Engineering Model Research Articles

Piecewise-scheduled thrust command control for in-service thrust performance improvement of gas turbine aero-engines: A hybrid fast design approach

Gas turbine aero-engines including turbofans, as the dominant powerplant for modern civil aircraft, convert the fossil energy in the jet fuel to propulsive forces via the thermo-dynamic cycle. Unfortunately, thrust regulation capabilities of gas turbine aero-engines are inevitably affected by uncertainties in measurements and gas path degradation during the life cycle, while quantification efforts for these uncertainties by traditional random analysis methods are usually considerable. In this paper, a piecewise-scheduled thrust command controller is proposed based on the improvement of the industrial baseline controller and current measurement levels, aiming at enhancing in-service thrust performance within a tolerable computational burden for engine fleets against these uncertainties. The proposed controller is equipped with a bank of embedded thrust maps for different flight cycle segments, which is fulfilled by a hybrid fast design approach incorporating a random analysis part and an analytical design part, as a new uncertainty quantification method. An industrial baseline controller with the identified thrust mode is also designed as the comparison basis. Simulations are carried out on a validated aero-thermal turbofan engine model with publically accessible uncertainty statistics. Simulation time for constructing the embedded thrust maps of the proposed controller is decreased by 99.8% on a desktop computer, compared to Monte-Carlo approach. Meanwhile, simulation results show that the proposed controller owns a bounded and tight thrust distribution for both new and severely degraded engine fleets at the take-off state within the permitted safety margin of the engine, which mitigates the significant under-thrust consequences from the baseline controller. Hence, the uncertainty control benefits of the proposed controller and its design efficiency are guaranteed.

The Medium Energy Electron Telescope (MEET): Instrument Fabrication and Testing

AbstractThis manuscript is a follow up study to Hogan et al., (2024, https://doi.org/10.1029/2023JA032221) which describes the science motivation and development of the Medium Energy Electron Telescope (MEET). Medium Energy Electron Telescope is a 1U instrument optimized to measure 30–400 keV electrons in the inner radiation belt with fine energy resolution to resolve dynamics of these populations which are not well understood. Here we present the fabrication and testing of MEET to certify its performance in preparation for in‐space measurements. Assembly of an engineering model is shown. This unit has undergone vibration testing and these results are discussed. The electronics of the instrument are tested after these environmental tests including electron measurements with pulse injection and a radioactive source. Test results indicate the instrument is viable for flight on a potential mission by satisfying noise requirements and reproduction of an input flux spectrum. The noise of expected input signals is quantified to be <∼6 keV allowing for <20% dE/E for 30 keV electrons. Strontium‐90 radioactive source tests were conducted. Using measured count rates from MEET's electron energy channels, we reproduce the strontium‐90 spectrum proving the instrument's ability to measure flux of 30–800 keV electrons with fine nominal energy resolution (<20% dE/E for <60 keV, and <10% for >60 keV electrons). These results demonstrate MEET's performance in a relevant environment via ground testing. Medium Energy Electron Telescope is available for future missions to provide novel electron measurements in the inner radiation belt.

Desing of an observer with real time monitoring speed and load torque for submersible induction motors

