Optimal System Design Research Articles

Optimal design and experimental testing of EMPI system for plasma disruption mitigation on J-TEXT

Plasma disruptions can cause significant damage to tokamak. Currently, the primary method for mitigating disruptions is the injection of a substantial amount of impurities. The electromagnetic injection method offers a high injection speed and rapid response time, making it a promising technique for impurity injection. The first Electromagnetic Pellet Injection System (EMPI), developed by the J-TEXT team, is capable of launching pellets at high velocities and features a specialized deceleration rail that ensures safe separation of the armature and pellet. However, this system lacks an armature recovery device and a vacuum system. In this work, a second generation EMPI has been developed, which has a vacuum system and a curved recovery rail. The curved recovery rail facilitates the smooth retrieval of the armature, enhancing the safety of the recycling process. Additionally, this new system employs an augmented rail design that improves launch performance. Test results indicate that the maximum current of the new EMPI has been reduced by approximately 60%, while the maximum launch speed has increased by around 20%.

Simultaneous optimal system and controller design for multibody systems with joint friction using direct sensitivities

AbstractReal-world multibody systems are often subject to phenomena like friction, joint clearances, and external events. These phenomena can significantly impact the optimal design of the system and its controller. This work addresses the gradient-based optimization methodology for multibody dynamic systems with joint friction using a direct sensitivity approach. The Brown–McPhee model has been used to characterize the joint friction in the system. This model is suitable for the study due to its accuracy for dynamic simulation and its compatibility with sensitivity analysis. This novel methodology supports codesign of the multibody system and its controller, which is especially relevant for applications like robotics and servo-mechanical systems, where the actuation and design are highly dependent on each other. Numerical results are obtained using a software package written in Julia with state-of-the-art libraries for automatic differentiation and differential equations. Three case studies are provided to demonstrate the attractive properties of simultaneous optimal design and control approach for certain applications.

Multiobjective design of automated order fulfillment systems: A case study for an existing surgical complex

We present a case study that introduces a systematic framework for designing an automated guided vehicle (AGV) order fulfillment system. The multi-stage framework integrated discrete event simulation (DES), super-efficiency data envelopment analysis (S-DEA) model, and Design of Experiments (DOE) methodologies for the design of an AGV system in an existing large-scale surgical complex within a comprehensive hospital in Singapore. Performance metrics derived from this model were consolidated into a single relative efficiency score using the S-DEA model. This efficiency score was then utilized in a DOE methodology, combined with a post-hoc model selection process, to robustly identify appropriate models for evaluating alternative system designs. The proposed framework optimizes AGV deployment by first identifying opportunities for AGVs to add value within the surgical system through a value-stream mapping approach. It then derives the optimal system design by balancing cost efficiency, manpower reallocation, and order fulfillment rates, facilitated by the S-DEA model augmented with the DOE approach and post-hoc model selection. Application of this framework to the surgical complex revealed several insights that can significantly enhance AGV efficiency prior to actual deployment. However, a key limitation of this case-study-based approach is its limited generalizability across different systems. Despite this, the methodology provides a robust foundation for AGV system design in similar healthcare environments.

R744 Refrigeration Solution for Small Supermarkets

Application based technology solution is nowadays preferred to achieve the performance of the cooling system configuration at its best. Therefore, benchmarking is an essential criterion which would add value to the optimum system design for various applications. In this study, a case study is carried out for a Milk Bar (small supermarket) to evaluate the potential of R744 cooling system for the similar cooling demand and tropical conditions. Field data collected during a particular time and temperature zone is further used to develop a yearly round performance of the HFC plug-in cabinets for MT and LT applications. The data is further used to develop a centralized R744 booster system that would meet the similar cooling load and demand of all the cabinets in the shop. Based on the cooling loads for the HFC unit, the yearly performance of the R774 booster system is calculated and compared to the existing plug-in solution. It is observed that the yearly electric energy consumption for the R744 centralized refrigeration system is 3.3 M W·h, 24% lower than with the existing solution based on HFCs. Moreover, the economic prospective of the R744 is further discussed to alternative material for the components and centralized unit structure which could empower the system with more reliability and an effective substitute to the synthetic technology for smaller supermarkets. This article is a translation of the article by Singh S, Pardiñas ÁÁ, Hafner A, Schlemminger C, Banasiak K. R744 Refrigeration Solution for Small Supermarkets. In: Proceedings of the 9th IIR Conference on the Ammonia and CO2 Refrigeration Technologies. Ohrid: IIF/IIR, 2021. DOI: 10.18462/iir.nh3-co2.2021.0030 Published with the permission of the copyright holder.

