Energy-efficient Machine Research Articles

A Novel Energy Efficient Multi-Dimensional Virtual Machines Allocation and Migration at the Cloud Data Center

A Novel Energy Efficient Multi-Dimensional Virtual Machines Allocation and Migration at the Cloud Data Center

Techno-Economic Development Methodology for Mini-Environments in Battery Cell Production

Due to the rising interest in electric vehicles, the demand for more efficient battery cells is increasing rapidly. To support this transformation, battery cells must become cheaper and environmentally friendly. Energy consumption during production is a major driver of costs and CO2 emissions. To fulfill societal demand, higher gravimetric and volumetric energy density of lithium-ion battery cells are required. Since these materials are significantly more sensitive to moisture, the production conditions need to be adapted accordingly. Therefore, the preparation of clean and extremely dry atmospheric conditions is crucial for production of high-quality battery cells. The technology approach "Mini-Environment" enables the substitution of conventional clean- and dry-rooms for significant energy and cost savings. However, the diverse product- and process-specific characteristics along the value chain require individual solutions to enable Mini-Environments for highly automated battery cell production. Due to multiple interacting requirements and characteristics, such as operator interventions, logistics interfaces and air handling procedures, there is a very high diversity of variants for the machine equipment design. Moreover, typical methodological procedures for development of quality-oriented and energy efficient production machines remain less applicable to the early stages of technological development. Both issues lead to the research questions, how to identify the independencies between costs, air handling, product and process characteristics and how to reach the most techno-economical machine design. To answer these questions, a systematic techno-economic development procedure was conducted. Following a four-phase analytical approach, based on an extended morphological box, cost analysis, techno-economic utility analysis and sensitivity loop, different Mini-Environment concepts were constructed, analyzed and cross-evaluated. The study also forecasts the CO2 emission rate reduction and cost savings compared to alternative machine concepts. In summary, a fully interlinked system design was adapted on a cell assembly line with a cost-saving potential of 56% for future battery cell production.

Energy efficient machine learning based SMART-A-BLE implemented Wireless Battery Management System for both Hybrid Electric Vehicles and Battery Electric Vehicles

Electric vehicles have a larger battery pack with more than 100's cells connected in series or parallel combinations to form many battery modules. A Battery Management system (BMS) continuously monitors Current, Voltage, and Temperature data to trigger control algorithms like cell balancing and thermal management using wired communication which is then sent to the cloud for real-time analytics [1]. Wired communication restricts the flexibility in battery module assembly and makes it difficult for battery swapping. The proposed wireless communication, along with the SMART-A-BLE algorithm for BMS in the electric vehicle, uses Bluetooth Low Energy [2], [3], [4], and [5]. SMART-A-BLE algorithm predicts the best connection interval using the Decision tree model based on the link quality factor captured based on real time latency. The model uses threshold latency, expected retransmission factor, data packet length, and the number of peripherals as input features. A look-up table-based algorithm is also proposed for resource constraint devices to achieve the optimal operating point where the slaves could transmit at both low latency and low current consumption with less congestion. SMART-A-BLE control algorithm outperforms the mathematical model-based algorithm in both ideal and interference conditions within a metallic container by maintaining robust connectivity.

Energy parameters assessment, under scale effect calculation of centrifugal fans with radial blades

The centrifugal fans with radial blades are used in various industrial energy systems, associated with high dustiness, due to their self-cleaning ability and their reliable operation. Radial fans are used in cement, asphalt industries and various dust collection facilities. The uniqueness of the different working environment and the specifics of each system require the use of a fan, designed for the particular application. Due to the dimensions of the machines, before the fan is produced, it normally goes through model tests. In model studies of rotodynamic fluid machines, differences are sought between the expected parameters from the application of the similarity theory and the obtained energy parameters. These differences are commonly referred to as the scale effect. Estimating the scale effect in model tests leads to the use of energy efficient machines for a given system. These are the one, that give the desired system parameters when operating in the region of maximum efficiency. Thus, the purpose of modeling is to transfer the efficiency obtained for a given mode of operation of the model machine to the prototype. The operation in the optimal mode of the prototype fan leads to energy savings and a reduction in energy costs for a given technological system. In the presented paper are evaluated the differences between expected and obtained parameters that help to realize a model for a centrifugal fan with radial blades. The results of model tests, conducted on two geometrically similar centrifugal fans with radial blades, where the inlet and outlet angles of the impeller are the same (β1=β2=900 ), are given. The fan impellers are semi-open type with 12 blades. Each of the impellers is tested in a geometrically similar helical chamber. Model tests have been conducted on a laboratory test-bench, meeting the requirements of AMCA 210 or ISO5801. The test-bench is equipped with the complete measuring equipment, according to the standards used. During the model tests, both the energy parameters of the fans and the parameters of the surrounding environment are measured. The results of the energy tests of the fans are given in the paper, as well as the coefficients concerning the scale effect are determined and analyzed.

