Simulation Engine Research Articles

Investigating the impact of solar PV and wind energy systems on the strength of a longitudinal power grid

The integration of inverter-based generation (IBG), such as solar photovoltaic (PV) and wind systems, presents unique challenges for power grid strength, especially in regions with limited conventional generation capacity. This study investigates the impact of such integration on the strength of Nigeria's power grid, focusing on sparsely connected northern region with abundant renewable resources. Using the network response short-circuit ratio (NRSCR) metric, which accounts for interactions between IBGs, we assess grid strength in three case studies. Simulations were conducted with the Power System Simulation for Engineering (PSS®E) software. Static analysis reveals inherent weakness in the northern region, characterized by low short-circuit capacity. Integrating IBGs at nine strategic locations shows varying responses: strong buses (NRSCR > 10) adapt well, while weak buses (NRSCR < 10), particularly in Kano and Katsina, constrain the operational capacity of the IBGs. Furthermore, close interaction among IBGs at these locations significantly impacts the system's dynamic response, although planned addition of conventional generators offers marginal improvement. This work highlights the potential of renewable resources to address energy shortages but also underscores the crucial need for detailed design studies at each point of interconnection (PoI), considering the longitudinal grid layout in the northern region and potential limitations imposed by IBG interactions. The findings also offer a structured approach that can be applied to power grids with comparable IBG integration levels, similar to the energy landscape of Nigeria.

Production and testing of mahua oil-based biodiesel synthesized through heterogeneous catalyst using experimental and numerical method.

A two-step treatment of mahua oil was conducted to synthesize mahua biodiesel using heterogeneous biomass-based catalyst derived from mahua shell. Mahua oil having higher free fatty acid (FFA) content (about 19%) was esterified to reduce the FFA content up to 1%. The esterification process was carried out using 200mL mahua oil, 5:1 molar ratio (methanol:oil), and 2.25 weight% of H2SO4 at a temperature of 60°C for 3h. Post esterification, a set of 16 experiments were created using a Box-Behnken design (BBD)-based response surface methodology (RSM) approach to conduct the transesterification of the esterified oil. Molar ratio, catalyst loading, reaction temperature, and reaction time were the four input variables chosen for the design of experiments. The optimized conditions for maximum biodiesel yield (87.7%) were found to be 14.88 molar ratio, 3.578% catalyst loading, 69.7°C reaction temperature, and 81.9min reaction time. The Diesel RK engine simulation tool which was experimentally validated for baseline diesel fuel was used for numerical simulation of mahua biodiesel. The performance, combustion, and emission behavior of mahua biodiesel analyzed using numerical simulation presented the sustainability of mahua biodiesel as an alternate fuel.

Technology Focus: Deepwater Fields (May 2024)

Without belittling our industry’s work to arrest climate change, the fact remains that relentless global efforts to secure energy during the past 4 decades have led to only a 7% reduction in the contribution of fossil fuels to the global energy supply. In light of the historic outcomes of COP28 and the stringent agreement of 198 countries to steer away from fossil fuels, it is evident that today’s oil and gas industry is at a crossroads and in an unprecedented race with time to address the trilemma to develop a progressive multitier energy model to balance transitioning to a low-carbon industry with maintaining production and fortifying its commitment to a sustainable future. Empowering, leveraging, and capitalizing on the current investments of our industry in the rapidly developing technologies that are revolutionizing both the renewable and fossil-fuel landscapes are exigently and prudently needed to reach this cornerstone goal. These technologies—such as carbon capture, use, and storage; winds; hydrogen; bioenergy; and geothermal—form more than 28% of the total energy consumption targeted to reduce greenhouse-gas emissions by 2050 and have a close affinity to the existing resources of the oil and gas industry. Deepwater offshore developments have always been the forefront projects of the industry. After about a decade of fragile outlook and waning investment in these ventures, the trend is showing a dynamic rise since 2022 based on the number of sanctioned projects worldwide and the associated annual capital of more than $200 billion. This is a remarkable affirmation of the resilience of these assets and the confidence in a greener, safer, and more cost-effective future of the industry in general. Rigorous engineering remains the key platform to accomplish the dizzying array of complex work on a daily basis and the linchpin in elevating the performance of deepwater projects from acquisition to abandonment. Opportunity still abounds for the operators of these assets to thrive and endure for the upcoming decades through advancing engineering prowess, bolstering their assets’ designs and operability, and collaborating with counterparts on potential synergies. These are prudent measures to appraise and invest in well-grounded solutions in order to harness the full potential of deepwater developments and improve their operational and overall efficiencies. Recommended additional reading at OnePetro: www.onepetro.org. SPE 217802 Fracturing 30,000-ft Wells in Deepwater—Planning and Execution Learnings From the Gulf of Mexico by K.F. Lizak, Shell, et al. OTC 32808 Validation of a Completion Digital Twin Applied to Packer Setting by L.A. Oliveira, Engineering Simulation and Scientific Software, et al. SPE 217277 Methane Recovery With Carbon Dioxide Sequestration as Hydrates in Deep Marine Environments: An Extensive Examination of Potential, Technical Advancements, and Economic Feasibility by Saket Kumar Shrivastava, SLB, et al.

