Real-world Operating Conditions Research Articles

Particle release from refit operations in shipyards: Exposure, toxicity and environmental implications

European harbours are known to contribute to air quality degradation. While most of the literature focuses on emissions from stacks or logistics operations, ship refit and repair activities are also relevant aerosol sources in EU harbour areas. Main activities include abrasive removal of filler and spray painting with antifouling coatings/primers/topcoats. This work aimed to assess ultrafine particle (UFP) emissions from ship maintenance activities and their links with exposure, toxicity and health risks for humans and the aquatic environment. Aerosol emissions were monitored during mechanical abrasion of surface coatings under real-world operating conditions in two scenarios in the Mallorca harbour (Spain). Different types of UFPs were observed: (1) highly regular (triangular, hexagonal) engineered nanoparticles (Ti-, Zr-, Fe-based), embedded as nano-additives in the coatings, and (2) irregular, incidental particles emitted directly or formed during abrasion. Particle number concentrations monitored were in the range of industrial activities such as drilling or welding (up to 5 ∗ 105/cm3, mean diameters <30 nm). The chemical composition of PM4 aerosols was dominated by metallic tracers in the coatings (Ti, Al, Ba, Zn). In vitro toxicity of PM2 aerosols evidenced reduced cell viability and a moderate potential for cytotoxic effects. While best practices (exhaust ventilation, personal protective equipment, dust removal) were in place, it is unlikely that exposures and environmental release can be fully avoided at all times. Thus, it is advisable that health and safety protocols should be comprehensive to minimise exposures in all types of locations (near- and far-field) and periods (activity and non-activity). Potential release to coastal surface waters of metallic engineered and incidental nanomaterials, as well as fine and coarse particles (in the case of settled dust), should be assessed and avoided.

System-Level Modeling and Thermal Simulations of Large Battery Packs for Electric Trucks

Electromobility has gained significance over recent years and the requirements on the performance and efficiency of electric vehicles are growing. Lithium-ion batteries are the primary source of energy in electric vehicles and their performance is highly dependent on the operating temperature. There is a compelling need to create a robust modeling framework to drive the design of vehicle batteries in the ever-competitive market. This paper presents a system-level modeling methodology for thermal simulations of large battery packs for electric trucks under real-world operating conditions. The battery pack was developed in GT-SUITE, where module-to-module discretization was performed to study the thermal behavior and temperature distribution within the pack. The heat generated from each module was estimated using Bernardi’s expression and the pack model was calibrated for thermal interface material properties under a heat-up test. The model evaluation was performed for four charging/discharging and cooling scenarios typical for truck operations. The results show that the model accurately predicts the average pack temperature, the outlet coolant temperature and the state of charge of the battery pack. The methodology developed can be integrated with the powertrain and passenger cabin cooling systems to study complete vehicle thermal management and/or analyze different battery design choices.

Denitrification performance and mechanism of biofilter constructed with sulfur autotrophic denitrification composite filler in engineering application

Sulfur autotrophic denitrification (SAD) is a promising technology due to its low cost and low sludge production. Based on previous studies on SAD materials as well as the denitrification mechanism of SAD technology, this study constructed two biofilters with a sulfur autotrophic denitrification composite filler (SADCF) to investigate the application potential of SAD technology. The feasibility of a SADCF-based biofilter was demonstrated, with a maximum nitrate volume load of 0.75 kg N/(m3·d) and low accumulation of nitrite and ammonium. In addition, an improved backwashing method (air–water backwashing) was obtained by comparing two different backwashing methods. Furthermore, some iron reducing bacteria (0.4% Geothrix) along with a rapid proliferation of the main sulfur-oxidizing bacteria (23.0% Thiobacillus and 27.7% Ferritrophicum) were found under real-world operating conditions. Overall, the results of this study provide a case reference for the operation of SADCF-based biofilters and the application of SAD technology in engineering.

Large-scale empirical study on the momentum equation’s inertia term

A common approach to reduce the Euler equations’ complexity for the simulation and optimization of gas networks is to neglect small terms that contribute little to the overall equations. An example is the inertia term of the momentum equation, which is said to be of negligible size under real-world operating conditions. However, this justification has always only been based on experience or single sets of artificial data points. This study closes this gap by presenting a large-scale empirical evaluation of the absolute and relative size of the inertia term when operating a real-world gas network. Our data consists of three years of fine-granular state data of one of the largest gas networks in Europe, featuring over 6,000 pipes with a total length of over 10,000 km. We found that there are only 120 events in which a subnetwork consisting of multiple pipes has an inertia term of high significance for more than three minutes. On average, such an event occurs less often than once every ten days. Therefore, we conclude that the inertia term is indeed negligible for real-world transient gas network control problems.

