Performance Simulation Model Research Articles

Numerical simulation of micro gas turbine engine based on time-marching one-dimensional method

The advancement of gas turbine technology serves as a crucial indicator of high-tech and technological prowess, with widespread applications in aviation, marine vessels, power generation, and clean energy. The precision of the performance simulation model stands as a pivotal component in the development of gas turbines. The present study deals with the one-dimensional modeling and validation of gas turbines by utilizing a time-marching scheme, in order to establish the correlation between the engine components and full-engine performance. The flow through the turbomachinery was solved by the one-dimensional model. The combustor was modelled as a compressible actuator disk. The accuracy of the turbomachinery solver was validated using the KJ66 compressor and turbine, and the predicted results demonstrated consistency with the experimental data or the three-dimensional simulations. The characteristic interface method was developed based on Riemann invariants, in order to provide the flow properties on both sides of the combustor. The results indicated that compared to the component-by-component approach, the developed characteristic interface method significantly reduced the computational time. Finally, the developed one-dimensional method was employed to simulate the performance of the KJ66 micro gas turbine engine. The predicted performance for the KJ66 gas turbine engine was consistent with the trend in the experiment results. The novelty of the present study was its modeling approach, and its ability to eliminate the dependence on the general component map, and obtain meridional distributions of flow parameters, which cannot be achieved in traditional zero-dimensional thermal cycle calculations. These results can provide an accurate and powerful tool for the analysis and optimization of component and full-engine performance in the preliminary design stage.

Numerical simulation and experimental study on guidance performance of hypersonic seeker under aerodynamic optical transmission effects

When a hypersonic seeker flies at high speed within the atmosphere, intense interaction with the incoming flow gradually develops into a complex turbulent flow field. This interaction results in complex thermal responses at the seeker window, causing aerodynamic optical effects such as image shift, jitter, and blur of the target image, thereby restricting the seeker's detection capability and accuracy. This paper uses a numerical simulation model for the guidance performance of a hypersonic seeker under aerodynamic optical transmission effects. The study focuses on an ellipsoidal seeker, with its supersonic flight simulation on the basis of the Reynolds-Averaged Navier-Stokes (RANS) equations to get a non-uniform gradient flow field. The correctness of the flow filed results can be verified by wind tunnel experiments. The transient temperature field of the seeker is solved using an unsteady thermal conduction-radiation coupled fluid-solid heat transfer method. Finally, the guidance performance of the hypersonic seeker under aerodynamic optical effects is predicted using the ray tracing method, which employs wavefront aberration, point spread function, degraded images, and image shift.

Evaluation and projection of changes in temperature and precipitation over Northwest China based on CMIP6 models

AbstractNorthwest China is much more sensitive to climate warming, and the climate has varied rapidly from warm and drought to warm and humid conditions. In addition, due to the complex terrain of Northwest China, the methods and parameterization schemes of different CMIP6 models, these models are mostly applied to arid areas in Northwest China or Central Asia, lacking climate data for plateau areas and eastern Lanzhou, specifically in filtering CMIP6 models and evaluating applicable models. In this paper, 34 CMIP6 climate models are used to evaluate and forecast future trends in Northwest China under the SSP126, SSP245 and SSP585 scenarios in the short, medium and long term. CMIP6 models of temperature and precipitation are identified by applying the interannual variability skill score (IVS) between CN05.1 datasets and historical CMIP6 models, which are suitable for Northwest China. Then, we assess the characteristics, warming and wetting deviations, and uncertainties in the prediction of climatic change according to CMIP6 models over Northwest China. The results show that CMIP6 models in precipitation and temperature applicable to Northwest China are AWI‐CM‐1‐1‐MR, BCC‐CSM2‐MR, FGOALS‐g3, INM‐CM4‐8, INM‐CM5‐0 and MRI‐ESM2‐0. The multi‐model ensemble mean (MMEM) has better capability than individual CMIP6 models in precipitation and temperature prediction. Spatiotemporal climatic change over Northwest China shows overall warming and wetting trends. The IVS provides the ability to estimate CMIP6 model simulation performance both temporally and spatially. The temperature simulation is quite good in the Tarim Basin and Hexi Corridor region, and the precipitation simulation is quite good in the plateau region, Altai Mountains, Tianshan Mountains and Hexi Corridor region. Cold and wet deviations occur in Northwest China due to the topography and few stations, which are common reasons. The main sources of uncertainties in temperature prediction during this century are model uncertainty (before the 2090s) and scenario variability (after the 2090s), and model uncertainty in precipitation for CMIP6 becomes the main source of uncertainty.

