System Design Optimization Research Articles

Optimization of Geothermal Heat Pump Systems for Sustainable Urban Development in Southeast Asia

This study examines the optimization of ground source heat pump (GSHP) systems and energy piles for sustainable urban development, focusing on Southeast Asia. GSHPs, which utilize geothermal energy for indoor HVAC needs, offer a sustainable alternative to traditional systems by utilizing consistent subsurface temperatures for heating and cooling. The study highlights the importance of understanding thermal movement within the soil, especially in soft marine clays prevalent in Southeast Asia, to improve GSHP system efficiency. Using a one-dimensional finite difference model, the study examines the effects of soil thermal conductivity and density on system performance. The results show that GSHP systems, especially when integrated with energy piles, significantly reduce electricity consumption and greenhouse gas emissions, underscoring their potential to mitigate the urban heat island effect in densely populated areas. Despite challenges posed by the region’s hot and humid climate, which could affect long-term effectiveness, the study highlights the need for further study, including field experiments and advanced modeling techniques, to optimize GSHP configurations and fully exploit geothermal energy in urban environments. The study’s insights into soil thermal dynamics and system design optimization contribute to advancing sustainable urban infrastructure development.

Auxiliary Heat System Design and Off-Design Performance Optimization of OTEC Radial Inflow Turbine

In this paper, solar energy is used as the auxiliary heat source of the ocean thermal energy radial inflow turbine, and the thermodynamic model of the circulation system is established. In addition, the ejector is introduced into the ocean thermal power generation system, and the process simulation is carried out using Aspen Plus V12. To address performance attenuation of the radial turbine under varying working conditions, shape optimization of a 30 kW OTEC radial turbine was conducted. Finally, the off-design performance variation in the radial inflow turbine is analyzed in the presence of a solar auxiliary heat source. The results show that the use of an auxiliary heat source can effectively improve the cycle efficiency of the system and is also conducive to the stable operation of the radial turbine. Under the condition of auxiliary heat source, the system cycle efficiency is increased by 2.269%.

Design Optimization of a Self-Circulated Hydrogen Cooling System for a PM Wind Generator Based on Taguchi Method

Design Optimization of a Self-Circulated Hydrogen Cooling System for a PM Wind Generator Based on Taguchi Method

Artificial intelligence based smoke flow mechanism analysis and prediction patterns of fire for large space building

The deposition and dynamics of smoke layers in large-space building fires are governed by a complex interplay of factors, making the prediction of such dynamics using traditional mathematical models challenging. In response, this study introduces a Back Propagation (BP) neural network model, devised from field simulation data, to efficiently forecast the temporal progression of smoke layers. It was observed that the model exhibits minimal training errors and high processing speeds, thereby fulfilling the stringent accuracy demands of fire engineering. Specifically, the model achieved a minimum relative error of 0.0005 and a maximum of 0.0845 across various prediction points, underscoring its reliability and precision. The ability of this BP neural network model to predict smoke layer changes significantly enhances the design optimization of smoke control systems swiftly and accurately in large buildings and supports rapid, informed decision-making during fire emergencies. Moreover, the model facilitates the development of engineering calculation models tailored for the quick prediction of fire smoke dynamics, which are essential for both theoretical research and practical applications. This approach not only conserves experimental resources but also advances the implementation of scientific, effective rescue operations in the event of large space building fires.

Modeling and analysis for landing airbag–lunar soil interaction using a CPU–GPU-based FMBD-DEM computational framework

To investigate the landing performance of airbag cushion systems on the discrete lunar soil, this study proposes a novel modeling approach that couples multi-physics fields, including the flexible multibody systems (FMBS), the granular systems, and the gas effects. In this model, the GPU parallelism technique is utilized to address challenges arising from the large number of particles and complex contact detection involving large deformation and overall motions, and an efficient contact implementation between finite elements and particles is introduced. Additionally, contact modeling between airbags is realized using master–slave techniques. Then, a modified conventional serial staggered procedure is employed to couple flexible multibody dynamics (FMBD) and discrete element method (DEM) modules. Finally, a series of numerical examples is presented to validate the proposed dynamic model. The validated model successfully simulates the interaction between landing airbags and lunar soil. Subsequent parametric analysis contributes to the design optimization of the landing system. Moreover, discussions regarding computational efficiency illustrate that the proposed CPU–GPU-based FMBD-DEM is advantageous in engineering scenarios involving flexible components and large-scale granular systems.