Relevance. When operating submersible equipment for oil production in aggressive environments and transferring wells to the cyclic operation mode, a decrease in the service life of the submersible installation for oil production is observed. In the first case, this is due to the formation of salt deposits and clogging of the working parts of the electric pump with mechanical impurities. In the second case – an increase in the number of starts of the submersible electric motor. To solve the existing problems, it is possible to implement closed control systems for submersible electric motors based on state variable observers, which determines the relevance of the study. Aim. To develop an observer with operational monitoring of the angular velocity of the rotor and the moment of resistance on the shaft of a submersible asynchronous motor at inconsistency of the initial conditions in various operating modes and its testing using modeling tools. Methods. The observer is built on the basis of known engine models in a fixed coordinate system α, β, the theory of IIR-filters to obtain a forecast of estimates of the angular velocity of the rotor and the torque on the shaft and their correction in real time. Results. The authors have proposed the original structure of an observer with operational monitoring of the angular velocity of the rotor and the moment of resistance on the shaft of a submersible asynchronous motor. Conclusions. The paper demonstrates the observer performance with inconsistency of initial conditions and electric motor model data in various operating modes. In all modes, stable estimates of the speed and torque of resistance on the electric motor shaft are obtained. At the same time, the error in estimating the angular velocity under the condition of changing the load on the shaft and starting in the loaded state is no more than 1.2%, which is acceptable in submersible electric motor control systems. It is revealed that the developed observer, under the condition of changing the active resistance of the stator and rotor in the ranges from –25 to +25% of the nominal value, obtains estimates of the angular velocity with an integral error of no more than 5%, except for starting the motor with a decrease in the active resistance of the rotor by 25% of the nominal value –5.53%, which is acceptable in engineering practice.

SIMULATION OF TORQUE VARIATIONS IN A DIESEL ENGINE FOR LIGHT HELICOPTERS USING PI CONTROL ALGORITHMS

This article presents the results of simulation research of a diesel engine for a light helicopter. The simulations were performed using the 1D software AVL Boost RT. The engine model includes elements such as cylinders, turbine, compressor, inlet and outlet valves, ambient environment definition, and fuel injection control strategy. The simulations aimed to evaluate the engine's response to step changes in the main rotor load, both increasing and decreasing power demands. Parameters analyzed included power deviation, torque, engine rotational speed, and stabilization time of the main rotor rotational speed. All tests were conducted using a single set of PI controller settings. The results demonstrate that these parameters are dependent on the magnitude of the step change in the main rotor load demand. The study compares the maximum engine rotational speed deviation from the nominal value for both increasing and decreasing main rotor load demands. The findings indicate that using PI regulator to control rotational speed in the diesel engine in a light helicopter significantly depends on the change in the load torque on the rotor.

Hall thruster model improvement by multidisciplinary uncertainty quantification

We study the analysis and refinement of a predictive engineering model for enabling rapid prediction of Hall thruster system performance across a range of operating and environmental conditions and epistemic and aleatoric uncertainties. In particular, we describe an approach by which experimentally-observed facility effects are assimilated into the model, with a specific focus on facility background pressure. We propose a multifidelity, multidisciplinary approach for Bayesian calibration of an integrated system comprised of a set of component models. Furthermore, we perform uncertainty quantification over the calibrated model to assess the effects of epistemic and aleatoric uncertainty. This approach is realized on a coupled system of cathode, thruster, and plume models that predicts global quantities of interest (QoIs) such as thrust, efficiency, and discharge current as a function of operating conditions such as discharge voltage, mass flow rate, and background chamber pressure. As part of the calibration and prediction, we propose a number of metrics for assessing predictive model quality. Based on these metrics, we found that our proposed framework produces a calibrated model that is more accurate, sometimes by an order of magnitude, than engineering models using nominal parameters found in the literature. We also found for many QoIs that the remaining uncertainty was not sufficient to account for discrepancy with experimental data, and that existing models for facility effects do not sufficiently capture experimental trends. Finally, we confirmed through a global sensitivity analysis the prior intuition that anomalous transport dominates model uncertainty, and we conclude by suggesting several paths for future model improvement. We envision that the proposed metrics and procedures can guide the refinement of future model development activities.

