About

Hongwei Sun joined the Mechanical and Industrial Engineering department at Northeastern University in September 2020. Prior to this, he was a full professor in the Mechanical Engineering department at the University of Massachusetts Lowell, where he began his academic career as an assistant professor in 2005. His educational background includes a PhD in Mechanical Engineering from the Institute of Engineering Thermophysics at the Chinese Academy of Sciences, obtained in 1998. His research focuses on multiphase thermal transport phenomena, acoustic wave bio and chemical sensors and actuators, thermal management of fibers and films, thermal energy storage materials and processing, microchannel cooling systems, nanoimprinting processes and applications, MEMS/NEMS fabrication, microfluidics and bioMEMS, as well as nanoscale magnetic assembly and applications. He has been recognized with awards such as the 2017 ASME International Conference on Nanochannels, Minichannels, and Microchannels Best Poster Award and the UML Entrepreneurial Faculty Award. His professional affiliations include the American Institute of Aeronautics and Astronautics, the American Society for Engineering Education, and the American Society of Mechanical Engineers.

Research topics

• Machine Learning
• Materials science
• Computer Science
• Composite material
• Artificial Intelligence
• Mechanics
• Nanotechnology
• Mechanical engineering
• Mathematics
• Acoustics
• Chemistry
• Computer vision
• Engineering
• Optoelectronics
• Electronic engineering

Selected publications

• Performance Improvement of Tree-Shaped Network With a Hybrid Lagrange Multipliers and Topology Optimization Method

  ASME Open Journal of Engineering · 2026-01-01

  articleOpen accessSenior author

  Abstract Dendritic path architecture, like a tree-like branching channel network, has attracted considerable attention in thermofluids applications due to its ability to deliver uniform flow distribution, low energy consumption, and high heat dissipation efficiency. However, the existing design theories still have drawbacks, such as the effect of flow and heat transfer at the junctions of bifurcated channels was not considered. In recent years, topology optimization (TO) has emerged as a powerful tool to advance the design of thermal management systems, such as cold plates, enabling enhanced thermal performance and reduced energy usage. However, a key limitation of the TO approach is the significant computational time required in the design process. This work presents a hybrid design method for a bifurcated channel structure through the Lagrange multipliers (LM)-based optimization method, followed by TO. This hybrid approach can not only enhance thermal dissipation efficiency and minimize flow resistance but also achieve a better performance with reduced computational time. The LM-based design method takes into consideration the effects of developing flow, thermal conductivity, and fluid properties on the dimensions of bifurcated channels. Next, the bifurcated channel was further optimized using the TO method to achieve the improved outlet temperature and pressure drop. The hybrid approach provides a new path for designing tree-like branching channel networks for a wide range of thermal applications.

  PublisherOA PDFDOI

• Predicting Economic Prospects of Rainwater Harvesting System: A Machine Learning Approach Based on Building Characteristics

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Authentication of Linderae Radix through plant metabolomics coupled with a machine learning-enhanced in situ hyperspectral imaging approach