Application of Renewable Energy in Indian agriculture

Energy plays a key role in agricultural production, post-production, rural domestic and livestock raising sectors, both directly as different forms of energy and fuel inputs for various purposes like operation of machinery, equipment, lighting, etc., and indirectly, for seed, fertilizers and chemicals production used in rural activities. India needs a secure, affordable and sustainable energy system to power effective economic growth well supported with renewable energy sources. Engineering interventions for effective (functionally, energetically and economically) systems is utmost needed for rural India. There is need for a revolution in mechanized agriculture, so that new energy efficient systems and machines can be recommended and introduced for small and marginal farmers. There is need to enhance the power availability and optimize the energy input to the rural sector to obtain the better income to agro-rural producers, traders and industrialists. Energy interventions are needed to use the locally available energy sources curtailing the use of fossil energies. The major targets in Indian perspective are the use of available and developed renewable and conventional energy sources and gadgets in rural society and agro-industry using all kinds of available biomass resources including solar electricity, heating and pumping at decentralised mode. We need to employ recent advanced technologies like plasma technology, nano-technology, IoT, artificial intelligence and robotics for effective generation of energy and valued products from rural local renewable resources. Research on solar energy use for production agriculture is challenging due to fluctuating need of torque depending on the agricultural field conditions. The use of batteries for storing and releasing power is another concern for long term use of solar photovoltaic (SPV) gadgets. Bio-CNG has emerged as an option for ex-situ management of crop residue. Thermo-chemical and bio-chemical conversion based electrical power routes are available and there is a need to promote these with better incentives. Energy efficient functionally improved mechanical systems to be introduced in the Indian farms need to be evolved.

Structural stability of Polylactide rope drums with a hybrid Gyroid-spokes infill structure, manufactured through fused filament fabrication

Additive manufacturing (AM) offers greater design freedom than conventional manufacturing processes. AM allows for components with complex infill structures of e. g. triply periodic-minimal surfaces (TPMS) that lead to significant weight reduction. Nevertheless, AM is mainly used in specialised engineering branches such as aerospace and medical engineering. This is due to high system cost and the high energy costs of the machines used, which utilise selective laser sintering (SLS), laser powder bed fusion (LPBF), or stereolithography (SLA). Fused filament fabrication (FFF) can offer cheaper and more energy-efficient machines. A series of tensile tests with FFF rope drum bodies made from polylactide (PLA) aims to investigate the stability of FFF machine elements. The test specimens possess a novel, hybrid infill structure comprised of straight spokes and a TPMS-gyroid surface. Compared to previous investigations, the specific breaking force – relative to the test specimens’ weight – increased by up to 159%. Whereas the infill density affects tensile strength as well as deformation, the infill distribution between the TPMS and spokes part of said hybrid structure affects especially the deformation behaviour. The results show that FFF machine elements such as the tested drum bodies have a realistic perspective for use in future products with regard to static strength.

Bayesian continual learning via spiking neural networks.

Among the main features of biological intelligence are energy efficiency, capacity for continual adaptation, and risk management via uncertainty quantification. Neuromorphic engineering has been thus far mostly driven by the goal of implementing energy-efficient machines that take inspiration from the time-based computing paradigm of biological brains. In this paper, we take steps toward the design of neuromorphic systems that are capable of adaptation to changing learning tasks, while producing well-calibrated uncertainty quantification estimates. To this end, we derive online learning rules for spiking neural networks (SNNs) within a Bayesian continual learning framework. In it, each synaptic weight is represented by parameters that quantify the current epistemic uncertainty resulting from prior knowledge and observed data. The proposed online rules update the distribution parameters in a streaming fashion as data are observed. We instantiate the proposed approach for both real-valued and binary synaptic weights. Experimental results using Intel's Lava platform show the merits of Bayesian over frequentist learning in terms of capacity for adaptation and uncertainty quantification.

