Validation Of Simulation Model Research Articles

Smart contracting for production supply in shared manufacturing: a practical simulation approach

Enhancing transparency in production processes, especially in shared manufacturing, relies heavily on sharing data. Information asymmetries and coordination problems between parties with conflicting interests pose a challenge in this multi-stakeholder interaction. Blockchain technology with smart contracting can be a solution due to its immutable data and decentralised data storage features. Designing and executing blockchain in industrial applications is a highly intricate task that requires extensive testing, expertise, and proficiency. This paper is the first to propose a holistic simulation model for evaluating the impact of smart contracting on shared manufacturing, including a novel approach to simulated smart contracting in time-lapse for Ethereum-based networks. The introduced model guides the design and implementation process of blockchain applications in shared manufacturing to address this challenge. A systematic literature review establishes ten design process requirements and ten smart contract functions. The implementation is developed based on the design benchmarks of three Ethereum-based frameworks to investigate the simulation model's respective feasibility and scalability. The simulation model validation demonstrates our approach's suitability for simulating smart contracting in shared manufacturing within a hybrid production. It enables fast and scalable simulations, offering an innovative approach to extensively testing blockchain applications before their introduction to ongoing industrial operations.

Dynamic Simulation and Analysis of Three Phase Induction Motor for Faults Detection using Matlab/Simulink

The dynamic simulation of three-phase induction motors under fault conditions is essential for understanding and mitigating the impacts of electrical faults on motor performance. This study aims to simulate and analyze the impact of electrical faults on three-phase induction motors to improve fault detection and isolation strategies. Utilizing MATLAB/Simulink software, the behavior of three-phase induction motors under both symmetrical and unsymmetrical faults is modeled and analyzed. The motor’s baseline parameters, include 3 Amps rated power and speed of 1500 RPM. Symmetrical faults, such as line-to-line-to-line (L-L-L), and unsymmetrical faults, like single-phase to ground faults, were simulated to observe their effects on motor operation. The d-q model was used to simulate motor dynamics, employing a block model approach to resolve reference frame theory issues. Major parameters analyzed include rotor speed, electromagnetic torque, and stator current. Through detailed simulations, key performance indicators such as torque fluctuations, current spikes, and decreases in rotor speed are examined. At 1.5 seconds, when the fault was introduced, the rotor speed, electromagnetic torque, and stator current were all affected. For instance, during a symmetrical fault, the rotor speed dropped from 1500 RPM to 1200 RPM, electromagnetic torque declined to -12 Nm, and the stator current increased to 7 Amps from the rated 3 Amps. Under an unsymmetrical single-phase-to-ground fault at the same instant, rotor speed decreased from 1500 RPM to 1400 RPM, electromagnetic torque declined to -12 Nm with greater distortion, and the stator current in the affected phases rose to 7 Amps from the rated 3 Amps. These results underscore the importance of robust fault detection and isolation mechanisms to enhance motor reliability and longevity. This work significantly contributes to engineering by offering validated simulation models and insights into parameter sensitivity, serving as both an educational resource and a foundation for advanced fault detection system development. The findings are applicable in academic research and industrial contexts, providing guidance for improving motor design and fault management strategies.

Spatial characterizations of bacterial dynamics for food safety: Modeling for shared water processing environments

Bacterial dynamics occurring in shared water environments during food processing are typically modeled assuming a homogeneous mixing profile. However, given the tank configurations, and water recirculation and reuse specifications used in many facilities, uniform mixing is not always applicable. Towards this goal, we here developed a novel reaction-diffusion-advection model that captures temporal and spatial variations in the water tanks under dynamic conditions. We utilize the dynamics involved in poultry chilling as an example, as this process features a comprehensive interplay of bacteria, water chemistry and water flow dynamics, as well as determining bacteria levels on carcasses moving into final phases of the food production chain, thus directly influencing public health risk. Well-posedness, existence and uniqueness of positive steady-state solutions with global stability are proved, as well as an estimation of the time scale of convergence to the steady-state solution provided. Simulations are used to verify the analytical results incorporating parameters informed by experimental data from generic, non-pathogenic E. coli, and predictively estimate the time to equilibrium. We show that during a typical 8 h processing shift, the model reaches steady state within 2 h, applying this result to validate model simulations against commercial data. The calibrated model predicts a distribution of E. coli levels on post-chill carcasses with mean and standard deviation of 3.35 ± 0.56 Log10 CFU/carcass, which closely compares to the experimentally observed distribution of 3.55 ± 0.64 Log10 CFU/carcass in an industrial setting. Our results reinforce the key role of space in quantifying essential mechanisms that govern water chemistry and E. coli dynamics during poultry chilling. Our model is an important tool to improve decision making for pathogen control during poultry chilling, as well as a blueprint from which models for processing other commodities like fresh produce and pork can be established.

