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Lian-Ping Wang

Lian-Ping Wang is a astronomy topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Lian-Ping Wang rather than just read about it. In short: Lian-Ping Wang is a mechanical engineer and academic, most known for his work on computational fluid dynamics, turbulence, particle-laden flow, and immiscible multiphase flow, and their applications to industrial and atmospheric processes. He is the chair professor of mechanics and aerospace engineering at the Southern University of Science and Technology in China, professor of mechanical engineering, and joint prof…

Key takeaways

  • Lian-Ping Wang belongs to astronomy; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Lian-Ping Wang to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Lian-Ping Wang from memory before moving on to harder problems.

Reference excerpt

Lian-Ping Wang is a mechanical engineer and academic, most known for his work on computational fluid dynamics, turbulence, particle-laden flow, and immiscible multiphase flow, and their applications to industrial and atmospheric processes. He is the chair professor of mechanics and aerospace engineering at the Southern University of Science and Technology in China, professor of mechanical engineering, and joint professor of physical ocean science and engineering at University of Delaware. Wang's research primarily focuses on fundamental physics in turbulent multiphase flows, utilizing computational fluid dynamics (CFD) modeling for intricate flows across various systems, including industrial, natural, and biological contexts. He developed traditional Navier-Stokes-based CFD methods and mesoscopic Boltzmann-equation based methods, like the lattice Boltzmann method and discrete unified gas kinetic scheme, as direct numerical simulation tools for complex turbulent and multiphase flows. He also devised numerical methods for studying complex fluid flow and transport in fuel cells and soil porous media, as well as the transport and retention of colloids and nanoparticles in the subsurface environment. Wang is an elected Fellow of the American Society of Mechanical Engineers and the American Physical Society. He was named in the World's Top 2% Scientists list by Stanford University in 2023 and in the Most Cited Chinese Researchers list by Elsevier in 2021 and 2022. In addition, he is an associate editor of the Journal of Fluid Mechanics and Theoretical and Applied Mechanics Letters, as well as a member of the Editorial Advisory Board for the International Journal of Multiphase Flow.

Education Wang received a bachelor's degree in mechanics in 1984 from Zhejiang University, before going to the US for PhD study, and subsequently obtained a PhD in mechanical engineering from Washington State University in 1990. During his PhD, he developed a theoretical model predicting the turbulent dispersion of sedimenting inertial particles, concurrently developing an empirical correlation for the integral time scale of fluid velocity observed by such particles, which came to be known as the Wang and Stock correction in multiphase flow literature.

Career and research During his postdoctoral tenure with Martin Maxey, they authored a paper on particle-laden turbulent flows, utilizing DNS to reveal novel effects of small-scale turbulence structure on particle behavior. At Penn State, he conducted a study on Kolmogorov refined similarity using high-resolution DNS flows, measuring various quantities related to the intermittency and scaling dynamics of fine-scale turbulence. In 1994, Wang joined the University of Delaware as an assistant professor of mechanical engineering, later becoming an associate professor in 2001 and professor in 2009. He serves as a chair professor of mechanics and aerospace engineering and director of the Center for Computational Science and Engineering at the Southern University of Science and Technology in China, professor of mechanical engineering, and joint professor of physical ocean science and engineering at the University of Delaware. During the period of 1998 to 2013, Wang's research concentrated on the turbulent collision rate and collision efficiency of inertial particles, where he played a role in establishing a theoretical foundation for the collision kernel, generating rigorous collision rate data from DNS, providing an analytical parameterization of the turbulent collision kernel, and studying the impact of turbulent collision on warm rain initiation. In 2012, he investigated the transport and retention of colloids and nanoparticles in porous media, considering the effects of physicochemical interaction forces. Using the lattice Boltzmann method and Lagrangian particle tracking, he explored multiscale reversible particle retention near grain surfaces, with factors like flow speed, ionic strength, and surface characteristics influencing the retention rate. In recent years, Wang developed a lattice Boltzmann-based particle-resolving simulation tool to study turbulence modulation by finite-size solid particles, revealing size-dependent characteristics. His group improved lattice Boltzmann method implementation for moving boundaries, enhancing numerical stability and computational efficiency, including the first DNS of turbulent pipe flow using the lattice Boltzmann method. He also developed lattice-Boltzmann models fully consistent with Navier-Stokes equations, such as the use of 2D rectangular or 3D cuboid lattices, and introduced a new D3Q27 lattice Boltzmann model enabling mesoscopic computation of local fluid vorticity, derived through an inverse design approach using hydrodynamic equations. Wang further applied the particle-resolving simulation tool to study the enhancement of particle drag in a turbulent background flow and dynamics of non-spherical particles.

Awards and honors 1998 – Francis Alison Young Scholars Award, University of Delaware 2006 – Distinguished Overseas Young Investigator Award, National Natural Science Foundation of China 2011 – Fellow, American Physical Society 2016 – Fellow, American Society of Mechanical Engineers 2016-2017 – Invitation Fellow, Japan Society for the Promotion of Science 2021, 2022 – Most Cited Chinese Researchers, Elsevier 2022 – Best Mechanical and Aerospace Engineering Scientists in China by Research.com

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Lian-Ping Wang

Start with the simplest possible case. Write down what Lian-Ping Wang claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Lian-Ping Wang before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Lian-Ping Wang ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Lian-Ping Wang

In research
Lian-Ping Wang appears in astronomy research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Lian-Ping Wang in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Lian-Ping Wang is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1965 births, 21st-century mechanical engineers, Academic staff of the Southern University of Science and Technology, so understanding it makes those chapters shorter.
In everyday life
Look for Lian-Ping Wang outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Lian-Ping Wang in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Lian-Ping Wang means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Lian-Ping Wang out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Lian-Ping Wang in simple terms?

Lian-Ping Wang is a mechanical engineer and academic, most known for his work on computational fluid dynamics, turbulence, particle-laden flow, and immiscible multiphase flow, and their applications to industrial and atmospheric processes. He is the chair professor of mechanics and aerospace engine…

Why does Lian-Ping Wang matter?

Because it connects several astronomy ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Lian-Ping Wang?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Lian-Ping Wang.

Tags

  • 1965 births
  • 21st-century mechanical engineers
  • Academic staff of the Southern University of Science and Technology
  • Fellows of the American Physical Society
  • Fellows of the American Society of Mechanical Engineers
  • Living people
  • University of Delaware faculty
  • Washington State University alumni
  • Zhejiang University alumni

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