Quasi-periodic solutions with multiple base frequencies exhibit the feature of 2π-periodicity with respect to each of the hyper-time variables.However,it remains a challenge work,due to the lack of effective solution...Quasi-periodic solutions with multiple base frequencies exhibit the feature of 2π-periodicity with respect to each of the hyper-time variables.However,it remains a challenge work,due to the lack of effective solution methods,to solve and track the quasi-periodic solutions with multiple base frequencies until now.In this work,a multi-steps variable-coefficient formulation is proposed,which provides a unified framework to enable either harmonic balance method or collocation method or finite difference method to solve quasi-periodic solutions with multiple base frequencies.For this purpose,a method of alternating U and S domain is also developed to efficiently evaluate the nonlinear force terms.Furthermore,a new robust phase condition is presented for all of the three methods to make them track the quasi-periodic solutions with prior unknown multiple base frequencies,while the stability of the quasi-periodic solutions is assessed by mean of Lyapunov exponents.The feasibility of the constructed methods under the above framework is verified by application to three nonlinear systems.展开更多
The dissolution of iron from the cathode and electrode/electrolyte interface(EEI)during long cycles significantly accelerates the aging process of LiFePO_(4)(LFP)/graphite batteries;there is a lack of systematic under...The dissolution of iron from the cathode and electrode/electrolyte interface(EEI)during long cycles significantly accelerates the aging process of LiFePO_(4)(LFP)/graphite batteries;there is a lack of systematic understanding of the spatial distribution of the EEI interface layer and the dissolve of Fe ions,especially in terms of the mechanism of the cathode-electrolyte interphase(CEI),solid electrolyte interphase(SEI),and iron dissolution.In this study,aged cells were subjected to continuous activation with constant current and multi-step segmented indirect activation(IA)and analyzed for capacity fade,impedance growth,and active Li^(+)mass loss at the EEI and nanoscale levels.The interaction between dissolved Fe^(2+)and the EEI in LFP/graphite pouch batteries was proposed and verified.The findings indicate that during IA process,the electric field facilitates the migration of solvated ions toward the electrodes,while simultaneously inhibiting the formation of organic species such as ROCO_(2)Li.The SEI primarily consists of a mixture of organic and inorganic small molecules,forming a continuous and uniform film on the electrode surface.This study demonstrates that IA favors the formation of a uniform EEI and offers constructive insights for advancing accelerated lifetime prediction strategies in lithium-ion batteries.展开更多
坝体抗震设计和评估需要准确计算无限水库动力响应.基于比例边界有限元法(scaled boundary finite element method,SBFEM)力学推导技术,推导了顺河向地震激励下等横截面无限水域频域响应计算公式,利用Fourier逆变换建立了时域响应控制方...坝体抗震设计和评估需要准确计算无限水库动力响应.基于比例边界有限元法(scaled boundary finite element method,SBFEM)力学推导技术,推导了顺河向地震激励下等横截面无限水域频域响应计算公式,利用Fourier逆变换建立了时域响应控制方程,通过线性叠加推导了顺河、横河、竖直三向组合地震激励下的无限水域频域和时域响应的SBFEM计算公式.结合有限元法,建立了无限水库频域和时域响应的FEM-SBFEM耦合方程.分析了地震激励下的二维、三维等横截面无限水库频域、时域响应,数值验证了所建立计算公式的正确性.所发展的FEM-SBFEM公式体系可推广应用于库底库岸具有吸收性的、横截面有任意几何形状的无限水库谐响应及瞬态响应分析.展开更多
为了模拟喷丸强化过程,实现喷丸强化效果快速预测,基于Abaqus软件采用离散元法-有限元法(Discrete Element Method-Finite Element Method,DEM-FEM)耦合建立随机多丸粒喷丸强化模型,并以TC4钛合金为研究对象,通过喷丸强化试验来验证耦...为了模拟喷丸强化过程,实现喷丸强化效果快速预测,基于Abaqus软件采用离散元法-有限元法(Discrete Element Method-Finite Element Method,DEM-FEM)耦合建立随机多丸粒喷丸强化模型,并以TC4钛合金为研究对象,通过喷丸强化试验来验证耦合模型的准确性。采用Box-Behnken设计(Box-Behnken Design,BBD)法,针对弹丸大小、喷丸速度和喷丸覆盖率3个工艺参数设计了三因素三水平的喷丸仿真试验方案,采用仿真分析获得表面残余应力值及表面粗糙度值,并通过Design-Expert软件进行数值拟合,最终得到喷丸工艺参数与表面残余应力和表面粗糙度之间的函数模型,采用响应面法分析弹丸大小、喷丸速度、喷丸覆盖率三因素之间的交互作用以及对喷丸强化效果的影响规律。结果表明,响应面预测模型结果与仿真计算结果误差低于5%,所建立的响应面预测模型具有较高的近似精度和可靠性,利用此模型可实现喷丸强化效果的有效预测。展开更多
Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parame...Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12172267 and 12302014).