Data Center Sustainability: Revisits and Outlooks

As energy-intensive entities, data centers are associated with significant environmental impacts, making their sustainability a subject of growing interest in recent years. In this article, we revisit data center sustainability and propose a forward-looking vision for improving data center sustainability. We argue that data center sustainability encompasses more than just energy efficiency and must be evaluated and optimized through a multi-faceted approach. To this end, we first present an overview of the sustainability metrics from five aspects. After that, we demonstrate the sustainability status of the latest data centers utilizing publicly available data center sustainability ratings. Furthermore, we examine the evolution of data center sustainability standards in Singapore to highlight several trending features. Based on the analysis, we identify several key elements of sustainable data centers. We then propose the Cognitive Digital Twin (CDT) architecture, which incorporates a digital twin engine for system-wide simulation and a decision engine for optimal control to improve data center sustainability. A case study is performed to optimize the chiller plant efficiency of a production data center in Singapore. The results demonstrate that the CDT can improve chiller plant energy efficiency by 5%, indicating around 140 metric tons of annual carbon emission savings.

Analysis and Experimental Study on Vibration Isolation Performance of Full-scale Turbofan Engine Mounting

At present, most of the turbofan engine use the articulated link mount to connect the engine to the wing, and the analysis and experimental research of articulated link mount are important means in the development of engine mount. In this paper, the vibration isolation performance analysis of a certain turbofan engine mounting was carried out, and a full-scale turbofan engine mounting vibration isolation test platform was established based on the four-terminal parameter method, and the engine simulation device - eccentric rotation excitation tester was developed, which simulates the real engine vibration through rotor imbalance, and the vibration isolation efficiency of a certain type of engine mount at the high and low pressure rotor frequency was obtained. The results show that the simulation and test results are very close, the error is within 15% at the low-frequency, and the error is within 5% at the high-frequency. The research of this paper can provide test evaluation means for my country to independently carry out the design and improvement for large passenger aircraft engine installation devices, and provide test basis for its design and finalization.