Study and optimization of the influence of the water tank temperature on the performance of a solar assisted multi-source heat pump drying system

Solar assisted air source heat pump drying (SASHPD) system has been widely studied due to its excellent energy saving and high dried quality of product. To improve system performance, the control logic of a solar assisted multi-source heat pump drying (SMSHPD) system was investigated, and the simulation model and the experimental platform were established in this paper. The seasonal operating characteristics of the air source heat pump drying (ASHPD), the SASHPD and the SMSHPD modes were studied. The energy consumption, coefficient of performance (COP) and specific moisture extraction rate (SMER) of the three drying systems were calculated and analyzed. Additionally, the water tank temperature were also measured to determine the seasonal control logic of the drying system. It was found that compared to the ASHPD system, in summer, the energy consumption of the SASHPD system was reduced by 33.06 %, and COP and SMER was increased by 49.4 % and 49.38 %, respectively. Moreover, because of the high ambient temperature and water tank temperature, the experimental results indicated that only the SASHPD control logic should be implemented in summer. The maximum error of the simulation results was 9.1 % and the accuracy of the simulation model was confirmed. In autumn, the lower water tank temperature after drying illustrated that the solar energy can be more fully utilized, which explained why the performance of the SMSHPD mode was increased by 6.5 % compared to the SASHPD mode. Consequently, it is necessary to implement the SMSHPD control logic in autumn. Moreover, the operating temperature of the water source heat pump drying mode in autumn was optimized and the optimal temperature range was 28–54 °C. The average COP in one week after optimization was 6.21 % higher than that of the original operating temperature. Based on the validated simulation model, the optimal operating temperatures in spring and winter were also calculated and the control logic of the SMSHPD system operating in the four seasons were all obtained. This paper has important guiding significance for the reasonable choice of the control logic of a solar combined air source heat pump drying system.

Development and validation of a cost-effective DIY simulation model for McDonald cerclage training.

The prevention of preterm birth is a challenging task for obstetricians. Cervical cerclage, used as both a primary and secondary prevention method for spontaneous preterm birth, is a crucial surgical intervention. It is essential that obstetricians can learn this procedure in a simulated environment before performing the stitches on high-risk patients. This study aimed to develop a simulator based on 3D printing and evaluate its validity for clinical training. The objectives of this study were (1) to design and construct a cost-effective simulator for McDonald cerclage with two different cervix models-a closed cervix and a cervix with bulging membranes-using common material from a DIY store and 3D printing technology and (2) to validate its effectiveness through feedback from learners and experts in cervical cerclage. The self-made simulator was evaluated by obstetricians using a questionnaire with Likert scale. Obstetricians and gynecologists assessed the simulator and found it useful for learning and practicing cervical cerclage. The simulator was deemed valuable for skill training. Cervical cerclage is a complex procedure that should be mastered through simulation rather than initial practice on real patients. Our simulator is a cost-effective model suitable for various clinical settings. It has been validated by obstetricians for both preventive and therapeutic cerclage, demonstrating its efficacy for training in cerclage techniques. Future research should focus on less skilled obstetricians and gynecologists and investigate how repeated use of the simulator can enhance their performance in cerclage stitching.

