A Computer Model for In Silico Trials on Pacemaker Energy Efficiency

Malaysia, as one of the top energy subsidizing countries, has announced to remove energy subsidies necessarily, not only to reduce energy consumption and the government budget deficit but also to improve overall efficiency and air quality. Therefore, this study evaluates the impacts of rationalizing energy subsidy and its energy efficiency improvement during 2010–2030 using a dynamic recursive computable general equilibrium model. Results revealed that reducing energy subsidies decreases energy consumption and emissions of all air pollutants. While the economic performance of the country improves in the long run due to stimulation in capital demand and investment, it reduces in the short run. Energy efficiency also improves by 1.1% and 2.3%, in the short run, in response to a reduction of 10% and 100% in energy subsidies, respectively. Energy efficiency improvements decrease the negative effects of pure subsidy policies on real GDP, trade, investment, and household consumption. The efficiency improvement policies also are effective in reducing more level of the rebound effect and lead to more energy saving in the economy, particularly in the petroleum products sector. The impacts on the rebound effect also differ across economic sectors. The results of this study provide new insights for energy subsidy policy and energy efficiency and suggest that additional tools and policies are required for improving the energy efficiency caused by phasing out energy subsidies. Malaysia, as one of the top energy subsidized countries, attempts to reduce the level of energy subsidies over time and, consequently, decline the use of fossil fuels in the economy. Therefore, this study analyzes the impacts of different subsidy reform policy on energy efficiency and, consequently, on economic and environmental performance and rebound effect of Malaysia by a recursive dynamic computable general equilibrium model.

Cyber-physical systems (CSPs) are demanding energy-efficient design not only of hardware (HW), but also of software (SW). Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Manage (DPM) are most popular techniques to improve the energy efficiency. However, contemporary complicated HW and SW designs requires more elaborate and sophisticated energy management and efficiency evaluation techniques. This paper is concerned about energy supply planning for real-time scheduling systems (units) of which tasks need to meet deadlines. This paper presents a model-based compositional energy planning technique that computes a minimal ratio of processor frequency that preserves schedulability of independent and preemptive tasks. The minimal ratio of processor frequency can be used to plan the energy supply of real-time components. Our model-based technique is extensible by refining our model with additional features so that energy management techniques and their energy efficiency can be evaluated by model checking techniques. We exploit the compositional framework for hierarchical scheduling systems and provide a new resource model for the frequency computation. As results, our use-case for avionics software components shows that our new method outperforms the classical real-time calculus (RTC) method, requiring 36.21% less frequency ratio on average for scheduling units under RM than the RTC method.

According to the federal law on energy saving and energy efficiency, in the industrial enterprises of Russia in 2020 is to be held mandatory energy audits, and measures aimed at saving resources. Experts estimate that total modernization of energy-intensive redistribution metallurgy, petrochemical and chemical industry will increase energy efficiency by 30%. For maximum effect, this problem must be addressed comprehensively, through the introduction of new energy-efficient technologies and the use of modern methods of analysis. In this article the author shows the possibility to solve tasks with high accuracy using a computer experiment without interfering with the work of the study unit. On the basis of computer modeling method in system “Aspen Hysys” was created column deizopentanizer model of the isomerization unit, allows to tracking the changes of process parameters in real time. By comparing the calculated data with the actual was confirmed its adequacy. This computer model allows to study the effect of the structure of the input feed of the column on the efficiency of the distillation process. It is shown that the changing of the input feed to the column with 20 and 76 plates on 33 and 67 thermal energy consumption in the reboiler is reduced by 11.4% and in the condenser at 11.9%. In this case, there is a possibility to reduce the consumption of irrigation by 19.5% without loss of quantity and quality of the extracted component. On the basis of the developed computer model is possible to carry out similar studies for the isomerization units of light gasoline fractions in order to improve their energy efficiency.

Recently Wireless sensor networks (WSNs) plays important role in monitoring physical environments. In mission critical applications like surveillance existence of coverage holes makes communication among sensor nodes impossible. Coverage hole may be defined as the failure of sensor nodes. There are various graph based hole detection approaches and algorithms proposed to detect the coverage hole and to heal the coverage hole. Energy efficiency and communication overhead are the important parameters in detecting the holes and successful delivery of data packets without delay. Most of the existing algorithms have disadvantages depending upon their computational model. This survey compares the various approaches and identifies their shortcomings based on computational model and communication overhead. A new model is also proposed for coverage hole detection.

