The practical applications of organic phase change materials (PCM) are greatly limited, due to their leakage in the melted state and unacceptably low thermal conductivity. To address such a challenge, we use a succule...
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The practical applications of organic phase change materials (PCM) are greatly limited, due to their leakage in the melted state and unacceptably low thermal conductivity. To address such a challenge, we use a succulent-based carbon aerogel (SCA), which consists of the epidermis, palisade tissue and spongy tissue, as an encapsulation scaffold for paraffin to fabricate PCM composites. The spongy tissue consisting of rich closed spherical cells allows a high loading efficiency (up to 95 wt%) for organic PCM, while the tightly-arranged palisade tissue and dense epidermis cells can act as two protective layers to prevent the leakage of liquid, enabling a mass loss as low as 1.3 wt% upon phase change. The PCM composites also show a high latent heat approaching that of pure paraffin and an excellent thermal cycling performance with 100% retention after being tested by using a differential scanning calorimeter 20 times. Moreover, the SCA not only serves as thermal conductive paths within the organic matrix, thereby remarkably enhancing the thermal conductivity of the PCM composites, but also acts as an effective photon captor and molecular heater, thus significantly increasing the light-to-thermal energy conversion efficiency of the PCM composites. As such, the SCA is an ideal multifunctional scaffold for PCM, which can advance the practical applications of PCM composites.
Paired carrier multiple access (PCMA) is one of the most common single-channel mixtures. It is still a great challenge to recover the transmitted bits from non-cooperative received PCMA signals, due to the high comple...
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Paired carrier multiple access (PCMA) is one of the most common single-channel mixtures. It is still a great challenge to recover the transmitted bits from non-cooperative received PCMA signals, due to the high complexity of existing single-channel blind source separation (SCBSS) algorithms. Hence, a double-direction delayed-decision-feedback sequence estimation (DD-DDFSE) algorithm for non-causal high-order channel is proposed to realise the separation of PCMA signals. The proposed algorithm employs the Viterbi algorithm (VA) with low-order channel twice instead of conventional VA with high-order channel once. The symbols estimated by the initial VA are utilised to equalise the causal and non-causal taps of the channel beyond the trellis state in the second VA. The relationship between the decision-feedback error from the initial VA and the performance of the second VA is also derived. Compared with state-of-the-art maximum likelihood sequence estimation algorithms, DD-DDFSE algorithm can not only decrease the computation complexity but also improve the separation performance.
Phase-change memory (PCM) compact modeling of the threshold switching based on a thermal runaway in Poole-Frenkel conduction is proposed. Although this approach is often used in physical models, this is the first time...
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Phase-change memory (PCM) compact modeling of the threshold switching based on a thermal runaway in Poole-Frenkel conduction is proposed. Although this approach is often used in physical models, this is the first time it is implemented in a compact model. The model accuracy is validated by a good correlation between simulations and experimental data collected on a PCM cell embedded in a 90 nm technology. A wide range of intermediate states is measured and accurately modeled with a single set of parameters, allowing multilevel programing. A good convergence is exhibited even in snapback simulation owing to this fully continuous approach. Moreover, threshold properties extraction indicates a thermally enhanced switching, which validates the basic hypothesis of the model. Finally, it is shown that this model is compliant with a new drift-resilient cell-state metric. Once enriched with a phase transition module, this compact model is ready to be implemented in circuit simulators. (C) 2018 The Japan Society of Applied Physics.
In recent years, the proton exchange membrane (PEM) fuel cell is regarded as the best choice in the next generation automobile power source owing to its high fuel conversion efficiency, low noise, almost zero emission...
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ISBN:
(数字)9781728130071
ISBN:
(纸本)9781728130088
In recent years, the proton exchange membrane (PEM) fuel cell is regarded as the best choice in the next generation automobile power source owing to its high fuel conversion efficiency, low noise, almost zero emissions, and low operating temperature. The working condition of PEM fuel cell depends upon several environmental parameters including the flow rate of fuel and oxidant, cell temperature, catalyst activity, and cell fittings. Mostly the data driven techniques are used to predict the voltage and power losses from a fuel cell in particular time. So instead of using a whole analytical model of fuel cell it is better to use Artificial Neural Network (ANN) model due to some of the parameters are very difficult to measure with respect to time. In this present work, it is investigated to develop a PEM fuel cell model using ANN technique. The experimental test on a real time fuel cell has been carried out to validate the ANN model. The different set of operating data is investigated with changing the environmental parameter. The ANN model is applied to emulate real operating conditions such as temperature, hydrogen consumption. After analysis the results it can be concluded that this presented model have good accuracy. Moreover, ANN learning methodology can be implemented to improve the PEM fuel cell stack efficiency. The model is implemented to determine the I-V performance of a single cell PEM fuel cell at different operating settings. The model could obtain the optimized values for the input variables corresponding to the value of objective function. Results showed a consistency between experimental data and the data made by the model. Therefore, it is indicated that the developed model is an effective method, which can predict the performance of fuel cell with high accuracy.
The organization of the CCD operation on the technical condition is considered. To control the development of the resource, taking into account the actual cyclograms of loading (during the flight of the aircraft, acco...
