با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Efficient sizing and optimization of multirotor drones based on scaling laws and similarity models
اندازه گیری موثر و بهینه سازی هواپیماهای بدون سرنشین چند موتور بر اساس قوانین مقیاس بندی و مدل های شباهت-2020 In contrast to the current overall aircraft design techniques, the design of multirotor vehicles generally
consists of skill-based selection procedures or is based on pure empirical approaches. The application of
a systemic approach provides better design performance and the possibility to rapidly assess the effect of
changes in the requirements. This paper proposes a generic and efficient sizing methodology for electric
multirotor vehicles which allows to optimize a configuration for different missions and requirements.
Starting from a set of algebraic equations based on scaling laws and similarity models, the optimization
problem representing the sizing can be formulated in many manners. The proposed methodology shows a
significant reduction in the number of function evaluations in the optimization process due to a thorough
suppression of inequality constraints when compared to initial problem formulation. The results are
validated by comparison to characteristics of existing multirotors. In addition, performance predictions
of these configurations are performed for different flight scenarios and payloads. Keywords: Multirotor drones | UAV | Design methodology | Sizing | Monotonicity analysis | Multidisciplinary Design Optimization |
مقاله انگلیسی |
2 |
System-level Power Integrity Optimization Based on High-Density Capacitors for enabling HPC/AI applications
بهینه سازی یکپارچگی قدرت در سطح سیستم مبتنی بر خازن های با چگالی بالا برای فعال کردن برنامه های HPC / AI-2020 In this work, we introduce platform-level power
integrity (PI) solutions to enable high-power core IPs and highbandwidth
memory (HBM) interface for HPC/AI applications.
High-complexity design methodology becomes more significant
to enable high-power operations of CPU/GPU/NPU that
preforms iteratively tremendous computing processes. In order
to achieve high-power performance at larger than 200W class,
system-level PI analysis and design guide at early design stage
is required to prevent drastic voltage variations at the bump
under comprehensive environments including SoC, interposer,
package and board characteristics. PI solutions based on highdensity
on-die capacitors are suitable for mitigating voltage
fluctuations by supplying quickly stored charges to silicon
devices. In adopting 2-/3-plate metal-insulator-metal (MIM)
capacitor with approximately 20nF/mm2 and 40nF/mm2, and
integrated stacked capacitor (ISC) with approximately
300nF/mm2, it is demonstrated that voltage properties (drop
and ripple) are able to be improved by system-level design
optimization such as power delivery network (PDN) design and
RTL-architecture manipulation. Consequently, system-level PI
solutions based on high-density capacitor are anticipated to
contribute to improving target performance of high-power
products in response to customer’s expectation for HPC/AI
applications. Keywords: HPC/AI | high-power applications | power integrity | power delivery network | decoupling capacitor | systemlevel design optimization |
مقاله انگلیسی |
3 |
Design optimization of a cross-flow He-Xe recuperator through second law analysis
بهينه سازي طراحي عبور متقابل جریان He-Xe از طريق تحليل قانون دوم-2020 Nuclear power conversion in space has been approached by various means since the first space missions, with the
advent of concepts such as thermoelectric, thermionic and thermodynamic conversion. Nowadays, thermal
cycles are under greater focus for being capable of providing higher conversion efficiencies. In this context, one
of the main concerns of engineers is the trade-off between power and mass. Therefore, this work aims the
optimization of a recuperator used in a regenerative closed Brayton cycle applied for power conversion in the
project of a small-scale nuclear reactor. The recuperator consists of a cross-flow, shell-and-tube heat exchanger
with a matrix of tubes distributed in a staggered configuration. In this work, the number of tubes and the mass
flow rate are varied. The number of tubes distributed axially is fixed as 4, whereas the quantity around the axis
can be 5, 7, 9, 12 and 16 tubes. The working fluid considered in this study is a mixture of noble gases He-Xe with
a molecular weight of 40 g/mol, whereas Inconel alloy 617 is applied as the recuperator material. The optimization
procedure was based on the entropy generation minimization and the heat exchanger effectiveness,
using the Computational Fluid Dynamics (CFD) technique to obtain the flow field. Optimum mass flow rates are
obtained for all the geometries at the points of minimum entropy generation number, around which lie the
ranges of tested mass flow rates. The ratio between the entropy generation number and effectiveness associated
with the optimum mass flow rate is considered a performance evaluation criterion, and the dependence of this
parameter with exchanger mass is assessed in order to select the most suitable geometry for the studied application.