From the analysis of available literary sources, it follows that the most effective way of introducing heat exchangers into production processes is by using computational fluid dynamics (CFD) modeling. This method also involves fine-tuning the components from which the model is formed. Energy efficiency, safety, and stable functionality of units are key aspects of production activities. CFD modeling provides ample opportunities for studying heat exchange processes in thermal installations, such as tubular heat exchangers. Optimizing the operation of such systems implies careful adjustments, including creating a three-dimensiona l model taking into account real dimensions, and flexible adjustment of various flow regimes and thermal conditions. 
 Process flow heating and cooling processes are standard practice in various industries. These operations are often performed in heat exchangers where the fluid flows through tubes in laminar conditions. Heat transfer mechanisms in such conditions are complex and not yet fully explored, as they include both forced and natural convection. This creates difficulties in accurately predicting the design of the heat exchanger. The use of simplified geometric models and limited information about oil refining processes in computer simulations significantly affects the analysis and optimization possibilities. This approach can significantly accelerate the development of new types of heat exchangers. Current research emphasizes design changes to improve heat transfer and other performance. 
 Previous studies note a lack of specific recommendations for design improvement, incomplete investigation of the causes of inaccurate results, and insufficient detail of important parameters. This study presents the model setup steps with emphasis on the important details of the heat exchanger. The grid adjustment process and the properties of the incoming oil are described. The impact of turbulence on crude oil and steam temperature changes is considered. The influence of speed on the heat exchange coefficient was also analyzed in detail, and prospects for further optimization were determined. The application of computer modeling in the oil and gas industry is important. It is a key tool for improving the efficiency, security, and sustainability of operations in the industry. 
 Computer modeling allows you to predict and optimize processes, create accurate virtual models of objects, analyze their security, and research new technologies. It also facilitates economic analysis, staff training, and innovation. As a result, computer simulation improves decisions, reduces risks, and promotes the development of the entire oil and gas industry. It is one of the effective methods of studying physical systems. Often, computer models are easier and more convenient to study, they allow conducting computational experiments, the real setting of which is difficult or can give an unpredictable result. All these aspects together are aimed at a deeper and more detailed understanding of heat exchange processes, which reveals the potential for improving this process in the future. 
 The main focus in this context is on the fundamental aspects of developing simulation models when using specialized software. The obtained results of the analysis and adjustment allow a deeper understanding of the model configuration process and the prospects for its further optimization. Increasing the flow rate promotes more efficient heat exchange due to the higher velocity of the medium. A linear dependence of the heat distribution along the length of the pipe and the heat transfer coefficient is obtained in the process of modeling.

The article deals with identifying and providing scientifically sound determination of design patterns for using LED sources to get energy efficient lighting with high quality parameters that affect the creation of comfortable indoor light environment. General research methods such as analysis, synthesis and generalization were used in this investigation, as well as additional methods such as historical retrospection, comparative analysis, monitoring method, study and generalization of experience (activity), scientific observation, computer and visual-graphic modeling, creative research experiment. As a result of the research the following patterns of designing the energy-efficient light environment by means of LED sources were defined: the design of lighting in a temporal context; the search for optimal solutions for combining natural and artificial lighting systems; the interdependence of improving energy efficiency and the quality of indoor light environment. The scientific novelty of the study consists in scientific development of an innovative direction in design theory – the study of design patterns of energy efficient lighting, which affect the creation of comfortable light environment in both aesthetic and psychophysiological senses. The practical significance of the obtained results lies in the fact that the main provisions and conclusions of this paper can be used in designing the light environment of modern energy efficient buildings, as well as to optimize and improve energy efficiency of existing lighting systems.KeywordsDesignIndoor light environmentEnergy efficiencyLighting of buildingsLuminairesLight-emitting diodes (LED)

Energy efficiency and economic growth: A retrospective CGE analysis for Canada from 2002 to 2012

The share of equipment and power use in smaller data centers (DCs) is comparable with that of more massive counterparts. However, they grabbed less attention in the literature despite being less energy-efficient. This study highlights the challenges of setting up a computational fluid dynamics (CFD) model of a 180-m2 small-size high-performance computing (HPC) DC and the validation procedure leading to a reasonably accurate model for the investigation of the thermal environment and potential energy efficiency improvements. Leaky floors, uneven placement of computing equipment and perforated tiles preventing separation of hot and cold air, low-temperature operation, and excessive cooling capacity and fan power were identified sources of energy inefficiency in the DC. Computational fluid dynamics model predictions were gradually improved by using experimental measurements for various boundary conditions (BCs) and detailed geometrical representation of large leakage openings. Eventually, the model led to predictions with an error of less than 1 °C at the rack inlet and less than 5 °C at the rack outlet. The ultimate objective was to use the validated CFD model to test various energy efficiency measures in the form of operational or design changes in line with the best practices. Impact of leakage between the raised floor and the room, reduced airflow rate, cold-aisle and hot-aisle separation, workload consolidation, and higher temperature operation were among the phenomena tested by using the validated CFD model. The estimated power usage effectiveness (PUE) value reduced from 1.95 to 1.40 with the proposed energy efficiency measures.