Enhancing Physiology Education Through Physiological Simulation

Advances in Physiology Education began publishing papers in 2005, using classic papers in physiology teaching [1]. This approach has several valuable outcomes: Classic experiments, conclusion of which form the foundation of much of our undergraduate teaching textbooks, require less specialized knowledge to understand and interpret the data and conclusions. It demonstrates the scientific method, demonstrating both "what do we know" and "how do we know." It promotes inquiry-based learning. It fosters data literacy, teaching students to interpret graphs and provides historical context for physiology [1]. Traditional practical classes achieve many of these outcomes while also providing a tactile experience, and have added benefits to students’ data literacy — in as much as the students perform the protocols themselves. Students will gain insight to what “squiggly lines” on a screen are measuring and the individual variability in what raw physiological recordings look like. Students will gain a greater appreciation for analytical techniques, and the manipulations and analysis that needs to be performed to interpret data. Students also gain an appreciation of the difference between textbook vs real data. Nevertheless, in person practical classes have limitations due to resource intensity, including space, materials, and curriculum time. HumMod (hummod.org) is a freely available mathematical model of Human Physiology, a "time-dependent physiologic simulation engine" [2]. JustPhysiology (justphysiology.com) is an education-focused web interface for HumMod [3]. We have written four physiology-based workshops utilizing simulated data from JustPhysiology. The JustPhysiology workshops were setup as a structured inquiry activity where students are expected to apply their content knowledge to interpret data, and students submitted their reports after each workshop for evaluation and feedback. In preparing the workshop activities for deployment, we trialed the activities with students who had previously completed our second year Human Physiology subject (and were thus familiar with the content). We then engaged students in focus group interviews —ascertaining the students’ perceptions of the learning experience. The outcome of the focus group interview suggests that students engaging with the workshop tasks enter into a positive learning cycle: [Formula: see text] The spontaneous emergence of a learning cycle, promoting deeper understanding of the physiology suggests that these data driven workshops will have improve student learning in Human Physiology.

Full-scale numerical simulations of standing-wave thermoacoustic engines with circular-pore and pin-array stacks

Thermoacoustic engines (TAEs) can convert thermal energy into acoustic energy with no moving parts. Previous numerical studies normally focused on the TAEs with a parallel-plate stack due to their simple structures and used two-dimensional (2-D) computational fluid dynamics (CFD) models to save computational costs. In this study, we conduct full-scale three-dimensional (3-D) CFD simulations on the standing-wave TAEs with more complicated circular-pore and pin-array stacks. Firstly, the dynamic behavior of the standing-wave TAEs in the start-up process is investigated. It is found that the optimal ratios of hydraulic radius rh to thermal penetration depth δk for the TAEs with circular-pore and pin-array stacks are 2 and 3.2, respectively. Secondly, the acoustic, hydrodynamic, and thermodynamic characteristics of the standing-wave TAEs in the steady-state process are explored. We find that when operating at optimal rh/δk, the TAE with a pin-array stack generates much larger acoustic power than that with a circular-pore stack. Examination of the vortex shedding at the stack ends indicates that the pin arrays exhibit less flow resistance than circular pores. At optimal rh/δk, the time-averaged transversal heat flux at the pin array ends is calculated to be around 1.2 × 105 W/m2, which is larger than 7.5 × 104 W/m2 of circular pores, corresponding to a better thermodynamic performance. The 3-D numerical investigations in this work give deeper insights into the performances of TAEs with circular-pore and pin-array stacks which were investigated in experiments, providing useful guidance for the future design and development of more sophisticated thermoacoustic devices for low-grade heat recovery.

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

Abstract. Snow dynamics play a critical role in the climate system, as they affect the water cycle, ecosystems, and society. In climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany in an elevation range of 150 to 1000 m over the time period 1987–2018. Two simulations stem from high-resolution regional climate models – the Weather Research &amp;amp; Forecasting (WRF) model at 1.5 km resolution and the COnsortium for Small scale MOdelling model in CLimate Mode (COSMO-CLM; abbreviated to CCLM hereafter) at 3 km resolution. Additionally, the hydrometeorological snow model Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) is run at point scale at the locations of the climate stations, based on the atmospheric output of CCLM. To complement the comparison, the ERA5-Land dataset (9 km), a state-of-the-art reanalysis land-surface product, is also compared. All four simulations are driven by the atmospheric boundary conditions of ERA5. Due to an overestimation of the snow albedo, the WRF simulation features a cold bias of 1.2 °C, leading to the slight overestimation of the snow depth in low-lying areas, whereas the snow depth is underestimated at snow-rich stations. The number of snow days (days with a snow depth above 1 cm) is reproduced well. The WRF simulation can recreate extreme snow depths, i.e. annual maxima of the snow depth, their timings, and inter-station differences, and thereby shows the best performance of all models. The CCLM reproduces the climatic conditions with very low bias and error metrics. However, all snow-related assessments show a strong systematic underestimation, which we relate to deficiencies in the snow module of the land-surface model. When driving AMUNDSEN with the atmospheric output of the CCLM, the results show a slight tendency to overestimate snow depth and number of snow days, especially in the northern parts of the study area. Snow depth extremes are reproduced well. For ERA5-Land (ERA5L), the coarser spatial resolution leads to larger differences between the model elevation and the station elevation, which contributes to a significant correlation of climatic biases with the elevation bias. In addition, the mean snow depth and number of snow days are strongly overestimated, with conditions that are too snowy in the late winter. Extreme snow depth conditions are reproduced well in the low-lying areas, whereas strong deviations occur with more complex topography. In sum, due to the high spatial resolution of convection-permitting climate models, they show the potential to reproduce the winter climate (temperature and precipitation) in southern Germany. However, different sources of uncertainties, i.e. the spatial resolution, the snow albedo parametrisation, and other parametrisations within the snow model, prevent their further use in a straightforward manner for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, such as in the context of climate change research.