Effect of geometric parameters on the heat transfer performance of a submerged coil condenser for heat-pump water heating

Water heating with heat pumps can contribute to reduce carbon dioxide emissions and meet sustainability goals due to its high thermal efficiency, versatility in terms of alternative energy sources and thermal energy storage capability. In this study, the heat transfer of the condenser subsystem of an accumulation heat-pump water heater is analyzed by means of 2D CFD simulations. After validating the simulation methodology with benchmark natural convection problems, the influence of different geometric parameters of the condenser coil on the heat transfer is studied, which include the distance between turns and the distance from the tank wall. For each case, Nusselt number correlations in terms of Rayleigh number are found via power-law fitting. According to the CFD results, a greater coil pitch improves the average heat transfer. Moreover, a shorter distance from the tank wall generates a velocity field that laterally diverts the plume that forms around each turn. Hence, the heat transfer is enhanced because the preheating effect from downstream turns is mitigated, while the velocity gradients are strengthened. Additionally, a condenser design is proposed with geometric parameters that promote effective heat transfer, and the performance is compared against a reference design with geometric parameters that limit the heat transfer but are common in commercial solutions. The proposed geometry has a 43% increase in the Nusselt number, with respect to the reference geometry. Also, based on a dynamic simulation model of heat pump performance, it is determined that, for the same operating conditions, the proposed geometry improves the overall coefficient of performance (COP) of the system by up to 7%. These results highlight the importance of the geometric design of the condenser in heat-pump water heating systems, since they can contribute to a better overall performance and a more efficient thermal storage.

Energy performance tradeoff assessment for varying design parameters in office buildings

ABSTRACT Energy performance and heat release from buildings can differ with variations in envelope and operational parameters. This study presents an evidence-based assessment for different study locations in Japan including Sapporo, Tokyo, and Osaka. The analysis used a comparative approach using a reference energy performance simulation model for large office buildings using EnergyPlus to assess the impact of design variations on building performance. The analysis included 66 simulations with 10 design parameters and 2 construction types. The study coupled energy performance with heat release from the building towards outdoor environments since heat release can influence the local thermal environments. Desirable tradeoffs were observed for timber constructions by increasing the cooling setpoint by 1°C reduced cooling and heating energy use by 10.7% and 11.9% in Sapporo, 8.5% and 22.7% in Tokyo, and 6.8% and 24.4% in Osaka. Similarly, cooling and heating peak loads were reduced by 5.9% and 2.4% in Sapporo, 5.4% and 2.0% in Tokyo, and 6.5% and 5.2% in Osaka. Additionally, nighttime heat release also decreased by 12.4% and 13.6% in Sapporo and Tokyo, and 6.2% and 5.7% in Osaka for concrete and timber constructions. The study provides early-stage design support for energy-efficient buildings and improved local thermal environments.

Informing building retrofits at low computational costs: a multi-objective optimisation using machine learning surrogates of building performance simulation models

Machine learning (ML) algorithms are increasingly used as surrogates for building performance simulation (BPS) models to leverage their energy predictive capabilities while reducing computational costs. In parallel, researchers are developing optimisation methods to inform building design and retrofit strategies but rarely employ ML-based BPS surrogates for this purpose. This study proposes a coupled modelling approach that leverages the capabilities of ML-based BPS surrogate models and multi-objective optimisation to inform holistic design and operation retrofits at low computational costs. The proposed methodology is demonstrated using an archetypal office building in Ottawa, Canada. The developed models achieved competitive predictive accuracies (adjusted R2: 0.90–0.99), identifying total and peak energy saving measures with up to 34% improvement in occupant thermal comfort at computational speeds 1266 times faster than a traditional BPS-based optimisation approach. Results offer a promising modelling workflow for design applications requiring extensive computations and scenario analyses, such as net-zero energy retrofits.

