Fox, David | Engineering Papers

DOE OSTI2022

Modeling and Experiments on a Dedicated Outdoor Air System Using Liquid Desiccant Heat and Mass Exchangers

Liquid desiccants can provide efficient dehumidification but have yet to see widespread adoption. Most systems studied previously use natural gas-combustion to heat and regenerate the desiccant, and a central chiller plant or cooling tower for removing the heat of absorption. Here we present modeling and experimental results on a novel packaged air conditioner integrating liquid-desiccant heat and mass exchangers with a vapor compression cycle. The packaged system does not need cold or hot water from a central plant or cooling tower and is suitable for rooftop unit air conditioners. The system uses the evaporator to cool the liquid desiccant that is absorbing moisture from the air, and the condenser to heat the liquid desiccant to regenerate it. Efficiency is improved by reducing the load on the evaporator for a given supply-air dewpoint. This paper presents the measured dehumidification efficiency for a 10- ton packaged air conditioner, focusing on dehumidifying ventilation air. The paper also presents a numerical model to predict the outlet conditions and dehumidification efficiency, which we compare with the measured data.

air conditioning↗

DOE OSTI2022

Enabling machine learning-ready HPC ensembles with Merlin

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. Here, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. As a producer–consumer workflow model, Merlin enables multi-machine, cross-batch job, dynamically allocated yet persistent workflows capable of utilizing surge-compute resources. Key features of Merlin are a flexible HPC-centric interface, low per-task overhead, multi-tiered fault recovery, and a hierarchical sampling algorithm that allows for $\mathscr{O}$(N) task execution and $\mathscr{O}$(N ln N) task queuing to ensembles of millions of tasks. In addition to Merlin’s design, we test the algorithm’s performance in an HPC center and demonstrate the ability to enqueue 40 million simulations in 100 s, with a 30 millisecond per-task overhead that is independent of ensemble size. Finally, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19.

97 MATHEMATICS AND COMPUTING↗