Experiment-based simulation of a cross-flow large-scale SOFC model

The multi-physical field full-coupling simulation of solid oxide fuel cell (SOFC) stack requires huge computational resources. Repeated iteration of highly non-linear calculation is easy to cause oscillation and lead to solution failure. At present, the simulation of SOFC stack models mainly focuses on the co-flow condition and counter-flow condition models. Most of them are simplified models that simplify the stack scale or physical field. In this paper, a SOFC decoupling model based on machine learning is established, and the full three-dimensional and multi-physical fields of the cross-flow large-scale SOFC stack are simulated. The model is divided into three parts for calculation, unit cell model, alternative mapping model, and cross-flow large-scale SOFC stack model. The alternative mapping model obtained by the BP neural network algorithm replaces the nonlinear multi-physics equations in the traditional model. Compared with the traditional method, the decoupling model can greatly reduce the computing resources and improve the stability of computing. In this paper, the experimental data of the single cell and the 30-layer stack are used to calibrate and verify the simulation results of stack. Studying the performance of the SOFC stack under different parameter conditions. Temperature, flow uniformity, gas mole fraction, and voltage distribution in the SOFC stack under different inlet flow rates and stack currents are obtained. Obtaining the output power and fuel utilization rate of the stack under different working conditions

Experiment-based simulation of a cross-flow large-scale SOFC model

The multi-physical field full-coupling simulation of solid oxide fuel cell (SOFC) stack requires huge computational resources. Repeated iteration of highly non-linear calculation is easy to cause oscillation and lead to solution failure. At present, the simulation of SOFC stack models mainly focuses on the co-flow condition and counter-flow condition models. Most of them are simplified models that simplify the stack scale or physical field. In this paper, a SOFC decoupling model based on machine learning is established, and the full three-dimensional and multi-physical fields of the cross-flow large-scale SOFC stack are simulated. The model is divided into three parts for calculation, unit cell model, alternative mapping model, and cross-flow large-scale SOFC stack model. The alternative mapping model obtained by the BP neural network algorithm replaces the nonlinear multi-physics equations in the traditional model. Compared with the traditional method, the decoupling model can greatly reduce the computing resources and improve the stability of computing. In this paper, the experimental data of the single cell and the 30-layer stack are used to calibrate and verify the simulation results of stack. Studying the performance of the SOFC stack under different parameter conditions. Temperature, flow uniformity, gas mole fraction, and voltage distribution in the SOFC stack under different inlet flow rates and stack currents are obtained. Obtaining the output power and fuel utilization rate of the stack under different working conditions

Study on the combined sewage sludge pyrolysis and gasification process : mass and energy balance

A combined pyrolysis and gasification process for sewage sludge was studied in this paper for the purpose of its safe disposal with energy self-balance. Three sewage sludge samples with different dry basis lower heat values (LHV(db)) were used to evaluate the constraints on this combined process. Those samples were pre-dried and then pyrolysed within the temperature range of 400–550 °C. Afterwards, the char obtained from pyrolysis was gasified to produce fuel gas. The experimental results showed that the char yield ranged between 37.28 and 53.75 wt% of the dry sludge and it changed with ash content, pyrolysis temperature and LHV(db) of the sewage sludge. The gas from char gasification had a LHV around 5.31–5.65 MJ/Nm3, suggesting it can be utilized to supply energy in the sewage sludge drying and pyrolysis process. It was also found that energy balance in the combined process was affected by the LHV(db) of sewage sludge, moisture content and pyrolysis temperature. Higher LHV(db), lower moisture content and higher pyrolysis temperature benefit energy self-balance. For sewage sludge with a moisture content of 80 wt%, LHV(db) of sewage sludge should be higher than 18 MJ/kg and the pyrolysis temperature should be higher than 450 °C to maintain energy self-sufficiency when volatile from the pyrolysis process is the only energy supplier; when the LHV(db) was in the range of 14.65–18 MJ/kg, energy self-balance could be maintained in this combined process with fuel gas from char gasification as a supplementary fuel; auxiliary fuel was always needed if the LHV(db) was lower than 14.65 MJ/kg.Accepted versio

Optimization of SOFC stack gas distribution structure based on BP Neural network and CFD

The flow field distribution of solid oxide fuel cells significantly affects the performance of the stack. The flow uniformity can be improved and the power generation efficiency can be improved by optimizing the gas distribution structure of the stack. Based on the simplified 6kW stack model, the stack gas distribution structure with two-stage buffer cavity was designed, and the stack model was numerically simulated by ANSYS Fluent software. The BP neural network model, which can predict the uniformity of the outlet of the integrated stack, is established successfully. The parameters of the gas distribution structure are analyzed and optimized by using the orthogonal test and BP neural network. The results show that at the same time considering pile distribution structure under the condition of surface area and uniformity, when the first stage inlet buffer chamber depth is 40 mm, the channel width is 40 mm, the secondary inlet buffer chamber depth is 80 mm, can effectively reduce the electric pile distribution structure, surface area, to reduce heat loss, at the same time guarantee the integrated electric reactor outlet flow uniformity of more than 96%, greatly improves the efficiency of power generation