The continuous convergence of machine learning algorithms, 5G and beyond (5G+) wireless communications, and artificial intelligence (AI) hardware implementation hastens the birth of federated learning (FL) over 5G+ mobile devices, which pushes AI functions to mobile devices and initiates a new era of on-device AI applications. Despite the remarkable progress made in FL, huge energy consumption is one of the most significant obstacles restricting the development of FL over battery-constrained 5G+ mobile devices. To address this issue, in this article, we investigate how to develop energy- efficient FL over 5G+ mobile devices by making a trade-off between energy consumption for “working” (i.e., local computing) and that for “talking” (i.e., wireless communications) in order to boost the overall energy efficiency. Specifically, we first examine energy consumption models for GPU computation and wireless transmissions. Then, we overview the state of the art of integrating FL procedure with energy-efficient learning techniques (gradient sparsification, weight quantization, pruning, etc.). Finally, we present several potential future research directions for FL over 5G+ mobile devices from the perspective of energy efficiency.

The hardware complexity of modern integrated circuits keeps increasing at a steady pace. Heterogeneous Multi-Processor Systems-on-Chips (MPSoCs) integrate general-purpose processing elements, domain-specific processors, dedicated hardware accelerators, reconfigurable logic, as well as complex memory hierarchies and interconnect. While offering unprecedented computational power and energy efficiency, MPSoCs are notoriously difficult to program. This chapter presents Models of Computation (MoCs) as an appealing alternative to traditional programming methodologies to harness the full capacities of modern MPSoCs. By raising the level of abstraction, MoCs make it possible to specify complex systems with little knowledge of the target architecture. The properties of MoCs make it possible for tools to automatically generate efficient implementations for heterogeneous MPSoCs, relieving developers from time-consuming manual exploration. This chapter focuses on a specific MoC family called dataflow MoCs. Dataflow MoCs represent systems as graphs of computational entities and communication channels. This graph-based system specification enables intuitive description of parallelism and supports many analysis and optimization techniques for deriving safe and highly efficient implementations on MPSoCs.KeywordsHeterogeneous multicoresModels of computationDataflow programmingSoftware abstractionStreaming modelsCompilersApplication mappingSchedulingMulticore simulationDesign space exploration

With continuing progress in VLSI and ASIC technologies, digital signal processing (DSP) algorithms have continued to find great use in increasingly wide application areas. DSP has gained popularity also in embedded systems although these systems set challenging constraints for implementations. Embedded systems contain limited resources, thus embedded DSP systems must balance tradeoffs between the requirements on computational power and computational resources. Energy efficiency has been important in batterypowered devices, but nowadays also the limited heat dissipation in small devices calls for low-power consumption. Successful implementation of DSP applications in embedded systems requires tailoring, which in turn sets challenges for design methodologies. For this special issue, we received 14 submissions and a collection of seven papers was finally accepted. The special issue is opened by “Observations on power-efficiency trends in mobile communication devices,” where the authors O. Silven and K. Jyrkka analyze the power consumption in the current mobile communication devices. Several bottlenecks in the current implementation style have been identified, thus the paper provides a good motivation for the following papers. In “The Sandbridge SB3011 platform,” the authors John Glossner et al. describe a system-on-a-chip (SoC) multiprocessor targeted as a software-defined radio platform. The platform provides solutions to the challenges in future mobile devices given in the previous paper. “A shared memory module for asynchronous arrays of processors” authored byMichael J. Meeuwsen et al. considers also chip multiprocessors. The presented shared memory module can be used for interprocess communication or to increase application performance by parallelizing computation. In “Implementing a WLAN video terminal using UML and fully automated design flow” by Petri Kukkala et al., an automated design flow for multiprocessor SoC is presented. The flow is based on UML descriptions and the authors demonstrate their design flow with a design case. Programming of chip multiprocessor platforms is considered in “pn: a tool for improved derivation of process networks” by Sven Verdoolaege et al. The paper discusses conversion of sequential programs to process networks allowing optimization of communication channels and buffers. In “A SystemC-based design methodology for digital signal processing systems,” the authors Christian Haubelt et al. describe a design flow for SoC designs containing automatic design space exploration, performance evaluation, and automatic platform-based system generation. The design flow is based on SystemC descriptions and the presented tools can automatically detect the underlying model of computation. Application-specific implementations are often used to speedup certain DSP tasks in embedded systems. In “Priority-based heading one detector in H.264/AVC decoding,” the authors Ke Xu et al. consider such implementations to speed up video decoding applications. The authors present a low-power decoder implementation for context-adaptive variable length coding defined in H.264 standard.