Determination of Aerodynamic Losses of Electric Motors

The energy efficiency of machines is nowadays an intensively studied problem. The efficiency of the induction motor is dominantly influenced by the rotor’s and stator winding’s temperature. The main goal of the research presented in this paper is to develop a methodology based on Computational Fluid Dynamics (CFD) analysis of internal and external aerodynamics, which is necessary for the optimisation of cooling of the induction motors. In this paper, the theoretical, as well as the numerical study of the internal and external aerodynamics of the induction motor, is described and verified by the experimental measurements. In the CFD-based numerical study, the Reynolds-averaged Navier–Stokes (RANS) turbulence modelling approach was applied to the flow field simulations inside and outside the induction motor. The complexity of the solved problem is increased not only by the geometric asymmetry but also by the flow’s asymmetric character caused by the fan’s rotation to cool the motor casing. This increases demand, especially on computational resources, as it is impossible to create a simplified numerical model incorporating symmetry. The volume flow of the cooling air and velocity between ribs was measured for the experimental study. Comparing the results of the Computational Fluid Dynamics (CFD) simulations and data obtained from the experimental measurement, we concluded that the results of CFD simulations are in good relationship with the results of experimental measurement and analytical approximations. An experimentally validated CFD model of the induction motor, the so-called digital twin, will be in the future used for virtual optimisation of the new designs concerning minimising losses and maximising efficiency, respectively.

Energy Efficiency in Military Camps

The article deals with the issue of the energy efficiency in military camps. The military camps are mostly built during military deployments in case of exercises or missions. As different kinds of temporary structures are typically used to shelter large part of the camps’ facilities, the area of improvement for better energy efficiency lies mainly in the utilization of energy efficient devices, machines and pieces of equipment that are used by deployed personnel. The resulting lower energy consumption does not only bring lower financial and logistical burden to the military units but also contribute to the lower environmental impact of military activities. And ultimately, lowering energy use, especially fossil fuel, may result in cleaner environment and significantly affect health of deployed personnel. Overall, there are many benefits brought by energy efficient devices and equipment in several overlapped areas in military camps and their surroundings.

Energy optimal point-to-point motion profile optimization

Position-controlled systems driving repetitive tasks are of significant importance in industrial machinery. The electric actuators used in these systems are responsible for a large part of the global energy consumption, indicating that major savings can be made in this field. In this context, motion profile optimization is a very cost-effective solution as it allows for more energy-efficient machines without additional hardware investments or adaptions. In particular, mono-actuated mechanisms with position-dependent system properties have received considerable attention in literature. However, the current state-of-the-art methods often use unbounded design parameters to describe the motion profile. This both increases the computational complexity and hampers the search for a global optimum. In this paper, Chebyshev polynomials are used to describe the motion profile. Moreover, the exact bounds on the Chebyshev design parameters are derived. This both seriously reduces the computational complexity and limits the design space, allowing the application of a global optimizer such as the genetic algorithm. Experiments validate the added value of the chosen approach. In this study, it is found that the energy consumption can be reduced by 62.9% compared to a standard reference motion profile.

Study on Energy Efficiency of an Off-Grid Vending Machine with Compact Heat Exchangers and Low GWP Refrigerant Powered by Solar Energy

The purpose of this research is to design an energy-efficient off-grid vending machine that has a refrigeration system powered by an environmentally friendly refrigerant. The paper presents details from the equipment’s design phase in terms of the resistance structure, photovoltaic panels area dimensioning, but also in terms of the necessary cooling load to be provided by the refrigeration system. The refrigerant chosen was R290 following a comparative analysis, which showed that it has an efficiency 6.6% higher than R134a. During the monitoring periods, the outdoors temperature varied between −4–28 °C, the relative humidity between 22–100% and yet only a slight indoor temperature variation of ±1.5 °C compared to the set value was recorded. The battery module managed to store enough energy from the photovoltaic panels to keep the vending machine functional through three consecutive days with adverse environmental conditions, as evidenced by the low variation of indoor temperature

Simulation platform for pattern recognition based on reservoir computing with memristor networks

Memristive systems and devices are potentially available for implementing reservoir computing (RC) systems applied to pattern recognition. However, the computational ability of memristive RC systems depends on intertwined factors such as system architectures and physical properties of memristive elements, which complicates identifying the key factor for system performance. Here we develop a simulation platform for RC with memristor device networks, which enables testing different system designs for performance improvement. Numerical simulations show that the memristor-network-based RC systems can yield high computational performance comparable to that of state-of-the-art methods in three time series classification tasks. We demonstrate that the excellent and robust computation under device-to-device variability can be achieved by appropriately setting network structures, nonlinearity of memristors, and pre/post-processing, which increases the potential for reliable computation with unreliable component devices. Our results contribute to an establishment of a design guide for memristive reservoirs toward the realization of energy-efficient machine learning hardware.