TPat: Transition pattern feature extraction based Parkinson’s disorder detection using FNIRS signals

Background and ObjectiveParkinson’s Disease (PD) is one of the most commonly observed neurodegenerative disorders worldwide. Many researchers have utilized machine learning (ML) models to detect PD and understand its underlying causes automatically. In this research, our primary objective is to automatically detect PD and extract meaningful results using the proposed ML model. Materials and MethodsIn this study, an FNIRS dataset collected from PD patients and control participants under three conditions—(i) rest, (ii) walking, and (iii) finger tapping—was utilized. A new explainable feature engineering (XFE) model was proposed to detect PD and automatically extract meaningful information under these conditions. The XFE model consists of four main phases: (i) feature extraction using the proposed channel transformation and transition pattern (TPat), (ii) feature selection employing cumulative weighted neighborhood component analysis (CWNCA), (iii) classification using the k-nearest neighbors (kNN) classifier, and (iv) channel network extraction to obtain explainable results. ResultsThe suggested TPat-based XFE model was applied to the FNIRS dataset. This dataset included three distinct cases. Our model achieved over 94% classification accuracy using leave-one-subject-out cross-validation (LOSO CV) and 100% classification accuracy using 10-fold cross-validation. Additionally, channel transitions for each case were identified and discussed. ConclusionsBased on the results and findings, the proposed model demonstrated high accuracy in FNIRS signal classification and provided explainable results. In this regard, the presented TPat-based XFE model contributed significantly to both ML and neuroscience.

Development of a Simulation Model for a New Rotary Engine to Optimize Port Location and Operating Conditions Using GT-POWER

The objective of this study is to develop a 1D CFD simulation model to identify the optimal design parameters, using GT-POWER prior to the optimization of a new rotary engine derived from a three-lobe gerotor pump (GP3 RTE) based on 3D CFD simulation. The models were compared based on their respective development stages (steps 1–4) to ascertain the impact of each parameter on performance. The step 4 model, which exhibited a similar trend to that observed in the 3D CFD results, was selected for further analysis and validation. The developed model accurately predicted GP3 RTE performance in terms of fuel consumption, indicated power, efficiency, and exhaust gas reticulation (EGR) behavior, approaching the accuracy of the CONVERGE model. Furthermore, the optimal intake/exhaust port locations and operating conditions of the GP3 RTE were derived using the developed step 4 model. The model provided a convenient and powerful tool for obtaining basic information regarding the unique behavior of the GP3 RTE, thereby enabling the optimization of the design parameters without the necessity for time-consuming three-dimensional design modifications.

Performance analysis of the adaptive cycle engine with cooling air: From propulsion performance, thermodynamic efficiency, exergy and infrared characteristics

The adaptive cycle engine gains significant attention due to its variable thermodynamic cycles and multi-duct characteristics, which are utilized for thermal management to enhance engine performance. This paper explores the features of multi-duct technology in adaptive cycle engines and aims to improve engine flight performance. An analysis is conducted on the impact of duct air bleed on engine propulsion characteristics, thermodynamic cycle properties, and exhaust system infrared radiation characteristics. Initially, a component-level model of the adaptive cycle engine was established, considering CDFS duct, bypass duct, and third duct air cooling devices. This model uses the ducted cold air to cool the temperature of low-pressure turbine exit, center cone wall, and main nozzle expansion section wall. Subsequently, a method for analyzing the infrared radiation of the engine exhaust system was proposed based on the above model, and the performance of engine components and the overall engine was analyzed in terms of energy, exergy, mechanical efficiency, and thermal efficiency. Finally, the numerical simulations of the propulsion performance, thermodynamic efficiency and infrared characteristics of the adaptive cycle engine with cooling air were carried out under conditions of ground takeoff, subsonic cruise, and supersonic cruise. The simulation results show that the cooling air can effectively enhance engine performance and infrared stealth capabilities under various flight conditions. The ducted air extraction measures proposed in the paper could effectively improve the propulsion efficiency and stealth characteristics of the engine, providing important references for the development of more efficient aeroengine.