Analysis and comparative assessment of charging dynamics in vertical multi-channel latent heat storage system with corrugated wavy channels

Latent heat thermal energy storage (LHTES) is a promising technology, but thermal response limitations caused by the relatively poor thermal conductivity of phase change materials (PCMs) remains an issue. In recent years, computational fluid dynamics (CFD) has proven to be a powerful and reliable tool in design optimization of different systems and problems in the energy sector. Building on that, the present work will use CFD to investigate the thermal performance enhancements achieved using vertical double-tube LHTES systems with multi-channel sinusoidal wavy geometries. The main parameters investigated here are the wavy channel height (H), pitch (P) and the number of channels (N) and the results are assessed in terms of melting rates and heat transfer coefficients. The analysis found that increasing the height from 1 mm to 3 mm, accelerated the phase change by 21 %, while reducing the pitch from 12.5 mm to 5 mm, enhanced the heat storage rates by 28 %. In addition, increasing the number of channels from 3 to 7 improved the charging rate by 64 %. Compared to a conventional straight channel, the enhanced wavy channel (N = 7, H = 3 mm, P = 5 mm) achieved 80 % higher thermal storage rate and 42 % faster charging time. The above findings provide new insight into the potential of utilizing compact vertical LHTES systems via customized wavy channels, with significant impact towards enhancing the existing thermal energy storage systems.

Implementation of adaptive neuro-fuzzy inference system in design and process optimization of latent heat storage system

This study focuses on implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) as an artificial neural network model to optimize latent heat thermal energy storage (LHTES) systems. Experimental data comprising seven input parameters (length, shell diameter, tube diameter, mass flow rate, difference in temperature, latent heat, and density) and two output parameters (charging and partial charging time) are collected and used to train the ANFIS model. Initially, the model is developed using a grid partition method, employing Triangular and Gbell membership functions and assigning diverse membership rules. This initial model demonstrated satisfactory accuracy based on quality metrics (R2, mean absolute, and root mean square error). Then, a subtractive clustering approach is applied to further enhance the model's accuracy, which groups different data clusters together. This enhanced model exhibited superior performance compared to the grid partition method, making it the optimal choice. In addition, an experimentally validated numerical model is developed using COMSOL Multiphysics and is utilized to crosscheck the predicted output of the ANFIS model. From a set of 100 combinations of storage capacity between 0.45 and 0.55 MJ, a fixed latent heat storage capacity of 0.456 MJ was identified by considering the combination with the least charging time based on the input parameters. Using this storage capacity, a minimum charging time of 223 min and a partial charging time of 180 min was determined. The numerical model also confirmed these optimal values.

Design Optimization of Family Mold Runner System for Injection Molding of Automotive Air Vent Parts: Development and Application of an Algorithm

Design Optimization of Family Mold Runner System for Injection Molding of Automotive Air Vent Parts: Development and Application of an Algorithm

A new adaptive Kriging-based optimization (AKBO) framework for constrained optimization problems: A case study on shared autonomous electric vehicle system design

Adaptive Kriging-based optimization (AKBO) efficiently performs optimization by employing adaptive sampling methods that sequentially update Kriging model. To solve constrained optimization problems, existing adaptive sampling methods add samples based on probability of feasibility (PoF) representing the probability that all constraints are satisfied. However, these methods have the limitation that samples are added inefficiently when the PoF is inaccurate. Therefore, this study proposes a new AKBO framework that is robust to instability of PoF in adaptive sampling. To this end, a near constraint boundary region that is expected to be close to the constraint boundary is defined based on a probability measure. Then, a near constraint boundary search (NCBS) algorithm, which explores the sample point that minimizes the objective function within the near constraint boundary region, is developed for global search, whereas a near optimum search (NOS) algorithm, which searches for better solutions that may exist near the current optimum candidate, is developed for local search. To alleviate the distortions of the Kriging model that may occur due to the highly nonlinear response, a truncated constraint function (TCF) method that limits the extremely violated constraint values used in the design of experiment (DoE) of the Kriging model is also proposed. This study verifies the performance of the proposed AKBO framework through the shared autonomous electric vehicle (SAEV) system design optimization problem. Comparison of optimum designs derived from various optimization methods shows that the proposed AKBO framework derives the feasible global optimum with high accuracy and efficiency.