The capacitated lot-sizing and energy efficient single machine scheduling problem with sequence dependent setup times and costs in a closed-loop supply chain network

The capacitated lot-sizing and energy efficient single machine scheduling problem with sequence dependent setup times and costs in a closed-loop supply chain network

Energy consumption, carbon emissions, product cost, and process time in incremental sheet forming process: A holistic review from sustainability perspective

Due to continuous worsening of the environment and depletion of natural resources, concept of sustainability in manufacturing sector is gaining an increasing attention. New processes and strategies characterized by efficient use of energy and materials, low processing time and zero or nearly zero green-house emissions are being explored. Incremental sheet forming (ISF) is a comparatively new sheet forming process with high economic payoff for small production runs and customized orders. The current study presents a comprehensive review on the sustainability aspects of the process. The process performance in terms of power demand, energy consumption, cost, CO2 emissions, processing time, and material wastage/usage is analyzed on the basis of published literature, and important conclusions and recommendations are drawn to utilize the process for cleaner, economical and time efficient production. The review establishes that ISF is more sustainable process than conventional forming methods for small production runs. Further, its sustainability performance can be further enhanced by choosing optimum process conditions especially lower spindle rotation, larger feed rate and step size. Shorter tool paths and advent of energy efficient machines is likely to further elevate the process performance. The review also identifies future work and presents fundamental guidelines and recommendations for establishing the sustainable ISF process.

On-line motion profile optimization for reciprocating mechanisms

Reciprocating mechanisms are widely used in industry because a complex movement is achieved by a simple rotation of the driven axis. Given the tendency to evolve towards more energy-efficient machines and flexible production, motion profile optimization offers a cost-effective solution as it results in large energy savings without any hardware adaptions. However, the existing optimizers are used off-line because the position-dependent parameters such as load torque and inertia of the system model must be known in advance. When the actual machine differs from the model, or when parameters change during operation due to process flexibility, the off-line determined motion profile is no longer optimal and results in unnecessary energy consumption.This paper therefore presents an on-line approach in which the varying inertia is estimated on the actual machine and used for updating the motion profile. The sliding discrete Fourier transform is proposed for real-time estimation and a gradient-based algorithm combined with Chebyshev polynomials is proposed for on-line optimization. Experimental validation on an industrial pick-and-place unit proves that the presented method leads to similar energy savings as off-line optimizers, but without prior knowledge of the parameters, and is moreover capable of handling mass changes during operation.

Improvement of refrigerating machine energy efficiency through radiative removal of condensation heat

In regions with a continental climate, refrigerators with air-cooled condensers operate at high condensation pressures during the summer season which reduces their efficiency and accelerates the wear of compressors. To reduce condensation pressure, it was proposed to use radiative cooling which is a way of heat removal through the planet's atmosphere to outer space in a form of infrared radiation. A refrigerating machine with an assembly of condensation heat removal including air and liquid cooling condensers connected in series has been developed. To reduce the condensation temperature, a pre-cooled heat-transfer agent is fed to the liquid cooling condenser during the day hours at high atmospheric temperatures. At night, the heat-transfer agent is cooled by radiative cooling. An experimental study of the operation of a 600 W refrigerating machine including a sealed piston compressor was conducted. R134a refrigerant was used. Supply of pre-cooled heat-transfer agent at +33.1 °С has provided a reduction of condensation temperature from +47.0 to +39.1 °С. The study was conducted at an atmospheric air temperature of +38.0 °С. The degree of pressure growth was decreased by 30 %. The refrigeration coefficient was increased by 11 %. In comparison with the conventional scheme with an air-cooled condenser, energy consumption by the system did not change in the daytime. The offered scheme of condensation heat removal reduces the pressure of condensation and provides stability for refrigerating machine operation. It can be used in stationary refrigerating machines at high daytime temperatures.

Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

AbstractIn cloud computing, virtual machine (VM) consolidation and migration are significant schemes to enhance the energy efficiency and costs of the cloud environment. The process of VM consolidation is to execute a more number of tasks with less power consumption. However, due to unreliable physical resources, energy consumption is increased. So, to solve this issue, improved beetle swarm optimization (IBSO) algorithm based on energy‐aware VM consolidation is presented in this article. IBSO algorithm joins together beetle swarm optimization (BSO) and particle swarm optimization (PSO). The proposed algorithm is defined with the effective representation of solution and multi‐objective functions such as power consumption, resource wastage, and SLA violation. Results of the work are depicted that the proposed approach outperformed different previous methods in terms of different evaluation measures.

Energy Efficiency in Machining Systems Based on Power-Law Model for Specific Energy Consumption

Energy and resource efficiency has become the core of manufacturing activities as more strict requirements for reducing the environmental burden. This paper deals with a policy to enhance the energy efficiency of manufacturing systems composed of CNC machine tools. We have proposed an empirical model to predict the power consumption of a machine tool based on a power function between specific energy consumption and material removal rate. This study extends our previous work and has two purposes. The first purpose is the theoretical grounds for the adequacy of our proposed model by observing power-law between material removal rate and specific energy consumption. Second, two energy efficiency criteria are derived: energy consumption effect and energy efficiency in machining, using the power-law model. It is demonstrated that a higher material removal rate provides lower electric energy demand and enhances energy efficiency through assessments using the proposed energy efficiency criteria.

Benchmarking In-Memory Computing Architectures

In-memory computing (IMC) architectures have emerged as a compelling platform to implement energy efficient machine learning (ML) systems. However, today, the energy efficiency gains provided by IMC designs seem to be leveling off and it is not clear what the limiting factors are. The conceptual complexity of IMCs combined with the absence of a rigorous benchmarking methodology makes it difficult to gauge progress and identify bottlenecks in this exciting field. This paper presents a benchmarking methodology for IMCs comprising: 1) a compositional view of IMCs that enables one to parse an IMC design into its canonical components; 2) a set of benchmarking metrics to quantify the performance, efficiency, and accuracy of IMCs; and 3) a strategy for analyzing the reported IMC data and metrics. We apply the proposed benchmarking methodology on an extensive database of IMC metrics extracted from >70 IC designs published since 2018, in order to infer and comprehend trends in this area. Our benchmarking effort indicates: 1) SRAM-based IMCs show a clear win in terms of energy efficiency and compute density over digital accelerators at the bank-level but the energy efficiency gap reduces dramatically when comparing at the processor level; 2) eNVM-based IMCs lag behind SRAM-based IMCs in terms of both energy efficiency and compute density, and surprisingly lag digital accelerators in terms of compute density; 3) the compute (bank-level) accuracy of IMCs, though a critical metric, is pervasively neglected in publications as is the energy vs. accuracy trade-off inherent to IMCs.

RESEARCH OF THE RHEOLOGICAL PARAMETERS OF FEED GRAIN IN THE PROCESS OF THE COMBINED IMPACT-CUTTING GRINDING

Taking into consideration current realities and various factors, which create difficulties for the effective functioning of Ukraine’s national economic system, the Agro-Industrial Complex of Ukraine acts as the most stable part of it, that is characterized by conservatism and is one of the main and stable cross-sectoral formations due to revenues. However, despite the strategic priority of the AIC, the high level of energy consumption by domestic producers does not enable providing an appropriate level of competitiveness of Ukraine’s AIC in domestic and foreign markets, at the same time the rationalisation of energy use necessitates the substantiation of the energy-saving variant of its development by implementing energy-efficient machines and technologies in the system of feed preparation and feeding animals. In the technological process of feed preparation, the share of energy consumption for grinding can be more than 50%. Thus, it becomes obvious that the profitability and competitiveness of the livestock industry largely depend on the energy efficiency of realizing this technological operation, and the reduction of energy intensity in this process is an urgent task. Modelling energy-saturated mechanical processes, particularly for the grinding operation, it is necessary to consider structural-mechanical properties of the product, its ability to deform in different modes of interaction with the executive body of the technological machine. The six-link mechanical-rheological model, which is built by combining the simplest models of rheological bodies and enables reproducing the structural and mechanical properties of the object of processing – maize grain with the required degree of reliability, has been offered in the article. Theoretical research of the rheological characteristics of the material, including the assessment of the relative deformation of the maize grain in the grinding process using he impact-cutting method depending on the moisture content and internal force factors, has been conducted. In the future, the obtained results will be used to evaluate the parameters of plastic and elastic absolute deformations in the material while studying the kinetics of the grinding process.