Preparation and performance of crosslinked poly(arylene ether)s containing azobenzene chromophores

A series of novel poly(arylene ether)s with crosslinked groups and different azobenzene chromophores contents (azo-CPAEs: PAE-allyl20%-azo20%, PAE-allyl20%-azo40%, PAE-allyl20%-azo60%) were synthesized from a new bisfluoro monomer, (2,6-difluorophenyl)-(4-hydroxyphenyl)methanone. Their chemical structures were characterized by means of UV-vis and FI-IR. The thermal properties of the polymers were investigated by TGA and DSC, indicating the polymers had high glass transition temperatures (Tg > 147 °C) and good thermal stability (Td5 > 360 °C) even when the contents of azobenzene chromophores was high to 60%. And the influence of thermal crosslinking on the performance of PAE-allyl20%-azo20%, a typical one of the series, was investigated. Tg of PAE-allyl20%-azo20% increased with the increase of heating time when heat-treated at 250 °C for 20, 40 and 60 min, indicating the crosslink degree of the polymer increased. After heat-treated for 60 min, Tg of PAE-allyl20%-azo20% increased to 175 °C from 147 °C before thermal crosslinking. Upon irradiation with a 532 nm neodymium doped yttrium aluminum garnet (Nd:YAG) laser beam, the remnant value of the polymer PAE-allyl20%-azo20% before and after the thermal crosslinking were 81 and 96%, respectively, meaning that the PAE-allyl20%-azo20% after thermal crosslink showed more stable photoinduced alignment than that before thermal crosslinking

Gambogic acid protects from endotoxin shock by suppressing pro-inflammatory factors in vivo and in vitro

Gambogic acid (GBA) targeted Heat shock protein 90 (Hsp90) and prohibited TNF-alpha/NF-kappa B signaling pathway. It can be inferred that the anti-inflammatory activity of GBA results from inhibiting the cytokine production via NF-kappa B signaling pathway. We used the RAW264.7 cell line and the endotoxin shock mouse model to confirm the hypothesis that GBA protects mice from endotoxin shock by suppressing cytokine synthesis. RAW264.7 cells were cultured and the endotoxin shocked mice model was constructed. ELISA was employed to evaluate the change of cytokine secretion levels. The effects of GBA on the activation of NF-kappa B signaling pathway were also determined by western blot and immune-fluorescent analysis. Cell viability was determined by MTT assay, and the cell migration was tested by wound healing assay. Our results demonstrated that GBA significantly inhibited the LPS-induced release of pro-inflammatory factors both in cell lines and mice serum, thereby protecting mice from endotoxin shock. Furthermore, we observed that the reduction of inflammatory cytokines interleukin 1-beta, interleukin 6 and TNF-alpha resulted from the Hsp90's client protein IKK degradation and the suppression of NF-kappa B pathway. Moreover, GBA suppressed the migration of LPS-induced RAW264.7 cells. Our results indicate that GBA has a potential both as an antitumor and anti-inflammatory therapeutic agent

Chromosome-level genome assembly and annotation of the prickly nightshade Solanum rostratum Dunal

Abstract The prickly nightshade Solanum rostratum, an annual malignant weed, is native to North America and has globally invaded 34 countries, causing serious threats to ecosystems, agriculture, animal husbandry, and human health. In this study, we constructed a chromosome-level genome assembly and annotation of S. rostratum. The contig-level genome was initially assembled in 898.42 Mb with a contig N50 of 62.00 Mb from PacBio high-fidelity reads. With Hi-C sequencing data scaffolding, 96.80% of the initially assembled sequences were anchored and orientated onto 12 pseudo-chromosomes, generating a genome of 869.69 Mb with a contig N50 of 72.15 Mb. We identified 649.92 Mb (72.26%) of repetitive sequences and 3,588 non-coding RNAs in the genome. A total of 29,694 protein-coding genes were predicted, with 28,154 (94.81%) functionally annotated genes. We found 99.5% and 91.3% complete embryophyta_odb10 genes in the pseudo-chromosomes genome and predicted gene datasets by BUSCO assessment. The present genomic resource provides essential information for subsequent research on the mechanisms of environmental adaptation of S. rostratum and host shift in Colorado potato beetles