Engineering topics

Ju, Feng

Publications and source records attributed to Ju, Feng.

3 records

DOE OSTI2023

Active predation, phylogenetic diversity, and global prevalence of myxobacteria in wastewater treatment plants

The operation of modern wastewater treatment plants (WWTPs) is driven by activated sludge microbiota, a complex assemblage of trophically interacting microorganisms. Microbial predation is crucial to fundamental understanding of how biological interactions drive microbiome structuring and functioning of WWTPs. However, predatory bacteria have received little attention regarding their diversity, activity, and ecological function in activated sludge, limiting the exploitation of food web interactions for wastewater microbiome engineering. Here, by using rRNA-stable isotope probing of activated sludge microbiota with 13 C-labeled prey bacteria, we uncovered diverse as-yet-uncultivated putative predatory bacteria that actively incorporated 13 C-biomass. Myxobacteria, especially Haliangium and the mle1-27 clade, were found as the dominant active predators, refreshing conventional views based on a few predatory isolates of Bdellovibrionota from WWTPs. The identified predatory bacteria showed more selective predation on prey compared with the protists dominated by ciliates, providing in situ evidence for inter-domain predation behavior divergence in activated sludge. Putative predatory bacteria were tracked over a two-year microbiome monitoring effort at a local WWTP, revealing the predominance of Myxococcota (6.5 ± 1.3%) over Bdellovibrionota (1.0 ± 0.2%) lineages. Phylogenetic analysis unveiled highly diverse myxobacteria inhabiting activated sludge and suggested a habitat filtering effect in global WWTPs. Further mining of a global activated sludge microbiome dataset revealed the prevalence of Myxococcota (5.4 ± 0.1%) species and potential impacts of myxobacterial predation on process performance. Collectively, our findings provided unique insights into the predating activity, diversity, and prevalence of Myxococcota species in activated sludge, highlighting their links with wastewater treatment processes via trophic regulation of enteric and functional bacteria.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Layer Time Control for Large Scale Additive Manufacturing Using High Performance Computing

This work proposes to optimize an additive manufacturing AM process to reduce energy and printing cost. The polymer AM process is inherently dependent on the time-temperature history of each layer to maintain geometric tolerances and mechanical integrity. Our preliminary study shows that regression-based layer time control model using thermal images could result in up to 30% build time reduction for simple geometries. This proposed work would use high-performance computing (HPC) to couple the data-driven model with thermal simulation for better predicting layer temperature profiles, improving throughput of large-scale additive manufacturing, and reducing its energy cost. We have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale AM via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreements with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation. In addition to the development of a layer time optimization method, we have developed a numerical framework for AM simulation with element activations in sync with toolpath, based on an open source finite element framework, DEAL.II. A major portion of this work was presented at SAMPE 2022 Conference and Exhibition on May 2022, and published in Proceedings of SAMPE 2022.

42 ENGINEERING↗

DOE OSTI2022

THE DESIGN OF LAYER TIME OPTIMIZATION IN LARGE SCALE ADDITIVE MANUFACTURING WITH FIBER REINFORCED POLYMER COMPOSITES

In this study, we have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale additive manufacturing (AM) via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreement with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation.

Jo, Eonyeon↗