Simulation Engine Research Articles

Photoelectrodes with Enhanced Carrier Generation and Collection Through Optical Simulation and Materials Engineering

AbstractThe efficiency of solar‐fuel conversion in photoelectrochemical (PEC) systems is hindered by significant losses of photons and photocarriers within the photoelectrodes. This study introduces an innovative ITO@In2S3 core‐shell nanowire structure designed to overcome these challenges through cutting‐edge materials engineering and sophisticated simulation techniques. Optical modeling and finite element simulations highlight the conflict between photocarrier generation and collection in planar In2S3 thin films and demonstrate how the core‐shell nanowire geometry can significantly enhance light absorption. The developed ITO@In2S3 nanowire photoanodes achieve a photocurrent of 11.3 mA cm−2 at 1.23 V versus RHE, which is nearly five times greater than that of planar ITO/In2S3 thin films. Characterization techniques, including UV–vis absorption and charge separation efficiency measurements, confirm the enhanced light absorption and improved carrier collection facilitated by broadening the optical absorption range and reducing carrier transport distances. This research not only deepens the understanding of the dynamics within thin‐film photoelectrodes but also paves the way for the development of more efficient technologies for solar fuel conversion.

Simulation of Responsive Envelopes in Current and Future Climate Scenarios: A New Interactive Computational Platform for Energy Analyses

Despite the strong interest concerning the responsive façades, today there are still few built examples and few tools to assess their benefits due to the complex description of the phenomenon. Energy simulations should consider the interactions between a time-varying environment and an environment-dependent envelope, increasing the intricacy of the problem; moreover, these strong environment–envelope interlinkages increase the importance of the location and climate scenarios considered. The aim of this study is to provide a tool to easily model these phenomena in different geographical and climate contexts. For this purpose, an innovative interactive computational platform (ICP) was developed based on EnergyPlus as a simulation engine, Python as a simulation manager, and Grasshopper as a user interface. Thanks to a single user-friendly environment, the users can simply select the climate scenario, the location, the responsive technology, and its main properties to set and run the dynamic energy simulation. After an overview of the current state of the art, this study provides a description of the structure and workflow adopted for developing this platform and details regarding its functioning and input management. Finally, the platform was tested to run an evolutionary optimization of an electrochromic window control strategy in different climate scenarios.

Equivalent Simulation Study of Delta-Rotor Engine

The mechanical structure, movement mode, and combustion process of a triangular rotor engine differ from those of a conventional engine with a crank connecting rod mechanism as the core. This makes it impossible to directly use existing simulation software to simulate the performance of the entire engine, rendering it difficult to conduct structural evaluation and performance prediction during the engine development stage. Therefore, using existing performance simulation software to establish a performance-equivalent model for a triangular rotor engine is crucial. To solve the above problems, this study proposes a performance-equivalent simulation modelling method for a triangular rotor engine based on a three-dimensional simulation model and the operating principles of the triangular rotor and four-stroke engines. Compared to various performance indicators obtained from the three-dimensional simulation model, the results show that the equivalent model established in this study has sufficient accuracy for key indicators such as the cylinder pressure, cylinder temperature, and in-cylinder quality under various working conditions. This can satisfy the requirements of the complete machine-performance simulation of a triangular rotor engine.

A methodology to replicate LTGC engine conditions in a single-zone model and validate gasoline surrogate formulations versus experimental measurements

