Combination Of Design Parameters Research Articles

Performance prediction and optimization of lateral exhaust hood based on back propagation neural network and genetic algorithm

The lateral exhaust hood (LEH) is commonly used to capture contaminated airflow in industrial plants. Its performance is influenced by numerous factors and exhibits a strong nonlinear relationship, optimizing this performance demands significant computational time and cost. This study proposes an LEH optimization design method that combines a backpropagation neural network (BPNN) with a genetic algorithm (GA), verified through experiments and numerical simulations. A BPNN prediction model is established based on 520 CFD simulation results. This study discusses seven factors influencing LEH performance, including the buoyant jet's velocity and temperature, the LEH's geometry and position, and the exhaust velocity. The results indicate that within the parameter range of the prediction model, there is a critical aspect ratio value that significantly affects LEH capture efficiency. The horizontal distance between the LEH and the pollution source significantly affects capture efficiency, especially when the LEH's installation height is low (H/D < 0.5). In addition, when combined with GA for fast searching, the expected combination of LEH design parameters can be obtained. This study aims to enhance the design efficiency of industrial exhaust hoods and improve worker health protection.

Optimal design of an inclined barrier for performance improvement of an oscillating water column device with an artificial neural network model

An oscillating water column (OWC) device with a surface-piercing inclined barrier has been studied in irregular waves. This study is to determine the optimal shape of an inclined barrier utilizing an Artificial Neural Network (ANN) model for the performance enhancement of the conventional OWC system. We employed the dual boundary element method (DBEM) as our numerical approach, which resolves two separate boundary integral equations (singular and hyper-singular) formulated at the coincident source points situated on either side of the degenerate barrier boundary. The design parameters affecting the performance of the OWC system have been optimized to maximize the power extraction using the trained ANN model. The training dataset was prepared using the DBEM, which is then used to train and validate the ANN model. Subsequently, the trained model was employed to make predictions using a broader dataset to identify the optimal combination of design parameters that maximize the conversion efficiency. The findings reveal that the well-trained ANN model can learn complex nonlinear relationships, make good predictions with high accuracy using a larger dataset of input conditions, and offer the optimal values of an inclined barrier appropriate for the wave conditions at the sea installation site.

The synergistic effect of multiple design factors on building energy consumption of office blocks: A case study of Wuhan, China

Design factors such as block morphology, building form and building envelope performance are key parameters affecting building energy consumption of urban blocks. However, the synergistic effect of multi-design factors on building energy consumption has not been studied in detail. This paper aims to investigate the synergistic effect of multi-design factors on building energy consumption of office blocks in Wuhan, China. This study employs field survey, satellite map and open street map to extract the typical building forms and construct parametric models of office blocks. Then, 81 representative scenarios considering the synergy of multiple design factors in office blocks are built. Then, based on the Rhino & Grasshopper parametric platform, a building energy assessment tool that considers the synergistic effect of multiple design factors is developed to simulate and record the building energy consumption of each scenarios. Finally, the synergistic effect of multi-design parameters on building energy consumption of office blocks is quantified using correlation and multiple linear regression methods. The findings reveal that the synergistic effect of multi-design factors on building energy consumption is 18.53%. Building energy consumption for high-rise pavilion typology is saved by 9.64% than multi-storey courtyard. The key design parameter combinations affecting building energy consumption in office block are building shape factor, solar heat gain coefficient, wall K-value, building width and window-wall ratio of west and east. This study contributes to a deeper understanding of the synergistic effects of multiple design parameters on building energy consumption in office blocks and presents a scalable model that can be applied globally to promote sustainable urban development.