Prediction of Efficiency, Performance, and Emissions Based on a Validated Simulation Model in Hydrogen–Gasoline Dual-Fuel Internal Combustion Engines

This study explores the performance and emissions characteristics of a dual-fuel internal combustion engine operating on a blend of hydrogen and gasoline. This research began with a baseline simulation of a conventional gasoline engine, which was subsequently validated through experimental testing on an AVL testbed. The simulation results closely matched the testbed data, confirming the accuracy of the model, with deviations within 5%. Building on this validated model, a hydrogen–gasoline dual-fuel engine simulation was developed. The predictive simulation revealed an approximately 5% increase in overall engine efficiency at the optimal operating point, primarily due to hydrogen’s combustion properties. Additionally, the injected gasoline mass and CO2 emissions were reduced by around 30% across the RPM range. However, the introduction of hydrogen also resulted in a slight reduction (~10%) in torque, attributed to the lower volumetric efficiency caused by hydrogen displacing intake air. While CO emissions were significantly reduced, NOx emissions nearly doubled due to the higher combustion temperatures associated with hydrogen. This research demonstrates the potential of hydrogen–gasoline dual-fuel systems in reducing carbon emissions, while highlighting the need for further optimization to balance performance with environmental impact.

Cost-effectiveness of semaglutide for secondary prevention of cardiovascular disease in the United States

Abstract Background In the SELECT trial, semaglutide produced a 20% reduction in the composite of cardiovascular death, non-fatal myocardial infarction (MI), and non-fatal stroke in individuals with overweight or obesity who did not have diabetes but had established pre-existing cardiovascular disease (CVD). Cost is an important barrier to widespread uptake of semaglutide, particularly in the US, and the cost-effectiveness for the secondary prevention of CVD is uncertain. Purpose To evaluate the cost-effectiveness of semaglutide vs. no obesity treatment for the secondary prevention of CVD from a US healthcare sector perspective. Methods The CVD Policy Model is an established, validated simulation model of fatal and non-fatal CVD outcomes and costs in the US population. Using data from the National Health and Nutrition Examination Survey, we created a nationally representative cohort of SELECT-eligible individuals (age ≥45, BMI ≥27kg/m2, no diabetes, pre-existing CVD). Healthcare costs associated with acute and chronic CVD were estimated from national survey and claims data and inflated to 2023 US dollars. The simulated control arm received usual care; the intervention arm received weekly semaglutide 2.4mg injections (assuming 8% discontinuation, which occurs year one). We reproduced the effectiveness of semaglutide based on results of the SELECT trial primary cardiovascular composite endpoint (non-fatal MI, non-fatal strokes and CVD death). The base case assumed 7% of patients receiving semaglutide developed injection-site reactions. Monthly cost of semaglutide was assumed to be $717 (US price net of rebates and discounts), but this was varied in sensitivity analyses. The analysis adopted a health system perspective over lifetime horizon. Results Approximately 4.7 million US adults would meet SELECT trial eligibility criteria. Over a lifetime horizon, semaglutide is projected to avert 538,000 major adverse cardiovascular events (241,000 nonfatal MIs; 80,000 nonfatal stokes, and 217,000 cardiovascular deaths) at an incremental cost of $613 billion. The incremental cost effectiveness ratio for semaglutide was $443,000 per quality-adjusted life year (QALY) gained. In order to meet cost-effectiveness thresholds of $50,000/QALY, $100,000/QALY, $150,000/QALY, drug costs would have to decline 90%, 79%, and 69% respectively. In sensitivity analyses, lowering drug cost to the current UK price (~ $220 per patient per month) would improve the incremental cost-effectiveness of semaglutide to <$150,000 per QALY gained, a potential upper threshold for cost-effectiveness in the US. Conclusion Widespread uptake of semaglutide in the SELECT-eligible U.S. population would prevent a large number of cardiovascular events, but its use would be low economic value at current US prices. Lowering prices in line with those in Europe (e.g., monthly price from $717 to $223) would make the therapy cost-effective at a threshold of $150,000 per QALY gained.

Quantifying the potential of evidence-based planting-pattern for reducing the outdoor thermal stress from a bio-meteorological perspective in tropical conditions of Indian cities.

