Filtern
Erscheinungsjahr
- 2022 (129) (entfernen)
Dokumenttyp
- Beitrag zu einem Tagungsband (129) (entfernen)
Sprache
- Englisch (129) (entfernen)
Schlagworte
- Concrete (8)
- Ultrasound (7)
- Structural health monitoring (5)
- Additive manufacturing (4)
- Corrosion (4)
- Monitoring (4)
- Non-destructive testing (4)
- Characterisation (3)
- Civil engineering (3)
- Digitalisation (3)
Organisationseinheit der BAM
- 8 Zerstörungsfreie Prüfung (62)
- 7 Bauwerkssicherheit (26)
- 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen (22)
- 9 Komponentensicherheit (19)
- 8.4 Akustische und elektromagnetische Verfahren (12)
- 8.0 Abteilungsleitung und andere (11)
- 5 Werkstofftechnik (8)
- 7.2 Ingenieurbau (8)
- 8.6 Faseroptische Sensorik (8)
- 3 Gefahrgutumschließungen; Energiespeicher (7)
Corrosive geothermal brines are a major challenge to geothermal power-plants. For cost reasons, plant designers favorize low alloyed steels, e.g., carbon steel, which are susceptible to uniform and localized corrosion when exposed to geothermal brines having acidic and saline properties. To solve such problem, coatings or inhibitors would be a protective solution as an alternative to the use of high alloyed materials.
This study investigated a coating system consisting of polyaniline/silicon dioxide basing on locally available resources. Protection against corrosion of carbon steel is shown by long-term exposure and electrochemical tests of coated carbon steels, performed in an artificial acidic and saline geothermal brine, comparable to real conditions at a site in Indonesia.
Therefore, an integrated coating system is presented for corrosion protection, combining the electro-chemical functionality of polyaniline and the physical advantages of silica.
Attenuation of ultrasonic signals in concrete has the potential to carry much information about the microstructure of the material. In this work a series of concrete specimens of varying porosities and pore size distributions were internally imaged with x-ray computed tomography (CT), and then subsequently examined with throughtransmission ultrasound. The CT images were used to quantify both capillary porosity of cement paste as well as internal interfaces that are likely to produce elastic wave scattering. Ultrasound signals were represented as a diffusion process such that absorption and scattering attenuation could be isolated. As implemented, the diffusion model was a poor predictor of capillary porosity, while diffusivity was a reasonable predictor of scattering interfaces. Diffusivity was found to scale extremely well with small scale porosity, which made it a good predictor of compressive strength.
Reliability assessment of Structural Health Monitoring (SHM) systems
poses new challenges pushing the research community to address many questions which are still open. For guided wave-based SHM it is not possible to evaluate the system performance without taking into account the target structure and applied system parameters. This range of variables would result in countless measurements.
Factors like environmental conditions, structural dependencies and wave characteristics demand novel solutions for performance analysis of SHM systems compared to those relying on classical non-destructive evaluation. Such novel approaches typically require model-assisted investigations which may not only help to explain and understand performance assessment results but also enable complete studies without costly experiments. Within this contribution, a multi input multi output approach using a sparse transducer array permanently installed on a composite structure to excite and sense guided waves is considered. Firstly, the method and the analysis of path-based performance assessment are presented considering an open-access dataset from the Open Guided Wave platform. Then, a performance analysis of a guided wave-based SHM system using Probability of Detection is presented. To explain some unexpected results, the model-assisted investigations are used to understand the physical phenomena of wave propagation in the test specimen including the interaction with damage. Finally, issues and future steps in SHM systems’ performance assessment and their development are discussed.
When it comes to higher accuracies, new technologies and real applications in additive manufacturing, there is one topic which cannot be avoided: The material response on the chosen processing parameters and its agreement and correspondence with literature data of the wrought material grade counterpart. In industrial Additive Manufacturing (AM) standards in terms of printing parameters, protection gas atmospheres or powder handling instructions are not obligatory. Therefore, the question must be answered whether the AM process is reproducible and reliable over different printing companies. This was the motivation to realize a round robin test between 8 European printing companies and academic partners. The consortium had printed and tested fatigue and tensile testing bars under plant-specific conditions. A commonly used cast aluminum alloy, AlSi10Mg, was chosen as test material for the PBF-LB/M process. Differences of the results between the partners and the scatter itself were discussed in detail.