Optimized Design of Carbon Nanotube Field-Effect Transistor Using Taguchi Method for Enhanced Current Ratio Performance

Abstract Presently, the integrated circuit (IC) industry grapples with obstacles in downsizing MOSFET technology further, hindered by its inherent physical constraints. Therefore, the substitution of silicon with carbon nanotubes (CNTs) holds promise for paving a novel path in semiconductor industries, driven by their diminutive dimensions and superior electrical properties. Hence, this project employed SILVACO ATLAS software in conjunction with the Taguchi method to refine a CNTFET design for optimal performance. In this work, response variables consists of on-current (Ion), current ratio (Ion/Ioff) and threshold voltage (Vth) are extracted. In this particular design, the Taguchi method was employed to ascertain the most effective combination of design parameters and materials to achieve optimal CNTFET performance, as assessed by the three key response variables. The design parameter and material that had been chosen were the diameter of carbon nanotube (Dcnt), dielectric material (K) and oxide thickness (tox). Each of the design parameters and material had three different values. For K, the values are 3.9 (SiO2), 25 (ZrO2) and 80 (TiO2). While for Dcnt and tox, the values are 4.0 nm, 6.0 nm, 8.0 nm and 2.0 nm, 4.0 nm, 6.0 nm respectively. According to the Taguchi optimization findings, the ideal combination of parameters comprises a CNT diameter of 4.0 nm, an oxide thickness of 2.0 nm, and the use of TiO2 (80) as the dielectric material. The ANOVA analysis underscores the significance of prioritizing optimization efforts towards the CNT diameter parameter. This is attributed to its substantial contribution, accounting for 93.55% of the variation in the Ion/Ioff value, surpassing the influence of dielectric materials and oxide thickness.

Comparative Assessment of ESI‐MS Softness for Inorganic Complexes: How Soft is Your ESI‐MS?

Electrospray ionization mass spectrometry (ESI‐MS) is a powerful tool for identifying and characterizing organometallic and coordination compounds. However, detection of fragile structures bound by weaker intermolecular forces can be significantly limited in ESI‐MS owing to the use of relatively harsh instrument conditions and configurations. In this study, a set of tests was developed to assess the softness of ESI‐MS systems. Two variants are presented: positive ion mode, utilizing a mixture of sodium ions and triphenylphosphine oxide producing [Na(OPPh3)n]+ ions (n = 1–4), and negative ion mode, utilizing Pd(PPh3)4 and sulfonated triphenylphosphine producing [Pd(L)(PPh3)n]– ions (n = 0–2), where softer instrument conditions preserve a higher proportion of the high‐coordinate ions and harsher conditions will result in increased detection of products of ion fragmentation. The results revealed notable variations in instrument softness, which were influenced by a combination of instrument design and experimental parameters. Meticulously optimizing experimental conditions and ESI‐MS parameters is essential to achieving the softest ionization possible, ensuring reliable analysis where applicable. This study offers valuable insight through straightforward tests that can be employed to assess the suitability of an instrument for specific research needs.

Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Building performance prediction is a significant area of research, due to its potential to enhance the efficiency of building energy management systems. Its importance is particularly evident when such predictions are validated against field data. This paper presents an intelligent computational model combining Monte Carlo analysis, Energy Plus, and an artificial neural network (ANN) to refine energy consumption and thermal comfort predictions. This model addresses various combinations of architectural building design parameters and their distributions, effectively managing the complex non-linear relationships between the response variables and predictors. The model’s strength is demonstrated through its alignment with R2 values exceeding 0.97 for both thermal discomfort hours and energy consumption during the training and testing phases. Validation with field investigation data further confirms its accuracy, demonstrating average relative errors below 2.0% for total energy consumption and below 1.0% for average thermal discomfort hours. In particular, an average underestimation of −12.5% in performance discrepancies is observed when comparing the building energy simulation model with field data, while the intelligent computational model presented a smaller overestimation error (of +8.65%) when validated against the field data. This discrepancy highlights the model’s potential and reliability for the simulation of real-world building performance metrics, marking it as a valuable tool for practitioners and researchers in the field of building sustainability.

