Search NASA⌕ Search

DOE OSTI · 2339619

Experimental investigation of surface roughness/wettability pattern effect on crystallization fouling over falling-film flow

Abstract

In many industries, including desalination, scaling in heat exchangers is a persistent issue, and most methods of scaling control involve environmentally hazardous chemical additives. Exploiting the effects of surface features and flow-imposed shear on salt deposition might introduce a new approach to scale management, opening the possibility of self-cleaning surfaces. In this work, we experimentally explore surface roughness and wettability patterns interaction with salt deposition and flow shear. Heat transfer and scale mass deposition on flat surfaces are measured during thin-film evaporation of calcium sulfate solution. A wide range of surface roughness (0.027–2.66 μm) is studied at fixed flow and heat flux conditions. Due to improved mixing, heat transfer is enhanced with rougher surfaces initially; however, degrades more quickly than smoother surfaces. Following a period of initial mass deposition fluctuation, a steady scaling stage is observed, with rough surfaces scaling more than smooth surfaces. Wettability patterned surfaces can suppress crystal nucleation without sacrificing overall surface wetting, resulting in antifouling performance and heat transfer enhancement. Scale mass deposition on a patterned surface is reduced by 60 %, and 27 % heat transfer improvement over the baseline surface is found. Furthermore, this study provides guidance to surface manipulation for heat transfer improvement and fouling mitigation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zaman, Mohammad Arafat, Wang, Sophie. 2024-01-12. Experimental investigation of surface roughness/wettability pattern effect on crystallization fouling over falling-film flow. https://doi.org/10.1016/j.desal.2024.117324

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