Compact CPW-fed Antenna with Controllable WLAN Band-rejection for Microwave Imaging

This work presents the design and experimental validation of a compact frequency reconfigurable coplanar waveguide (CPW)-fed ultra-wideband (UWB) antenna with a capability to on-demand reject WLAN frequencies within the range of 5.15 GHz to 5.85 GHz, specifically tailored for applications in microwave imaging. The design and experimental results are presented and discussed. The bandwidth enhancement is obtained by using T-shaped slots between the feedline and the ground plane of the antenna, and the WLAN band is rejected by using an open loop resonator (OLR) placed on the antenna backside. Switching between UWB with and without WLAN band-notched modes is performed using a PIN diode and a bias circuit. The simulation results corroborate well with the experimental data and clearly showed an interesting frequency reconfigurable behavior for use in microwave imaging applications. An antenna performance simulation and analysis model is presented for breast tumor detection, and the tumor effect has been noticed for both operating modes of the antenna.

Machine learning for high-precision simulation of dissolved organic matter in sewer: Overcoming data restrictions with generative adversarial networks

Understanding the transformation process of dissolved organic matter (DOM) in the sewer is imperative for comprehending material circulation and energy flow within the sewer. The machine learning (ML) model provides a feasible way to comprehend and simulate the DOM transformation process in the sewer. In contrast, the model accuracy is limited by data restriction. In this study, a novel framework by integrating generative adversarial network algorithm-machine learning models (GAN-ML) was established to overcome the drawbacks caused by the data restriction in the simulation of the DOM transformation process, and humification index (HIX) was selected as the output variable to evaluate the model performance. Results indicate that the GAN algorithm's virtual dataset could generally enhance the simulation performance of regression models, deep learning models, and ensemble models for the DOM transformation process. The highest prediction accuracy on HIX (R2 of 0.5389 and RMSE of 0.0273) was achieved by the adaptive boosting model which belongs to ensemble models trained by the virtual dataset of 1000 samples. Interpretability analysis revealed that dissolved oxygen (DO) and pH emerge as critical factors warranting attention for the future development of management strategies to regulate the DOM transformation process in sewers. The integrated framework proposed a potential approach for the comprehensive understanding and high-precision simulation of the DOM transformation process, paving the way for advancing sewer management strategy under data restriction.

Opportunities and Barriers of Calibrating Residential Building Performance Simulation Models Using Monitored and Survey-Based Occupant Behavioural Data: A Case Study in Northern Spain

The performance gap caused by occupant behaviour (OB) is one of the main challenges to the accuracy of building performance simulations (BPS) models. Calibration of BPS models has shown great improvements in tertiary and single residential buildings. Nevertheless, the calibration in collective residential buildings is still uncertain. This study aims to identify the opportunities and barriers to the calibration of collective residential building BPS models for the analysis of heating energy consumption. For this, the research calibrates a real case study of a social rental housing building located in northern Spain. The method involves the adjustment of input data based on OB clusters, developed by monitorization and survey data and the statistical comparison of the results of normative models, calibrated models and real data. The results show an average improvement of 67% in hourly indoor temperature and 16% in hourly heating energy consumption in calibrated models, but still with a considerable performance gap. The main barriers to a higher accuracy are the wide diversity and lack of uniformity of OB patterns, uncertainty of parameters, and use of auxiliary heating systems. However, deeper monitorization and survey campaigns with the use of OB clusters can be a promising opportunity.