The impact of declined natural greenery and increased built surfaces exacerbates heat stress in urban areas causing limited usage of outdoor spaces. Greenery strategies such as trees are capable of mitigating outdoor thermal stress gain because of their phytological properties. While urban greenery guidelines have suggested the ad-hoc procedure of tree planting-schemes based on aesthetic-value, soil-water preservation etc., understanding of their morphological character help in regulating extreme thermal condition. Hence, this study aims to investigate the most efficient planting pattern based on canopies densities and trees clusters for reducing the outdoor thermal stress from bio-meteorological perspective.It initiates with the measurement of the site's morphological and meteorological attributes in existing commercial market of Bhopal City which has a humid sub-tropical climate (Aw, Koppen climate categorization). Furthermore, it leads to the development of 4-different iterated clusters incorporating moderate to high-density canopies and their overlaps pattern to estimate reduction potential in outdoors using field surveys and validated simulation model. The reduction potential in terms of magnitude and duration of thermal stress is quantified across 3-thermal variables i.e., air temperature, mean radiant temperature and universal thermal climate index. Results indicate highly-dense canopies are more effective in reducing greater magnitude of thermal stress along longer duration. Also overlapped planting pattern within the same canopy density does not make significant difference in stress reduction as compared to the changing the densities. This study will help planners and landscape architects to adopt evidence-based planting-pattern strategies for improving outdoor microclimate.

Assessing the reliability of a ship energy performance simulation tool through on-board data

To mitigate the environmental impact of shipping on climate change, it is nowadays urgent to explore energy efficiency and cost-effective measures by means of reliable digital tools. These are becoming essential for assisting the design of new ships and the refurbishment of the existing ones, as well as for assessing and optimizing the energy performance of a ship during its entire life-cycle. This paper focuses on the verification of the reliability of a novel ship energy dynamic simulation tool, developed for the calculation of the energy, economic, and environmental performance of large ships, the design and optimization of energy system layouts and operational logics, as well as the definition of design and management guidelines for ships. The reliability of the developed model was verified through the use of data measured onboard an existing sample cruise ship. The verification procedure is conducted for assessing the reliability and for exploiting the potentiality of dynamic analysis for a more precise evaluation of ships energy loads, temperature levels, and necessary sizes of possible saving technologies. The validated tool will enable reaching two different goals: i) to evaluate different energy system layouts and to define the optimal one for achieving the present imposed targets and constraints, ii) to obtain a large amount of data for allowing the implementation of the digital twin approach in the maritime sector. The model validation was successfully achieved and very low or negligible deviations, lower than 4%, of numerical data vs. measurements were observed. Through the validated simulation tool approximately 23.4 GWh of primary energy from polluting fuels over two weeks is computed. In addition, the model provided insights into the flow rate, temperature levels, and energy flows of the ship energy system, necessary for identifying the amount of thermal energy to be recovered as well as potential solutions to enhance the energy efficiency of the entire system.

Unequal area facility layout problem considering transporters interaction– a queuing theory and machine learning approach

ABSTRACT This study presents a novel analytical framework that merges queueing theory with deep neural networks to optimize facility layout and transporter selection in manufacturing systems. It addresses critical factors such as stochastic service times of facilities, random demand, transporter capacity, speed, and transportation batch size. Three objectives are considered: minimizing material handling costs (MHC), work-in-process (WIP), and the interaction probability of transporters (IP). The latter objective focuses on reducing instances where transporters cross paths to prevent accidents or disruptions. WIP is computed using a multi-class open queueing network model, while IP is determined using a deep neural network. The model facilitates the identification of facilities and the assignment of suitable transporters, considering empty transporter travels to minimize MHC and WIP. Results from the model are compared to a simulation model for validation across various scenarios, demonstrating acceptable accuracy. Additionally, a multi-objective meta-heuristic optimization algorithm is employed to solve the model. The effectiveness of the optimization method is evaluated against other approaches, highlighting its applicability in enhancing manufacturing system performance.