Combat Effectiveness Evaluation of UAV Intelligent Swarm Based on AHP-Fuzzy Comprehensive Evaluation

In order to evaluate the combat effectiveness of UAV intelligent swarm, this paper puts forward the establishment of UAV intelligent swarm combat effectiveness evaluation index system based on availability, credibility and combat ability, analyzes the factors affecting the combat effectiveness of UAV intelligent swarm, establishes the effectiveness evaluation model, determines the weight of each factor by AHP, and evaluates the effectiveness index by fuzzy evaluation matrix. Using this method to evaluate the combat effectiveness of UAV intelligent swarm, the evaluation result is objective and reasonable, which provides theoretical reference for the optimal design and use of UAV intelligent swarm system.

The Frank-Wulf method in modeling information systems

Subject of research: the process of choosing the best option for the structure of an information system under given conditions at the design stage. Purpose of research: to increase the efficiency and quality of the validity of decision-making, to reduce time and financial costs when choosing the structure of an information system at the design stage. Methods and objects of research: the object of research is an information system. The problem of nonlinear programming is formulated. A mathematical model of choosing the optimal variant of an information system is presented. By the method of expert assessments, information system projects with point values are determined. Information system projects with maximum values of scores are selected to determine the optimal option. The method of the conditional Frank-Wolf gradient determines the optimal solution of a nonlinear mathematical model. The essence of gradient methods is a sequential change in the value of the objective function when moving from the starting point of the domain of acceptable solutions to the optimal value of the function. The algorithm of the Frank-Wulff method includes the following steps: determining the initial value of the point of the domain of acceptable solutions, calculating the gradient, determining the optimal solution to a linear programming problem, moving to a new point, checking the condition for the end of the iterative process. The iterative process of determining the optimal solution ends when the calculated value of the calculation accuracy is less than the given value. Main results of research: preliminary projects of the information system were determined using the method of expert assessments, the optimal solution of the mathematical model of nonlinear programming by the Frank-Wolf method was found. A mathematical model for determining the optimal design of an information system will improve the efficiency and quality of the validity of decisions made, reduce financial costs and design time of information systems. The results obtained can be used in further research on this topic.

PULPO: A framework for efficient integration of life cycle inventory models into life cycle product optimization

AbstractThis work presents the PULPO (Python‐based user‐defined lifecycle product optimization) framework, developed to efficiently integrate life cycle inventory (LCI) models into life cycle product optimization. Life cycle optimization (LCO), which has found interest in both the process systems engineering and life cycle assessment (LCA) communities, leverages LCA data to go beyond simple assessments of a limited number of alternatives and identify the best possible product systems configuration subject to a manifold of choices, constraints, and objectives. However, typically, aggregated inventories are used to build the optimization problems. Contrary to existing frameworks, PULPO integrates whole LCI databases and user inventories as a backbone for the optimization problem, considering economy‐wide feedback loops between fore‐ and background systems that would otherwise be omitted. The open‐source implementation combines functions from Brightway2 for the manipulation of inventory data and pyomo for the formulation and solution of the optimization problem. The advantages of this approach are demonstrated in a case study focusing on the design of optimal future global green methanol production systems from captured CO2 and electrolytic H2. It is shown that the approach can be used to assess sector‐coupling with multi‐functional processes and prospective background databases that would otherwise be impractical to approach from a standalone LCA perspective. The use of PULPO is particularly appealing when evaluating large‐scale decisions that have a strong impact on socioeconomic systems, resulting in changes in the technosphere on which the background system is based and which is often assumed constant in standard LCO approaches regardless of the decisions taken. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