In this work, surrogate fuels for three premium-grade gasoline fuels were formulated and its autoignition behavior was validated, together with that of an existing surrogate for E10 regular gasoline, by comparison to experimental data measured in the Sandia Low-Temperature Gasoline Combustion research engine facility. Surrogate fuels have been previously designed by matching fuel properties such as octane number, fuel/air stoichiometric ratio and hydrogen/carbon ratio. However, similar octane rating can be obtained with several blends that will show different ignition characteristics. To avoid this issue, the surrogate fuels shown in this paper have been formulated by matching the main components of the fuel detailed hydrocarbon analysis. Validation of the surrogates was performed by replicating engine experiments run with the real fuel in a 0-D internal combustion engine reactor model with detailed chemistry. A fitted heat-loss model had to be implemented to properly replicate the experimental in-cylinder thermodynamic conditions, which invalidates the adiabatic assumption sometimes applied to engine simulations. Furthermore, minor species present in the EGR and residuals, such as CO and NOx, had to be taken into account to replicate the experimental low-temperature heat release (LTHR) trends. Therefore, accounting for heat losses and detailed in-cylinder composition are essential to accurately simulate the engine data. The bottom dead center temperature deviation required to obtain the same combustion timing as in the experiments was used as a metric to evaluate the accuracy of simulations. The surrogates gave good performance for the whole explored range, and replication of LTHR is a key factor in matching the experimental data. Chemical kinetic analyses showed that replication of LTHR is controlled by the ability of the surrogate to properly balance radical generation from low-temperature chain branching decomposition with radical consumption from stable species. Surrogate components that act as a sink of radicals tend to suppress the LTHR of the fuel, which may lead to large deviations between simulations and experiments. Especial attention should be paid to these fuel species when selecting the surrogate components.

Engineering affinity of humanized ScFv targeting CD147 antibody: A combined approach of mCSM-AB2 and molecular dynamics simulations

This study aims to assess the effectiveness of mCSM-AB2, a graph-based signature machine learning method, for affinity engineering of the humanized single-chain Fv anti-CD147 (HuScFvM6-1B9). In parallel, molecular dynamics (MD) simulations were used to gain valuable insights into the dynamics and affinity of the HuScFvM6-1B9-CD147 complex. The result analysis involved integrating free energy changes calculated from the mCSM-AB2 with binding free energy predictions from MD simulations. The simulated structures of the modified HuScFvM6-1B9-CD147 domain 1 complex from MD simulations were used to highlight critical residues participating in the binding surface. Interestingly, alterations in the pattern of amino acids of HuScFvM6-1B9 at the complementarity determining regions interacting with the 31EDLGS35 epitope were observed, particularly in mutants that lost binding activity. The predicted mutants of HuScFvM6-1B9 were subsequently engineered and expressed in E. coli for subsequent binding property validation. Compared to WT HuScFvM6-1B9, the mutant HuScFvM6-1B9L1:N32Y exhibited a 1.66-fold increase in binding affinity, with a KD of 1.75 × 10−8 M. While mCSM-AB2 demonstrates insignificant improvement in predicting binding affinity enhancements, it excels at predicting negative effects, aligning well with experimental validation. In addition to binding free energies, total entropy was considered to explain the discrepancy between mCSM-AB2 predictions and experimental results. This study provides guidelines and identifies the limitations of mCSM-AB2 and MD simulations in antibody engineering.

Visual Modeling Method for Ship Piping Network Programs in Engine Simulators

Visual Modeling Method for Ship Piping Network Programs in Engine Simulators

A multiphase flow model of water droplets dielectrophoretic-induced air dehumidification phenomena