Optimization algorithm of 500kVGIS circuit breaker electric field simulation based on GPU acceleration

It proposes an optimization approach for electric field simulation in 500kV gas-insulated switchgear (GIS) circuit breakers that use GPU acceleration. Accurate modelling of electric field distribution within GIS circuit breakers is critical for assuring the dependability and safety of high voltage power systems. However, the computational complexity of simulating these systems presents substantial obstacles to established approaches. The goal of this work is to improve the speed, efficiency, and scalability of electric field simulations by leveraging GPU parallel processing capabilities. They methodically build and refine the simulation algorithm, utilizing techniques such as parallel algorithm design, GPU architecture optimization, and efficient memory management. Through rigorous performance studies, they demonstrate significant increases in computational efficiency relative to CPU-based techniques, with an average speed up of 15x. Furthermore, the scalability of the GPU-accelerated method is evaluated, demonstrating near-linear scalability up to 8 GPUs. Validation against experimental observations and analytical models indicates that the simulation findings are accurate and robust. This study helps the progress of electric field simulation tools for GIS circuit breakers by providing insights into electric field behaviour and aiding design optimization and performance evaluation in high voltage power systems.

Complementary assessment and design optimization of a hybrid renewable energy system integrated with open-loop pumped hydro energy storage

The intermittent nature of renewable energy resources is a major obstacle in utilizing such resources for power generation despite their abundance. In this paper, a dynamic analysis of an integrated system based on photovoltaic (PV) panels and wind turbines coupled with pumped hydro energy storage is presented. When the power generation exceeds the power demand, surplus electricity is used to pump the water to the upper reservoir. The natural inflow of the river obviates the necessity for oversized reservoirs and saves costs. A recently introduced multi-objective equilibrium optimizer algorithm is developed to find the optimal size of the integrated hybrid energy system. The objectives are to minimize the levelized cost of electricity and maximize the reliability of the proposed system. The results show that the correlation between solar and wind power generation in the selected location is mildly strong with a value of 0.3. The total energy generation from hydro, solar, and wind resources are 77,508.7 kWh, 103,323 kWh, and 47,375.4 kWh, respectively. Comparing the results with other algorithms indicates that the equilibrium optimizer has a better performance in finding the optimal design.

An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers

The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development.

Theoretical model and control-parameter classification analysis for solid oxide fuel cell systems with cooling air bypass into burner

The burner is central to the thermal management of solid oxide fuel cell (SOFC) system. Using cooling air bypass (CA) into burner is simple and effective to control the excessive temperature. A novel theoretical model for SOFC systems equipped with CA into burner is developed and verified with multiple experimental data. A comprehensive control-parameter classification analysis is carried out with the model, revealing quantitatively the impacts of the fuel utilization (Uf), air flow rate (Vair), and maximum burner temperature (TBM), on the system performance. The results confirm CA can minimize thermal shock to the stack. Favorable combinations of (Uf, Vair, TBM) satisfying the thermal constrains of maximum cell temperature and temperature difference are identified, and capable of achieving a system alternating current efficiency of 55 % or more. The theoretical model can help the system design optimization targeting the specifics of the stack.

