Collaborative Research: IRES Track 1: International Research Experience for Students in Big Data Applications in Energy and Related Infrastructure

合作研究：IRES Track 1：能源及相关基础设施大数据应用学生的国际研究经验

• 批准号: 1952490
• 负责人: Bimal Nepal
• 金额: $ 13.98万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952490&HistoricalAwards=false
• 关键词: Collaborative; Research; IRES; Track; International

The necessity of preparing a data-savvy and global workforce has been widely recognized in the published literature. The World Economic Forum projects that big data and machine learning would create a very large number of jobs in the global marketplace. Among other industries, energy and related infrastructure have tremendous need and potential for applications of big data to enhance the safety and efficiency of energy production, distribution, and infrastructure support. The project “Collaborative Research: International Research Experience for Students (IRES) in Big Data Applications in Energy and Related Infrastructure” has three distinct goals: 1) prepare students with professional skills to join a diverse and global STEM/knowledge workforce in advanced data science and analytics; 2) increase enthusiasm and research skills of undergraduate students from underrepresented population groups who otherwise might not have an international opportunity for professional development; and 3) foster collaboration between the faculty and students from Asian (economically, one of the fastest growing regions in the world) and U.S. universities. This project will support 27 undergraduate students (over a three-year period) for a 6-week long high quality research internship experience. These students will work on energy related real-world big data and machine learning projects at a reputed international research institute in Malaysia. Out of nine participants per year, three each from the participating institutions, Texas A&M University, North Dakota State University, and University of Nevada, Las Vegas, will be recruited for this program. To broaden the participation, efforts will be to recruit about two-third students from female and underrepresented population groups. The proposed IRES site, Universiti Teknologi Petronas (UTP), Perak, Malaysia, is a highly ranked research institution in the ASEAN region (a 10-Nation bloc with a combined GDP of nearly $3 trillion). The UTP has a very strong connection with the energy industry. This project will unveil novel applications of big data and machine learning tools and techniques in energy and related infrastructure domains including energy production and operations issues such as reliability, environmental impact, and distribution logistics in an interdisciplinary manner. Students will gain intellectual experiences and understanding on assessing reliability of the energy networks utilizing degradation or physics-of-failure models and tools such as stochastic modeling, Bayesian updating, and optimization models for maintenance planning. Lastly, students will work in a cohort of multi-university and multinational teams mentored by renowned international faculty and researchers. It will further enable these students to join global workforce by gaining global research and cultural experiences. The project will benefit multiple groups and entities including the students, universities from both countries, faculty members, and industries. Finally, the well stablished Engineering Global Preparedness Inventory scale will be used to objectively assess the impact of IRES activity on students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在已发表的文献中，人们普遍认识到有必要培养一个精通数据的全球性员工队伍。世界经济论坛预测，大数据和机器学习将在全球市场上创造大量就业机会。在其他行业中，能源和相关基础设施对大数据的应用具有巨大的需求和潜力，以提高能源生产，分配和基础设施支持的安全性和效率。该项目“合作研究：国际研究经验的学生（IRES）在能源和相关基础设施的大数据应用”有三个不同的目标：1）准备学生的专业技能，加入先进的数据科学和分析的多元化和全球STEM/知识劳动力;（二）提高来自代表性不足的人口群体的本科生的热情和研究技能，否则他们可能没有国际机会，专业发展;和3）促进教师和学生之间的合作，从亚洲（经济，在世界上增长最快的地区之一）和美国大学。该项目将支持27名本科生（为期三年）进行为期6周的高质量研究实习体验。这些学生将在马来西亚一家著名的国际研究机构从事与能源相关的真实世界大数据和机器学习项目。在每年的9名参与者中，将为该方案招募来自参与机构的各3名参与者，即德克萨斯农工大学、北达科他州州立大学和内华达州大学、拉斯维加斯。为了扩大参与，将努力从女性和代表性不足的人口群体中招收约三分之二的学生。拟议的IRES站点位于马来西亚霹雳州的Universiti Teknologi Petronas（UTP），是东盟地区（一个10国集团，总GDP近3万亿美元）排名靠前的研究机构。UTP与能源行业有着非常紧密的联系。该项目将以跨学科的方式揭示大数据和机器学习工具和技术在能源和相关基础设施领域的新应用，包括能源生产和运营问题，如可靠性，环境影响和分销物流。学生将获得知识经验和理解，利用退化或故障物理模型和工具，如随机建模，贝叶斯更新和优化模型进行维护规划，评估能源网络的可靠性。最后，学生将在由知名国际教师和研究人员指导的多所大学和跨国团队中工作。它将进一步使这些学生通过获得全球研究和文化经验加入全球劳动力队伍。该项目将使多个团体和实体受益，包括学生，两国大学，教师和行业。最后，完善的工程全球准备清单规模将被用来客观地评估IRES活动对学生的影响。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Predictive Analysis of Fluid-Hammer Effect on LNG Regasification System Pipeline Network

• DOI: 10.1109/rams51457.2022.9894025
• 发表时间: 2022-01
• 期刊: 2022 Annual Reliability and Maintainability Symposium (RAMS)
• 作者: Ajinkya Zalkikar;Bimal P. Nepal;H. Husin;O. Yadav;A. Banerjee

Life cycle assessment of water treatment processes in Malaysia: A quest for greener practice

• DOI: 10.1063/5.0121697
• 发表时间: 2023
• 期刊: 24TH TOPICAL CONFERENCE ON RADIO-FREQUENCY POWER IN PLASMAS
• 作者: W. Leong;Adamaris Cruz;Salman Wahab;Stephon Wilder;Fiorentina Richelle Asun;S. Masrura;E. Khan;Y. Ho