How unemployment benefit duration shapes startup motivation and growth

AbstractBusiness creation is economically important, and unemployment precedes the creation of a substantial share of new firms. Yet, most research has focused on analyzing the effects of unemployment insurance policies on re-employment outcomes, ignoring self-employment. In this paper, we analyze how the potential duration of unemployment benefits, a fundamental design choice of unemployment insurance systems, affects whether new firms are founded out of opportunity or necessity and their growth potential. To this end, we construct a comprehensive dataset on German firm founders that links administrative social insurance information with business survey data. Exploiting reform and age-related exogenous variation in the potential duration of unemployment benefits, we find that longer potential benefit duration implies longer actual unemployment and, as a consequence, more necessity entrepreneurship and worse startup outcomes in terms of sales and employment growth. We explain this overall effect of potential benefit duration through a mix of compositional and individual-level duration effects. Our findings underline that new firms started out of unemployment are a highly heterogeneous group and suggest that the (optimal) design of unemployment insurance systems has important externalities on whether innovation- and growth-oriented firms are started out of unemployment.

Optimal powertrain system design and V2V and V2I based hierarchical control strategy of FRIDEV under vehicle-following scenarios

In this paper, the front- and rear-independent-drive electric vehicle (FRIDEV) is selected as the target vehicle for its good dynamic and economy performance. Based on the analysis of the cost, efficiency, and loss characteristics of different motors, the interior permanent magnet synchronous motor (IPMSM) and induction motor (IM) are applied in the target powertrain system. Then, the proposed powertrain system is optimized based on the analysis of driving cycles. The optimal control strategy for the target powertrain system is developed, which consists of the driving mode switching algorithm and the driving torque distribution based on adaptive particle swarm optimization (APSO). Furthermore, in order to achieve the efficient autonomous driving of the target vehicle under the vehicle-following scenarios, a hierarchical control strategy is proposed with the combination of upper-level control of the speed planning strategy and the lower-level control of powertrain system control strategy. The upper-level control of speed planning strategy is designed, which can determine and track the target vehicle speed based on the information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Finally, simulation analysis in MATLAB/Simulink and virtual driving experiments are conducted to verify the superiority of the proposed powertrain system and hierarchical control strategy.

A Water-Energy-Carbon Nexus Approach for the Optimal Design and Configuration of Green Roofs and Photovoltaic Systems

A Water-Energy-Carbon Nexus Approach for the Optimal Design and Configuration of Green Roofs and Photovoltaic Systems

Optimal design and numerical study of the air system in inter-stage guide disc cavity

Optimal design and numerical study of the air system in inter-stage guide disc cavity

Optimizing a Hybrid Wind-Solar-Biomass System with Battery and Hydrogen Storage Using Generic Algorithm-Particle Swarm Optimization for Performance Assessment

This paper investigates the optimal design of a hybrid renewable energy system, integrating wind turbines, solar photovoltaic systems, biomass, and battery and hydrogen storage to ensure a reliable energy supply at the lowest annual cost for a residential load in Kern County, USA. The hybrid generic algorithm particle swarm optimization (GAPSO) algorithm was adopted to determine the optimal configuration of parameters and cost-effectiveness, considering technical, economic, environmental, and social performance indicators. The generic algorithm (GA) and particle swarm optimization (PSO) validate the effectiveness of the proposed technique, showcasing its efficiency in system optimization. The findings indicate that GAPSO outperforms GA and PSO due to its rapid convergence, lowest final fitness value, and stable optimization process. The hybrid GAPSO's performance, combined with the different capacities of wind turbines (4,561 kW), solar PV (8,480 kW), biomass (2,261 kW), battery banks (8,000 kWh), and fuel cells (2,392 kW), resulted in an annual cost of $6,239,193; energy cost and net present value of $0.48/kWh and $101,333,937. The system maintained a supply loss of 0.8%, achieved an availability index of 99.2%, a renewable energy fraction of 88.87%, GHGs emission of 953615 kg, land use of 3842875 m2, and water consumption 528678 L respectively. GAPSO achieved a 2.17% and 0.01% improvement in cost-effectiveness and 11.11% increase in reliability compared to GA and PSO.