Air humidity in indoor spaces plays a critical role in human comfort and health. Dehumidification systems are used for building humidity controls, but they can take significant energy consumption, especially in geographic locations with high outdoor humidity and warm climates. Consequently, there is a growing demand for innovative dehumidification processes that consume minimal energy. Dielectrophoretic air dehumidification represents one such promising approach. However, it has not garnered significant attention due to the absence of engineering models and simulation tools capable of evaluating its performance and limitations at large-scale airflows. A new numerical multiphase CFD model, which is also experimentally validated, is developed in a customized Reacting Foam solver based on OpenFOAM® version 9. The newly developed model seeks to decrease substantial energy consumption and lower costs by leveraging the dielectrophoretic phenomenon to regulate moisture levels in the air. The solver integrates a hybrid Eulerian-Lagrangian framework to track the droplet's trajectory and growth rate while solving the continuum equations for the moist air. An electrospray produces electrically charged droplets, which grow during their in-flight trajectories as water vapor condenses onto their surfaces. The role of electrostatic forces in promoting vapor condensation within a high-gradient electrical field is investigated, and the dielectrophoretic vapor nucleation process on charged water droplets is discussed. The CFD model was validated against results from the literature and from proof-of-concept experiments conducted by the authors, which showed a 2 % air dehumidification with a single electrospray and airflow rate of 5 cubic feet per minute. The simulation results indicated that augmenting the number of electrically charged spray droplets increased the dehumidification of the air to 25 %. The initial mean droplet diameter, the orientation of the injector and relative humidity significantly influence the assessment of dehumidification. Scaling up this approach to larger airflow volumes is identified as a potential future research direction.

A comprehensive propulsion system analysis: an integrated approach utilising engine simulation and free-running model experiments

Regulations concerning the environmental impact and energy efficiency of ships are becoming increasingly stringent. To meet these demanding criteria, new technologies are actively being developed to achieve emission reductions while maintaining efficient fuel energy conversion into propulsion power. However, the propulsion system behaviour in a dynamic environment of the actual sea is highly nonlinear and complex, and a profound understanding of the mutual interactions is still lacking. This paper proposes an integrated approach for comprehensive propulsion system analysis. The key feature of this technique is the utilisation of an intelligent propeller drive controlled by a marine diesel engine simulator (MDES) to drive the free-running model of a ship, providing real-like behaviour of the engine model in real-time in response to actual measured values. The core of the MDES consists of a continuous cycle-mean value dynamic engine model supplemented with a detailed combustion cycle evaluation. The developed fast calculation algorithm of crank-angle resolved thermodynamic equations of the gas state in the engine cylinder enables the complete engine model to be integrated into the real-time environment of a physical model ship. Thus, the propulsion system's performance can be analysed under conditions that closely mimic those of the actual sea environment.

Investigating Methods to Generate Models for Turbine Maps Using Machine Learning

<div>For turbocharged engine design, manufacturer-provided turbocharger maps are typically used in simulation analysis to understand key engine performance metrics. Each data point in the turbocharger map is generated by physically testing the hardware or through CFD analysis—both of which are time-consuming and expensive. As such, only a modest set of data can be generated, and each data map must be interpolated and extrapolated to create a smooth surface, which can then be used for engine simulation analysis.</div> <div>In this article, five different machine learning algorithms are described and compared to experimental data for the prediction of Cummins Turbo Technologies (CTT) fixed geometry turbines within and outside of the experimental data range. The results were validated against xxx-provided test data. The results demonstrate that the Bayesian neural networks performed the best, realizing a 0.5%–1% error band. In addition, it is extrapolatable when suitable manually created extra data points are incorporated within the dataset at low and high turbine speeds.</div>

Optimization of the full-load combustion schemes of n-butanol/biodiesel reactivity-controlled compression ignition (RCCI) toward high-efficiency and low-emission engines