Experience in modeling an asphaltene depositions curve and reducing the intensity of their formation by optimizing oil production technology

Relevance. The need to study the formation of asphaltene deposits in oil production wells. Among the known methods of combating these complications, the greatest interest is in optimizing oil production technology to prevent or reduce the intensity of the formation of these deposits. Aim. To study the possibility of constructing a curve for the formation of asphaltene deposits on the laboratory installation “Wax Flow Loop” and to determine the possibility of optimizing oil production technology to prevent the formation of these deposits. Methods. Laboratory studies on the “Wax Flow Loop“ installation, modeling based on the component composition of oil in the “PVTSim“ software product, calculations in the “Engineering Simulator of Technological Processes“ software product. Results. Based on the results of laboratory studies and numerical calculations, it was possible to construct a curve for the formation of asphaltene deposits. The correctness of the curve was validated against actual data on plugging a production well. The calculation of the influence of installing an electric centrifugal pump and choke-fitting a well showed that the first technology for changing oil production can significantly reduce the intensity of the formation of asphaltene deposits by introducing additional energy. The second technology only slightly shifts the thermobaric conditions of liquid extraction, which does not significantly affect the intensity of the formation of asphaltene deposits. Conclusions. The laboratory installation "Wax Flow Loop" allows correctly forming the curve of asphaltene deposits formation in the production tubing of oil producing wells. Methods for optimizing oil production technology make it possible to prevent the formation of asphaltene deposits with correct modeling and numerical calculations and selection of downhole pumping equipment.

Thermodynamic simulation study on compression ignition engine operating with oil obtained via catalytic pyrolysis of waste polythene covers

In recent years, waste-to-energy systems are receiving more attention due to their dual benefit of reduction of pollution caused by waste materials and reducing the overconsumption of fossil fuels. Since plastic waste is readily available and has a high heating value, it is a viable source of energy.; plastic garbage makes an excellent source of energy. Today, a feedstock made up of several types of plastic is used to quickly pyrolyze and produce plastic pyrolysis oil (PPO) or waste plastic oil (WPO). The oil could be utilized in internal combustion engines to produce heat and power. In the current work, waste polythene covers were transformed into value added products by utilizing fly ash catalyst and oil obtained is examined for suitability to run in the diesel engine. In the primary phase simulation study is performed on a four-stroke single cylinder DI Compression ignition engine using Diesel-RK engine simulator, results indicated that, waste plastic oil was found to emit higher oxides of nitrogen compared to diesel therefore in the secondary phase the same engine simulated by implementing exhaust gas recirculation at 3.5 %,7 %,10.5 % and 14 % rates. In the end engine parameters get optimized using 3D parametric optimization technique.Simulation Results indicate that, performance parameters such as brake power, brake thermal efficiency and brake specific fuel consumption were found to be decreased slightly under EGR conditions. Furthermore, combustion parameters such as cylinder peak pressure and peak temperature decrease up on implementing the exhaust gas recirculation, similarly the emission characteristics such as CO2, PM and smoke found to be increase with increase in EGR ratio while oxides of nitrogen emissions significantly decreased under EGR conditions. In order to reduce the emissions further, a 3D parametric optimization technique was adopted and optimal results obtained when engine runs at compression ratio of 16.5, injection timing of 19.38 and EGR percentage of 4.8 %.