文摘Quasi-periodic solutions with multiple base frequencies exhibit the feature of 2π-periodicity with respect to each of the hyper-time variables.However,it remains a challenge work,due to the lack of effective solution methods,to solve and track the quasi-periodic solutions with multiple base frequencies until now.In this work,a multi-steps variable-coefficient formulation is proposed,which provides a unified framework to enable either harmonic balance method or collocation method or finite difference method to solve quasi-periodic solutions with multiple base frequencies.For this purpose,a method of alternating U and S domain is also developed to efficiently evaluate the nonlinear force terms.Furthermore,a new robust phase condition is presented for all of the three methods to make them track the quasi-periodic solutions with prior unknown multiple base frequencies,while the stability of the quasi-periodic solutions is assessed by mean of Lyapunov exponents.The feasibility of the constructed methods under the above framework is verified by application to three nonlinear systems.
基金supported by the National Key R&D Program of China(2021YFB2401800)the support from Beijing Nova Program(20230484241)+2 种基金the support from the China Postdoctoral Science Foundation(2024M754084)the Postdoctoral Fellowship Program of CPSF(GZB20230931)the support from Initial Energy Science&Technology Co.,Ltd(IEST)。
文摘The dissolution of iron from the cathode and electrode/electrolyte interface(EEI)during long cycles significantly accelerates the aging process of LiFePO_(4)(LFP)/graphite batteries;there is a lack of systematic understanding of the spatial distribution of the EEI interface layer and the dissolve of Fe ions,especially in terms of the mechanism of the cathode-electrolyte interphase(CEI),solid electrolyte interphase(SEI),and iron dissolution.In this study,aged cells were subjected to continuous activation with constant current and multi-step segmented indirect activation(IA)and analyzed for capacity fade,impedance growth,and active Li^(+)mass loss at the EEI and nanoscale levels.The interaction between dissolved Fe^(2+)and the EEI in LFP/graphite pouch batteries was proposed and verified.The findings indicate that during IA process,the electric field facilitates the migration of solvated ions toward the electrodes,while simultaneously inhibiting the formation of organic species such as ROCO_(2)Li.The SEI primarily consists of a mixture of organic and inorganic small molecules,forming a continuous and uniform film on the electrode surface.This study demonstrates that IA favors the formation of a uniform EEI and offers constructive insights for advancing accelerated lifetime prediction strategies in lithium-ion batteries.
文摘坝体抗震设计和评估需要准确计算无限水库动力响应.基于比例边界有限元法(scaled boundary finite element method,SBFEM)力学推导技术,推导了顺河向地震激励下等横截面无限水域频域响应计算公式,利用Fourier逆变换建立了时域响应控制方程,通过线性叠加推导了顺河、横河、竖直三向组合地震激励下的无限水域频域和时域响应的SBFEM计算公式.结合有限元法,建立了无限水库频域和时域响应的FEM-SBFEM耦合方程.分析了地震激励下的二维、三维等横截面无限水库频域、时域响应,数值验证了所建立计算公式的正确性.所发展的FEM-SBFEM公式体系可推广应用于库底库岸具有吸收性的、横截面有任意几何形状的无限水库谐响应及瞬态响应分析.
文摘为了模拟喷丸强化过程,实现喷丸强化效果快速预测,基于Abaqus软件采用离散元法-有限元法(Discrete Element Method-Finite Element Method,DEM-FEM)耦合建立随机多丸粒喷丸强化模型,并以TC4钛合金为研究对象,通过喷丸强化试验来验证耦合模型的准确性。采用Box-Behnken设计(Box-Behnken Design,BBD)法,针对弹丸大小、喷丸速度和喷丸覆盖率3个工艺参数设计了三因素三水平的喷丸仿真试验方案,采用仿真分析获得表面残余应力值及表面粗糙度值,并通过Design-Expert软件进行数值拟合,最终得到喷丸工艺参数与表面残余应力和表面粗糙度之间的函数模型,采用响应面法分析弹丸大小、喷丸速度、喷丸覆盖率三因素之间的交互作用以及对喷丸强化效果的影响规律。结果表明,响应面预测模型结果与仿真计算结果误差低于5%,所建立的响应面预测模型具有较高的近似精度和可靠性,利用此模型可实现喷丸强化效果的有效预测。
文摘Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.