Model Predictive Control of a Stand-Alone Hybrid Battery-Hydrogen Energy System: A Case Study of the PHOEBUS Energy System

Model predictive control is a promising approach to robustly control complex energy systems, such as hybrid battery-hydrogen energy storage systems that enable seasonal storage of renewable energies. However, deriving a mathematical model of the energy system suitable for model predictive control is difficult due to the unique characteristics of each energy system component. This work introduces mixed integer linear programming models to describe the nonlinear multidimensional operational behavior of components using piecewise linear functions. Furthermore, this paper develops a new approach for deriving a strategy for seasonal storage of renewable energies using cost factors in the objective function of the optimization problem while considering degradation effects. An experimentally validated simulation model of the PHOEBUS Energy System is utilized to compare the performance of two model predictive controllers with a hysteresis band controller such as utilized for the real-world system. Furthermore, the sensitivity of the model predictive controller to the prediction horizon length and the temporal resolution is investigated. The prediction horizon was found to have the highest impact on the performance of the model predictive controller. The best-performing model predictive controller with a 14-day prediction horizon and perfect foresight increased the total energy stored at the end of the year by 18.9% while decreasing the degradation of the electrolyzer and the fuel cell.

Controlling coating thickness distribution for a complex geometry with the help of simulation

This paper aims to develop a proper and valid simulation model for electroplating complex geometries. Since many variables influence the quality of the deposited coating and its thickness distribution, it is challenging to conduct efficient research only through experiments. In contrast, simulation can be an efficient way to optimize the electroplating experiments. Despite its potential, simulation has seen limited commercial use in the electroplating industry due to its inherent complexity and difficulty in achieving accurate precision for intricate geometries. The present study addresses the aspects that can enhance the electroplating simulation’s accuracy, which has been typically overlooked in the literature, such as the effect of current efficiency and its dependency on the current density, the input data for the electrode kinetics, the surface topology changes, and the differences between 2 and 3D simulations. The simulation model was validated by experimental results related to the coating thickness of Ni plating on a T-joint geometry. The results showed good agreement with the experimental ones, confirming the model’s ability to precisely predict the coating thickness and distribution and promote its broader utilization in the industry. Finally, the developed model was used to determine the optimal current density regime for achieving uniform coating thickness distribution on a T-joint sample.

Assessing turbulence–flame interaction of thermo-diffusive lean premixed H2/air flames towards distributed burning regime

Simultaneous laser-induced fluorescence of OH radicals and particle image velocimetry, and quasi-simultaneous 1D Raman/Rayleigh and 2D Rayleigh scattering measurements are used to investigate the effects of Karlovitz number (by varying the equivalence ratio) and residence time (by varying the axial measurement location) on the internal flame structures of lean premixed hydrogen/air turbulent jet flames. The turbulent flow fields, instantaneous macroscopic flame structures, and thermochemical states of a set of lean premixed hydrogen/air turbulent flames with varying initial equivalence ratio of 0.3, 0.4, and 0.45 at a constant bulk velocity of 100m/s are discussed. With an increasing equivalence ratio, the Karlovitz number derived from the turbulent flow fields decreases rapidly from 7690 to 260 and 100, determined at a downstream location of x/D=7. At the highest Karlovitz number (i.e., the lowest equivalence ratio), a distributed burning is observed in the jet flame as turbulent transport dominates over molecular mixing, and the effects of differential diffusion and flame curvature are suppressed. With decreasing Karlovitz number, intense burning regions characterized by elevated local equivalence ratio, high water mole fraction, and super-adiabatic flame temperatures are observed in association with positive flame curvature. The same combustion diagnostics are applied to another lean premixed hydrogen/air turbulent flame with an initial equivalence ratio of 0.4 and a bulk velocity of 200m/s at selected downstream locations of x/D=3.5, 7, 10.5, and 14 to assess the effects of developing turbulence and residence time. Corresponding Karlovitz numbers are 680, 730, 775, and 690, as the local turbulent intensity changes along downstream locations. While flame structures reveal characteristics towards distributed burning at lower x/D, a locally intense burning region appears at higher x/D together with positive curvature. This is mainly because the turbulence develops with increasing x/D and the flame surface is more disturbed and curved by turbulent eddies with increasing turbulence length scales. The highly diffusive hydrogen is locally concentrated by positively curved flame surfaces, resulting in fuel-rich burning regions at a higher temperature. This indicates that, as velocity fluctuations increase in axial direction, turbulence can also promote thermo-diffusive instabilities to a certain extent, increasing the mixture inhomogeneity in temperature space by interacting with the differential diffusion effect of hydrogen at atmospheric pressure.Novelty and significanceThe novelty of the current work is the focus on turbulence–chemistry interactions of premixed hydrogen/air jet flames at high turbulence and ultra lean conditions. Comprehensive experimental results including the turbulent flow fields (particle image velocimetry), instantaneous flame structures (laser-induced fluorescence of OH radicals) and internal thermochemical states (quasi-simultaneous 1D Raman/Rayleigh and 2D Rayleigh scattering imaging) of hydrogen/air jet flames are analyzed and discussed. The data sets with well-characterized boundary conditions and high measurement accuracy, are crucial for validation and development of numerical simulation models.