One-shot battery degradation trajectory prediction with deep learning

The degradation of batteries is complex and dependent on several internal mechanisms. Variations arising from manufacturing uncertainties and real-world operating conditions make battery lifetime prediction challenging. Here, we introduce a deep learning-based battery health prognostics approach to predict the future degradation trajectory in one shot without iteration or feature extraction. We also predict the end-of-life point and the knee-point. The model correctly learns about intrinsic variability caused by manufacturing differences, and is able to make accurate cell-specific predictions from just 100 cycles of data, and the performance improves over time as more data become available. Validation in an embedded device is demonstrated with the best-case median prediction error over the lifetime being 1.1% with normal data and 1.3% with noisy data. Compared to state-of-the-art approaches, the one-shot approach shows an increase in accuracy as well as in computing speed by up to 15 times. This work further highlights the effectiveness of data-driven approaches in the domain of health prognostics.

Measurements of water consumption for the development of new test regimes for domestic water meters

The reduction of water consumption and the technical developments of the last decades, such as the use of tap aerators, water-saving appliances or the use of electronic measuring principles for water consumption measurements at household level, lead to increased and novel demands on flow metering technology. Nowadays, the meters used must be able to detect less water volume with a constant meter size and constantly changing flows. This makes it necessary.to check whether the demanded measurement accuracy is maintained under these changed boundary conditions. Against this background, the question arises to what extent the existing test regimes to assess the measuring performance of water meters are still appropriate. In order to answer this question, it is necessary to set up a metrological infrastructure that will enable water meters to be tested close to real-world operation conditions. Therefore, the following paper focusses on flow dynamics within the water consumption at the household level. One key factor to realize a representative test regime are reliable and representative water consumption measurements at household level. Within the “Metrology for real-world domestic water metering” project (MetroWaMet) within the European Metrology Programme for Innovation and Research (EMPIR) water consumption measurements were carried out in different European countries. Based on the water consumption measurements discussed essential consumption characteristics can be derived in terms of amplitudes and amplitude distributions, gradients or durations. One important outcome of the study is that water consumption in the four different European countries is comparable. This means based on the available consumption data a generally applicable test regime can be derived which can be extended if necessary. The guide developed regarding the planning and carrying out of consumption measurements can be used to collect further reliable and representative consumption data.

Parametric analysis and optimization of a latent heat thermal energy storage system for concentrated solar power plants under realistic operating conditions

High-temperature latent heat thermal energy storage (LHTES) systems are currently being considered for integration into concentrated solar power (CSP) plants; however, the challenge is to properly design the LHTES system under real-world operating conditions. Thus, this numerical investigation studied the effects of the LHTES system's design parameters on its performance under periodic steady-state with charging and discharging ‘cutoff’ temperatures to mimic its real-world operation. The study found that with the incorporation of cutoff temperatures, the system's specific energy and storage effectiveness decreased by 74% and 68%, respectively, due to lower useful charging and discharging times. Furthermore, the study demonstrated that the system's useful charging and discharging time could be augmented by increasing the shell radius (R) or length (L) of the system, or by decreasing the system's tube radius (ro) or the velocity of the heat transfer fluid (um) that flows through the system. The system's geometrical parameters (R, L, and ro) and um also substantially influenced its performance, but in a different manner than their influence on charging-discharging times. For example, increasing R deteriorated the system's performance substantially. Thus, we proposed optimized designs that achieved high charging-discharging times as well as good performance levels, using the response surface methodology.

A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter

An accurate state of charge (SOC) estimation in battery management systems (BMS) is of crucial importance to guarantee the safe and effective operation of automotive batteries. However, the BMS consistently suffers from inaccuracy of SOC estimation. Herein, we propose a SOC estimation approach with both high accuracy and robustness based on an improved extended Kalman filter (IEKF). An equivalent circuit model is established, and the simulated annealing-particle swarm optimization (SA-PSO) algorithm is used for offline parameter identification. Furthermore, improvements have been made with noise adaptation, a fading filter and a linear-nonlinear filtering based on the traditional EKF method, and rigorous mathematical proof has been carried out accordingly. To deal with model mismatch, online parameter identification is achieved by a dual Kalman filter. Finally, various experiments are performed to validate the proposed IEKF. Experimental results show that the IEKF algorithm can reduce the error to 2.94% under dynamic stress test conditions, and robustness analysis is verified with noise interference, hence demonstrating its practicability for extending to state estimation of battery packs applied in real-world operating conditions.

Development and Analysis of Improved Departure Modeling for Aviation Environmental Impact Assessment

Accurate modeling of aircraft fuel consumption, emissions, and noise is crucial in evaluating new air transportation operational procedures and policies to abate negative environmental impacts. The Aviation Environmental Design Tool (AEDT) is a comprehensive software package developed to address this requirement. Although the modeling of departure operations around airports is of great interest to policy makers and communities, AEDT’s default departure procedures, assumptions of maximum takeoff thrust, and current weight estimations do not fully represent real-world operational flight conditions. With more access to flight data, this paper first presents the development of improved departure profiles that fine-tune the previous modeling assumptions to more accurately reflect real-world operations. We then present a systematic analysis of the new departure profiles through a large-scale computer experiment and follow-up statistical analysis. The result provides comprehensive and valuable insights on the sensitivity of assumptions, quantification of the new profiles’ impacts on estimating aviation environmental impacts, and variability among different aircraft models. Lastly, the statistical learning method elastic net is used to find the driving factors behind the complex patterns observed in the noise results.

