The Effect of the Novel FeNiCoAlTa Shape Memory Alloy Treatments on its Corrosion Behavior When Used as a Pipe Coupler
Bibliographic record
Abstract
Pipelines are used to transport natural gas and oil. The common way to join the pipes together is welding. Welded areas and heat affected zones have proven to be the weakest part of the pipeline, these areas are often exposed to the environment and as a consequence exhibit more cracks and failures than the rest of the pipe. Such failures lead to leaks of gases, oil, and process fluids that are harmful to the environment. In addition, leaks cause a production loss which is very costly. In the past decades, there have been various leaks that lead to fatalities and that resulted in major environmental impacts. Shape memory alloys (SMAs), also known as smart alloys, are engineering materials that gained their name due to their unique capability of remembering the shape they had before deformation and returning to it. They undergo reversible solid-to-solid phase transformation (martensitic transformation) when a load, temperature or magnetic field is applied to them, and can recover their original shape if the external stimulus is removed. The most commercially available SMAs are nickel–titanium (Ni-Ti or Nitinol) alloys, which are used mostly in orthodontic and medical applications. These alloys are expensive, have limited temperature range of application, and are difficult to process for large scale applications. Due to these limitations, the current research interest has shifted towards developing new SMAs that are cheaper, exhibit a similar shape memory effect, and that are corrosion resistance. Among the new SMAs competing with Ni-Ti smart alloys are iron-based SMAs (Fe-based SMAs). Since the discovery of the shape memory effect in Fe-30Mn-1Si in early 1980s, the Fe-based SMAs alloys have attracted the researcher's attention due to their low cost, good mechanical properties, high temperature range of application, workability and weldability. However, they exhibited relatively poor shape recovery which made it necessary to treat them using cycles of thermomechanical treatment known as ‘training’ to enhance their shape memory effect. The Fe-based SMAs have shown a promising potential to be used as pipe couplers in oil and gas applications. Pipe couplers made from SMAs can be installed easily, and can be a good replacement for welding in pipes. Welding involves heat that affects the structure of the piping metal and the resulted heat affected zone usually possess a favorable location for failure and cracking. This has made Fe- based SMAs pipe couplers a better option due to their low cost and long-term reliability. The use of these alloys to join pipes is aimed to replace the welding process. The SMAs pipe couplers use the shape memory effect to apply a contact pressure onto the surface of the pipes to be coupled. Compared to currently available couplers that work by brazing, smart alloy couplers are easier to install, require lower installation temperatures, and have similar coupling capabilities. The seal is more temperature resistant than that provided by brazed couplers. In this research, the corrosion resistance of the Novel FeNiCoAlTa (Fe-28%Ni-17%Co-11.5%Al-2.5%Ta) SMA that was developed by the National Aeronautics and Space Administration (NASA) was investigated for using as a pipe coupler. The effect of two different cycles of treatment on the alloy was investigated. In addition, the alloy's corrosion resistance was compared to the resistance of the most common SMA (Ni-Ti or Nitinol). The corrosion potential, polarization resistance and potentiodynamic polarization of the alloys were compared at different temperatures and different PHs. Moreover, the corrosion resistance of the alloy was compared to the resistance of different carbon steel, and stainless steels that are commonly used in oil and gas applications, when electrochemically tested in 3% NaCL solution. The research also investigated the galvanic corrosion of FeNiCoAlTa SMA when coupled to carbon steel and different types of stainless steels. It was found that even though increasing the treatment cycles improved the shape memory effect of the alloy, it reduced its corrosion resistance. One has to decide on whither to enhance the shape memory effect of a pipe coupler or to improve its corrosion resistance. Nevertheless, the alloy exhibited good corrosion resistance, even after increasing the cycles of the treatment showing that it could be a potential replacement to pipe welding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".