Neutronic Analysis of the Conceptual Molten Uranium Breeder Reactor Using MCNP and SCALE Tools

The Molten Uranium Breeder Reactor (MUBR) is a radically new reactor concept with a mixed-energy spectrum. MUBR is fueled with molten uranium metal in large-diameter fuel tubes and is cooled by circulating molten uranium fuel through a heat exchanger. The reactor has heavy water as moderator, and the reactivity of the reactor is primarily controlled by the voiding effect of the moderator through an innovative control cavity structure design. Because the MUBR design is vastly different from most existing fission reactors, neutronics analysis must be performed for many different combinations of design parameters to identify viable and optimum design configurations. To facilitate the neutronics analysis, a proprietary program called MUBR6gen is being developed to provide a pipeline tool to expedite the process. MUBR6gen employs two well-established neutronics codes, i.e., MCNP and SCALE, to perform standard neutronics calculations for MUBR by automating input preparation and output processing. In addition, MUBR6gen ensures consistency of the MCNP and SCALE inputs and compares the outputs of the two codes to warrant the simulation results. Augmented with MUBR6gen, standard neutronics analysis was carried out on a small-scale MUBR design, which serves as a model problem in the paper. The neutronics performance characteristics of the model reactor were obtained and discussed in a code-to-code pattern. An overall very good agreement between the results of the two neutronics codes was established. Based on the success of the model problem analysis, further neutronics analysis using MUBR6gen was extended for a set of MUBR variant designs. Meaningful and promising fuel cycle analysis results for the 10 different designs were achieved and discussed. These results are used to identify the best MUBR candidates in terms of fuel lifetime and utilization efficiency for future applications.

Examination of convective heat transfer and entropy generation performance in twisted elliptical tubes using response surface method

In this paper, the convective heat transfer (CHT) and flow properties of twisted elliptical tubes (TETs) are numerically simulated within the range of Reynolds numbers (Re) 710 to 4790. The effects of design parameters such as twist ratio (P/D), aspect ratio (A/B), and Re on the CHT and flow resistance properties of TETs are highlighted. The response surface method (RSM) is used to research the effect of interaction between design parameters on the performance of CHT in TETs and establish the relationship between the objective function, i.e., Nusselt number (Nu), friction factor (f), and design parameters. The objective function fitted by RSM are optimized by the non-dominated sequential genetic algorithm (NSGA-II) to obtain the Pareto optimal solution. Besides, the entropy generation minimization principle explores entropy generation characteristics to reveal the mechanism of CHT enhancement in TETs. The results show that the CHT performance, flow resistance, and total entropy generation in TET increase, decrease, and increase with the growth of Re, respectively. They all increase with the reduction of P/D or the growth of A/B. The maximum values of Nu and pressure drop (ΔP) occur when A/B = 2.16 and P/D = 11.90; the minimum value of total entropy generation is reached when A/B = 1.34 and P/D = 26.33; and the Pareto optimal solution corresponds to the best combination of design parameters of Re = 3980, P/D = 13.32, and A/B = 1.82.

Dynamics optimization of small branch pipes in nuclear power plants based on machine learning algorithms

Vibration fatigue failure of small branch pipes poses a great threat to the safe operation of nuclear power plants. However, the transient and wide-band vibration problems are not adequately considered in ASME and RCC-M code, resulting in repeated fatigue failures. In addition, the current research mainly focuses on vibration test methods and fatigue analysis methods, neglecting the study of pipeline vibration characteristics. Therefore, innovative approaches were essential for effectively managing complex dynamic loads. In this study, an innovative approach combining backpropagation artificial neural networks (BP-ANN) and non-dominated sorting Genetic Algorithm II (NSGA-II) was proposed to optimize the vibration of these pipes. The goal was mitigating vibration-induced failures by enhancing operational stability. The methodology progressed through several key stages. Firstly, BP-ANN was utilized for regression analysis, correlating pipe characteristics to vibration effects. Through regression analysis, the complex interrelationships governing the pipes' dynamic behavior was revealed. Subsequently, based on the regression model, NSGA-II was used to derive an optimal combination of design parameters to minimize the vibration response. The proposed technique was validated on an L-shaped cantilevered pipe via finite element simulations and physical experiments. The analysis case shows that the BP-ANN model demonstrated excellent accuracy in predicting vibration responses. Meanwhile, NSGA-II successfully revealed the trade-offs between conflicting objectives, generating a Pareto-optimal set balancing stability under different excitation directions. This study highlights the potential of machine learning methods for dynamic optimization of small branch pipes in nuclear power plants, and verifies the accuracy and effectiveness of the method through experiments. The research results provide a new idea for small branch pipe design and vibration control of nuclear power plant, which contributes to enhancing the safety and reliability of small branch pipes.