Development of optimal energy management strategy for proton exchange membrane fuel cell-battery hybrid system for drone propulsion

Drones powered by fuel cells have been developed to ensure long flight distances. Due to the low dynamic responsiveness of proton exchange membrane fuel cell (PEMFC) systems, they must be integrated with batteries to fully meet the power demand profiles of drones. In this study, a dynamic model of a PEMFC-battery hybrid system for drone propulsion is developed in MATLAB-Simulink® to establish the optimal energy management strategy (EMS) for ensuring efficient and safe operation. The proposed PEMFC system comprises two modules of an open-cathode PEMFC stack, which are connected in parallel. To estimate the dynamic behavior and performance of PEMFC, one-dimensional dynamic model of PEMFC considering two-phase transport through the gas diffusion layer is developed and simulated. A zero-dimensional dynamic model of the battery was also developed based on an equivalent circuit model. The resulting PEMFC-battery hybrid system model was simulated to determine the power required for the propulsion of the drone according to a flight scenario comprising five phases: takeoff hover, climb, cruise, descent, and landing hover. Three different EMSs are developed and simulated to define the optimal one for the PEMFC-battery hybrid system by comparing the total hydrogen consumption and the final SOC of the battery As a result, the EMS that equally splits the required power between two PEMFCs exhibits the lowest hydrogen consumption during the flight simulation. This study helps provide the optimal system design and control scheme of drones powered by fuel cells.

Heat pump and thermal energy storage: Influences of photovoltaic, the control strategy, and price assumptions on the optimal design

Combining heat pump, thermal energy storage, and photovoltaic is a common option to increase renewable energy usage in building energy systems. While research finds that optimal system design depends on the control, design guidelines neglect an influence of (1) photovoltaic, (2) the supervisory control, and (3) prices assumptions on the design of heat pump and thermal energy storage. We analyze how these influences may impact the optimal design. To analyze possible impacts, a pre-screening approach is used to identify relevant model input combinations with a potentially high influence on the optimal design. Applying a simulation-based design optimization with a detailed building energy system model, we optimize three cases: no photovoltaic, no supervisory control, and a state-of-the-art supervisory control. Results indicate that cost optimal design does not change with photovoltaic or the supervisory control, but heat pump size increases with higher electricity prices. For the storage, maximizing the size achieves high self-sufficiency degrees, but cost optimal design changes only for selected price assumptions. Overall, we find that neglecting photovoltaic and surplus control in the system design is a valid assumption. However, price assumptions should be included to allow simple but optimal design rules in current guidelines.

Employing a two stage process with resampling, decision rules, and fine acquisition to optimize the acquisition stage of GNSS receiver performance

The optimal design of Global Navigation Satellite System (GNSS) software receivers should enable the accurate estimation of the receiver's position, velocity, and time under various environmental conditions. The software receivers consist of three sections: acquisition, tracking, and navigation. This paper specifically centers on the acquisition sections of the Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou, and Galileo satellite navigation systems. The acquisition stage necessitates the prompt and precise transmission of the satellite list within the receiver's view, along with Doppler frequency estimation and phase code offset, to facilitate a seamless transition to the tracking stage with the utmost speed and accuracy. Conventional acquisition demonstrates commendable speed, but lacks accuracy, especially in environments with a low Carrier-to-Noise Ratio (CNR). Contrastingly, precise acquisition methods will impede the speed of the acquisition stage. In this paper, a novel acquisition method is proposed. By integrating concepts like resampling, two-stage acquisition, and fine-acquisition, this method enables the attainment of higher accuracy without compromising speed. The results demonstrate that the proposed method enhances accuracy by 7.275% compared to conventional methods and boosts speed by 76.97% compared to methods focused on accuracy improvement.

