Energy absorption performance has been a long-pursued research topic in designing desired materials and structures subject to external dynamic loading.Inspired by natural bio-structures,herein,we develop both numerica...Energy absorption performance has been a long-pursued research topic in designing desired materials and structures subject to external dynamic loading.Inspired by natural bio-structures,herein,we develop both numerical and theoretical models to analyze the energy absorption behaviors of Weaire,Floret,and Kagome-shaped thin-walled structures.We demonstrate that these bio-inspired structures possess superior energy absorption capabilities compared to the traditional thin-walled structures,with the specific energy absorption about 44%higher than the traditional honeycomb.The developed mechanical model captures the fundamental characteristics of the bio-inspired honeycomb,and the mean crushing force in all three structures is accurately predicted.Results indicate that although the basic energy absorption and deformation mode remain the same,varied geometry design and the corresponding material distribution can further boost the energy absorption of the structure,providing a much broader design space for the next-generation impact energy absorption structures and systems.展开更多
Silicon monoxide(SiO)(silicon[Si]mixed with silicon dioxide[SiO_(2)])/graphite(Gr)composite material is one of the most commercially promising anode materials for the next generation of high-energy-density lithium-ion...Silicon monoxide(SiO)(silicon[Si]mixed with silicon dioxide[SiO_(2)])/graphite(Gr)composite material is one of the most commercially promising anode materials for the next generation of high-energy-density lithium-ion batteries.The major bottleneck for SiO/Gr composite anode is the poor cyclability arising from the stress/strain behaviors due to the mismatch between two heterogenous materials during the lithiation/delithiation process.To date,a meticulous and quantitative understanding of the highly nonlinear coupling behaviors of such materials is still lacking.Herein,an electro–chemo–mechanics-coupled detailed model containing particle geometries is established.The underlying mechanism of the regulation between SiO and Gr components during electrochemical cycling is quantitatively revealed.We discover that increasing the SiO weight percentage(wt%)reduces the utilization efficiency of the active materials at the same 1C rate charging and enhances the hindering effects of stress-driven flux on diffusion.In addition,the mechanical constraint demonstrates a balanced effect on the overall performance of cells and the local behaviors of particles.This study provides new insights into the fundamental interactions between SiO and Gr materials and advances the investigation methodology for the design and evaluation of next-generation high-energydensity batteries.展开更多
Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networ...Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.展开更多
Neuromodulation represents a cutting edge class of both invasive and non-invasive therapeutic methods which alter the activity of neurons.Currently,several different techniques have been developed-or are currently bei...Neuromodulation represents a cutting edge class of both invasive and non-invasive therapeutic methods which alter the activity of neurons.Currently,several different techniques have been developed-or are currently being investigated–to treat a wide variety of neurological and neuropsychiatric disorders.Recently,in vivo and in vitro studies have revealed that neuromodulation can also induce myelination,meaning that it could hold potential as a therapy for various demyelinating diseases including multiple sclerosis and progressive multifocal leukencepalopathy.These findings come on the heels of a paradigm shift in the view of myelin's role within the nervous system from a static structure to an active co-regulator of central nervous system plasticity and participant in neuron-mediated modulation.In the present review,we highlight several of the recent findings regarding the role of neural activity in altering myelination including several soluble and contact-dependent factors that seem to mediate neural activitydependent myelination.We also highlight several considerations for neuromodulatory techniques,including the need for further research into spatiotemporal precision,dosage,and the safety and efficacy of transcranial focused ultrasound stimulation,an emerging neuromodulation technology.As the field of neuromodulation continues to evolve,it could potentially bring forth methods for the treatment of demyelinating diseases,and as such,further investigation into the mechanisms of neuron-dependent myelination as well as neuro-imaging modalities that can monitor myelination activity is warranted.展开更多
