A novel low power hybrid cache using GC-EDRAM cells

In a typical embedded CPU, large on-chip storage is critical to meet high performance requirements. However, the fast increasing size of the on-chip storage based on traditional SRAM cells makes the area cost and energy consumption unsustainable for future embedded applications. Replacing SRAM with DRAM on the CPU’s chip is generally considered not worthwhile because DRAM is not compatible with the common CMOS logic and requires additional processing steps beyond what is required for CMOS. However a special DRAM technology, Gain-Cell embedded-DRAM (GC-eDRAM)  [1], [2], [3] is logic compatible and retains some of the good properties of DRAM (small and low power). In this paper we evaluate the performance of a novel hybrid cache memory where the data array, generally populated with SRAM cells, is replaced with GC-eDRAM cells while the tag array continues to use SRAM cells. Our evaluation of this cache demonstrates that, compared to the conventional SRAM-based designs, our novel architecture exhibits comparable performance with less energy consumption and smaller silicon area, enabling the sustainable on-chip storage scaling for future embedded CPUs.     

A new analysis of two phase flow on hydrogen production from water electrolysis

It is clear that the entire world have to research, develop, demonstrate and plan for alternative energy systems for shorter term and also longer term. As a clean energy carrier, hydrogen has become increasingly important. It owes its prestige to the increase within the energy costs as a result of the equivocalness in the future availability. Two phase flow and hydrogen gas flow dynamics effect on performance of water electrolysis. Hydrogen bubbles are recognized to influence energy and mass transfer in gas-evolving electrodes. The movement of hydrogen bubbles on the electrodes in alkaline electrolysis is known to affect the reaction efficiency. Within the scope of this research, a physical modeling for the alkaline electrolysis is determined and the studies about the two-phase flow model are carried out for this model. Internal and external forces acting on the resulting bubbles are also determined. In this research, the analytical solution of two-phase flow analysis of hydrogen in the electrolysis is analyzed.     

Can hydrogen be the sustainable fuel for mobility in India in the global context?

This article provides a critical assessment of H₂ from the standpoint of more widespread use as a sustainable fuel for Indian mobility applications in the global context. The potential techno-economic advantages of utilizing H₂ for automobiles rather than battery electric vehicles or conventional internal combustion engine vehicles are emphasized. The present assessment demonstrates that H₂ production, storage, and distribution costs are the primary challenges, and a significant improvement is still necessary for H₂ to compete either against the internal combustion engine vehicle or the battery electric vehicle to win the race, arguably. The secondary challenges have also been demonstrated, which include the cost of the fuel cell stack and the modifications associated with internal combustion engine vehicles, as well as regulatory and safety concerns, which impede the widespread usage of H₂. It is critical that policy-making for sustainable mobility in India is possible with the aid of a National H₂ Energy Road-Map. This in turn can achieve a cost target of $0.5/kg for H₂.     

Comprehensive numerical investigations on the thermal performance of heat recirculating micro combustor with pin fins for micro-thermal voltaic system applications

Aiming at improving the relatively low energy output and energy conversion efficiency of the micro-thermal voltaic (MTPV) system, an innovative heat recirculating micro combustor with pin fins is designed. The effects of pin fins arrangement, hydrogen/air equivalent ratio on the energy output and performance of CHMC, HMCP and HMCI are compared and investigated. The result shows that when the V_in is 6 m/s and Φ is 1.0, the emitter power of CHMC is 72.76W, and that of HCMP and HCMI micro combustor are 75.99W and 76.35W. and the emitter efficiency of CHMC, HCMP and HCMI is 41.93%, 43.26% and 44.01%. HMCI has better energy output capability compared with CHMC and HMCP. Even though, HMCI brings a higher pressure drop, it is within the acceptable range. When the V_in is 6 m/s, the pressure drop from the pin fins only accounts for 26.4% of the total pressure drop for HMCI. Through the study of equivalent ratio, it is found that HMCI has good adaptability in different equivalent ratio range. This work provides new ideas for the development of MTPV system in the future.     

Recent insights in synthesis and energy storage applications of porous carbon derived from biomass waste: A review