Analyze of Education and Training Using Ship Engine Simulators at STIP Jakarta Indonesia

On this research paper, we analyze Cadet Engineer, junior engineer, senior level Operational engineers, and senior management level engineers will carry out experiments using speed data from various scenarios on the ship engine simulator and then the experimental results will use educational and training analysis on the ship engine simulator to find out the right time. Later analysis also revealed that human errors occur due to the following reasons: Lack of knowledge of the overall system and fundamental operations, Insufficient confirmation of operations and Incomplete knowledge of the sequence of operations. the role of the instructor will be explained to reduce the causes of human error and to improve educational performance according to the implementation and development of each scenario based on expert justification and reference to IMO and SOLAS Standards. various safety procedure scenarios for all Engineers in the hope of minimizing accidents that cause loss of life at sea. For the purpose of training engine plant operations using a PC-based Engine Room Simulator (ERS), describe the features of human errors by incompetent trainees in marine engine plant operations and demonstrate the necessary improvements in training to reduce such errors from an educational technology point of view. Examination of these data has shown that errors occur primarily due to errors of omission and inadvertent action. Further analysis was carried out on these errors by the engineer trainees to determine the characteristics of errors caused in the process of acquiring procedural knowledge for operations according to the implemented development of various safety scenarios.

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

Recent Developments in Using a Modified Transfer Matrix Method for an Automotive Exhaust Muffler Design Based on Computation Fluid Dynamics in 3D

The present work aims to investigate the newly modified transfer matrix method (MTMM) to predict an automotive exhaust muffler’s transmission loss (AEMTL). The MTMM is a mixed method between a 3D-CFD (Computation Fluid Dynamics in 3D), namely AVL FIRETM M Engine (process-safe 3D-CFD Simulations of Internal Combustions Engines), and the classic TMM for the exhaust muffler. For all the continuous and discontinuous sections of the exhaust muffler, the Mach number of the cross-section, the temperature, and the type of discontinuity of the exhaust gas flow were taken into consideration to evaluate the specific elements of the acoustic quadrupole that define the MTMM coupled with AVL FIRETM M Engine for one given muffler exhaust. Also, the perforations of intermediary ducts were considered in the new MTMM (AVL FIRETM M Engine linked with TMM) to predict the TL (transmission loss) of an automotive exhaust muffler with three expansion chambers. The results obtained for the TL in the frequency range 0.1-4 kHz agree with the experimental results published in the literature. The TMM was improved by adding the AVL FIRETM M Engine as a valuable tool in designing the automotive exhaust muffler (AEM).

Engineering application study of high-performance aluminum alloy 6A13-T6

To address the application challenges of the novel high-strength aluminum alloy material 6A13-T6 in engineering, experimental and numerical simulation studies were conducted. Firstly, material tensile tests, axial compression tests, and pure bending tests were conducted, and the applicability of the specification was evaluated. Subsequently, finite element models were employed to analyze the overall mechanical performance and economic indicators of three typical engineering structures (grid shells, grid trusses, and glass curtain walls) based on the test results. The findings indicate that 6A13-T6 exhibits a non-proportional yield strength (f0.2) of 340 MPa, an ultimate strength of 352 MPa, and a post-necking elongation of 14.5%. Design calculations for axially compressed and flexural members made of 6A13-T6 high-strength aluminum alloy can be conservatively carried out following Chinese design specification. The use of 6A13-T6 aluminum alloy can reduce material consumption in structures, with a maximum reduction of up to 29% compared to other commonly used aluminum alloys and up to 72% compared to common steel materials. Therefore, the new high-strength aluminum alloy 6A13-T6 has obvious engineering application value and has been used in a hotel curtain wall project.

Modelling effluent reuse in the pulp and paper industry to predict consequences on conductivity.

