Sustainable Energy Modelling and Simulation

燃烧产生的烟尘颗粒是导致气候变化和疾病的主要因素。它们与肺癌和肺气肿等呼吸系统疾病、致癌基因毒性和不良妊娠结局有关。为了维护地球和物种的健康，需要进行减少并最终消除颗粒排放的研究。德沃金教授的研究项目的主要目标是：1)开发详细的数值模型来表征燃烧产生的颗粒的形成和排放；2)利用机器学习来生成新的算法方法来估计烟灰颗粒的性质和浓度；3)广泛传播知识和数值工具，用于工业设计和学术研究；4)推进建筑能源领域的可持续技术发展。发电和运输，通过数值模拟和实验。研究方法将涉及计算流体动力学计算，包括燃烧的化学和物理现象，以及统计粒子动力学建模。这些计算将利用大型计算集群上的并行计算来生成将流动条件与颗粒属性联系起来的数据集。该研究将使用计算和测量数据为机器学习方法提供信息，以生成人工智能算法，这些算法可以预测各种系统中的烟尘颗粒现象（如颗粒成核）、性质（如大小和形状）和浓度。德沃金教授的研究将通过高性能并行计算和人工智能方法的新应用，推动燃烧产生的颗粒建模领域的发展。他对机器学习的新颖使用将导致计算效率和预测能力的重大进步。他的研究将允许建立模型和算法来预测或估计烟灰颗粒形成的各个方面，这将用于更好地理解粒子物理学，并有助于设计交通和发电部门的低排放设备。他的工作将对这些行业产生重大影响，提供新的见解和先进的数值工具。最后，他的研究将直接支持减少大气中有害污染物的存在，特别是在人类活动集中的城市。

Combustion-generated soot particles are a major contributor to climate change and disease. They have been linked to respiratory illnesses such as lung cancer and emphysema, cancer-causing genotoxicity, and adverse pregnancy outcomes. Research toward the reduction and eventual elimination of particle emissions is needed to support the health of our planet and species. The main objectives of Prof. Dworkin's research program are to i) develop detailed numerical models to characterize the formation and emissions of combustion-generated particles, ii) leverage machine learning towards the generation of new algorithmic methods to estimate soot particle properties and concentrations, iii) widely disseminate knowledge and numerical tools for use in industry design and academic research, and iv) advance sustainable technology development in building energy, power generation, and transportation, through numerical modelling and experimentation. The research approach will involve Computational Fluid Dynamics calculations that incorporate chemical and physical phenomena of combustion, and statistical particle dynamics modelling. These computations will utilize parallel computing on large compute clusters to generate datasets that relate flow conditions to particle properties. The research will use computed and measured data to inform machine learning methods towards the generation of artificial intelligence algorithms that can predict soot particle phenomena (such as particle nucleation), properties (such as size and shape), and concentration, in a variety of systems. Prof. Dworkin's research will advance the field of combustion-generated particulate modelling through high-performance parallel computing, and the novel application of artificial intelligence methods. His novel use of machine learning will lead to major advances in computational efficiency and predictive capability. His research will permit the creation of models and algorithms that predict or estimate aspects of soot particle formation, which will be used to gain a better understanding of particle physics, and contribute to the design of lower-emitting devices in the transportation and power generation sectors. His work will have a major impact on these industries, providing new insights, and advanced numerical tools. Finally, his research will directly support the reduction of harmful pollutants present in our atmosphere, especially in cities where anthropogenic activities are concentrated.