•A computationally efficient method to characterise a latent heat store is proposed.•Method based on a dynamic neural network.•Experimental data collected to compile training data.•Neural network ...tested against experimental data for testing.•Results demonstrate that a dynamic neural network is suitable for this application.
Latent heat storage systems utilising phase change materials have potential to offer several advantages over sensible heat storage, including higher energy storage densities and thermal modulation. Despite these advantages, only a few commercialised products incorporating this technology exist due to several engineering challenges. One problem is how to model this technology in a computationally efficient manner which allows simulating this technology with variable heat sources such as solar thermal and heat pump systems and assess their long-term system performance. In this study, the application of a dynamic neural network for this purpose is investigated, where a Layered Digital Dynamic Neural (LDDN) type network is trained using experimental data to approximate the outlet fluid temperature of a latent heat storage system given inlet fluid temperature and mass flow rate.
To acquire the training data necessary for the neural network, an experimental apparatus was designed, built and operated under laboratory conditions. Twenty experiments were conducted to obtain training data where the latent heat storage system was charged to different operating temperatures ranging from 25 to 70 °C. The mass flow rate of the heat exchanger fluid flowing through the heat exchanger was also varied: 0.045 and 0.05 kg/s such that the flow of heat exchanger fluid remained turbulent. These data were then presented to the network for training and optimisation of the network architecture using the Bayesian Regularization training algorithm. It was found, that the LDDN type architecture was suitable to characterise the thermal operational behaviour of a latent heat storage system with good accuracy and with little computational effort once trained. Based on an energy analysis, the neural network response predicted the quantity of energy stored and discharged with approximately 5% and 7% accuracy respectively when presented with data not used during the training process. These results indicate that a dynamic neural network may be a computationally efficient method to model the non-linear operational characteristics of a latent heat storage system. It may therefore be implemented within a simulation environment such as TRNSYS or Simulink.
•Direct-Metal-Laser-Sintered (DMLS) aluminium Heat Exchangers (HEX)•DMLS HEX filled with Phase Change Materials (PCM) for Li-Ion cells isothermalisation.•Effect of PCM mass and HEX geometry on Li-Ion ...cells isothermalisation.•Single discharge and consecutive charge/discharge cycles tests conducted.
This study investigates the effect of mass and geometry as design parameters of a Lithium-Ion (Li-Ion) cell Thermal Management System (TMS) based on a Direct-Metal-Laser-Sintered (DMLS) structure filled on Phase Change Material (PCM). As part of a TMS, complex heat exchanger geometries are produced using DMLS technique, filled with a PCM, and experimentally tested using an established methodology. The design parameters investigated are PCM mass and DMLS heat exchanger geometry to assess the effects of increased thermal energy storage capacity and enhanced equivalent thermal conductivity respectively on the overall performance of a Li-Ion pouch cell. Constant discharge rate tests and stress sequences are used to reproduce realistic Li-Ion cell operating conditions. It has been shown that all PCM TMS effectively improve the isothermalisation of the cell under a single discharge at rate 3C compared to natural convection and decreased the cell maximum temperature by at least 5 °C. The enhanced heat transfer performance of finned designs further improves the temperature uniformity and decreases the cell maximum temperature. When tested under stress sequences, the TMS characterised by a higher thermal mass maintained functional isothermalisation for 5 cycles; this is an increase of two cycles than for the TMS with a lower thermal mass. By using a Pareto front analysis based on thermal and geometrical variables, specific designs are indicated as the best combination of thermal performance and additional weight for single discharge tests (i.e., intermittent load) and stress sequences (i.e., constant load).
Redox-based random-access memory (ReRAM) has the potential to successfully address the technological barriers that today's memory technologies face. One of its promising features is its fast ...switching speed down to 50 ps. Identifying the limiting process of the switching speed is, however, difficult. At sub-nanosecond timescales three candidates are being discussed: An intrinsic limitation, being the migration of mobile donor ions, e.g., oxygen vacancies, the heating time, and its electrical charging time. Usually, coplanar waveguides (CPW) are used to bring the electrical stimuli to the device. Based on the data of previous publications, we show, that the rise time of the effective electrical stimulus is mainly responsible for limiting the switching speed at the sub-nanosecond timescale. For this purpose, frequency domain measurements up to 40 GHz were conducted on three Pt\TaO x \Ta devices with different sizes. By multiplying the obtained scattering parameters of these devices with the Fourier transform of the incoming signal, and building the inverse Fourier transform of this product, the voltage at the ReRAM device can be determined. Finally, the rise time of the voltage at the ReRAM device is calculated, which is a measure to the electrical charging time. It was shown that this rise time amounts to 2.5 ns for the largest device, which is significantly slower than the pulse generator's rise time. Reducing the device's rise time down to 66 ps is possible, but requires smaller features sizes and other optimizations, which we summarize in this paper.
