Computational models for predicting transient temperature distributions, residual stresses, and residual deflections for girth-butt welds are described. Comparisons of predicted and measured temperatures for a two-pass welded pipe show agreement to within 9 percent and 17 percent of the measured values for passes one and two, respectively, the model for predicting residual stresses and residual deflections is based on a finite-element representation recognizing individual passes, temperature dependent elastic-plastic constitutive behavior, elastic unloading for material in the nonlinear stress-strain range, and changes in geometry due to the deformation of each weld pass. Load incrementation and incremental stress-strain relations are also used. Results for a two-pass girth-butt welded pipe show good correlation between residual stresses and residual deflections obtained from the computational model and data obtained from a welded 304 stainless steel pipe.
There is a growing consensus that gravel‐bed roughness should be parameterized based on bed‐surface topography, not only sediment size. One benefit is the possible identification of various spatial scales of surface roughness and evaluation of their respective contributions to flow resistance (and also to bedload transport). The absence of relationships between roughness at the different scales is apparent in previous work, which currently limits roughness parameterization from topography and application in flow modeling. This study examines the use of moving‐window detrending on gravel‐bed digital elevation models (DEMs) for isolating roughness scales and their respective signatures. A large data set of 35 water‐worked gravel‐bed patches from both the laboratory and the field was used for the analysis. The measured bed topography was separated into two distinct DEMs: one representing grains, the other representing small bedforms. For all DEMs, bed‐elevation parameters measuring vertical roughness, imbrication, and spatial correlations were determined. Our results show distinct topographic signatures between grain and bedform DEMs. We show strong positive linear relationships between grain vertical roughness and the size of the bed‐surface material. Surface sediment arrangement also determined bedform shape, with groupings of coarse sediment forming humps on the surface, and finer sediment sheltered in hollows. Patch‐scale vertical roughness could not be estimated simply as the sum of grain and bedform vertical roughness. Instead, our results suggest weighted summation and the existence of universal weighting coefficients. Practical applications for studies on gravel‐bed roughness and flow modeling using DEMs are discussed.
Evidence of downstream fining in sediment size along the length of a gravel bar has frequently been observed. However, there is limited quantitative information on the variation of other roughness statistics. Developments in high-resolution topographic data acquisition provide the opportunity for assessing roughness variations along and across a gravel bar, to quantify existing theoretical observations of bar sorting. Here, close-range photogrammetry is used for the first time to assess intra-bar variations in roughness, at 14 different locations on a single gravel bar in the Whakatiwai River, New Zealand. An extensive range of roughness parameters are used, including the standard deviation of elevations, skewness, kurtosis, inclination index, and horizontal roughness lengths from second-order structure functions. A reduction with distance down bar was found in all roughness parameters, except skewness, along with a decrease in the variability of the data at the bar tail for all parameters. Lateral variation in roughness parameters was also assessed, showing evidence of an increase in roughness parameters with distance from the water edge. These findings can be used to validate and calibrate existing flow resistance equations and morphodynamic models. General trends in roughness statistics indicate coarser sediment at the bar head and near the river bank. These trends reflect the formative flows and are used to infer sedimentation patterns, which suggest that the gravel bar 2 undergoes development through lateral accretion. Although complexities in the sedimentation patterns are evident, due to multiple cycles of erosion and deposition, a greater understanding of these patterns is needed for the implementation of successful river management for this river, and others.
A combined analytical and experimental method for inferring the residual stresses in a pipe formed by joining two sections with a girth butt weld is described. The relieved surface strains due to cutting the pipe in two are measured and fitted in a least-square sense to the strains predicted by an analysis of the sectioned pipes. This permits a prediction of the through-thickness distribution of the residual longitudinal normal and radial shear stresses at the site of the cut. The procedure is used to infer the residual stress distribution due to last-pass-heat-sink welding of two sections of 16-in- (400-mm-) dia pipe.
In fluvial research, comparisons of laboratory and field data sets are rare or outdated; therefore, future research would benefit from the integration of laboratory and field data sets. We use close‐range photogrammetry as a tool to help bridge that interface. Close‐range photogrammetry is a technique that is readily applied in both laboratory and field environments to capture submillimetre topographic data of natural and replica surfaces of gravel‐bed rivers. Digital Elevation Models (DEMs) of difference (DoDs) are presented to quantitatively assess the replicability of four surfaces, using the casting process. Replication results with accuracies of <35 mm, including observed localized areas of particle dislodgement, suggest casting is a suitable process to integrate laboratory and field data sets in fluvial morphology, allowing natural surfaces (e.g., from the field) to be analysed in a controlled laboratory environment. This enables the isolation of parameters, such as the microtopography of the surface or water submergence. Further, we highlight the importance of considerations of the wider scale morphology required to contextualize patch‐scale field research using close‐range photogrammetry. We demonstrate this with an example of the influence of vegetation on DEM quality and roughness statistics. These wider morphological features are difficult to simulate in the laboratory, albeit have a control on patch‐scale processes. The successful replication of natural surfaces using casting and the use of tools such as close‐range photogrammetry provide a bridge for future research that requires the integration of laboratory experiments, field experiments, and numerical modelling.
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