In the last few decades, global warming, environmental pollution, and an energy shortage of fossil fuel may cause a severe economic crisis and health threats. Storage, conversion, and application of regenerable and dispersive energy would be a promising solution to release this crisis. The development of porous carbon materials from regenerated biomass are competent methods to store energy with high performance and limited environmental damages. In this regard, bio-carbon with abundant surface functional groups and an easily tunable three-dimensional porous structure may be a potential candidate as a sustainable and green carbon material. Up to now, although some literature has screened the biomass source, reaction temperature, and activator dosage during thermochemical synthesis, a comprehensive evaluation and a detailed discussion of the relationship between raw materials, preparation methods, and the structural and chemical properties of carbon materials are still lacking. Hence, in this review, we first assess the recent advancements in carbonization and activation process of biomass with different compositions and the activity performance in various energy storage applications including supercapacitors, lithium-ion batteries, and hydrogen storage, highlighting the mechanisms and open questions in current energy society. After that, the connections between preparation methods and porous carbon properties including specific surface area, pore volume, and surface chemistry are reviewed in detail. Importantly, we discuss the relationship between the pore structure of prepared porous carbon with surface functional groups, and the energy storage performance in various energy storage fields for different biomass sources and thermal conversion methods. Finally, the conclusion and prospective are concluded to give an outlook for the development of biomass carbon materials, and energy storage applications technologies. This review demonstrates significant potentials for energy applications of biomass materials, and it is expected to inspire new discoveries to promote practical applications of biomass materials in more energy storage and conversion fields.     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Digital twin-based analysis of volumetric error mapping procedures

The evaluation of the volumetric accuracy of a machine tool is an open challenge in the industry, and a wide variety of technical solutions are available in the market and at research level. All solutions have advantages and disadvantages concerning which errors can be measured, the achievable uncertainty, the ease of implementation, possibility of machine integration and automation, the equipment cost and the machine occupation time, and it is not always straightforward which option to choose for each application. The need to ensure accuracy during the whole lifetime of the machine and the availability of monitoring systems developed following the Industry 4.0 trend are pushing the development of measurement systems that can be integrated in the machine to perform semi-automatic verification procedures that can be performed frequently by the machine user to monitor the condition of the machine. Calibrated artefact based calibration and verification solutions have an advantage in this field over laser based solutions in terms of cost and feasibility of machine integration, but they need to be optimized for each machine and customer requirements to achieve the required calibration uncertainty and minimize machine occupation time.This paper introduces a digital twin-based methodology to simulate all relevant effects in an artefact-based machine tool calibration procedure, from the machine itself with its expected error ranges, to the artefact geometry and uncertainty, artefact positions in the workspace, probe uncertainty, compensation model, etc. By parameterizing all relevant variables in the design of the calibration procedure, this simulation methodology can be used to analyse the effect of each design variable on the error mapping uncertainty, which is of great help in adapting the procedure to each specific machine and user requirements. The simulation methodology and the analysis possibilities are illustrated by applying it on a 3-axis milling machine tool.     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Fabrication of alumina ceramics with functional gradient structures by digital light processing 3D printing technology

Alumina ceramics with different unit numbers and gradient modes were prepared by digital light processing (DLP) 3D printing technology. The side length of each functional gradient structure was 10 mm, the porosity ratio was controlled to 70%, and the number of units were (1 × 1 × 1 unit) and (2 × 2 × 2 unit) respectively. The different gradient modes were named FCC, GFCC-1, GFCC-2 and GFCC-3. SEM, XRD, and other characterization methods proved that these gradient structures of alumina ceramics had only α-Al2O3 phase and good surface morphology. The mechanical properties and energy absorption properties of alumina ceramics with different functional gradient structures were studied by compression test. The results show that the gradient structure with 1 × 1 × 1 unit has better mechanical properties and energy absorption properties when the number of units is different. When the number of units is the same, GFCC-2 and GFCC-3 gradient structures have better compressive performance and energy absorption potential than FCC structures. The GFCC-2 gradient structure with 1 × 1 × 1 unit has a maximum compressive strength of 19.62 MPa and a maximum energy absorption value of 2.72 × 105 J/m3. The good performance of such functional gradient structures can provide new ideas for the design of lightweight and compressive energy absorption structures in the future.     

Cation and polyhedron substitution strategies: Effects on local crystal structure and on Bi3+ and Eu3+ co-doped inverse garnet phosphors’ luminescence property

Following the rapid growth of lightning technology, the development of red-emitting phosphors is effective for improving color temperature and color rendering index for w-LEDs devices. Herein, a single phased garnet phosphor with cation and polyhedron substitution modification was firstly prepared. For Mg₃Gd₂Ge₃O₁₂: Bi³⁺, Eu³⁺, the intensity has been remarkably improved by about 16% compared to the one without Bi³⁺ sensitization. The energy transfer mechanism is identified in this work. Based on cation and polyhedron substitution strategies, novel phosphors with different compositions were obtained and further modified the PL properties. With Lu³⁺ substitution, the bond lengths between Bi³⁺ ion and anion ligands are decreased and the site symmetry has been strengthened, which leads to a 21 nm blue shift when Lu³⁺ totally replaced Gd³⁺ ions. In addition, Lu³⁺ and [SiO₄] substitution strategies both effectively increased symmetric rigid structure, which leads to a significant improvement in thermal stability, indicating the samples own great potential in optical applications This work provides a new insight to synthesis red-emitting phosphors for warm white-LEDs.