Preparation and performance analysis of polyamide-based rapid repair material for asphalt pavement shallow groove

In order to effectively remedy shallow grooves on asphalt pavements, this study presents an innovative solution, termed Polyamide-based Asphalt Pavement Shallow Groove Rapid Repair Material (PA-SGRRM). This material employs polyamide 6 (PA6) as the matrix resin, in conjunction with polypropylene (PP), ethylene-vinyl acetate copolymer (EVA), and maleic anhydride-grafted polyethylene-octene copolymer (POE-g-MAH). It is manufactured through an extrusion granulation process. At the outset, a molecular dynamics approach was employed to assess the compatibility of the primary constituents and the adhesion between the matrix resin and asphalt, confirming the rationale behind the material selection. Subsequently, the orthogonal experimental design method was utilized to investigate the influence of design parameters (POE-g-MAH content, PP content, and EVA content) on the flexural strength, tensile strength, low-temperature toughness, and melt flow rate of PA-SGRRM. Following this, we prepared samples with the optimal proportions of EVA and POE-g-MAH and conducted a separate analysis to evaluate the impact of PP on the saturated moisture content of PA-SGRRM. Finally, we employed Scanning electron microscopy (SEM) to observe the microstructures of the low-temperature fracture surface and ambient temperature tensile fracture surface of the PA-SGRRM and employed Fourier transform infrared spectroscopy (FTIR) to deduce the primary chemical reactions occurring within the PA-SGRRM. The results demonstrated that POE-g-MAH played a crucial role in enhancing the compatibility of the simulated hybrid system consisting of PP and PA6. Additionally, it was observed that adhesion between PA6 and asphalt existed, with a gradual decrease as temperature rose. The content of POE-g-MAH and PP were the two primary parameters influencing the performance of PA-SGRRM. POE-g-MAH exerted a significant adverse impact on the flexural strength, tensile strength, and melt flow rate of PA-SGRRM, while substantially improving its low-temperature toughness. The PP positively reduced the water absorption characteristics of PA-SGRRM. Based on the experimental results, the optimal combination of design parameters for PA-SGRRM is recommended.

Theoretical study of the synergic relationships between the design parameters in energy-saving building design

With the rapid development of the economy, people have increasingly higher requirements for the comfort of living spaces, and the result is the sharp increase in building energy consumption. Several design parameters influence living space comfort and building energy efficiency. Since the same design standard can include different design parameter combinations, synergic relationships may exist between these criteria for one case. Identifying these synergic relationships requires an inverse problem approach. This paper established a model by combining an improved genetic algorithm (IGA) and numerical calculation to determine the synergic design parameter relationships (e.g. the thermophysical building material properties and energy-saving factors). For Isum=0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ext{I}}}_{{\ ext{sum}}}= {0} $$\\end{document}, the shading coefficient significantly influenced the linear function between the thermal conductivity and insulation thickness. In this case, the insulation thickness was exponentially related to the shading coefficient, while the thermal conductivity of the insulation material significantly impacted the synergic relationship. For ESR=65%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{ESR}}{=65}\\%$$\\end{document}, the insulation thickness was a segmented function of the shading coefficient. The results verified that the proposed model was efficient and reliable for identifying the synergic relationships between energy-saving parameters. In engineering applications, designers can select the optimal design parameter combination based on the relationship curve between the parameters in this paper according to the local market conditions and specific design requirements.

Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring.

This study addresses the need for advanced machine learning-based process monitoring in smart manufacturing. A methodology is developed for near-real-time part quality prediction based on process-related data obtained from a CNC turning center. Instead of the manual feature extraction methods typically employed in signal processing, a novel one-dimensional convolutional architecture allows the trained model to autonomously extract pertinent features directly from the raw signals. Several signal channels are utilized, including vibrations, motor speeds, and motor torques. Three quality indicators-average roughness, peak-to-valley roughness, and diameter deviation-are monitored using a single model, resulting in a compact and efficient classifier. Training data are obtained via a small number of experiments designed to induce variability in the quality metrics by varying feed, cutting speed, and depth of cut. A sliding window technique augments the dataset and allows the model to seamlessly operate over the entire process. This is further facilitated by the model's ability to distinguish between cutting and non-cutting phases. The base model is evaluated via k-fold cross validation and achieves average F1 scores above 0.97 for all outputs. Consistent performance is exhibited by additional instances trained under various combinations of design parameters, validating the robustness of the proposed methodology.

Research on the Decision-Making Method for the Passive Design Parameters of Zero Energy Houses in Severe Cold Regions Based on Decision Trees

As the field of zero energy building design and research continues to progress, the use of data analysis methods is on the rise. These methods are applied to create assessment criteria, compare performance, and aid in design decision making. Decision trees, as a data-driven approach, offer interpretability and predictability, assisting designers in summarizing their design experience and serving as a foundation for design references. However, the current application of decision tree methods in the zero energy house sector primarily focuses on HVAC systems, lacking a comprehensive exploration from an architectural design perspective. Therefore, this study presents an empirical method for building and applying models based on decision trees, using zero energy house cases in severely cold regions of China as samples. Through an analysis of the interactions among various passive design parameters and the use of EnergyPlus for performance simulations, a decision tree model is established. This model aids in determining the recommended combinations of passive design parameters that meet the criteria of low energy consumption. Moreover, feature weighting highlights the most influential passive design parameters on building energy consumption, including the length of the architectural gestalt plane, the roof shape, and the ground thermal resistance. This research provides valuable methods and guidance for the design and construction of zero energy houses in severely cold regions of China.

Performance-based seismic design method for disc spring-based self-centering viscous dissipative braced steel frame

Disc spring-based self-centering viscous damper (SCVD) has emerged as a novel technology, offering velocity-dependent energy dissipation and exceptional self-centering capabilities. Integrating this damper in structural bracing systems can effectively enhance passive control over critical structural demands such as peak deformation, residual displacement, and floor acceleration. Nevertheless, a seismic design method specifically tailored for self-centering viscous dissipative braced steel frames (SCVDFs) needs further refinement. This study proposes a performance-based seismic design (PBSD) method for SCVDFs, which is grounded in the performance-based plastic design (PBPD) approach. The proposed procedure primarily considers the effects of two critical hysteresis parameters: the post-activation stiffness ratio and the viscous damping ratio. To verify the effectiveness of the design method, a set of SCVDFs was designed, each utilizing different combinations of design parameters. Nonlinear static and dynamic analyses were performed on these archetypes to evaluate their seismic performance. The evaluation findings demonstrated that the properly designed SCVDFs can achieve the expected design performance and effectively suppress the peak inter-story drift, residual inter-story drift, and peak absolute floor acceleration of the structure, thereby achieving a favorable trade-off in the design outcome and maximizing the advantages of the SCVDF in seismic resilience.