Face and content validity of a novel post-thyroidectomy haematoma simulation model

Can a new simulation model help increase confidence in trainees and reduce postoperative complications?

Validation and verification of simulation model for initial subcooled LPG filling into LFSS pipelines and fuel gas skid on LPG dual-fuel carrier

Validation and verification of simulation model for initial subcooled LPG filling into LFSS pipelines and fuel gas skid on LPG dual-fuel carrier

(Invited) Numerical Simulation of an Atmospheric Pressure Streamer Discharge and Induced Chemical Reactions

Streamer discharges are a type of non-thermal plasma produced by high-voltage electrical discharges, and have great potential as highly reactive chemical reaction fields. This is mainly due to the generation of high energy electrons that produce chemically active species. The applications of non-thermal plasma have attracted considerable interest in a wide range of industries. However, each radical has a different role in the plasma application and their reaction mechanism is also different. It's therefore important to understand exactly when, where and how much of these reactive species are produced in order to improve the treatment efficiency of atmospheric pressure plasma applications. Against this background, the aim of this study is to develop a simulation model capable of accurately predicting the chemical reactions of radicals. With a sufficiently validated simulation model, it would be possible to use such a model to design chemical reaction fields.The model consists of the first-order electrohydrodynamic model for electrons and positive and negative ions within the drift-diffusion approximation. Axis-symmetric coordinates were used to simulate the streamer discharge in air at atmospheric pressure. The electron transport and source parameters were calculated using two-term bltzmann equation and published electron impact cross-sections. The chemical reaction model used in this study includes electron impact collisions (excitation, ionisation, dissociation, recombination, attachment and detachment), ion recombination and the reactions of neutrals. In order to demonstrate the validity of the simulation, the light emission of the discharge is first compared between experiment and simulation. Then the spatial and temporal variations of each radical are simulated and compared with the simulation. In this talk, I will present the process of validation and verification (V&V) of the streamer discharge simulation and then the mechanism of radical production revealed by the simulation.

The population-level effects of omitting chemotherapy guided by a 21-gene expression assay in node-positive breast cancer: a simulation modeling study

BackgroundA recent trial showed that postmenopausal women diagnosed with hormone receptor-positive, human epidermal growth factor receptor-2 (HER2)-negative, lymph node-positive (1–3 nodes) breast cancer with a 21-gene recurrence score of ≤ 25 could safely omit chemotherapy. However, there are limited data on population-level long-term outcomes associated with omitting chemotherapy among diverse women seen in real-world practice.MethodsWe adapted an established, validated simulation model to generate the joint distributions of population-level characteristics of women diagnosed with early-stage breast cancer in the U.S. Input parameters were derived from cancer registry, meta-analyses, and clinical trial data. The effects of omitting chemotherapy on 10-year distant recurrence-free survival, life-years, and quality adjusted life-years (QALYs) were modeled for premenopausal and postmenopausal women. QALYs were discounted at 3%. Results were evaluated for subgroups stratified by race and ethnicity. Sensitivity analyses included testing results across a range of inputs. The model was validated using the published RxPONDER trial data.ResultsIn premenopausal women, the 10-year distant recurrence-free survival rates were 85.3% with chemo-endocrine and 80.1% with endocrine therapy. The estimated life-years and QALYs gained with chemotherapy in premenopausal women were 2.1 and 0.6, respectively. There was no chemotherapy benefit in postmenopausal women. There was no variation in the absolute benefit of chemotherapy across racial or ethnic subgroups. However, there were differences in distant recurrence-free survival rates, life-years, and QALYs across groups. Sensitivity analysis showed similar results. The model closely replicated the RxPONDER trial.ConclusionsModeled population-level outcomes show a small chemotherapy benefit in premenopausal women, but no benefit among postmenopausal women. Simulation modeling provides a useful tool to extend trial data and evaluate population-level outcomes.