Conceptual design and multi-objective optimization of a hybrid system based on direct ammonia protonic ceramic fuel cell and alkali metal thermal electric converter

As a power generation device, direct ammonia protonic ceramic fuel cells (NH3–PCFCs) also produce a significant amount waste heat, which not only results in energy wastage but also abnormal operation if the waste heat is not removed. To address these concerns and enhance generation electricity capacity, a novel waste heat recovery system based upon NH3-PCFC and alkali metal thermal electric converter (AMTEC) is first proposed, wherein the AMTEC converts the high-grade exhaust heat produced by the NH3-PCFC to extra electricity. Considering ammonia decomposition sluggish kinetics, various irreversible overpotential losses of NH3-PCFC as well as thermodynamic losses within the integrated system, the energetic/exergetic performance parameters evaluating the NH3-PCFC, AMTEC and the hybrid system are formulated. The general performance characteristics of the proposed system are revealed, along with a sensitive analysis conducted under specific operational conditions or structural parameters. Numerical calculation exhibited that the maximum attainable power density of the hybrid system has been promoted 20.5 % than that of stand-alone NH3-PCFC system. The multi-objective genetic algorithm is utilized to optimize the hybrid system performance for trade-off the power output and efficiency. Optimized results show the power density and corresponding efficiency of proposed system can respectively reach 6762.3 W m−2 and 49.8 % in the corresponding optimization conditions.

Design, tuning, and blackbox optimization of laser systems

Chirped pulse amplification (CPA) and subsequent nonlinear optical (NLO) systems constitute the backbone of myriad advancements in semiconductor manufacturing, communications, biology, defense, and beyond. Accurately and efficiently modeling CPA+NLO-based laser systems is challenging because of the complex coupled processes and diverse simulation frameworks. Our modular start-to-end model unlocks the potential for exciting new optimization and inverse design approaches reliant on data-driven machine learning methods, providing a means to create tailored CPA+NLO systems unattainable with current models. To demonstrate this new, to our knowledge, technical capability, we present a study on the LCLS-II photo-injector laser, representative of a high-power and spectro-temporally non-trivial CPA+NLO system.

Experimental investigation on effective aerosol scavenging using different spray configurations with pre-injection of water mist for Fukushima Daiichi decommissioning

During the decommissioning of the Fukushima Daiichi nuclear power plant, it is important to consider the retrieval of resolidified debris both in air and underwater configurations. For the subsequent retrieval of debris from the reactor building, the resolidified debris must be cut into smaller pieces using various cutting methods. During the cutting process, aerosol particles are expected to be generated at the submicron scale. It has been noted that such aerosols sizing within the Greenfield gap (0.1–1 μm) are difficult to remove effectively using traditional spraying methods. Therefore, to improve the aerosol removal efficiency of the spray system, a new aerosol agglomeration method was recently proposed, which involves injecting water mist to enlarge the sizes of the aerosol particles before removing them using water sprays. In this study, a series of experiments were performed to clarify the proper spray configurations for effective aerosol scavenging and to improve the performance of the water mist. The experimental results showed that the spray flow rate and droplet characteristics are important factors for the aerosol-scavenging efficiency and performance of the water mist. The results obtained from this study will be helpful for the optimization of the spray system design for effective aerosol scavenging during the decommissioning of the Fukushima Daiichi plant.

Design Optimization of A Triple-Layer Flat-Panel Detector for Three-Material Decomposition.

Spectral radiography and fluoroscopy with multi-layer flat-panel detectors (FPD) is being actively investigated in a range of clinical applications. For applications involving contrast administration, maximal contrast resolution is achieved when overlaying background anatomy is completely removed. This calls for three-material decomposition (soft tissue, bone, and contrast) enabled by measurements in three energy channels. We have previously demonstrated the feasibility of such decomposition using a triple-layer detector. While algorithmic solutions can be adopted to mitigate noise in the material basis images, in this work, we seek to fundamentally improve the conditioning of the problem through optimized system design. Design parameters include source voltage, the thickness of the top two CsI scintillators, and the thickness of two copper interstitial filters. The design objective is to minimize noise in the basis image containing contrast, chosen as gadolinium in this work to improve separation from bone. The optimized design was compared with other designs with unoptimized scintillator thickness and/or without interstitial filtration. Results show that CsI thickness optimization and interstitial filtration can significantly reduce noise in the gadolinium image by 35.7% and 42.7% respectively within a lung ROI, which in turn boosts detectability of small vessels. Gadolinium and bone signals are separated in all cases. Visualization of coronary vessels is enabled by the combining optimized system design and regularization. Results from this work demonstrate that three-material decomposition can be significantly improved with system design optimization. Optimized designs obtained from this work can inform imaging techniques selection and triple-layer detector fabrication for spectral radiography.