Carbon dioxide evaporation heat transfer coefficient prediction in porous media using Machine learning algorithms based on experimental data

The prediction of the internal heat transfer coefficient during evaporation is vital for vapor-compression refrigeration and closed-loop power cycles. Accurate measurements and understanding of CO2 heat transfer in porous evaporators are essential for optimal system design across various operating conditions. This study utilizes a reference dataset derived from previous experiments that investigated the impact of porous evaporators on CO2′s internal heat transfer coefficient under sub-critical conditions, employing gravel sand as the porous medium. The dataset encompasses key factors: gravel sand porosities ranging from 39.8 % to 44.5 %, evaporator inlet pressures between 3700 and 4300 kPa, CO2 mass flow rates from 10.7x10-5–18x10-5kg.s−1, and porous tube effective diameters spanning 1.53 x10-3–3.4x10-3m. Employing three machine learning techniques (SVM, GPR, OBEM), the study predicts the internal heat transfer coefficient using regression models. The models’ predictions are analyzed and compared to expected values for validation, evaluating their performance using four statistical criteria. Results indicate SVM, GPR, and OBEM models achieved RMSEs of 1.5471, 1.8212, and 3.6978, respectively, while MAE errors were 1.1479, 1.2418, and 2.9787, respectively. Comparison with the dimensional analysis method reveals the effectiveness of the proposed models in accurately predicting internal heat transfer coefficients. The models exhibit low uncertainty and maintain prediction quality on an extended dataset without overfitting concerns. Overall, this research contributes valuable insights for designing heat exchangers and systems in vapor-compression refrigeration and closed-loop power cycles.

Chemical-Inspired Material Generation Algorithm (MGA) of Single- and Double-Diode Model Parameter Determination for Multi-Crystalline Silicon Solar Cells

The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing solar energy conversion efficiency, a significant barrier to the widespread adoption of solar energy. Accurate modeling and estimation of PV parameters are essential for the optimal design, control, and simulation of PV systems. Traditional optimization methods often suffer from limitations such as entrapment in local optima when addressing this complex problem. This study introduces the Material Generation Algorithm (MGA), inspired by the principles of material chemistry, to estimate PV parameters effectively. The MGA simulates the creation and stabilization of chemical compounds to explore and optimize the parameter space. The algorithm mimics the formation of ionic and covalent bonds to generate new candidate solutions and assesses their stability to ensure convergence to optimal parameters. The MGA is applied to estimate parameters for two different PV modules, RTC France and Kyocera KC200GT, considering their manufacturing technologies and solar cell models. The significant nature of the MGA in comparison to other algorithms is further demonstrated by experimental and statistical findings. A comparative analysis of the results indicates that the MGA outperforms the other optimization strategies that previous researchers have examined for parameter estimation of solar PV systems in terms of both effectiveness and robustness. Moreover, simulation results demonstrate that MGA enhances the electrical properties of PV systems by accurately identifying PV parameters under varying operating conditions of temperature and irradiance. In comparison to other reported methods, considering the Kyocera KC200GT module, the MGA consistently performs better in decreasing RMSE across a variety of weather situations; for SD and DD models, the percentage improvements vary from 8.07% to 90.29%.