Biodiesel and n-butanol, as popular alternative fuels, can be used in advanced reactivity-controlled compression ignition (RCCI) engines for efficient and clean combustion. However, the complex interactions between n-butanol and biodiesel, as well as the best combustion schemes at different loads, remain poorly understood. Given this, the objective of this paper is to identify optimal fuel organizations for n-butanol/biodiesel RCCI engines across different load scenarios by combining engine simulation with a global optimization algorithm, explore the potential synergies of control parameters, and analyze microscopic dual-fuel interactions based on the integrated average reaction path analysis. Results show that the optimal scheme at low load employs almost homogeneous fuel-air mixtures at elevated temperature atmosphere above 390 K, with n-butanol energy share exceeding 96% and optimal biodiesel injection timing between −70 and −50 °CA. Higher n-butanol ratio coupled with increased intake temperature improves engine performance. For mid load, biodiesel injection timing is retarded to −55 to −30 °CA, and biodiesel proportion is increased to introduce reactivity stratification, while maintaining its energy share below 20% to mitigate NOx emissions. Notably, biodiesel density is identified as the primary physical property influencing NOx formation. At high load, a split combustion scheme involving premixing and subsequent diffusion combustion is optimal. The physical effects of n-butanol addition minimally impact the low-temperature ignition of biodiesel, while its chemical effects significantly defer the low-temperature reactivity. The reaction path analysis reveals that n-butanol competes with biodiesel for OH radical consumption, with a large proportion of OH and HO2 consumed in the cyclic reactions of nC4H9OH and nC4H8OH. Adjusting the reactivity stratification affects the reaction state of n-butanol entrained by the biodiesel jets. Higher reactivity gradient intensifies biodiesel reactions to induce the pyrolysis of the involved n-butanol through nC4H8OH => 2C2H4 + OH, therefore resulting in more staged heat release.

0-D Dynamic Performance Simulation of Hydrogen-Fueled Turboshaft Engine

In the last few decades, the problem of pollution resulting from human activities has pushed research toward zero or net-zero carbon solutions for transportation. The main objective of this paper is to perform a preliminary performance assessment of the use of hydrogen in conventional turbine engines for aeronautical applications. A 0-D dynamic model of the Allison 250 C-18 turboshaft engine was designed and validated using conventional aviation fuel (kerosene Jet A-1). A dedicated, experimental campaign covering the whole engine operating range was conducted to obtain the thermodynamic data for the main engine components: the compressor, lateral ducts, combustion chamber, high- and low-pressure turbines, and exhaust nozzle. A theoretical chemical combustion model based on the NASA-CEA database was used to account for the energy conversion process in the combustor and to obtain quantitative feedback from the model in terms of fuel consumption. Once the engine and the turbomachinery of the engine were characterized, the work focused on designing a 0-D dynamic engine model based on the engine’s characteristics and the experimental data using the MATLAB/Simulink environment, which is capable of replicating the real engine behavior. Then, the 0-D dynamic model was validated by the acquired data and used to predict the engine’s performance with a different throttle profile (close to realistic request profiles during flight). Finally, the 0-D dynamic engine model was used to predict the performance of the engine using hydrogen as the input of the theoretical combustion model. The outputs of simulations running conventional kerosene Jet A-1 and hydrogen using different throttle profiles were compared, showing up to a 64% reduction in fuel mass flow rate and a 3% increase in thermal efficiency using hydrogen in flight-like conditions. The results confirm the potential of hydrogen as a suitable alternative fuel for small turbine engines and aircraft.

A cloud-based simulation framework for high-fidelity room impulse response dataset generation: case study, validation and computational performance

Increased cost-effective computational resources in the cloud in combination with state-of-the-art simulation algorithms has opened doors for generating large quantities of simulated high-fidelity room impulse responses. Such datasets can, e.g., be used for training machine learning based audio signal processing algorithms such as speech enhancement or dereverberation, as well as for carrying out virtual prototyping of audio devices in different acoustic conditions. The authors have previously presented a cloud-based simulation engine that is well suited for such large-scale dataset generation tasks. The simulation engine leverages a hybrid wave/geometrical solver, in combination with various geometry pre-processing technology and spatial audio post-processing technology. In this paper, we present a dataset generation case study using this simulation engine, as well as a novel validation study into certain aspects of the spatial audio post-processing. Finally, we present an analysis into computational performance when leveraging multi-node computational architectures to run extremely large wave-based simulations using the simulation framework. To the best of our knowledge, this is the first time a multi-node wave-based acoustics solver presented, enabling wave-based simulations with billions of degrees of freedom.