Transport and clogging of microplastic particles in porous media: Microscale experiments and statistical analysis

In recent years, the migration and distribution of microplastics (MPs) in the natural environment have garnered worldwide attention. However, little is known about the transport and intercept of MPs in infiltration systems. In infiltration systems, MPs could affect the flow through porous media, leading to complex flow and removal dynamics in various engineering applications. This will threaten the ecosystem and human health due to the characteristics of MPs. In this study, a two-dimensional porous media flow cell was developed to visualize the transport and intercept of microplastic particles in porous media. Statistical data on pore characteristics were gathered by tracking changes in pore clogging state under different particle concentrations and flow rates. It was found that the size ratio dt/dp was the critical factor influencing pore throat clogging probability. Pore throats were categorized into persistent-clogging, occasional-clogging, and non-clogging based on their clogging probability at different dt/dp. Additionally, the parameter dt/dpU that distinguished the occasional-clogging zone from the non-clogging zone decreased with increasing particle concentration. The clogging probability in the occasional-clogging zone was influenced by dt/dp, particle flux, and flow velocity in the pore throat. Furthermore, two distinct clogging mechanisms, independent and dependent clogging, were observed, determined by the states of neighboring pores. These findings have implications for assessing the interception performance of MPs in filtration systems. The results enhance our understanding of MPs transport and interception dynamics in porous media and contribute to the optimization of filtration system design and operation.

Design optimization of a marine refrigeration system with waste heat recovery channel

This paper presents the design of an optimized marine refrigeration system to be used as a preservation facility for fishermen on boats. This aims to help preserve and maintain the high quality of the catch during fishing operations. This is to replace the manual ice box method. The system is tested in an actual environment offshore. The lowest temperature reached inside the refrigeration when full load is -1.7°C with a maximum time of 6 hours, while the temperature of the fish inside the refrigeration is 2.5°C. These results show the capability of the enhanced marine refrigeration system to achieve the desired temperature for the catch to maintain its good quality. In addition, the usability and user experience survey results indicated that the target beneficiaries agreed that the following aspects of the marine refrigeration system include usefulness and functionality, ease of use, design capacity, dimension, aesthetics, and kerosene as the resources used are excellent. Keywords: Absorption, Kerosene, Marine Refrigeration

Stochastic optimization of solar-based distributed energy system: An error-based scenario with a day-ahead and real-time dynamic scheduling approach

Distributed energy systems (DES) have garnered global attention as a promising solution for the expansion of renewable energy sources. However, stochastic uncertainty in renewable sources poses significant challenges in the collaboration optimization of system design and operation. In this study, a comparison analysis was conducted to assess the effectiveness of the conventional distribution-based scenario generation (DS) method for stochastic optimization of a distributed energy system in residential buildings. The results revealed that the DS method inaccurately captured extreme scenarios and exhibited limitations in operation optimization, leading to significant performance evaluation bias. To address these challenges, a novel stochastic optimization approach was developed based on error-based scenarios and a day-ahead and real-time dynamic scheduling strategy (ES-DRS). This approach incorporated solar energy prediction errors to more accurately characterize extreme scenarios, while also considering dynamic dual-scale meteorological boundary condition for rolling operation optimization. Furthermore, the buffer storage and coverage periods in ES-DRS were investigated and discussed during dynamic scheduling. Results demonstrated that when the buffer storage and coverage period were set at 36.22 kWh in summer and 24 h, respectively, the DES with ES-DRS achieved optimal multi-objective performance. This resulted in an annual total cost of 3.21 × 104 USD and CO2 emission of 5.82 tons, representing reductions of 26.45% and 61.06%, respectively, compared to the conventional strategy. Overall, this research contributes to advancing uncertainty analysis and scenario-based optimization in DES, highlighting the potential benefits of adopting the ES-DRS approach to maximize overall performance from both economic and environmental perspectives.