A sensitivity study on the starting simulations of turbojet engines

Abstract Gas turbine engine starting models require a lot of calibration to represent reality with acceptable accuracy due to the lack of high-quality component rig data in the sub-idle region. A detailed sensitivity study is presented in this paper to guide such calibration efforts. A thermodynamic component-matching type transient model of a single-spool turbojet engine with shaft and heat-soakage dynamics is employed for this purpose. Turbomachinery component maps are extended to sub-idle using an in-house map smoothing tool and the strategies presented by Kurzke recently. These extension strategies make use of the correlations hidden in the already available regions of the maps and ensure physical consistency. However, they contain some uncertainty, even when an experimentally obtained zero-speed line is available. Combustor sub-idle efficiency, stability limits, and delay are taken from the literature. Due to the chaotic nature of a combustor in the sub-idle region, a precise prediction of the combustor efficiency seems impossible. Effects of uncertainties related to sub-idle turbomachinery map extensions, burner efficiency, and heat soakage are investigated in this paper. Two popular fuel control strategies are employed and compared to see how controls deal with these uncertainties. It is concluded that turbomachinery torque characteristics and turbine capacities are the most important parameters when calibrating a starting model with a control based on rotational acceleration while burner efficiency and heat soakage are added on top of these with a control based on fuel flow rate.

Fault Detection on Short-Haul or Highly Dynamic Flights Using Transient Flight Segments

Abstract A machine learning-based approach is presented, which allows to detect persistent engine faults after a single flight. It utilizes transient in-flight measurements and a transient engine model. The time series of the residuals between the measured data and the data resulting from performance synthesis is evaluated using moving windows containing at least one transient segment. A continuous wavelet transformation and a pretrained convolutional neural network are utilized on the residuals for feature extraction. The fault detection is carried out via a one-class support vector machine, trained exclusively on nominal engine operation data. Therefore, the approach requires no a-priory knowledge of the effects of engine faults on the in-flight measurements. Under the assumption of persistent faults, all windows of a single flight, which contain at least one transient segment, are considered in order to improve the reliability of the fault detection. This approach is validated using measured data of a small helicopter engine that replicates the dynamic flight of the corresponding model helicopter on a ground test bed. Consequently, step changes as well as complex variations of the shaft power output are considered. Four standard gas path sensors are considered. The one-class support vector machine is used successfully to detect two types of total pressure sensor anomalies. Assuming a typical number of transient segments for an average short haul flight, it turns out that persistent faults can be detected within one flight with a probability of above 90%.

Transient NOx emission modeling of a hydrogen-diesel engine using hybrid machine learning methods

One promising approach to reduce carbon foot print of internal combustion engines (ICEs) is using alternative fuels like hydrogen, particularly by converting medium and heavy-duty diesel engines to dual-fuel hydrogen-diesel engines. To minimize elevated NOx emissions from hydrogen-fueled engine, fast and accurate emission models are essential for engine model-based control and for engine calibration and optimization using hardware-in-the-loop (HIL) setups. In this study, a fast-response NOx emissions sensor is used to measure the transient NOx emissions from a dual-fuel hydrogen-diesel engine. Subsequently, steady-state models (SSMs), quasi steady-state models (QSSMs), and transient sequential models (TSMs) in the form of black-box (BB) and gray-box (GB) models are developed for transient NOx emissions prediction. GB models utilize both information from a one dimensional (1D) physical engine model and experimental data for training, while BB models only use experimental data. SSMs are optimized artificial neural networks (ANNs) trained using steady-state data, QSSMs are optimized ANNs trained using transient data, and TSMs are time-series networks trained using transient data. Long short-term memory (LSTM) and gated recurrent unit (GRU) networks are used as the time-series deep learning networks. The results showed that the 1D physical model has the poorest performance with successive model performance improvement from SSM to QSSM and from QSSM to TSM. The developed BB TSM model in this study can predict transient NOx emissions with an R2 value greater than 0.96 at 89,000 predictions per second which makes this model suitable for real-time engine model-based control where computational efficiency is crucial. The developed GB TSM model can predict transient NOx emissions with an R2 value greater than 0.97 but it is computationally more expensive. The extra accuracy of the GB TSM models makes them the best choice for HIL setups where more computational power is available, and accuracy is more crucial.