Simulation and experimental study of motorcycle engine fueled with hho enriched gasoline

Simulation and experimental study of motorcycle engine fueled with hho enriched gasoline

Onboard amino acid salt-based CO2 integrated absorption and mineralization: Kinetics, mechanism, economics and life cycle

The International Maritime Organization's ambitious greenhouse gas reduction strategy is driving efforts to capture carbon from marine engine exhaust gas. Onboard carbon capture and storage (OCCS) faced significant challenges, particularly due to the substantial thermal energy requirements during the capture process and the complexities of transporting captured CO2 to storage facilities. The integrated CO2 absorption and mineralization (IAM) process, a thermodynamically downhill process, offered a potential solution for rapidly absorbing and sequestering CO2 in-situ. In this study, a single-step amino acid salt-based IAM process was employed to address carbon emissions from ships. Under optimized conditions, over 85 % of the CO2 in simulated marine engine exhaust gas was captured and completely converted to calcium carbonate using a potassium glycinate/ calcium hydroxide blend absorption slurry. This approach tackled the key challenges of effectively capturing CO2 from ships, while minimizing energy consumption, and ensuring the safe storage of the captured CO2. A case study on a 1700 TEU container ship evaluated the economic benefits and global warming potential of the OCCS system, indicating that capturing CO2 from ships could be profitable (87.2 $/ton CO2) and calcium carbonate could be a commercial product. Despite high-carbon-intensity absorbents (such as calcium hydroxide) partially offset the carbon reduction effort, resulting in a decline of carbon capture efficiency from 85 % to 44 %. The proposed amino acid salt-based IAM method could be considered a promising technique for reducing carbon emissions from ships, providing a feasible technical means for achieving the International Maritime Organization's ambition strategy for zero-carbon shipping.

Method for Function Expansion of Real Machine-Based Engine Simulator and How It Works in Reality

Method for Function Expansion of Real Machine-Based Engine Simulator and How It Works in Reality

A denoising algorithm based on ARIMA and competitive K-SVD for the diagnosis of rolling bearing faults

Rolling bearings are extensively employed in industrial production as essential support components for rotating machinery. However, under conditions of high load and harsh operation, bearings are highly susceptible to failure. The weak vibration signals associated with these failures may be obscured by complex harmonic interference and strong noise, posing challenges for the accurate diagnosis of rolling bearing failures. In this paper, an autoregressive integrated moving average and competitive K-singular value decomposition (ARIMA-CK-SVD) algorithm is proposed to realize effective extraction of faulty pulse signals in a strong interference environment. First, the ARIMA model is used to preprocess the original signal to eliminate the interference of harmonic components. Second, a method is proposed for the adaptive selection of parameters in the ARIMA model, with consideration given to the characteristics of K-SVD. Subsequently, a competitive mechanism is introduced during the dictionary update phase of the algorithm to adjust the pattern of atomic updates and eliminate noise atoms. Finally, the effectiveness of the ARIMA-CK-SVD has been validated through simulation experiments and engineering tests.

An efficient pseudoelastic pure P‐mode wave equation and the implementation of the free surface boundary condition

AbstractBased on the elastic wave equation, a pseudoelastic pure P‐mode wave equation has been recently derived by projecting the wavefield along the wavefront normal direction. This pseudoelastic pure P‐mode wave equation offers an accurate simulation of P‐wave fields with accurate elastic phase and amplitude characteristics. Moreover, considering no S‐waves are involved, it is computationally more efficient than the elastic wave equation, making it an excellent choice as a forward simulation engine for P‐wave exploration. Here, we propose a new pseudoelastic pure P‐mode wave equation and apply the stress image method to it to implement the free surface boundary condition. The new pseudoelastic wave equation offers significantly improved computational efficiency compared to the previous pseudoelastic wave equation. Additionally, the wavefields simulated by this new pseudoelastic wave equation exhibit clear physical interpretations. We evaluate the accuracy of the new wave equation in simulating elastic P‐waves by employing a model with high‐velocity contrasts. We find that this new equation, which purely admits P‐waves, though having exact amplitude and phase behaviour as the elastic waves for transmission components, the amplitudes slightly suffer in the scattering scenario. The difference in amplitude between the elastic and our pseudoelastic increases as the contrast in velocity at the interface (interlayer velocity ratio) increases, especially the S‐wave velocities. This has negative implications on scattering from the free surface boundary condition or the sea bottom interface, especially if the shear wave velocity below the surface or the sea bottom is high. However, in cases where, like for land data in the Middle East, the transition to a free surface is smoother, the accuracy of the pseudoelastic equation is high. In all cases, regardless of the interlayer velocity ratio, the accuracy of the pseudoelastic wave equation in simulating the elastic case, for scattered waves, exceeds that of the acoustic wave equation in phase and amplitude.