Model development and parameter calibration of a combined-cycle power generation unit via metaheuristic optimization techniques

Mathematical modeling of combined-cycle units has produced a variety of valuable results, primarily significant advances in their energy efficiency and education of power plant operators. These positive results motivate the need for more complex modeling and simulation techniques with the goal of greater accuracy and accessibility. This study presents a simplified and enhanced version of a gas turbine model for dynamic performance simulation. The theoretical improvements have been proved through modeling philosophy and simulation accuracy attained by metaheuristic optimization techniques. The model has been constructed using thermodynamic and mathematical concepts, with parameter optimization and calibration performed using metaheuristic optimizers. The suggested model’s resilience has been demonstrated by simulation results.

Evaluating nine different air-sea flux algorithms coupled with CAM6

The accuracy of air-sea flux algorithms significantly impacts the simulation performance of climate models. In this study, we evaluated nine widely-used air-sea flux algorithms: those used in the BCC model, the CAM model, the ECMWF model, and the WRF model (with three options), as well as three other commonly-used algorithms: COARE, UA, and VM. An offline comparison revealed significant discrepancies in the flux calculations obtained with different algorithms. Subsequently, we conducted nine sensitivity experiments by coupling these algorithms into the Community Atmosphere Model version 6 (CAM6) to assess the extent and manner in which the selection of flux algorithms influenced the simulated climate. The online simulations introduced complexities due to interactions between the atmosphere and surface fluxes, making the results more intricate. The iterative process of the model created negative feedback between wind speed and momentum flux, as well as between air temperature and heat flux, leading to inconsistencies between online and offline test results. Based on the comprehensive evaluation metric Distance between Indices of Simulation and Observation (DISO), a concept of combined or segmented algorithm was introduced, and a segmented algorithm based on wind speed was found resulting in an overall superior simulation performance compared to the previous nine experiments.

Advanced cooling channel structures for enhanced heat dissipation in aerospace

Thermal protection is crucial for active cooling runner structures. It’s highly effective for achieving thermal insulation in aerospace applications. This study focuses on evaluating the thermal insulation performance of the heat dissipation runner structure under varying flow rates. The results demonstrate the exceptional thermal insulation capabilities of the heat dissipation runner structure, with a noticeable positive impact on thermal protection through appropriate flow rate increments. A simulation and analysis model for thermal insulation performance is developed and validated against experimental findings. By delving into robust heat transfer theory and turbulence mechanisms, it is observed that the local turning radius and length of the runner exhibit an inverse relationship with the heat dissipation performance of the structure. Through localized optimization of the runner design and implementation of a series–parallel connection mechanism, eight distinct heat dissipation runner structures are meticulously crafted. Analysis of temperature uniformity and flow consistency within the runner structure is conducted using thermal resistance, inlet pressure loss, and comprehensive evaluation criteria. Results underscore the strong thermal insulation performance of the eight runner structures, highlighting a direct correlation between the number of parallel runners and enhanced heat dissipation efficiency, while revealing an inverse correlation with the number of local turns. Notably, at a flow rate of 0.0005 m3/s, the thermal insulation performance of the eight flow channel structures peaks. Furthermore, it is observed that increasing the series connection of the runners only serves to complicate the structural design without significant benefits to heat dissipation. Thus, it is recommended to minimize the series connection direction in the runner design to optimize thermal management effectiveness.

Collaborative modeling-based improved moving Kriging approach for low-cycle fatigue life reliability estimation of mechanical structures

To effectively estimate the reliability level of low-cycle fatigue (LCF) life of mechanical structures, a novel method of collaborative modeling-based improved moving Kriging (IMKCM) approach is proposed by fusing improved moving Kriging method and collaborative modeling strategy. In this method, the improved moving Kriging model is developed by the Kriging model, moving least squares method and artificial hummingbird algorithm, in which the Kriging model is selected as the basis function, the artificial hummingbird algorithm is used to define the optimal radius of compact support region and determine the effective modeling samples, and the moving least squares method is adopted to resolve the unknown coefficients; the collaborative modeling strategy is developed from the decomposition and coordination method, in which the mathematical models of the decomposed sub-objectives are synchronously built by the cell array theory, and the mathematical model between the objective and the sub-objectives is established by the improved moving Kriging model. Furthermore, a numerical example (i.e., nested nonlinear function) and an engineering example (i.e., turbine blisk LCF life reliability estimation) are employed to verify the effectiveness of the IMKCM approach. The results show that the proposed IMKCM method holds excellent abilities in modeling features and simulation performance.

