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End date: 2023 × Start date: 2023 × End date: 2024 × Start date: 2020 × DDC: 500 × DDC: 620 × Has files: Yes × Type: Article × Type: Text ×

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Now showing 1 - 4 of 4
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    Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China
    ([London] : Nature Publishing Group UK, 2021) Xing, Xiaofan; Wang, Rong; Bauer, Nico; Ciais, Philippe; Cao, Junji; Chen, Jianmin; Tang, Xu; Wang, Lin; Yang, Xin; Boucher, Olivier; Goll, Daniel; Peñuelas, Josep; Janssens, Ivan A.; Balkanski, Yves; Clark, James; Ma, Jianmin; Pan, Bo; Zhang, Shicheng; Ye, Xingnan; Wang, Yutao; Li, Qing; Luo, Gang; Shen, Guofeng; Li, Wei; Yang, Yechen; Xu, Siqing
    As China ramped-up coal power capacities rapidly while CO2 emissions need to decline, these capacities would turn into stranded assets. To deal with this risk, a promising option is to retrofit these capacities to co-fire with biomass and eventually upgrade to CCS operation (BECCS), but the feasibility is debated with respect to negative impacts on broader sustainability issues. Here we present a data-rich spatially explicit approach to estimate the marginal cost curve for decarbonizing the power sector in China with BECCS. We identify a potential of 222 GW of power capacities in 2836 counties generated by co-firing 0.9 Gt of biomass from the same county, with half being agricultural residues. Our spatially explicit method helps to reduce uncertainty in the economic costs and emissions of BECCS, identify the best opportunities for bioenergy and show the limitations by logistical challenges to achieve carbon neutrality in the power sector with large-scale BECCS in China.
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    Data-Driven Discovery of Stochastic Differential Equations
    (Beijing : Engineering Sciences Press, 2022) Wang, Yasen; Fang, Huazhen; Jin, Junyang; Ma, Guijun; He, Xin; Dai, Xing; Yue, Zuogong; Cheng, Cheng; Zhang, Hai-Tao; Pu, Donglin; Wu, Dongrui; Yuan, Ye; Gonçalves, Jorge; Kurths, Jürgen; Ding, Han
    Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system's dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.
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    Calculation of the steady states in dynamic semiconductor laser models
    (Dordrecht [u.a.] : Springer Science + Business Media B.V, 2022) Radziunas, Mindaugas
    We discuss numerical challenges in calculating stable and unstable steady states of widely used dynamic semiconductor laser models. Knowledge of these states is valuable when analyzing laser dynamics and different properties of the lasing states. The example simulations and analysis mainly rely on 1(time)+1(space)-dimensional traveling-wave models, where the steady state defining conditions are formulated as a system of nonlinear algebraic equations. The performed steady state calculations reveal limitations of the Lang-Kobayashi model, explain nontrivial bias threshold relations in lasers with several electrical contacts, or predict and explain transient dynamics when simulating such lasers.
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    Simulation and analysis of high-brightness tapered ridge-waveguide lasers
    (Dordrecht [u.a.] : Springer Science + Business Media B.V, 2023) Koester, Jan-Philipp; Wenzel, Hans; Wilkens, Martin; Knigge, Andrea
    In this work, a simulation-based analysis of a CW-driven tapered ridge-waveguide laser design is presented. Measurements of these devices delivered high lateral brightness values of 4 W · mm - 1mrad - 1 at 2.5W optical output power. First, active laser simulations are performed to reproduce these results. Next, the resulting complex valued intra-cavity refractive index distributions are the basis for a modal and beam propagation analysis, which demonstrates the working principle and limitation of the underlying lateral mode filter effect. Finally, the gained understanding is the foundation for further design improvements leading to lateral brightness values of up to 10 W · mm - 1mrad - 1 predicted by simulations.