N-BodyPat: Investigation on the dementia and Alzheimer's disorder detection using EEG signals

The N-body problem is a remarkable research topic in physics. We propose a new feature extraction model inspired by the N-body trajectory and test its feature extraction capability. In the first part of the research, an open-access electroencephalogram (EEG) dataset is used to test the proposed method. This dataset has three classes, namely (i) Alzheimer's Disorder (AD), (ii) frontal dementia (FD), and (iii) control groups. In the second step of the study, the EEG signals were divided into segments of 15 s in length, which resulted in 4,661 EEG signals. In the third part of the study, the proposed new self-organized feature engineering (SOFE) model is used to classify the EEG signals automatically. For this SOFE, two novel methods were presented: (i) a dynamic feature extraction function using a graph of the N-Body orbital, termed N-BodyPat, and (ii) an attention pooling function. A multileveled and combinational feature extraction method was proposed by deploying both methods. A feature selection function using ReliefF and Neighborhood Component Analysis (RFNCA) was used to choose the most informative features. An ensemble k-nearest neighbors (EkNN) classifier was employed in the classification phase. Our proposed N-BodyPat generates seven feature vectors for each channel, and the utilized EEG signal dataset contains 19 channels. In this aspect,133 (=19 × 7) EkNN-based outcomes were created. To attain higher classification performance by employing these 133 EkNN-based outcomes, an iterative majority voting (IMV)-based information fusion method was applied, and the most accurate outcomes were selected automatically. The recommended N-BodyPat-based SOFE achieved a classification accuracy of 99.64 %.

Simulation of intra-chamber processes in a low-thrust rocket engine with a hydrogen-air mixture and counterflow cooling

The principles and methods of improvement of thrust, efficiency and cooling of rocket engines for space flight safety are of great interest for aerospace industry. The development of reliable and durable low-thrust rocket engines for space missions seems to be one of the important areas of development of the rocket and space industry. Issues related to the implementation of a new direction related to the design of propulsion systems for orientation systems of upper stages of launch vehicles using environmentally friendly fuel components are considered. A mathematical model is developed and numerical modelling of processes in the combustion chamber of a low-thrust rocket engine with gaseous fuel components (hydrogen and oxygen) is carried out. The model takes into account the presence of a cooling jacket. The mathematical model includes the effects of turbulence, combustion of a gaseous fuel mixture, kinetics of hydrogen combustion, and radiation. As a result of calculations, distributions of flow quantities in the combustion chamber and thermal loads on its walls are obtained. The influence of hydrogen mass flow rate on the efficiency of the working process and the dependence of thrust on hydrogen mass flow rate are discussed. The results of numerical modelling are compared with the data of a physical experiment obtained through fire tests of an engine model made of stainless steel on a three-dimensional printer.

Mixed-Flow Turbofan Engine Model for the Conceptual Design of Sustainable Supersonic Airplanes

Current research efforts on commercial supersonic flight aim to overcome past challenges by designing a new generation of sustainable supersonic airplanes. Achieving this goal requires careful consideration of the propulsion system during the design process. This study proposes a mixed-flow turbofan engine model coupled with emission estimation routines to increase the reliability of the conceptual design of future supersonic aircraft. The model enables parametric analyses by analyzing variations in main engine design parameters (πc,πf, BPR) as function of the system and mission requirements, such as the Mach number, and suggesting applicability boundaries. The overall methodology was applied to a low-boom Mach 1.5 case study, allowing for both on-design and off-design analyses and generating a propulsive database to support preliminary mission simulations and chemical emission estimation. Finally, the accuracy and reliability of the engine model was validated against GSP 11 data for a generic mixed-flow turbofan engine. A modified version of the Fuel Flow Method, originally developed by Boeing, allowing for emissions estimation throughout the mission for a supersonic engine using biofuels. The application of the methodology led to the definition of an engine with a πc of 30 and BPR of 0.7 for the selected case study, which was successful in meeting the initial mission requirements.