  Journal of Pharmaceutical Analysis · 2025-10-28 · 2 citations

  articleOpen access

  Linderae Radix, a medicinally significant herb with a history of over 2,000 years, is highly esteemed for its potential to promote longevity. Derived from the tuberous roots of Lindera aggregata ( L. aggregata ), it encounters difficulties in being distinguished from non-medicinal parts, such as non-fusiform taproots and old roots in the herbal drug market. To address the problem, this study developed a new strategy that integrates non-targeted plant metabolomics with a machine learning-enhanced hyperspectral imaging (HSI) approach for in situ quality assessment. Firstly, a comprehensive metabolomics analysis was conducted using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and gas chromatography-mass spectrometry (GC-MS) to identify 25 and 48 differential metabolites, respectively. Then, combined with machine learning algorithms, HSI in the 400–1,000 nm band achieved visual in situ assessment of different types of L. aggregata roots. Second derivative (2 nd D)-Savitzky-Golay (SG) smoothing-logistic regression (LR) models achieved 93.33% accuracy of the test set in spectral classification. Moreover, spectral pre-processing and characteristic wavelength selection led to high prediction accuracies for the content of significant components in L. aggregata using standard normal variate (SNV)-competitive adaptive reweighted sampling (CARS)-least squares support vector machine (LSSVM) and SNV-CARS-extreme learning machine (ELM) ( > 0.87 for the test set). This is the first study to provide a visual representation of the content of marker compounds in L. aggregata roots, offering a rapid, non-destructive method for assessing the quality of Linderae Radix. It scientifically justifies the medicinal use of tuberous roots and illuminates rapid quality evaluation through morphological identification. • A rapid, non-destructive method for assessing the quality of Linderae Radix. • Integrated metabolomics, HSI, and machine learning for rapid quality assessment. • Machine learning for establishing classification and quantitative prediction models . • Visualize the spatial distribution of the norisoboldine content in drug slices.

  PublisherDOI

• Real-Time Wetting Area Measurement of Micro- and Nanostructured Surfaces with an Acoustic Wave Device

  Langmuir · 2025-10-03

  articleSenior authorCorresponding

  Wetting characterization on textured surfaces is essential for applications such as improving fluid dynamics in microfluidic devices, enhancing antifouling coatings in maritime environments, and optimizing ink deposition in printing processes. Common methods, including contact angle measurement and optical microscopic imaging methods, often fail to provide in situ, real-time characterization of the interface between liquid and a textured surface. This work studies the wetting of micro- and nanopillar surfaces with an acoustic wave device, quartz crystal microbalance (QCM), which is capable of capturing the real-time partial wetting and transition from Cassie to Wenzel states. The frequency response of the micropillar QCM device was found to have a linear correlation with the area of liquid penetration on the substrate with a limit of detection (LOD) of 0.3% area coverage. Furthermore, the real-time wetting characterization capabilities of the QCM device were validated through experiments involving BSA (bovine serum albumin) adsorption on micropillar-coated surfaces. The device can detect the dynamic wetted behavior of the surface due to protein adsorption. In addition, measurement of the wetting on the nanopillar surface with QCM was demonstrated. The developed method offers a promising solution for the real-time characterization of the penetrated area on the surface, enhancing the interaction between liquids and surfaces in applications such as microfluidic systems, coating technologies, and biomedical devices.

  PublisherDOI

• A New Interdisciplinary Engineering Course - "Nanoscale Transport Phenomena for Manufacturing Nanodevices"

  2025-04-01

  articleOpen access
  PublisherOA PDFDOI

• Machine learning-based framework for assessing the financial viability of decentralized rainwater harvesting systems

  Results in Engineering · 2025-09-11

  articleOpen access

  • RF model achieved R² > 0.9, MAE < 0.096, and RMSE < 0.206 for BCR. • Water tariffs and CV contributed 20.8% and 20.5%, respectively. • Web app provides planning-level assessments with error < 10%. Urban rainwater harvesting (RWH) systems are essential for sustainable water management, but their adoption is limited by the challenges in assessing their economic feasibility. Traditional methods often fail to account for the nonlinear and multivariable factors affecting RWH systems. This study develops a machine learning-based framework to overcome these limitations and predict the economic viability of decentralized rooftop RWH systems. Using data from 1,841 RWH systems across 20+ countries and 150+cities, four machine learning algorithms—Random Forest (RF), AdaBoost, Light Gradient Boosting Machine (LGBM), and Multi-Layer Perceptron (MLP)—were evaluated. The RF model demonstrated optimal performance for Benefit-Cost Ratio (BCR) prediction (R²=0.906, MAE=0.096, RMSE=0.209), outperforming the Net Present Value (NPV) (R²=0.939, MAE=49,249, RMSE=512,453) and Payback Period (PBP) models (R²=0.883, MAE=13.53, RMSE=32.74). SHAP analysis identified water tariffs (20.8%) and rainfall variation (20.5%) as key BCR predictors; the catchment area (43.3%) and total built area (36.3%) dominated NPV forecasts, while project lifetime (19.7%) was the primary factor for PBP. Additionally, a user-friendly web application was developed using Streamlit, enabling users to input regional and building-specific parameters to assess the economic feasibility of RWH systems. This framework offers a robust tool for engineers, property owners, and policymakers to make informed decisions, promoting the global adoption of sustainable water management practices.

