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The oxidation of sulfur in aqueous solutions is an important process in several geochemical and industrial contexts. In concrete technology, it is particularly relevant for the protection of steel reinforcement in slag cement-based concretes, as the presence of sulfides affects the passivation of the steel and corrosion initiation in the presence of chlorides. However, the knowledge about the kinetics of sulfide oxidation in alkaline solutions, such as concrete pore solutions, is incomplete. To address this issue, we prepared solutions with 0.80 M NaOH, and nominally 10, 100 and 350 mM HS−, respectively, and followed the evolution of the sulfur species during bubbling with oxygen-containing gas (concentration 100% or 22%) using X-ray absorption near-edge structure (XANES) spectroscopy at the sulfur K-edge. Polysulfide, thiosulfate, sulfite and sulfate were identified and quantified via linear combination fitting of the spectra, using previously measured standards. A preliminary analysis of the results shows that the initial rate constant of sulfide oxidation depends strongly on the HS− concentration, while it less strongly depending on the oxygen concentration, in line with previous results for lower pH values. Approaches to fit the data to obtain the rate constants of all relevant reactions will be discussed.
Zurzeit befindet sich die VDI-Richtlinie 3783 Blatt 1 in der Überarbeitung, wobei ein Modellwechsel auf ein Lagrange‘sches Partikelmodell erfolgt. Für die Berechnung der Gasausbreitung mit Gebäuden ist ein Windfeld erforderlich. Mit AUSTAL wird auch das diagnostische Windfeldmodell TALdia zur Verfügung gestellt, das mittels empirischer Ansätze die Rezirkulationszonen von Gebäuden und Hindernissen modelliert. Ein prognostisches Windfeldmodell auf Grundlage der Navier-Stokes-Gleichungen sollte bei komplexen Bebauungs-situationen bessere Berechnungsergebnisse liefern, was aber mit einem größeren Berechnungsaufwand verbunden ist. Im Rahmen eines IGF- Forschungsvorhabens wurde das prognostische Windfeldmodell OFWind als Open Source – Software entwickelt. Im Vergleich mit den experimentellen Ergebnissen des Forschungsvorhabens wird die Anwendbarkeit der beiden Windfeldmodelle bei drei komplexen Bebauungssituationen untersucht.
Im vorliegenden Bericht wurde untersucht, Spanndrahtbrüche in einem Brückenträger sowohl mittels Schallemissionsanalyse (SEA) als auch mit (eingebetteter) verteilter faseroptischer akustischer Sensorik (DAS) zu erkennen. Die Ergebnisse zeigen, dass die Bruchsignale anhand beider Messverfahren detektiert werden können. Die jeweils detektierten Drahtbrüche werden miteinander verglichen. Durch eine Gegenüberstellung werden SEA und DAS detailliert dargestellt.
For hazard assessment purposes, the dispersion of gases in complex urban areas is often a scenario to be considered. However, predicting the dispersion of heavy gases is still a challenge. In Germany, the VDI Guideline 3783, Part 1 and 2 is widely used for gas dispersion modelling. Whilst Part 1 uses a gauss model for calculating the dispersion of light or neutrally buoyant gases, Part 2 uses wind tunnel experiments to evaluate the heavier-than-air gas dispersion in generic built up areas. In practice, with this guideline, it is often not possible to adequately represent the existing obstacle configuration. To overcome this limitation, computational fluid dynamics (CFD) methods could be used. Whilst CFD models can represent obstacles in the dispersion area correctly, actual publications show that there is still further research needed to simulate the atmospheric flow and the heavy gas dispersion. This paper presents a modified k-ε-turbulence model that was developed in OpenFOAM v5.0 (England, London, The OpenFOAM Foundation Ltd Incorporated) to enhance the simulation of the atmospheric wind field and the heavy gas dispersion in built-up areas. Wind tunnel measurements for the dispersion of neutrally buoyant and heavy gases in built-up environments were used to evaluate the model. As a result, requirements for the simulation of the gas dispersion under atmospheric conditions have been identified and the model showed an overall good performance in predicting the experimental values.
Proof load testing may be performed to confirm the reliability of the bridge for an existing classification or to prove the reliability for a higher classification. In this paper, a probabilistic decision analysis approach is applied to the scenario for the evaluation of target proof load in the situation where information on the bridge resistance model is lacking. In this case, the resistance model is established by proof loading and taking very basic prior knowledge into account. The decision scenario is modelled in the context of the proof load test planner who shall choose the required load level for assessment of a bridge. The choice of the load level depends on the risks due to the testing and the expected benefit gain from the test. Information acquired about the loading response from monitoring during the proof load testing is modelled by taking basis in the model uncertainty formulation. The optimal proof load level for classification of a single lane, simply supported bridge of 8m span subjected to live load from very heavy (gross weight > 80 tons) transport vehicles was calculated. The optimal proof load level was identified as leading to a positive expected benefit gain to the decision maker while also satisfying target reliability criteria for remaining service life. The analysis was performed for the evaluation of bridge performance with respect to five classifications of very heavy transport vehicles with different vehicle weights and configurations.
The 2010 CCQM-K79 'Comparison of value-assigned CRMs and PT materials: Ethanol in aqueous media' is the second key comparison directly testing the chemical measurement services provided to customers by National Metrology Institutes (NMIs) and Designated Institutes (DIs). CCQM-K79 compared the assigned ethanol values of proficiency test (PT) and certified reference materials (CRMs) using measurements made on these materials under repeatability conditions. Nine NMIs submitted 27 CRM or value-assigned PT materials for evaluation. These materials represent many of the higher-order reference materials then available for this commercially and forensically important measurand.
The assigned ethanol mass fraction in the materials ranged from 0.1 mg/kg to 334 mg/kg. All materials were stored and prepared according the specifications provided by each NMI. Samples were processed and analyzed under repeatability conditions by one analytical team using a gas chromatography with flame ionization detection (GC-FID) method of demonstrated trueness and precision.
Given the number of materials and the time required for each analysis, the majority of the measurements were made in two measurement campaigns ('runs'). Due to a shipping delay from one NMI, an unanticipated third campaign was required. In all three campaigns, replicate analyses (three injections of one preparation separated in time) were made for one randomly selected unit of each of the 27 materials. Nine of the 27 materials were gravimetrically diluted before measurement to provide solutions with ethanol mass fraction in the established linear range of the GC-FID method. The repeatability measurement value for each analyzed solution was estimated as the mean of all replicate values. The within- and between-campaign variance components were estimated using one-way ANOVA. Markov Chain Monte Carlo Bayesian analysis was used to estimate 95% level-of-confidence coverage intervals for the mean values.
Uncertainty-weighted generalized distance regression was used to establish the key comparison reference function (KCRF) relating the assigned values to the repeatability measurements. On the basis of leave-one-out cross-validation, all of the assigned values for all 27 materials were deemed equivalent at the 95% level of confidence. These materials were used to define the KCRF.
Parametric bootstrap Monte Carlo was used to estimate 95% level-of-confidence coverage intervals for the degrees of equivalence of materials, d ± U95(d), and of the participating NMIs, D ± U95(D). Because of the very wide range of ethanol mass fraction in the materials, these degrees of equivalence are expressed in percent relative form: %d ± U95(%d) and %D ± U95(%D). The median of the absolute values of the %D for the participating NMIs is less than 0.05% with a median U95(%D) of less than 1%. These results demonstrate that the participating NMIs have the ability to correctly value-assign CRMs and proficiency test materials for ethanol in aqueous media and similar measurands.