Modelacion del impacto del deshielo en lo caudales en la cuenca del Lago Mansfiled Hollow en Connecticut, USA

Storm runoff predictions are essential for minimizing flood hazards and increasing resilience to extreme weather events. In this study, an analysis was conducted to simulate snowmelt runoff in the Mansfield Hollow Lake Watershed, which is a tributary of the Thames River watershed in Connecticut, New England. The United States Army Corp of Engineers (USACE) model HEC-HMS was applied to simulate snowmelt runoff during the winter-spring of 2010 and 2019. The Mansfield Hollow Lake Watershed is composed of three main tributaries, namely the Fenton, Mount Hope, and Natchaug Rivers. These runoff simulations and the watershed response to snowmelt are crucial for evaluating the potential impacts of watershed management decisions, particularly during high-flow periods. The HEC-HMS model was calibrated during the 2010 event and validated for the 2019 events. The study found that for the snow storms during 2010 and 2019 events, HEC-HMS model provided highly accurate predictions of snowmelt runoff with R-squared and, Nash - Sutcliffe correlation values exceeding 0.76. These findings highlight the efficacy of HEC-HMS model for simulating snowmelt runoff and demonstrate the utility of such model in predicting and managing flood risks. The results of this study provide valuable insights into the potential impacts of snowmelt runoff and will inform future watershed management decisions in the Mansfield Hollow Lake Watershed and similar regions.

A Comparison of Model Predictive Control Architectures for Application to Electrified Aircraft Propulsion Systems

Abstract As electrified aircraft propulsion (EAP) systems continue to mature, more sophisticated hardware and software are being developed to balance operations among electric machines and gas-turbine engines. In hybrid-electric propulsion systems, the increased complexity resulting from integrating turbine-engine shafts with electric machines necessitates control methodologies to account for various physical domains. Ideal controllers for hybrid-electric engines manage systems, subsystems, and their interactions in a coordinated fashion, able to account for safety and performance goals while being computationally efficient. In a previous work, linear model predictive control (MPC) schemes were implemented in centralized and distributed frameworks on a nonlinear turbofan engine model as a proof of concept. However, these schemes were not evaluated for computational complexity, prompting further study. The research presented here develops hierarchical MPC schemes to reduce the computational burden of the previous MPC schemes. A two-tier framework is implemented, where a slower sampling MPC controls electric machines and determines fan-speed tracking goals for a faster sampling controller, which is either a MPC or a proportional-integral (PI) controller. The proposed designs are compared to the centralized MPC investigated previously, and performance is measured via fan speed tracking error, energy storage state-of-charge, and computation time. Results reveal that the hierarchical MPC scheme employing a lower-level PI controller improves computation time while maintaining comparable tracking and state-of-charge regulation to the centralized scheme.

Prediction of Remaining Useful Life of Aero-engines Based on CNN-LSTM-Attention

Accurately predicting the remaining useful life (RUL) of aircraft engines is crucial for maintaining financial stability and aviation safety. To further enhance the prediction accuracy of aircraft engine RUL, a deep learning-based RUL prediction method is proposed. This method possesses the potential to strengthen the recognition of data features, thereby improving the prediction accuracy of the model. First, the input features are normalized and the CMAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset is utilized to calculate the RUL for aircraft engines. After extracting attributes from the input data using a convolutional neural network (CNN), the extracted data are input into a long short-term memory (LSTM) network model, with the addition of attention mechanisms to predict the RUL of aircraft engines. Finally, the proposed aircraft engine model is evaluated and compared through ablation studies and comparative model experiments. The results indicate that the CNN-LSTM-Attention model exhibits superior prediction performance for datasets FD001, FD002, FD003, and FD004, with RMSEs of 15.977, 14.452, 13.907, and 16.637, respectively. Compared with CNN, LSTM, and CNN-LSTM models, the CNN-LSTM model demonstrates better prediction performance across datasets. In comparison with other models, this model achieves the highest prediction accuracy on the CMAPSS dataset, showcasing strong reliability and accuracy.