Continuous Eddy Simulation (CES) of Transonic Shock-Induced Flow Separation

Reynolds-averaged Navier–Stokes (RANS), large eddy simulation (LES), and hybrid RANS-LES, first of all wall-modeled LES (WMLES) and detached eddy simulation (DES) methods, are regularly applied for wall-bounded turbulent flow simulations. Their characteristic advantages and disadvantages are well known: significant challenges arise from simulation performance, computational cost, and functionality issues. This paper describes the application of a new simulation approach: continuous eddy simulation (CES). CES is based on exact mathematics, and it is a minimal error method. Its functionality is different from currently applied simulation concepts. Knowledge of the actual amount of flow resolution enables the model to properly adjust to simulations by increasing or decreasing its contribution. The flow considered is a high Reynolds number complex flow, the Bachalo–Johnson axisymmetric transonic bump flow, which is often applied to evaluate the performance of turbulence models. A thorough analysis of simulation performance, computational cost, and functionality features of the CES model applied is presented in comparison with corresponding features of RANS, DES, WMLES, and wall-resolved LES (WRLES). We conclude that CES performs better than RANS, DES, WMLES, and even WRLES at a little fraction of computational cost applied for the latter methods. CES is independent of usual functionality requirements of other methods, which offers relevant additional advantages.

Annual evaluation of the visual-thermal comfort and energy performance of thermotropic glazing in a reference office room of China

Hydrogel-based thermotropic glazing, which achieves the dynamic characteristics by maneuvering the light-scattering behaviors, has a great potential in improving building energy performance, visual and thermal comfort. The visual-thermal comfort and energy performance of the thermotropic glazing with different transition temperature was evaluated by using an experimental validated building performance simulation model. The performance of the thermotropic glazing was compared with conventional double-clear glazing and low-emissivity double glazing under five cities within the five major climate zones across China. The objective is to comprehensively understand the applicability of the thermotropic glazing in different climatic conditions of China as well as to determine the optimal transition temperature. The results showed that: (1) The thermotropic glazing with a transition temperature of 30 °C could achieve better energy performance, visual and thermal comfort in Xiamen, Nanjing, and Kunming. (2) Low transition temperature is not an essential requirement for the application of the thermotropic glazing, which could result in increasing energy use intensity and this effect is more significant in colder regions. (3) The thermotropic glazing could contribute to building energy savings (up to 16.3% and 2.3% i.e., in Xiamen) compared with conventional double-clear glazing and low-emissivity double glazing with low SHGC. (4) The thermotropic glazing applied in Xiamen, Nanjing and Kunming could obtain better visual and thermal comfort (i.e., increase of the desired range of illuminance sUDI500–2000lux, is up to 85.19% and a promotion of 29.18% in time proportion of desirable thermal comfort in Kunming) when compared with low-emissivity double glazing and conventional double-clear glazing.