Lignite and sub-bituminous coals from western U.S. contain high amounts of moisture （sub-bituminous： 15%-30%, lignites： 25%-40%）. German and Australian lignites （brown coals） have even higher moisture content, 5...Lignite and sub-bituminous coals from western U.S. contain high amounts of moisture （sub-bituminous： 15%-30%, lignites： 25%-40%）. German and Australian lignites （brown coals） have even higher moisture content, 50% and 60%, respectively. The high moisture content causes a reduction in plant performance and higher emissions, compared to the bituminous （hard） coals. Despite their high-moisture content, lignite and sub-bituminous coals from the western U.S. and worldwide are attractive due to their abundance, low cost, low NOx and SOx emissions, and high reactivity. A novel low-temperature coal drying process employing a fluidized bed dryer and waste heat was developed in the U.S. by a team led by GRE （Great River Energy）. Demonstration of the technology was conducted with the U.S. Department of Energy and GRE funding at Coal Creek Station Unit 1. Following the successful demonstration, the low-temperature coal drying technology was commercialized by GRE under the trade name DryFiningTM fuel enhancement process and implemented at both units at Coal Creek Station. The coal drying system at Coal Creek has been in a continuous commercial operation since December 2009. By implementing DryFining at Coal Creek, GRE avoided $366 million in capital expenditures, which would otherwise be needed to comply with emission regulations. Four years of operating experience is described in this paper.展开更多
This study investigates turbulent particle-laden channel flows using direct numerical simulations employing the Eulerian-Lagrangian method.A two-way coupling approach is adopted to explore the mutual interaction betwe...This study investigates turbulent particle-laden channel flows using direct numerical simulations employing the Eulerian-Lagrangian method.A two-way coupling approach is adopted to explore the mutual interaction between particles and fluid flow.The considered cases include flow with particle Stokes number varying from St=2 up to St=100 while maintaining a constant Reynolds number of Reτ=180 across all cases.A novel vortex identification method,Liutex(Rortex),is employed to assess its efficacy in capturing near-wall turbulent coherent structures and their interactions with particles.The Liutex method provides valuable information on vortex strength and vectors at each location,enabling a detailed examination of the complex interaction between fluid and particulate phases.As widely acknowledged,the interplay between clockwise and counterclockwise vortices in the near-wall region gives rise to low-speed streaks along the wall.These low-speed streaks serve as preferential zones for particle concentration,depending upon the particle Stokes number.It is shown that the Liutex method can capture these vortices and identify the location of low-speed streaks.Additionally,it is observed that the particle Stokes number(size)significantly affects both the strength of these vortices and the streaky structure exhibited by particles.Furthermore,a quantitative analysis of particle behavior in the near-wall region and the formation of elongated particle lines was carried out.This involved examining the average fluid streamwise velocity fluctuations at particle locations,average particle concentration,and the normal velocity of particles for each set of particle Stokes numbers.The investigation reveals the intricate interplay between particles and near-wall structures and the significant influence of particles Stokes number.This study contributes to a deeper understanding of turbulent particle-laden channel flow dynamics.展开更多
文摘Energy absorption performance has been a long-pursued research topic in designing desired materials and structures subject to external dynamic loading.Inspired by natural bio-structures,herein,we develop both numerical and theoretical models to analyze the energy absorption behaviors of Weaire,Floret,and Kagome-shaped thin-walled structures.We demonstrate that these bio-inspired structures possess superior energy absorption capabilities compared to the traditional thin-walled structures,with the specific energy absorption about 44%higher than the traditional honeycomb.The developed mechanical model captures the fundamental characteristics of the bio-inspired honeycomb,and the mean crushing force in all three structures is accurately predicted.Results indicate that although the basic energy absorption and deformation mode remain the same,varied geometry design and the corresponding material distribution can further boost the energy absorption of the structure,providing a much broader design space for the next-generation impact energy absorption structures and systems.
文摘Silicon monoxide(SiO)(silicon[Si]mixed with silicon dioxide[SiO_(2)])/graphite(Gr)composite material is one of the most commercially promising anode materials for the next generation of high-energy-density lithium-ion batteries.The major bottleneck for SiO/Gr composite anode is the poor cyclability arising from the stress/strain behaviors due to the mismatch between two heterogenous materials during the lithiation/delithiation process.To date,a meticulous and quantitative understanding of the highly nonlinear coupling behaviors of such materials is still lacking.Herein,an electro–chemo–mechanics-coupled detailed model containing particle geometries is established.The underlying mechanism of the regulation between SiO and Gr components during electrochemical cycling is quantitatively revealed.We discover that increasing the SiO weight percentage(wt%)reduces the utilization efficiency of the active materials at the same 1C rate charging and enhances the hindering effects of stress-driven flux on diffusion.In addition,the mechanical constraint demonstrates a balanced effect on the overall performance of cells and the local behaviors of particles.This study provides new insights into the fundamental interactions between SiO and Gr materials and advances the investigation methodology for the design and evaluation of next-generation high-energydensity batteries.