This analysis leads to the optimum design point at the geometry of 9 tubes around the recuperator axis,
yielding a lost available work of 929.76 W for an ambient temperature of 298 K. Keywords: Optimization | Recuperator | CFD | Second law | Entropy |
مقاله انگلیسی |
4 |
A FBWM-PROMETHEE approach for industrial robot selection
رویکرد FBWM-PROMETHEE برای انتخاب ربات های صنعتی-2020 In recent years, the selection of a robot for particular industrial purposes is one of the most challenging problems in the manufacturing environment based on automation and smartness for real-time decision-making. At present, several types of industrial robots with various capabilities, features, facilities, and specifications are available in the market. This makes the decision-making process more and more complicated due to the increase in complexity, advanced technologies, and features that are continually being incorporated into the robots by several manufacturers. The decision-maker needs to identify and select the best-suited robot to attain the desired output with precise application ability, and minimum cost. This paper tries to solve the robot selection problem using Fuzzy Best-Worst Method and PROMETHEE as the two most appropriate multi-criteria decision-making (MCDM) methods for weighting criteria and ranking of decision alternatives, respectively. Keywords: Industrial engineering | Multidisciplinary design optimization | Manufacturing engineering | Technology management | Operations management | Industry management | Business management | Industrialization | Industrial robots | Fuzzy best-worst method | PROMETHEE | MCDM | Robot selection | Criteria |
مقاله انگلیسی |
5 |
Energy- and exergy-based optimal designs of a low-temperature industrial waste heat recovery system in district heating
طرح های بهینه مبتنی بر انرژی و اگزرژی سیستم کمکی برای بازیابی زباله های صنعتی با دمای پایین در گرمایش منطقه-2020 This paper illustrates how the choice of indicators changes the design of a waste heat recovery system in district
heating. A prospective system in Grenoble (France) aims to valorize waste heat from the French National
Laboratory of Intense Magnetic Fields (LNCMI) by injecting it at 85 °C to the nearby district heating network. We
optimize its design for three possible waste heat temperatures: 35 °C (current), 50 °C (viable) and 85 °C (innovative).
As major components, the system includes a thermal storage (ranging from 10 MWh to 40 MWh) and
may include a heat pump depending on the waste heat’s temperature. Different optimizations are guided by two
energetic indicators (one source-oriented, the other demand-oriented) and by the overall exergy efficiency. The
system’s annual performance is assessed through the Sankey and Grassman diagrams and compared between
optimal designs. Yearly simulation included optimal management of the thermal storage, through mixed-integer
linear programming. The demand-oriented optimal design suggests recovering waste heat at 35 °C with a heat
pump and a 40-MWh storage, granting the highest coverage of residential needs (49%). On the other hand, the
source-oriented optimal design suggests recovering waste heat at 85 °C without heat pump and with a 40-MWh
storage, reaching the highest recovery of waste heat (55%). Exergy analysis supports the source-oriented design,
as it reaches the highest global exergy efficiency (27%). Our prospective techno-economic and exergo-economic
analyses should complement these results and may change some conclusions, especially regarding the storage
capacity. Keywords: Waste heat recovery | District heating network | Design optimization | Energy management | Exergy optimization |
مقاله انگلیسی |
6 |
Energy storage and management system design optimization for a photovoltaic integrated low-energy building
ذخیره سازی انرژی و بهینه سازی طراحی سیستم مدیریت برای یک ساختمان یکپارچه فتوولتائیک-2020 This study aims to analyze and optimize the photovoltaic-battery energy storage (PV-BES) system
installed in a low-energy building in China. A novel energy management strategy considering the battery
cycling aging, grid relief and local time-of-use pricing is proposed based on TRNSYS. Both single-criterion
and multi-criterion optimizations are conducted by comprehensively considering technical, economic
and environmental performances of the system based on decision-making strategies including the
weighted sum and minimum distance to the utopia point methods. The single-criterion optimizations
achieve superior performances in the energy supply, battery storage, utility grid and whole system aspect
respectively over the existing scenario of the target building. The multi-criterion optimization considering
all performance indicators shows that the PV self-consumption and PV efficiency can be increased
by 15.0% and 48.6% while the standard deviation of net grid power, battery cycling aging and CO2
emission can be reduced by 3.4%, 78.5% and 34.7% respectively. The significance and impact of design
parameters are further quantified by both local and global sensitivity analyses. This study can provide
references for the optimum energy management of PV-BES systems in low-energy buildings and guide
the renewable energy and energy storage system design to achieve higher penetration of renewable
applications into urban areas. Keywords: Solar photovoltaic | Battery energy storage | Energy management | Optimization | Sensitivity analysis |
مقاله انگلیسی |
7 |
Problems of engineering entrepreneurship in Africa: A design optimization example in solar thermal engineering
مشکلات کارآفرینی مهندسی در آفریقا: نمونه ای از بهینه سازی طراحی در مهندسی حرارت خورشیدی-2020 This paper addresses Africa’s challenges and opportunities to engineering entrepreneurs. A business environmental scan is done in line with the standard PESTLE analysis, identifying at least twenty generic problems across the continent. Focus is directed to an opportunity in solar water heating, where inade- quate electricity supply combines with a plentiful solar resource amidst environmental protection aware- ness, to make investments potentially worthwhile. Three home level market segments are identified. Key issues in the PESTLE scan are linked with available materials to formulate and solve a design optimization model for these segments. A competition-less product emerges for rural homes. Another – for small urban homes – can be retailed at 50% of current equivalent system prices, and yet, still make profitsfor the entrepreneur. Both these systems attain average temperatures in excess of 57 °C, the fatal levelfor most pathogenic bacteria. The 3rd and larger system for rich urban homes incorporates a supplemen-tary electric heater that is programmable to kick in half an hour before water withdrawal if solar energy has failed to maintain water temperature above 60 °C. The entrepreneur can still make profit if the pro- duct retails at 52% of the equivalent competition price.© 2019 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Africa | Design optimization | Engineering entrepreneurship | PESTLE analysis | Solar water heating |
مقاله انگلیسی |
8 |
Introducing reinforcement learning to the energy system design process
معرفی یادگیری تقویتی به فرآیند طراحی سیستم انرژی-2020 Design optimization of distributed energy systems has become an interest of a wider group of researchers due the
capability of these systems to integrate non-dispatchable renewable energy technologies such as solar PV and
wind. White box models, using linear and mixed integer linear programing techniques, are often used in their
design. However, the increased complexity of energy flow (especially due to cyber-physical interactions) and
uncertainties challenge the application of white box models. This is where data driven methodologies become
effective, as they demonstrate higher flexibility to adapt to different environments, which enables their use for
energy planning at regional and national scale.