A performance-related design for hot-mix asphalt (HMA) incorporating multiple design parameters

Traditionally, the mix design was majorly the trial and error process of optimising aggregate gradation and asphalt content. With the progress of concept and technology, the number of design gyrations (N des) has been gradually regarded as another important design variable. This research explored the potential advantage of mixes designed incorporating multiple design parameters. The paper is divided into two parts. Part 1 designed the varied aggregate gradations with different levels of N des to evaluate the effect of incorporating the N des variable in a traditional process. Part 2 further explored the possible combinations of multiple design parameters, involving binder type, N des, RAP addition and aggregate gradation. The result showed that by selecting a suitable level of N des, the performance of mixes with different aggregate gradations can be well balanced to a certain extent. Besides, the performance of HMAs may be largely improved and some marginal materials can be reutilised by selecting a suitable combination of multiple design parameters.

Archimedes Screw Pump Efficiency Based on Three Design Parameters using Computational Fluid Dynamics Software – Ansys CFX

Utilization of Archimedes screw pumps as water lifting pumps has become widespread in past decade due to frequent occurrences of floods in Malaysia. The problem of insufficient drainage in various urban areas exacerbates the impact of heavy rainfall, prompting efforts to mitigate this issue with minimal maintenance cost and low impact to the environment. Thus, this study is aiming to study the design parameters of screw pump to obtain the optimal efficiency of the Archimedes screw pump specifically for flood mitigation in Malaysia. The main design parameters affecting pump’s efficiency are rotor profile, pitch length, length of the pump, rotational speed, inclination angle, and material selection. However, only three design parameters were considered in the study, that are the angle of inclination, the number of blades, and the angular velocity of the rotating pump. These three design parameters are selected as many previous findings focusing on varying angle of inclination with number of blades with constant rotational speed. Thus, this study will find the highest efficiency when these three design parameters are integrated with variation of rotational speeds at 25, 30 and 40 RPM. Basically, screw pump is designed using SOLIDWORKS and simulations with specific boundary conditions are conducted using the ANSYS-CFX software, which utilizes computational fluid dynamics (CFD) techniques. These boundary conditions are based on previous study by Rosly et al in 2016. The inlet flow rate of 0.002 m3/s and diameter of the screw pump are constant while the other three main parameters are varying within the acceptable ranges which are reported from prior studies. The outcomes found that the highest torque is generated by a single rotating blade at 5.65 Nm which rotates at 30 RPM at 30° angle of inclination. Meanwhile, the highest efficiency of 24.04% is obtained with a single rotating blade at 40 RPM with 20° angle of inclination. Based on the findings, it is concluded that these three main design parameters of screw pump may not be sufficient to obtain the optimal efficiency for the specific boundary conditions used in the simulation study. Thus, several combinations of design parameters should be considered in the future to increase the screw pump’s efficiency.

Techno-economic assessment and grid impact of Thermally-Integrated Pumped Thermal Energy Storage (TI-PTES) systems coupled with photovoltaic plants for small-scale applications

The growing share of Renewable Energy Sources (RES) is rising the amount of curtailed energy to preserve grid security. With the aim of evaluating a complementary storage solution to electric batteries for both new and revamping RES power plants, this study investigates the performance of a Thermally Integrated Pumped Thermal Energy Storage (TI-PTES) system integrated with a Photovoltaic (PV) power plant aimed to enhance the energy self-sufficiency of small-scale users. The assessment is carried out for a case study characterized by a demand of electricity, heating and cooling that vary throughout the year. The studied PTES system is based on the integration of a High Temperature Heat Pump (HTHP), two Thermal Energy Storage (TES) sections and an Organic Rankine Cycle (ORC) power plant. Results show the influence of the main design parameters, such as PV size, HTHP size and TES capacities on the overall system performance, evaluated by means of the energy self-sufficiency, energy self-consumption, round-trip efficiency, Levelized Cost of Storage and the Grid Impact indicator, which quantifies the energy exchanges with the grid (feed-ins and withdrawals) to the overall user demand. The influence of seasonality on the energy performance indicators was studied as well. For the case study considered, the best combination of design parameters for the PV-PTES system is identified with reference to a PV plant whose yearly energy production equals the energy demand and for a 55 kWe High Temperature Heat Pump, a 10 kW ORC and a storage volume of 48 m3 for each of the two TES units. The PTES system is characterized by a LCOS of 0.72 $/kWh, a round-trip efficiency of 28.2 % and its integration offers several advantages over the use of a PV plant without any storage section. In particular, the PTES unit leads to a substantial increase in the self-sufficiency, within the range 1–14 %, and a considerable decrease of the grid impact indicator, within the range 2–28 %.