Exploring the potential for energy conservation in French households through hybrid modeling

In view of the escalating energy costs and global environmental concerns, it has become imperative to achieve energy efficiency in modern buildings. A large fraction of the energy consumed in buildings is for such things as heating, ventilation, and air conditioning. Fuelled by the reality of the need to improve the energy efficiency of heating, ventilation, and air conditioning systems while not sacrificing indoor environmental quality, this study employs computational fluid dynamics modeling to optimize heating, ventilation, and air conditioning system operation and design, although the design and operation of the system are most likely more complex. Through advanced simulation techniques, the research investigates airflow distribution, thermal comfort, and pollutant dispersion within air-conditioned spaces. The study shows how computational fluid dynamics can be used to predict temperature gradients, air circulation optimization, and the effect of some of the architectural features including window placement, insulating quality and air circulation strategies. Furthermore, computational fluid dynamics is combined with Building Energy Modeling to run real world operation result, thus enabling data driven optimization. By integrating the controller with common power saving and heating, ventilation, and air conditioning control strategies, such as time and daily charging frequency switching, the capability to develop heating, ventilation, and air conditioning systems that dynamically respond to fluctuating loads is enhanced. It also outlines how computational fluid dynamics can be used for evaluating new energy saving strategies such as demand controlled ventilation and smart thermostat integration as well as renewable energy coupling. In addition, computational fluid dynamics is used not only to consider the associated system inefficiencies that impact consuming energy and creating discomfort for occupants such as airflow structure and heat accumulation at localized sites, but also to identify alternative configurations that demonstrate gains in energy or water consumption along with movement comfort. Additionally, the potential of the digital twin technology and its ability for real-time heating, ventilation, and air conditioning performance monitoring and predictive adaptation based on the real-time data streams is discussed. A comprehensive computational base for developing next-generation, energy-efficient, environment-friendly heating, ventilation, and air conditioning systems that meet regulatory standards and improve occupant well-being is provided. As a piece of work to address rising energy consumption in buildings, especially in HVAC systems, and the lack of efficiency of traditional designing methods, this paper is articulated. The existence of affordable and simple models that are often utilized to predict airflow and temperature dynamics across HVAC systems is emphasized, but it points to the fact that these models often fail to provide sufficiently accurate insights; there is energy inefficiency, poor thermal comfort, and high operational costs. However, Computational Fluid Dynamics fills in this gap by providing much more accurate ways of simulating real-world HVAC performance for dynamic conditions. It also suggests a Computational Fluid Dynamics modeling in conjunction with the Building Energy Model to improve energy efficiency and comfort.

Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications

This paper presents an energy-focused analysis of structural materials used in passenger cars, with a particular emphasis on the impact of construction materials on total energy consumption throughout the vehicle’s life cycle. Three production periods (2000, 2010, and 2020) were analysed for B- and C-segment vehicles using inventory data from Life Cycle Assessment databases, the scientific literature, and certified dismantling stations. The embodied energy of key material groups—steel, aluminium, plastics, and other materials—was calculated based on representative mass shares and material-specific energy intensity indicators. The computational model was supplemented with statistical analyses (Kolmogorov–Smirnov test, Levene’s test, ANOVA, and Tukey’s post hoc tests) to verify whether observed temporal trends were statistically significant. The results indicate that total material-related energy inputs increased from approximately 57 GJ to 64 GJ per vehicle, while the specific energy intensity per kilogram decreased from 47.6 MJ/kg to 42.6 MJ/kg. Aluminium exhibited a pronounced reduction in unit energy intensity due to the rising share of secondary materials, whereas plastics and other materials showed substantial increases. Steel remained the largest contributor in absolute terms because of its dominant mass share. This study highlights the growing importance of the production phase in the environmental balance of modern vehicles, particularly in the context of the rising share of lightweight materials and recycling-based components. The results emphasise the importance of energy-efficient material use and underscore the significance of material selection and recycling strategies in reducing energy demand within the automotive sector.