Case Study: Advanced Technology Underpins Efficiency and Climate-Related Goal Gains

_ Today’s gas-compression operators are under increasing pressure to reduce greenhouse gas (GHG) emissions. Fortunately, multiple technologies are available that can support that objective while enabling operators to move more gas cost-effectively. Embracing such advancements can positively impact a reduced-carbon future without compromising operational efficiency or profitability. To choose the right technologies for a gas-compression package, it’s critical for operators to have a clear understanding of their business and GHG emissions-related goals. Operators also need clarity concerning the impact of evolving requirements on total cost of ownership (TCO) as well as potential financial ramifications. Producing more gas at a lower cost with lower GHG emissions is possible with new technologies. Operators can support their success by choosing the right engine driver for a gas-compression package based on their unique needs and site conditions. It’s critical to consider an engine’s power density and TCO, as that informs an operator’s selections and can lower a gas-compression package’s operating cost in terms of dollars per thousand cubic feet ($/Mcf) of gas throughput. An engine, such as the Cat G3600 A4 Gen 2, that can deliver a 10% power increase compared to the previous model can allow operators to use fewer engines to perform the same amount of work. This advantage can lower capital requirements, reduce operating expenses, and decrease fuel consumption. Fewer engines operating in the field may also contribute to lower GHG emissions. Modernizing Existing Assets Operators prioritize flexibility, durability, and lower TCO. They can support those objectives by choosing an engine platform that allows performance upgrades across the life of the engine. This enables operators to apply advanced technology in their existing fleet year after year without incurring the substantial cost of purchasing a new engine. Engine upgrade kits offer operators an option to maximize and modernize active assets in a streamlined way, as the upgrades can be applied conveniently and cost-effectively at the time of a scheduled overhaul to minimize disruption. For example, by using an upgrade kit for an A4 Gen 1 engine model referenced above, operators can lower volatile organic compounds up to 32%, formaldehyde up to 24%, carbon monoxide up to 13%, methane emissions up to 33%, and total GHG emissions up to 5% on a grams per brake horsepower-hour (g/bhp-hr) basis* while saving the traditional capital expenditure of a new engine. (*Site conditions: Same power level between the G3600 A4 Gen 1 and Gen 2, 85MN, 905 BTU/scf, 100% load, 25°C ambient, 500-ft altitude.) Such updates are relatively cost neutral when applied as part of a scheduled major overhaul and require minimal or no change to the compression package. This makes upgrade kits a wise choice to ensure equipment keeps pace with industry demands.

Research on assembly constraints reconstruction based on Unreal Engine

Abstract In the process of digital twin modeling, the assembly constraints of the model in the Unreal Engine usually requires manual intervention. To realize the intelligence and high efficiency of digital twin modeling, an Unreal Engine based assembly constraints reconstruction method is proposed. The method explores the high-fidelity assembly constraints mapping from the modeling software to the Unreal Engine, divides the model in the modeling software into two parts: the entity structure and the assembly constraints, and reconstructs the assembly constraints in the Unreal Engine, to realize the transformation from the modeling software model to the Unreal Engine model. Finally, the proposed method is applied to the digital twin engineering of various institutions to verify its rapidity and effectiveness in practical application scenarios.

Impact of Urbanization on Urban Heat Island Dynamics in Shillong City, India Using Google Earth Engine and CA-Markov Modeling

Impact of Urbanization on Urban Heat Island Dynamics in Shillong City, India Using Google Earth Engine and CA-Markov Modeling