The objective of this study was to assess, through simulation, conductivity variations in pulp and paper circuits when recycling waste water treatment plant (WWTP) effluent with a view to reducing fresh water use in a tissue mill. WWTP effluent was recycled in the process for different uses. A PS2000 digital model coupled with the PHREEQC chemical simulation engine was used to identify and quantify the main sources of conductivity: caustic soda, sodium bisulphite and acetate production through anaerobic microbial activity. Recycling WWTP effluent enables fresh water uptake to be reduced by 50% when used for pulp dilution or white water, by 81% when used in paper machine showers, and up to 96% for all uses combined. As fresh water use decreases, circuit closure increases along with, consequently, COD and conductivity. COD build-up can be controlled by best available techniques application. Recycling WWTP effluent has a strong impact on conductivity. However, the impact of high conductivity levels on additives performance is limited in the case of the mill studied. Acetate concentration could be controlled by better agitation of tanks or the introduction of air by pumps. Furthermore, limiting acetate production can reduce the need for caustic soda to control the pH.

Reinforcement Learning-Based Switching Controller for a Milliscale Robot in a Constrained Environment

This work presents a reinforcement learning-based switching control mechanism to autonomously move a ferromagnetic object (representing a milliscale robot) around obstacles within a constrained environment in the presence of disturbances. This mechanism can be used to navigate objects (e.g., capsule endoscopy, swarms of drug particles) through complex environments when active control is a necessity but where direct manipulation can be hazardous. The proposed control scheme consists of a switching control architecture implemented by two sub-controllers. The first sub-controller is designed to employs the robot's inverse kinematic solutions to do an environment search of the to-be-carried ferromagnetic particle while being robust to disturbances. The second sub-controller uses a customized rainbow algorithm to control a robotic arm, i.e., the UR5 robot, to carry a ferromagnetic particle to a desired position through a constrained environment. For the customized Rainbow algorithm, Quantile Huber loss from the Implicit Quantile Networks (IQN) algorithm and ResNet are employed. The proposed controller is first trained and tested in a real-time physics simulation engine (PyBullet). Afterward, the trained controller is transferred to a UR5 robot to remotely transport a ferromagnetic particle in a real-world scenario to demonstrate the applicability of the proposed approach. The experimental results show an average success rate of 98.86\% calculated over 30 episodes for randomly generated trajectories.

Advancing wastewater treatment technologies: The role of chemical engineering simulations in environmental sustainability

Wastewater treatment stands as a critical component in mitigating environmental pollution and safeguarding public health. This review delves into the pivotal role of chemical engineering simulations in advancing wastewater treatment technologies towards enhanced environmental sustainability. The modernization of wastewater treatment processes necessitates a comprehensive understanding of the complex physical, chemical, and biological interactions involved. Chemical engineering simulations offer a powerful toolset for modeling and optimizing these processes with a focus on efficiency, effectiveness, and sustainability. Firstly, simulations enable the prediction and analysis of pollutant removal mechanisms within treatment systems. By simulating various operating conditions and configurations, engineers can optimize treatment processes to achieve higher removal efficiencies while minimizing resource consumption and waste generation. Secondly, simulations aid in the design and development of innovative treatment technologies, such as membrane filtration, advanced oxidation processes, and biological nutrient removal systems. Through computational modeling, engineers can assess the performance and feasibility of these technologies under different scenarios, facilitating informed decision-making and accelerating their implementation. Furthermore, chemical engineering simulations play a crucial role in addressing emerging challenges in wastewater treatment, including the removal of emerging contaminants such as pharmaceuticals, microplastics, and endocrine-disrupting chemicals. By simulating the behavior of these contaminants within treatment systems, engineers can devise targeted strategies for their removal, thus mitigating their adverse environmental and public health impacts. Chemical engineering simulations serve as indispensable tools for advancing wastewater treatment technologies towards greater environmental sustainability. By facilitating the optimization, design, and innovation of treatment processes, simulations contribute to the development of more efficient, cost-effective, and environmentally friendly solutions for managing wastewater and protecting our ecosystems. Embracing these simulation-driven approaches is essential for achieving the ambitious goals of sustainable development and ensuring a cleaner, healthier future for generations to come.