Long-term retention is one of the major challenges concerning the reliability of redox-based resistive switching random access memories based on the valence change mechanism (VCM). The stability of ...the programmed state has to be ensured over several years, leaving a sufficient read window between the states, which is even more challenging at large statistics. Thus, the underlying physical mechanisms have to be understood and experimental data have to be evaluated accurately. Here, it shows that the retention behavior of the high resistive state (HRS) is more complex than that of the low resistive state and requires a different evaluation method. In this work, we experimentally investigate the retention behavior of 5M VCM devices via accelerated life testing and show the difficulties of commonly used evaluation methods in view of the HRS. Subsequently, we present a new evaluation method focusing on the standard deviation of the HRS current distribution. Hereby, an activation energy for the degradation process can be extracted, which is essential for the prediction of the devices’ behavior under operating conditions. Furthermore, we reproduce the experimentally observed behavior with our 3D Kinetic Monte Carlo simulation model. We confirm the plausibility of our evaluation method and are able to connect the calculated activation energy to the migration barriers of oxygen vacancies that we implemented in the model and that we believe play a key role in the experimentally observed degradation process.
We fabricated high quality Nb/Al2O3/Ni(0.6)Cu(0.4)/Nb superconductor-insulator-ferromagnet-superconductor Josephson tunnel junctions. Using a ferromagnetic layer with a steplike thickness, we obtain ...a 0-pi junction, with equal lengths and critical currents of 0 and pi parts. The ground state of our 330 microm (1.3lambda(J)) long junction corresponds to a spontaneous vortex of supercurrent pinned at the 0-pi step and carrying approximately 6.7% of the magnetic flux quantum Phi(0). The dependence of the critical current on the applied magnetic field shows a clear minimum in the vicinity of zero field.
We demonstrate the fabrication of a 3D memory architecture based on the resistive switching effect. Resistive memory (RRAM) is under wide investigation since it is non-volatile, promises fast ...operation and can be integrated into high density architectures like crossbar arrays. Here, silver-doped methyl-silsesquioxane (MSQ) is integrated in crossbar array structures for the following reasons. First, the material at the same time provides good planarization properties so that emerging lithography techniques like nanoimprint lithography (NIL) are applicable. Second, we could prove that silver-doped MSQ can be used as resistive switching material on the nano scale. Using this technique, crossbar arrays with a minimum feature size of only 100
nm are stacked on each other and the functionality is proved by electrical characterization. This comprises the doubling of the memory density and furthermore even higher integration is in principle not limited by this technique, while the CMOS overhead increases only slightly.
The formation mechanism of 2-dimensional electron gases (2DEGs) at heterointerfaces between nominally insulating oxides is addressed with a thermodynamical approach. We provide a comprehensive ...analysis of the thermodynamic ground states of various 2DEG systems directly probed in high temperature equilibrium conductivity measurements. We unambiguously identify two distinct classes of oxide heterostructures: For epitaxial perovskite/perovskite heterointerfaces (LaAlO3/SrTiO3, NdGaO3/SrTiO3, and (La,Sr)(Al,Ta)O3/SrTiO3), we find the 2DEG formation being based on charge transfer into the interface, stabilized by the electric field in the space charge region. In contrast, for amorphous LaAlO3/SrTiO3 and epitaxial γ-Al2O3/SrTiO3 heterostructures, the 2DEG formation mainly relies on the formation and accumulation of oxygen vacancies. This class of 2DEG structures exhibits an unstable interface reconstruction associated with a quenched nonequilibrium state.
Memristive switches are promising devices for future nonvolatile nanocrossbar memory devices. In particular, complementary resistive switches (CRSs) are the key enabler for passive crossbar array ...implementation solving the sneak path obstacle. To provide logic along with memory functionality, "material implication" (IMP) was suggested as the basic logic operation for bipolar resistive switches. Here, we show that every bipolar resistive switch as well as CRSs can be considered as an elementary IMP logic unit and can systematically be understood in terms of finite-state machines, i.e., either a Moore or a Mealy machine. We prove our assumptions by measurements, which make the IMP capability evident. Local fusion of logic and memory functions in crossbar arrays becomes feasible for CRS arrays, particularly for the suggested stacked topology, which offers even more common Boolean logic operations such as and and nor .
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