Multi-Objective Optimization Research on the Integration of Renewable Energy HVAC Systems Based on TRNSYS

Well-designed passive buildings can drastically reduce building energy consumption, and optimal design of air conditioning systems is the key to achieving low operating energy consumption in near-zero energy buildings. TRNSYS was used to build the simulation model for a near-zero-energy building and its air conditioning system in Beijing. The Taguchi method was used to sort the design parameters that affect system performance according to the degree of influence and find the best combination of design parameters to optimize the system, which increased the solar fraction of the system by 4.6% and reduced the annual operating energy consumption by 7.32%. For the optimized system, a multi-objective optimization function of the life cycle costs and carbon emissions was established. By comparing the energy consumption, life cycle costs, and carbon emissions of the air conditioning system under different system configurations, optimal configuration solutions under different design target weights were obtained. It was found that using a ground source heat pump system + solar collector system had better energy-savings benefits, but the operating costs were slightly higher. The application of absorption refrigeration can reduce the system operating costs but will increase the initial investment. The best economic benefits were achieved using the ground source heat pump system + solar collector system for heating in winter and the ground source heat pump system for cooling in summer, and the best environmental benefits were obtaining using the ground source heat pump system + solar collector system for heating in winter and the ground source heat pump system + solar absorption refrigeration system in summer, which provides a reference for the optimization design and research of air conditioning systems in near-zero energy buildings.

Development of an impact based pyroshock test setup using finite element and response surface methods

In this study, a simulation and test-based procedure is created and applied to develop mechanical pyroshock test setup. In order to build an effective design, preliminary input parameters are defined with finite element simulations. The agreement between impact simulations and experimental data was achieved for resonant plate, metallic anvil plate and gun system. The obtained Shock Response Spectrum (SRS) curves are not well fitted to explicit finite element simulations for nonmetallic anvil plate materials, such as felt, which also have significant contribution to results. Therefore, further improvement is established by using test-based Response Surface Method (RSM). After validating the relationship statistically, response optimizer was used to find the best combination of design parameters to achieve target SRS curve. Finally, the prediction was checked experimentally to show the effectiveness of the developed methodology.

Application of a Multidisciplinary Design Process to Assess the Influence of Requirements and Constraints On the Design of Military Engines

Abstract The design of supersonic military aircraft is a complex multidisciplinary optimization (MDO) process in which the dependencies and strong interactions between engine and aircraft must be imperatively considered. Applying a fully coupled propulsion-airframe design system is a highly challenging task, since it requires a set of numerically stable analysis tools capable of optimizing multiple design variables simultaneously. To improve computational efficiency, the application of low-fidelity design of experiment (DOE) methods aid in narrowing down the selection of suitable combinations of design parameters. This approach allows the division of the multidisciplinary process into subsystems, each of which can be served by specialized engineers. Interactions between the disciplines are then considered by exchanging DOE-based sensitivities. This paper presents the multidisciplinary design process developed at the German Aerospace Center (DLR), - in which the airframe and propulsion system are designed simultaneously whilst effectively utilizing DOE-based sensitivities. Guiding the work is an application case on the preliminary design of military engine concepts considering its effects on overall integrated aircraft architecture. The design process is used to investigate the influence of important engine parameters such as overall pressure ratio (OPR), bypass ratio (BPR) and turbine entry temperature (T4) on the design of military aircraft. Furthermore, the impacts of thrust requirements and technological constraints of the engine are analyzed