Investigation of Dead Time Losses in Inverter Switching Leg Operation: GaN FET vs. MOSFET Comparison

This paper investigates the commutation transients of MOSFET and GaN FET devices in motor drive applications during hard-switching and soft-switching commutations at dead time operation. This study compares the switching behaviors of MOSFETs and GaN FETs, focusing on their performance during dead time in inverter legs for voltage source inverters. Experimental tests at various phase current levels reveal distinct switching characteristics and energy dissipation patterns. A validated simulation model estimates the experimental energy exchanged and dissipated during switching transients. The results demonstrate that GaN FETs exhibit lower overall losses at shorter dead times compared to MOSFETs, despite higher reverse conduction voltage drops. The study provides a quantitative framework for selecting optimal dead times to minimize energy losses, enhancing the efficiency of GaN FET-based inverters in low-voltage motor drive applications. Finally, a dead time optimization strategy is proposed and described.

Implementation and Validation of a High-fidelity Simulation Model for Surgical Resident Training: Management of Acute Intraoperative Hemorrhage during Robot-assisted Surgery

ObjectivesTo improve our previous simulation-based training module by using sustainable material to mold an anatomically accurate terrain and reproducing major vascular injuries encountered during robot-assisted nephrectomy. MethodsThe simulator was built with a pump, gauge, and valve linked via silicone tubing. Artificial blood was made from cornstarch, water, and red dye, and pumped through 3D-Med artificial vessels with the dimensions of an average renal artery. Silicone was used to emulate the pliability of organic tissue and mold an anatomically accurate terrain. Eight urologic residents participated in the pilot simulation. We employed validated assessment tools including Non-Technical Skills for Surgeons and Objective Structured Assessment of Technical Skills forms to guide debrief sessions moderated by an expert physician after individual performance evaluations. ResultsThe apparatus demonstrated high reproducibility across all simulation scenarios, enhancing resident problem-solving skills. Residents’ pre-simulation surveys revealed significant concern regarding their acute hemorrhage management. Residents’ post-simulation survey demonstrated average realism scores increased from 4.375 to 4.75. Residents also felt the simulator enhanced learning, offering valuable practice and knowledge applicable to their surgical specialty. ConclusionsThe management of acute hemorrhage during robot-assisted surgery remains a space for additional surgical education and training. Our simulation successfully provided a reliable, reproducible training for residents to practice their technical and non-technical skills in managing acute hemorrhage.

Lithium-Ion Battery Thermal Runaway: Experimental Analysis of Particle Deposition in Battery Module Environment

In this paper, a novel experimental setup to quantify the particle deposition during a lithium-ion battery thermal runaway (TR) is proposed. The setup integrates a single prismatic battery cell into an environment representing similar conditions as found for battery modules in battery packs of electric vehicles. In total, 86 weighing plates, positioned within the flow path of the vented gas and particles, can be individually removed from the setup in order to determine the spatial mass distribution of the deposited particles. Two proof-of-concept experiments with different distances between cell vent and module cover are performed. The particle deposition on the weighing plates as well as the particle size distribution of the deposited particles are found to be dependent on the distance between cell vent and cover. In addition, the specific heat capacity of the deposited particles as well as the jelly roll remains are analyzed. Its temperature dependency is found to be comparable for both ejected particles and jelly roll remains. The results of this study help researches and engineers to gain further insights into the particle ejection process during TR. By implementing certain suggested improvements, the proposed experimental setup may be used in the future to provide necessary data for simulation model validation. Therefore, this study contributes to the improvement of battery pack design and safety.