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders, part of the energy-intensive clinker production process can be dispensed. However, as numerous raw materials are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced binder formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of alkali-activated binder formulations can significantly accelerate research.
The "Sequential Learning App for Materials Discovery" (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings.
Our material database already includes more than 120,000 data points of alternative binders and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials.
Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.
This work focuses on the design and fabrication of low-cost and fast-response of an electronic nose (E-nose) based on semiconductor gas sensors, for discriminating some synthetic gases such as ammonia (NH3), ethanol (C2H5OH), and hydrogen sulfide (H2S). Additionally, the capability of separating different concentration levels of each considered gases was checked. Dataset treatment of E-nose by using Principal Component Analysis (PCA) showed a good discrimination of the different synthetic gases. Furthermore, perfect classification was reached of different concentration levels of the analysed gases by using Discriminant Function Analysis (DFA). In the light of these results, it could be stated that the developed E-nose system constitutes an inexpensive, rapid, simple to use, and efficient tool for synthetic gases detection.
The studied aluminium alloy is EN AW-2618A (2618A). It is very widely used for exhaust gas turbo-charger compressor wheels. Due to long operating times, high cycle fatigue (HCF) and material aging under the influence of temperatures up to 230 °C is particularly relevant for the wheels. The wheels are typically milled from round wrought blanks. From such round blanks, different testpieces are extracted and a comprehensive series of HCF tests is conducted at room temperature. The tests investigate the materials fatigue performance in the T61 state for two load-ratios, namely R = -1 and R = 0.1. Additionally, two overaged material states are tested, accounting for the aging process the material undergoes during long operating times at high temperatures. The experimental results are evaluated and compared to each other.
Furthermore, the design process of notched specimens is presented. With the notched specimens, it is aimed to quantify the notch sensitivity of the material. Relating thereto, two potential model parameters for the fatigue lifetime model are introduced. Finally, the extended research data management in this project is highlighted and its advantages for sustainable use in material science and engineering applications are shown.
To improve ultrasonic imaging of concrete structures, we transferred a seismic migration technique, the Reverse Time Migration (RTM), to non-destructive testing. A 2D elastic RTM algorithm was tested on synthetic ultrasonic echo data. Compared to the typically used synthetic aperture focusing technique (SAFT) as well as our acoustic RTM algorithm, the presented elastic RTM results show an enhancement in imaging vertical reflectors and complex features inside the 2D numerical concrete model.
Temperature compensation strategies play a key role in the implementation of guided wave based structural health monitoring approaches. The varying temperature influences the performance of the inspection system inducing false alarms or missed detection, with a consequent reduction of reliability. This paper quantitatively assesses two temperature compensation methods, namely the optimal baseline selection (OBS) and the baseline signal stretch (BSS), with the aim to extend their use to the case of distributed sensor networks (DSN). The effect of temperature separation between baseline time-traces in OBS and BSS are investigated considering multiple couples of sensors employed in the DSN. A decision strategy that uses frequent value warning to define the optimal baseline or stretching parameter is found to be effective analyzing data from two several experiments, which use different frequency analysis with either predominantly A0 mode or S0 mode data or both. The focus is given on the fact that different paths are available in a sensor network and several possible combinations of results are available. Nonetheless, introducing a frequent value warning it is possible to increase the efficiency of the OBS and BSS approach making use of fewer signal processing algorithms. In addition, the effectiveness of those approach is quantified using damage indicators as metric, which confirms that the performance of OBS and BSS quantitatively agree with predictions and also demonstrate that the use of compensation strategies improve detectability of damage with a higher reliability of the system.
The work on Tribology, digitalisation, and the harmonised procedures developed in i-TRIBOMAT Open Innovation Testbed was presented. The project develops procedures and shares ideas on how to deal with characterisation techniques and provide services to end users in different places and locations, which is realised by adopting a common and widely agreed procedures. i-TRIBOMAT is active also via YouTube platform to enhance communication and spread the word amongst the interested parties and society.