Hydrological Simulation Study in Gansu Province of China Based on Flash Flood Analysis

The calibration and validation of hydrological model simulation performance and model applicability evaluation in Gansu Province is the foundation of the application of the flash flood early warning and forecasting platform in Gansu Province. It is difficult to perform the simulation for Gansu Province due to the fact that it covers a wide range, from north to south, with multiple climate types and diverse landforms. The China Flash Flood Hydrological Model (CNFF) was implemented in this study. A total of 11 model clusters and 289 distributed hydrological models were divided based on hydrology, climate, and land-use factors, among others. A spatiotemporally mixed runoff method and the Event-Specific Geomorphological Instantaneous Unit Hydrograph (GIUH) were applied based on large-scale fast parallel computation. To improve model calibration and validation efficiency, the RSA method (Regionalized Sensitivity Analysis) was used for CNFF model parameter sensitivity analysis, which could reduce the number of model parameters that need to be adjusted during the calibration period. Based on the model sensitivity analysis results, the CNFF was established in Gansu Province to simulate flood events in eight representative watersheds. The average NSE, REQ, and ET were 0.76 and 0.73, 9.1% and 12.6%, and 1.2 h and 1.7 h, respectively, in the calibration and validation period. In general, the CNFF model shows a good performance in multiple temporal and spatial scales, thus providing a scientific basis for flash flood early warning and analysis in Gansu Province.

Establishment and Calibration of Performance Simulation Model for Hydrogen Injector of Fuel Cell

Establishment and Calibration of Performance Simulation Model for Hydrogen Injector of Fuel Cell

Assessing Statistical Indices for Calibrating Building Performance Simulation Models via Continuous Time Series

In engineering planning practice, building simulation is becoming increasingly crucial for evaluating buildings’ energetic and thermal behaviour. The appropriate application in the form of models that represent reality precisely, therefore, faces the challenge of realistic parameter specification for uncertain BES model properties. Many of them can only be restricted to a probable range of values. Additional knowledge can be obtained for the case of available measurement data via inverse modelling. This approach minimises cost function values representing discrepancies between associated time series (measured and simulated data sets). Much research has been published in this field for energy consumption characteristics. These approaches differ in several aspects from measured continuous physical state quantities. This article applies general cost indices to air temperature measurement series. For isolated effects such as time delay and amplitude attenuation, the impact on the characteristic values is examined, and their response is evaluated. This survey leads to the conclusion that there is no single parameter suitable for identifying all effects, and a combination is recommended. A new proposal is made for such a combined statistical index, and exemplary applied for measured temperature curves from a castle near Potsdam.

Validating a Building Performance Simulation Model of a naturally ventilated Double Skin Facade

Double Skin Facades (DSF) are regaining popularity as a way to increase the climate resilience of buildings. Building Performance Simulation (BPS) is commonly used for their assessment, but modelling DSFs with BPS is challenging due to their complex thermophysical behaviour. Several research works have evaluated the capabilities and limitations of BPS for modelling specific DSF configurations. This work presents a validation study based on experimental data from a full-scale naturally ventilated double-skin façade, compared against results from the BPS software IDA-ICE. The study found that in periods with low solar irradiation, the different modelling strategies had a minor influence on the results, with a high agreement between measurements and simulation. In contrast, periods with solar irradiation showed a higher sensitivity to the modelling strategy, with more significant deviations from the measurement results.

An electrochemical-thermal-aging effects coupled model for lithium-ion batteries performance simulation and state of health estimation

Numerous interrelated coupled aging mechanisms contribute to the degradation of lithium-ion batteries (LIBs), making it challenging to confirm the influence of aging effects on the performance and state of health (SOH). Inspired by electrochemical impedance spectroscopy (EIS) analysis and observed morphological changes, this paper considers double layer resistance increase, solid electrolyte interface (SEI) film growth, and cathode aggregate crack propagation as the main aging effects, and the influence of temperature is considered, leading to development of an electrochemical-thermal-aging coupled model. The model achieves high-precision simulation for different temperature and aging conditions by updating the model parameters based on the aging effects and internal temperature. Compared with experimental data, the relative error of voltage simulation is within 1%. Additionally, the aging effects parameters exhibit a consistent trend with data analysis and mechanism inference, and this strong correlation is beneficial for estimating SOH. The estimation error of SOH based on four linear regression methods is less than 0.5%. This novel electrochemical-thermal-aging effects coupling model addresses two major challenges in battery management system (BMS): performance simulation and SOH estimation under real-world operating conditions.