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Experimental Studies of 3D Frustrated Magnets
No full textFrustrated magnets are a promising class of materials which are being scrutinized for decades in view of their interesting ground states and potential applications [1]. In this context, we investigated the magnetic and thermodynamic properties of two 3D geometrically frustrated magnets using PPMS and SQUID magnetometer measurements. Our goal is to understand the ground state properties of K2Fe2(SO4)3, a Fe2+ based double trillium lattice compound, and Li3Nd3W2O12, a Nd3+ based hyper-Kagome lattice compound.The polycrystalline samples were synthesized using solid-state reactions. The structural and magnetic properties were investigated via X-ray powder diffraction, magnetization, and heat capacity measurements. The K2Fe2(SO4)3 crystallizes in a cubic unit cell with a space group P213. It does not undergo a magnetic transition down to 2 K despite having a Curie-Weiss temperature of CW ∼ −21 K. The isothermal magnetization exhibits two field-induced features at C1 ∼ 1.61 T and C2 ∼ 5.95 T. Preliminary measurements on Li3Nd3W2O12 will also be discussed.Reference:[1] L. Balents, Nature 464, 199 (2010)
A new approach for joint assimilation of cosmic-ray neutron soil moisture and groundwater level data into an integrated terrestrial model
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How can GRACE/-FO data assimilation enhance our understanding of anthropogenic effects on the hydrological system in Europe?
No full textPossibilities for reducing FWI calculation costs for GPR multi-offset data
No full textGround penetrating radar (GPR) is an important shallow geophysical exploration method and can be widely applied in near surface applications. Thereby, the GPR full-waveform inversion (FWI) utilizes all information contained in the data including dynamics and kinematics and therefore has theoretically the highest imaging possibilities. One of the bottlenecks of FWI is its high computational cost and inability to meet the real-time needs for experimental applications. To improve the efficiency of GPR FWI, we provide two methods that can reduce the calculation time. The first is source encoding, which can reduce the number of forward simulations by simultaneously exciting multiple sources in one simulation and mitigating crosstalk noise by encoding the sources. The second is source subsampling, which selects a subset of sources to participate in the inversion, gradually uses more sources during the inversion process, and in the final stage uses all the sources. Numerical experiments show that compared with traditional FWI, both methods effectively reduce the calculation time by providing a similar reconstruction of the medium properties
Leveraging Systems Knowledge to Enhance Sustainable Practices in a Thriving Pulp and Paper Industry
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Gaussian Process Supported Stochastic MPC for Distribution Grids
No full textThe efficacy of control systems for distribution grids can be influenced by different sources of uncertainty. Stochastic Model Predictive Control (SMPC) can be employed to compensate for such uncertainties by integrating their probability distribution into the control problem. An efficient SMPC algorithm for online control applications is the stochastic tube SMPC, which is able to treat the evaluation of the chance constraints analytically. However, this approach is efficient only when the calculation of the constraint back-off is applied to a linear model. To address this issue, this work employs Gaussian Processes to approximate the nonlinear part of the power flow equations based on offline training, which is integrated into the SMPC formulation. The resulting SMPC is first validated and then tested on a benchmark system, comparing the results with Deterministic MPC and SMPC that excludes Gaussian Processes. The proposed SMPC proves to be more efficient in terms of cost minimization, reference tracking and voltage violationreduction