CFD Simulation of Pre-Chamber Spark-Ignition Engines—A Perspective Review

The growing demand to reduce emissions of pollutants and CO2 from internal combustion engines has led to a critical need for the development of ultra-lean burn engines that can maintain combustion stability while mitigating the risk of knock. One of the most effective techniques is the pre-chamber spark-ignition (PCSI) system, where the primary combustion within the cylinder is initiated by high-energy reactive gas jets generated by pilot combustion in the pre-chamber. Due to the complex physical and chemical processes involved in PCSI systems, performing 3D CFD simulations is crucial for in-depth analysis and achieving optimal design parameters. Moreover, combining a detailed CFDs model with a calibrated 0D/1D model is expected to provide a wealth of new insights that are difficult to gather through experimental methods alone, making it an indispensable tool for improving the understanding and optimization of these advanced engine systems. In this context, numerous previous studies have utilized CFD models to optimize key design parameters, including the geometric configuration of the pre-chamber, and to study combustion characteristics under various operating conditions in PCSI engines. Recent studies indicate that several advanced models designed for conventional spark-ignition (SI) engines may not accurately predict performance under the demanding conditions of Turbulent Jet Ignition (TJI) systems, particularly when operating in lean mixtures and environments with strong turbulence–chemistry interactions. This review highlights the pivotal role of Computational Fluid Dynamics (CFDs) in optimizing the design of pre-chamber spark-ignition (PCSI) engines. It explores key case studies and examines both the advantages and challenges of utilizing CFDs, not only as a predictive tool but also as a critical component in the design process for improving PCSI engine performance.

Participating mass of large-scale complex dynamic machine foundation: taking the national facility for earthquake engineering simulation (NFEES) as an example

Participating mass of large-scale complex dynamic machine foundation: taking the national facility for earthquake engineering simulation (NFEES) as an example

Teaching Aero-Engine Performance: From Analytics to Hands-On Exercises Using Gas Turbine Performance Software

Abstract In this paper, we present an approach for teaching turbojet engine performance that combines mathematical analysis with the use of performance software to provide students with a comprehensive and in-depth understanding of engine performance and how it is affected by design parameter choices. In the first part, we present the derivation of a universally valid analytical model for turbojet engine cycle design, which is the basis of the propulsion courses at RWTH Aachen University. The analytical model allows for the calculation of specific thrust as well as thermal and propulsive efficiencies as a function of pressure ratios and burner exit temperature. These are all expressed as differentiable functions. Their mathematical extremes form a basis for examining the intricate relationships in turbojet engine design. In the second part, we move from theory to practice. We utilize the GasTurb software, computing turbojet engine cycles for a wide range of cycle design parameters. The analytically derived trends are validated and errors introduced by simplifications are assessed. Differences between the analytical approach and software-based performance simulations of real engines, like temperature-dependent gas properties and secondary air systems, are emphasized and discussed, thus providing a bridge to real-world applications. In summary, this paper describes a two-step approach to teaching turbojet engine design using analytics and performance software. Each approach alone can be used to enrich and extend existing performance courses. Combining the approaches has significant benefits, improving understanding and inspiring students to pursue further research in the field of propulsion.