  PublisherDOI

• Short-chain PFAS in coastal sediments: PFBS-driven antimicrobial resistance and pathogen risks

  Water Research · 2025-09-09 · 4 citations

  article1st author
  PublisherDOI

• Enhanced Thermal Conductivity and Leakage Resistance of Microparticle Impregnated Composite Phase Change Materials

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• A high throughput co-flow millifluidic device for homogeneous nanoparticle synthesis

  Journal of Industrial and Engineering Chemistry · 2025-01-27 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

• Towards a new era of rainwater utilization: implementation status, barriers, and prospects of decentralized rainwater harvesting systems in China

  Environmental Reviews · 2025-01-01 · 10 citations

  articleOpen access

  This article explores the potential of decentralized rainwater harvesting (RWH) systems as an extension of conventional water sources in China. Through a comprehensive literature review, we identify key economic, technical, and social barriers hindering widespread adoption, including high costs, lack of standardized design tools, and low public awareness. Our analysis reveals significant regional disparities in economic viability, with benefit–cost ratios ranging from 0.16 in arid regions to 3.2 in humid areas, influenced by factors such as rainfall patterns, roof area, and water demand. The study highlights innovative solutions to address these challenges, including low-cost treatment technologies (e.g., gravity-driven microfiltration and solar disinfection), gravity-based distribution systems for high-rise buildings, and neighborhood-scale approaches to enhance cost-effectiveness. Additionally, we propose decision–support tools and regionalized design aids to optimize system performance. Policy measures such as subsidies, tax incentives, and mandatory installation in new buildings are recommended to accelerate adoption. These findings offer valuable guidance for the implementation of decentralized RWH systems, emphasizing the need for interdisciplinary collaboration to overcome barriers and promote RWH as a sustainable solution for urban water management challenges, not only in China but also in other parts of the world.

  PublisherOA PDFDOI

Recent grants

• MRI: Acquisition of a Nanoimprint Lithography System for Nanomanufacturing and Nano/Micro Fabrication

  NSF · $508k · 2009–2012

• I-Corps Teams: A Low Cost and High Performance Anode for Lithium Ion Batteries with Application to Electric Vehicles

  NSF · $50k · 2016–2017

• Nanoscale Metal Network Enhanced Phase Change Materials

  NSF · $432k · 2016–2020

• Collaborative Research: A Low-Cost, "Digital" Biosensing Platform with Single Protein Biomarker Sensitivity

  NSF · $245k · 2021–2023

• EAGER: Magnetically Assembling and Soldering of Nanoscale Metal Network into Phase Change Material

  NSF · $99k · 2013–2015

Frequent coauthors

• Majid Charmchi

  University of Massachusetts Lowell

  31 shared

• Junwei Su

  Zhongnan Hospital of Wuhan University

  29 shared

• Hamed Esmaeilzadeh

  University of Massachusetts Lowell

  20 shared

• Pengtao Wang

  Oak Ridge National Laboratory

  18 shared

• Zhiyong Gu

  17 shared

• Siqi Ji

  Northeast Petroleum University

  15 shared

• Ilia Chiniforooshan Esfahani

  Northeastern University

  10 shared

• Minghao Song

  University of Massachusetts Lowell

  9 shared

Education

• Ph.D.

  Chinese Academy of Sciences

  1998

• B.S., Power Engineering

  Harbin Engineering University

  1992

Awards & honors

• 2017 ASME International Conference on Nanochannels, Minichan…