基金supported by the U.S.Department of Energy’s Office on Energy Efficiency and Renewable Energy(EERE)under the Advanced Manufacturing Office,award number DE-EE0009111。
文摘Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.
基金the Medical Scientist Training Program(T32GM007250)Predoctoral Training in Molecular Therapeutics Program(T32GM008803)。
文摘Neuromodulation represents a cutting edge class of both invasive and non-invasive therapeutic methods which alter the activity of neurons.Currently,several different techniques have been developed-or are currently being investigated–to treat a wide variety of neurological and neuropsychiatric disorders.Recently,in vivo and in vitro studies have revealed that neuromodulation can also induce myelination,meaning that it could hold potential as a therapy for various demyelinating diseases including multiple sclerosis and progressive multifocal leukencepalopathy.These findings come on the heels of a paradigm shift in the view of myelin's role within the nervous system from a static structure to an active co-regulator of central nervous system plasticity and participant in neuron-mediated modulation.In the present review,we highlight several of the recent findings regarding the role of neural activity in altering myelination including several soluble and contact-dependent factors that seem to mediate neural activitydependent myelination.We also highlight several considerations for neuromodulatory techniques,including the need for further research into spatiotemporal precision,dosage,and the safety and efficacy of transcranial focused ultrasound stimulation,an emerging neuromodulation technology.As the field of neuromodulation continues to evolve,it could potentially bring forth methods for the treatment of demyelinating diseases,and as such,further investigation into the mechanisms of neuron-dependent myelination as well as neuro-imaging modalities that can monitor myelination activity is warranted.
文摘Lignite and sub-bituminous coals from western U.S. contain high amounts of moisture （sub-bituminous： 15%-30%, lignites： 25%-40%）. German and Australian lignites （brown coals） have even higher moisture content, 50% and 60%, respectively. The high moisture content causes a reduction in plant performance and higher emissions, compared to the bituminous （hard） coals. Despite their high-moisture content, lignite and sub-bituminous coals from the western U.S. and worldwide are attractive due to their abundance, low cost, low NOx and SOx emissions, and high reactivity. A novel low-temperature coal drying process employing a fluidized bed dryer and waste heat was developed in the U.S. by a team led by GRE （Great River Energy）. Demonstration of the technology was conducted with the U.S. Department of Energy and GRE funding at Coal Creek Station Unit 1. Following the successful demonstration, the low-temperature coal drying technology was commercialized by GRE under the trade name DryFiningTM fuel enhancement process and implemented at both units at Coal Creek Station. The coal drying system at Coal Creek has been in a continuous commercial operation since December 2009. By implementing DryFining at Coal Creek, GRE avoided $366 million in capital expenditures, which would otherwise be needed to comply with emission regulations. Four years of operating experience is described in this paper.
文摘This study investigates turbulent particle-laden channel flows using direct numerical simulations employing the Eulerian-Lagrangian method.A two-way coupling approach is adopted to explore the mutual interaction between particles and fluid flow.The considered cases include flow with particle Stokes number varying from St=2 up to St=100 while maintaining a constant Reynolds number of Reτ=180 across all cases.A novel vortex identification method,Liutex(Rortex),is employed to assess its efficacy in capturing near-wall turbulent coherent structures and their interactions with particles.The Liutex method provides valuable information on vortex strength and vectors at each location,enabling a detailed examination of the complex interaction between fluid and particulate phases.As widely acknowledged,the interplay between clockwise and counterclockwise vortices in the near-wall region gives rise to low-speed streaks along the wall.These low-speed streaks serve as preferential zones for particle concentration,depending upon the particle Stokes number.It is shown that the Liutex method can capture these vortices and identify the location of low-speed streaks.Additionally,it is observed that the particle Stokes number(size)significantly affects both the strength of these vortices and the streaky structure exhibited by particles.Furthermore,a quantitative analysis of particle behavior in the near-wall region and the formation of elongated particle lines was carried out.This involved examining the average fluid streamwise velocity fluctuations at particle locations,average particle concentration,and the normal velocity of particles for each set of particle Stokes numbers.The investigation reveals the intricate interplay between particles and near-wall structures and the significant influence of particles Stokes number.This study contributes to a deeper understanding of turbulent particle-laden channel flow dynamics.