Chemical engineering and the circular water economy: Simulations for sustainable water management in environmental systems

In the face of escalating water scarcity and environmental degradation, the imperative for sustainable water management has never been more urgent. Chemical engineering emerges as a pivotal discipline in the pursuit of solutions to these pressing challenges. This review explores the role of chemical engineering in advancing the circular water economy paradigm through simulations aimed at fostering sustainable water management within environmental systems. The circular water economy concept advocates for the efficient utilization, recycling, and reclamation of water resources to minimize waste and maximize resource efficiency. Chemical engineering techniques play a fundamental role in realizing this vision through the design and optimization of water treatment processes, resource recovery systems, and advanced simulation methodologies. This review delves into the application of computational simulations within the realm of chemical engineering to model and analyze various aspects of the water cycle. Such simulations enable the assessment of complex environmental systems, aiding in the identification of optimal strategies for water resource allocation, pollution control, and ecosystem preservation. Key areas of focus include the simulation of wastewater treatment processes, such as biological, physical, and chemical treatment methods, to enhance pollutant removal efficiency and promote water reuse. Furthermore, advanced modeling techniques facilitate the evaluation of innovative technologies like membrane filtration, adsorption, and electrochemical processes for the purification and desalination of water resources. Moreover, chemical engineering simulations enable the assessment of integrated water management strategies, encompassing aspects of urban water systems, industrial processes, and agricultural practices. By considering the interconnectedness of various sectors, holistic approaches to water resource management can be formulated, promoting resilience and sustainability in the face of changing environmental conditions. The integration of chemical engineering simulations into the framework of the circular water economy offers a promising avenue for advancing sustainable water management practices. Through comprehensive modeling and analysis, informed decision-making can pave the way towards a more resilient and equitable water future, ensuring the long-term viability of our environmental systems.

Nanophotonic chalcogenide waveguides for supercontinuum generation pumped at 1550 nm

Nanophotonic chalcogenide waveguides (ChGs) fabricated from Ge28Sb12Se60 (GeSbSe) glass were designed and fabricated for efficient supercontinuum (SC) generation. Dispersion engineering and nonlinear optical simulations were used to optimize the cross-sectional dimensions of the strip waveguides for pumping at 1550 nm. The waveguides were fabricated using e-beam lithography and plasma etching to achieve submicron confinement and high optical quality. By pumping anomalous dispersion engineered waveguides with femtosecond pulses at 1550 nm wavelength, supercontinuum generation spanning over 750 nm was demonstrated on-chip. The findings provide experimental evidence of the potential applications of GeSbSe waveguides in nonlinear photonics, highlighting their advantages in terms of performance and spatial efficiency.

A Predictive Toxicokinetic Model for Nickel Leaching from Vascular Stents.

In vitro testing methods offer valuable insights into the corrosion vulnerability of metal implants and enable prompt comparison between devices. However, they fall short in predicting the extent of leaching and the biodistribution of implant byproducts under in vivo conditions. Physiologically based toxicokinetic (PBTK) models are capable of quantitatively establishing such correlations and therefore provide a powerful tool in advancing nonclinical methods to test medical implants and assess patient exposure to implant debris. In this study, we present a multicompartment PBTK model and a simulation engine for toxicological risk assessment of vascular stents. The mathematical model consists of a detailed set of constitutive equations that describe the transfer of nickel ions from the device to peri-implant tissue and circulation and the nickel mass exchange between blood and the various tissues/organs and excreta. Model parameterization was performed using (1) in-house-produced data from immersion testing to compute the device-specific diffusion parameters and (2) full-scale animal in situ implantation studies to extract the mammalian-specific biokinetic functions that characterize the time-dependent biodistribution of the released ions. The PBTK model was put to the test using a simulation engine to estimate the concentration-time profiles, along with confidence intervals through probabilistic Monte Carlo, of nickel ions leaching from the implanted devices and determine if permissible exposure limits are exceeded. The model-derived output demonstrated prognostic conformity with reported experimental data, indicating that it may provide the basis for the broader use of modeling and simulation tools to guide the optimal design of implantable devices in compliance with exposure limits and other regulatory requirements.