This study introduces a data driven approach based on reinforcement learning to design distributed energy
systems. Two different neural network architectures are used in this work, i.e. a fully connected neural network
and a convolutional neural network (CNN). The novel approach introduced is benchmarked using a grey box
model based on fuzzy logic. The grey box model showed a better performance when optimizing simplified
energy systems, however it fails to handle complex energy flows within the energy system. Reinforcement
learning based on fully connected architecture outperformed the grey box model by improving the objective
function values by 60%. Reinforcement learning based on CNN improved the objective function values further
(by up to 20% when compared to a fully connected architecture). The results reveal that data-driven models are
capable to conduct design optimization of complex energy systems. This opens a new pathway in designing
distributed energy systems. Keywords: Distributed energy systems | Energy hubs | Reinforcement learning | Optimization | Data driven models | Machine learning |
مقاله انگلیسی |
9 |
Problems of engineering entrepreneurship in Africa: A design optimization example in solar thermal engineering
مشکلات کارآفرینی مهندسی در آفریقا: یک نمونه بهینه سازی طراحی در مهندسی حرارتی خورشیدی-2019 This paper addresses Africa’s challenges and opportunities to engineering entrepreneurs. A business environmental
scan is done in line with the standard PESTLE analysis, identifying at least twenty generic
problems across the continent. Focus is directed to an opportunity in solar water heating, where inadequate
electricity supply combines with a plentiful solar resource amidst environmental protection awareness,
to make investments potentially worthwhile. Three home level market segments are identified. Key
issues in the PESTLE scan are linked with available materials to formulate and solve a design optimization
model for these segments. A competition-less product emerges for rural homes. Another – for small
urban homes – can be retailed at 50% of current equivalent system prices, and yet, still make profits
for the entrepreneur. Both these systems attain average temperatures in excess of 57 C, the fatal level
for most pathogenic bacteria. The 3rd and larger system for rich urban homes incorporates a supplementary
electric heater that is programmable to kick in half an hour before water withdrawal if solar energy
has failed to maintain water temperature above 60 C. The entrepreneur can still make profit if the product
retails at 52% of the equivalent competition price. Keywords: Africa | Design optimization | Engineering entrepreneurship | PESTLE analysis | Solar water heating |
مقاله انگلیسی |
10 |
Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning
مدل انرژی کامل ساختمان برای کنترل بهینه HVAC: یک چارچوب عملی مبتنی بر یادگیری تقویتی عمیق-2019 Whole building energy model (BEM) is a physics-based modeling method for building energy simula- tion. It has been widely used in the building industry for code compliance, building design optimization, retrofit analysis, and other uses. Recent research also indicates its strong potential for the control of heat- ing, ventilation and air-conditioning (HVAC) systems. However, its high-order nature and slow computa- tional speed limit its practical application in real-time HVAC optimal control. Therefore, this study pro- poses a practical control framework (named BEM-DRL) that is based on deep reinforcement learning. The framework is implemented and assessed in a novel radiant heating system in an existing office building as a case study. The complete implementation process is presented in this study, including: building en- ergy modeling for the novel heating system, multi-objective BEM calibration using the Bayesian method and the Genetic Algorithm, deep reinforcement learning training and simulation results evaluation, and control deployment. By analyzing the real-life control deployment data, it is found that BEM-DRL achieves 16.7% heating demand reduction with more than 95% probability compared to the old rule-based control. However, the framework still faces the practical challenges including building energy modeling of novel HVAC systems and multi-objective model calibration. Systematic study is also needed for the design of deep reinforcement learning training to provide a guideline for practitioners. Keywords: HVAC | Energy efficiency | Whole building energy model | Optimal control | Deep reinforcement learning |
مقاله انگلیسی |