In the past 20 years, the US Navy has used the physical scale modeling (PSM) technique to design effective cathodic protection (CP) systems for the ships underwater hull, nickel-aluminum-bronze props and other hull components. In more recent years, a number of computational techniques have been devised in an attempt to fulfill this purpose. Physical models have proven highly adept at ICCP design, since modeled information provides a direct relationship to the actual hull and can be scaled up directly because of confidence in the physically measured data. Boundary element (BE) models have been correspondingly devised that mimic actual hull design and even the PSM layout, but because the BE method is a computational methodology, the calculated data requires systematic validation with a physical analog to insure confidence in the control response. BE literature has discussed design issues regarding mesh layout, intrinsic geometric complexities, accuracy of material response input, the predictive engineering design capability for zonal response, and assessment of electric field response. It does not significantly discuss the accuracy of the BE model calculated work predictive design capability, without the need for "tweaking," and ultimately a rigorous validation of both the mesh and resultant system design technique. This paper presents validation requirements, for any BE model, that is inherently robust enough to be used for CP design and control, and a proposed four-point methodology that will allow for the comprehensive validation of the BE model to predict the ICCP control responses and system performance behavior.
The goal of impressed current cathodic protection (ICCP) design for ship hulls, under the Navy Ship's Technical Manual (NSTM, Chapter 633), is to provide a uniform potential distribution at -0.85 V, ± 0.05 V, versus a silver/silver chloride (Ag/AgCl) reference cell, over the wetted hull surface during all operational aspects of an active ship. To accomplish this, the physical scale modeling (PSM) technique, combined with a rigid design protocol, has been used extensively by the U.S. Navy to provide optimal and retrofit upgrade designs of ICCP systems for hulls. The ICCP design guidance, provided by the protocol, defines the hull properties, hull damage and general power supply requirements. PSM is utilized to determine optimal placement of ICCP components (anodes and reference cells) and to evaluate performance for up to a 15% wetted hull coatings loss under static (pierside) and dynamic (underway) conditions. Data are provided which illustrate the use of the design protocol criteria, along with the integrated PSM technique, to determine ICCP system design and evaluate performance.
The truth values used for validation of computational models are measured values from either actual structures or experiments.The accuracy of computational models will depend on the accuracy in which key parameters can be measured. Therefore it is imperative that there is a clear understanding of what and how parameters are being measured. An incomplete understanding of the experimental process, including measurement sensors, will corrupt the computational model validation process.In this work computational modeling has been used to further the understanding and assist in the design of a sensor used to measure off-board electrical fields. Previously data from a series of dipole models was generated using NRL's physical scale modeling experimental facility. Results were compared with both analytical and computational solutions. Variations were observed between results. Boundary element methods were used to extensively model tank geometry, water depth, sensor orientation and to some degree sensor geometry. It was determined that the sensor as designed was not adequate for the off-board electrical measurements required. In this work boundary element modeling is used to assist in the design of a new off-board electrical field sensor. Dipole models which consider the vertical and horizontal placement of half-cells on the sensor are used to quantify characteristics of the new sensor. Comparisons are provided between analytical, computational and measured results once the sensor design is finalized.
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