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The organization of the CCD operation on the technical condition is considered. To control the development of the resource, taking into account the actual cyclograms of loading (during the flight of the aircraft, according to the results of the trend analysis of the gas compressor units, control of the GTU operation), it is proposed to use the dependence of the resource on the parameter characterizing the mode (PCM). To establish the connection of the resource with PCM, it was proposed to use the Larsen-Miller diagram, supplemented by a number of constructions, as well as the TC (throttle characteristic) of the GTE, obtained experimentally or as a result of the “factory” GTE tests.
Today’s telemetry ground stations are migrating from traditional serial PCM data distribution to Telemetry over IP architectures. The Range Commanders Council has published IRIG 218-10 TELEMETRY TRANSMISSION OVER INT...
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There is a significant need to build efficient non-von Neumann computing systems for highly data-centric artificial intelligence related applications. Brain-inspired computing is one such approach that shows significa...
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There is a significant need to build efficient non-von Neumann computing systems for highly data-centric artificial intelligence related applications. Brain-inspired computing is one such approach that shows significant promise. Memory is expected to play a key role in this form of computing and, in particular, phase-change memory (PCM), arguably the most advanced emerging non-volatile memory technology. Given a lack of comprehensive understanding of the working principles of the brain, brain-inspired computing is likely to be realized in multiple levels of inspiration. In the first level of inspiration, the idea would be to build computing units where memory and processing co-exist in some form. Computational memory is an example where the physical attributes and the state dynamics of memory devices are exploited to perform certain computational tasks in the memory itself with very high areal and energy efficiency. In a second level of brain-inspired computing using PCM devices, one could design a co-processor comprising multiple cross-bar arrays of PCM devices to accelerate the training of deep neural networks. PCM technology could also play a key role in the space of specialized computing substrates for spiking neural networks, and this can be viewed as the third level of brain-inspired computing using these devices. (C) 2018 Author(s).
Proton Exchange Membrane Fuel Cell (PEMFC) has become one of the most promising energy technologies at the present time. Several factors are driving the growing interest in this technology. Modeling different phenomen...
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Proton Exchange Membrane Fuel Cell (PEMFC) has become one of the most promising energy technologies at the present time. Several factors are driving the growing interest in this technology. Modeling different phenomena occurring in PEMFC plays an important role in this development and performance. The performance of a Proton Exchange Membrane Fuel Cell (PEMFC) depends on the characteristics of the membrane, gas diffusion layer (GDL), catalyst and operating parameters such as operating pressure, cell operation temperature, relative humidity, and mass flow rate of feed gases, channel geometries and design of the stack. Recent studies on the compilation of factors affecting durability and performance of PEMFC indicate that the performance of fuel cell strongly depends on the performance of its membrane. In this paper, a three-dimensional PEM fuel Cell model has been developed and is used to investigate the effects of geometry membrane on cell performance. The numerical results indicated that a thinner membrane corresponds to the higher current density, the hydrogen and oxygen consumption and, accordingly water production is high. Finally, the numerical results of the proposed CFD model are compared with the available experimental data and that represent good agreement.
Latent heat is the most important property for phase change material (PCM) as thermal energy storage material. It is closely related to the internal properties of the material. The aim of this article is to examine th...
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Latent heat is the most important property for phase change material (PCM) as thermal energy storage material. It is closely related to the internal properties of the material. The aim of this article is to examine the effect of multi walled carbon nanotube (MWCNT) on the microstructural and thermal properties of a paraffin based PCM and their influence on energy storage. Thermal stability, functional group and morphology of a pure PCM and MWCNT mixed PCM have been analysed using DSC, FTIR, and FESEM, respectively. Microstructural observation shows that MWCNT is homogeneously incorporated into the PCM matrix. FTIR spectra of pure and MWCNT mixed PCM are similar, indicating that there was no chemical interaction. It was found from DSC result that MWCNT/RT44HC composite PCM shows a 2.7% increase in latent heat of fusion. The newly introduced composite material is suitable for energy storage in solar thermal and solar photovoltaic thermal applications.
Proton exchange membranes based on perfluorosulfonic acid (PFSA) ionomers are the industry standard for proton exchange membrane fuel cells. These ionomers offer remarkable proton conductivity and chemical stability, ...
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Proton exchange membranes based on perfluorosulfonic acid (PFSA) ionomers are the industry standard for proton exchange membrane fuel cells. These ionomers offer remarkable proton conductivity and chemical stability, especially when fully hydrated with water. Performance, however, rapidly decreases as the membranes dry out, thereby requiring external humidification equipment. Because of this, there exists a need for new ionomers that can maintain high proton conductivity at low humidification levels. Perfluoro bissulfonimides have acid strengths similar to those of sulfonic acids and can be used to extend the polymer side chain with an additional protogenic group. These polymers have been designated PerfluoroSulfonImide Acid (PFIA) or PerfluoroIonene Chain Extended (PFICE) ionomers. Compared to PFSAs, these ionomers have improved conductivities under all humidity conditions, however, the benefit of these improvements are most significant under the driest conditions.
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