The impacts of DC/AC ratio, battery dispatch, and degradation on financial evaluation of bifacial PV+BESS systems

As the price of solar modules has decreased, oversizing PV system becomes a general practice. Without proper energy management, the oversized systems could lead to over-generation waste which cause a loss in revenue. Battery energy storage system (BESS) can be integrated to the PV system for utilizing the over-consumption energy and increasing the system’s financial benefits. This paper highlights the influence of technical and financial factors on the photovoltaic (PV) and PV with BESS design. To analyze the impacts of the factors on PV systems, thirty-six system configurations with a variety of geographic locations, tariff structures, billing methods, and PV system technology are simulated using System Advisor Model (SAM) as a tool with result validation from measurements on a real oversized residential grid-tied bifacial PV+BESS system. To study the financial criteria that affect the optimal system design, we conducted the sensitivity analysis of the DC/AC ratio, battery replacement threshold, and battery size to observe the optimal system design based on three financial outputs: levelized cost of energy (LCOE), net present value (NPV), and payback period. The findings show that the optimal DC/AC ratio for residential-scale PV systems lies between 1.1 - 1.3 and can go up to 1.6 with BESS. PV+BESS systems can reduce the payback period by 20% and boost NPV by 80% compared to the PV-only systems. The sensitivity study of the battery replacement shows that the battery can be degraded until 50% of its maximum capacity for the lowest LCOE. To achieve the optimal grid-tied PV system design, each economic parameter should be evaluated together.

Thermophysical Investigation of Multiform NiO Nanowalls@carbon Foam/1-Octadecanol Composite Phase Change Materials for Thermal Management.

Multiform NiO nanowalls with a high specific surface area were constructed in situ on carbon foam (CF) to construct NiO@CF/OD composite phase change materials (CPCMs). The synthesis mechanism, microstructures, thermal management capability, and photothermal conversion of NiO@CF/OD CPCMs were systematically studied. Additionally, the collaborative enhancement effects of CF and multiform NiO nanowalls on the thermal properties of OD PCMs were also investigated. NiO@CF not only maintains the porous 3D network structure of CF, but also effectively prevents the aggregation of NiO nanosheets. The chemical structures of NiO@CF/OD CPCMs were analyzed using XRD and FTIR spectroscopy. When combined with CF and NiO nanosheets, OD has high compatibility with NiO@CF. The thermal conductivity of NiO@CF/OD-L CPCMs was 1.12 W/m·K, which is 366.7% higher than that of OD. The improvement in thermal conductivity of CPCMs was theoretically analyzed according to the Debye model. NiO@CF/OD-L CPCMs have a photothermal conversion efficiency up to 77.6%. This article provided a theoretical basis for the optimal design and performance prediction of thermal storage materials and systems.

H∞ optimum design and nonlinear seismic performance assessment of tuned-mass-damper-inerter (TMDI) supported by an auxiliary structure

Tuned Mass Damper Inerter (TMDI) and its simplified form, Tuned Inerter Damper (TID), are highly effective in mitigating seismic responses in buildings, particularly when rigidly connected to the ground. However, practical constraints often limit the feasibility of this ideal setup. This study introduces a novel configuration: the AS-type TMDI, which connects the TMDI to an auxiliary structure (AS) adjacent to the main building to simulate a ground connection. To design the AS-type TMDI, this research builds upon insights from studies on TMDIs in adjacent buildings and develops an analogous 3-DOF model representing the TMDI-linked adjacent-building system. This model incorporates two buildings of different heights, allowing the TMDI to be connected at any floor level. Utilizing fixed-point theory, the study derives an analytical solution for the optimal design of TMDI systems within this novel configuration. Additionally, a method is introduced to determine the optimal stiffness and design procedure for the AS itself. The performance of the proposed AS-type TMDI is evaluated through comprehensive nonlinear seismic analysis of a benchmark nine-story steel frame structure, accounting for material and geometric nonlinearities. Results demonstrate that the AS-type TMDI performs comparably to a virtually grounded TMDI in effectively reducing the seismic responses. Furthermore, this innovative configuration outperforms both a conventional retrofitting technique, where the building is rigidly connected to the AS, and a system where a viscous damper alone connects the main building to the AS. This highlights the AS-type TMDI's potential to significantly improve the building’s seismic performance.