perspective of the IoE, interconnecting billions of heterogeneous devices and systems is one of the major challenges to be solved. Complex high-performance devices interact with highly specialized lightweight electronic devices, such as e.g. smartphones and smart sensors. Data is often measured, stored, and shared continuously with neighboring devices or in the cloud. Thereby, the abundance of data being collected and processed raises privacy and security concerns. Conventional cryptographic operations are typically based on deterministic algorithms requiring high circuit and system complexity, which makes them unsuitable for lightweight devices. Many applications do exist, where strong cryptographic operations are not required, such as e.g. in device identification and authentication. Thereby, the security level mainly depends on the quality of the entropy source and the trustworthiness of the derived keys. Statistical properties such as the uniqueness of the keys are of great importance to precisely distinguish between single entities. In the past decades, hardware-intrinsic security, particularly physically unclonable functions (PUFs), gained a lot of attraction to provide security features for IoT devices. PUFs use their inherent variations to derive device-specific unique identifiers, comparable to fingerprints in biometry. The potentials of this technology include the use of a true source of randomness, on demand key derivation, as well as inherent key storage. Combining these potentials with the unique features of PE technology opens up new opportunities to bring security to lightweight electronic devices and systems. Although PE is still far from being matured and from being as reliable as silicon technology, in this thesis we show that PE-based PUFs are promising candidates to provide key derivation suitable for device identification in the IoE. Thereby, this thesis is primarily concerned with the development, investigation, and assessment of PE-based PUFs to provide security functionalities to resource constrained printed devices and systems. As a first contribution of this thesis, we introduce the scalable PE-based Differential Circuit PUF (DiffC-PUF) design to provide secure keys to be used in security applications for resource constrained printed devices. The DiffC-PUF is designed as a hybrid system architecture incorporating silicon-based and inkjet-printed components. We develop an embedded PUF platform to enable large-scale characterization of silicon and printed PUF cores. In the second contribution of this thesis, we fabricate silicon PUF cores based on discrete components and perform statistical tests under realistic operating conditions. A comprehensive experimental analysis on the PUF security metrics is carried out. The results show that the silicon-based DiffC-PUF exhibits nearly ideal values for the uniqueness and reliability metrics. Furthermore, the identification capabilities of the DiffC-PUF are investigated and it is shown that additional post-processing can further improve the quality of the identification system. In the third contribution of this thesis, we firstly introduce an evaluation workflow to simulate PE-based DiffC-PUFs, also called hybrid PUFs. Hereof, we introduce a Python-based simulation environment to investigate the characteristics and variations of printed PUF cores based on Monte Carlo (MC) simulations. The simulation results show, that the security metrics to be expected from the fabricated devices are close to ideal at the best operating point. Secondly, we employ fabricated printed PUF cores for statistical tests under varying operating conditions including variations in ambient temperature, relative humidity, and supply voltage. The evaluations of the uniqueness, bit aliasing, and uniformity metrics are in good agreement with the simulation results. The experimentally determined mean reliability value is relatively low, which can be explained by the missing passivation and encapsulation of the printed transistors. The investigation of the identification capabilities based on the raw PUF responses shows that the pure hybrid PUF is not suitable for cryptographic applications, but qualifies for device identification tasks. The final contribution is to switch to the perspective of an attacker. To judge on the security capabilities of the hybrid PUF, a comprehensive security analysis in the manner of a cryptanalysis is performed. The analysis of the entropy of the hybrid PUF shows that its vulnerability against model-based attacks mainly depends on the selected challenge building method. Furthermore, an attack methodology is introduced to assess the performances of different mathematical cloning attacks on the basis of eavesdropped challenge-response pairs (CRPs). To clone the hybrid PUF, a sorting algorithm is introduced and compared with commonly used supervised machine learning (ML) classifiers including logistic regression (LR), random forest (RF), as well as multi-layer perceptron (MLP). The results show that the hybrid PUF is vulnerable against model-based attacks. The sorting algorithm benefits from shorter training times compared to the ML algorithms. If the eavesdropped CRPs are erroneous, the ML algorithms outperform the sorting algorithm.