Performance Evaluation of Real-time DustTrak Monitors for Outdoor Particulate Mass Measurements in a Desert Environment

Using the filter-based gravimetric technique as the reference method, this study aimed to evaluate the performance of the TSI DustTrak DRX® aerosol monitor, which offers real-time measurements, and establish suitable factors for bias correction in real-world operating conditions. Overall, the DustTrak demonstrated high precision in tracking the PM2.5 and PM10 mass concentrations; however, the measurements significantly deviated from those obtained via the reference method, showing an overestimation by a factor of ~2 for the fine fraction and an underestimation by ~20% for the coarse one. Applying a correction factor based on a constant proportion produced an acceptable level of accuracy, but utilizing one adjusted for humidity achieved even better results. These factors can also be used to enhance the accuracy of DustTrak measurements under comparable conditions, i.e., for aerosol with similar properties in similar environments. Additionally, the proposed approaches can serve as a model for field calibration and measurement data correction in other studies employing various types of real-time optical monitors.

Design and Development of Low Cost, Portable, On-Field I-V Curve Tracer Based on Capacitor Loading for High Power Rated Solar Photovoltaic Modules

This paper outlines a novel design of low-cost, portable, fast, and precise Current-Voltage Curve Tracer (IVCT) with automated parameter extraction for high power rated Solar Photovoltaic (SPV) modules to effectively and efficiently determine the outdoor operating status of SPV power generators. The developed IVCT is based on a Raspberry Pi microprocessor, a super-capacitive load, heat sinkable discharge resistances, and sensors with high sensitivity and resolution for measuring light irradiance, module temperature, current, and voltage. The proposed Outdoor Test Facility (OTF) consists of an Current-Voltage ( $I-V$ ) and a Power-Voltage ( $P-V$ ) curve tracer that uses a dynamic loading supercapacitor to safely and quickly scan the SPV module performance characteristics under real-world operating conditions. It also helps to achieve uniform sampling with better data accuracy. It uses Raspberry Pi as a central processing unit for low-cost data acquisition, data logging, and data computation. Furthermore, results from on-field testing of various small-scale SPV modules show that the $I-V$ tracer can acquire higher-resolution characteristics curves and perform accurate model parameter recognition in real-time. Proposed IVCT can measure individual SPV modules without altering the electrical interconnection circuit, and the operating point can be shifted to 20 $A$ and $45~V$ in few seconds. The proposed system recomposes the SPV module $I-V$ characteristics based on this variance, with accuracies of 1 to 3% for the region near maximum power.

OPTIMAL HEIGHT OF STEEL PLATE GIRDERS WITH CORRUGATED WEBS

Abstract. This work is carried out in order to study the classical problem of determining the optimal height of a plate girder with given moment of resistance in relation to the features of the real operation of a beam with a corrugated web. A bi-symmetric plate girder with a wavy web is considered. The structural model parameters of the girder correspond to the Zenam technology, which determines the structural limitations of the task. The physical model is the 1st class double-tee girder according to the stress-strain state (in accordance with the current design standards of Ukraine SCS (State Construction Standards of Ukraine) В.2.6-198). The mathematical model is considered as a task of minimizing the objective function (beam mass) taking into account design factors. The height is a project variable. The objective function is formulated as one-parameter taking into account the bending strength of the beam. The structural coefficients and their influence on the objective function are considered. Both the shear strength constrain and overall wall shape stability constrain are taken into account. Deflections are determined taking into account the shear deformations of the thin web. The minimum values of the beam height are considered when the permissible deflection is not exceeded. It is shown that the introduction of the conditions of shear strength and wall stability into the resolving equation indicates the lower limit of the calculated variable value. The constraint is inactive in the area of feasible solutions. Areas of rational decisions are shown. Numerical studies have been conducted for corrugated beam structures. Design requirements in accordance with design standards are used as method constraints. The optimal values of the beam height are obtained according to the minimum mass criterion in the range of applied spans and loads. The effective values of spans and loads for beams with corrugated walls in the range of optimal parameters and technological limitations have been determined. Further research is needed to explore constructive solutions that would minimize the values of constructive coefficients, taking into account specific effects such as local tensions and real-world operating conditions.

Data augmentation for automated pest classification in Mango farms

Mangos are native to South and Southeast Asian regions. They are one of the favorite fruits consumed globally, with an overall estimated consumption reaching up to 50.65 million metric tons in 2017. However, the production of mango is usually severely affected by pests that attack the fruit, stem, root or mango leaf. Addressing the need for an early stage automated or semi-automated pest identification system, the research presented in this paper proposes an advanced machine learning (ML) technique for analyzing large-scale mango fields and identification of the onset of biological threats using computer vision and deep-learning technologies. The ML technique presented in the paper extends the pre-trained VGG-16 deep-learning model to supplement the last layer with a fully connected network training of consisting of 2-layers. In addition, the research presented in the paper also considers the real-world operational conditions commonly faced by Indonesian farmers for collecting and processing visual information obtained from the Mango farms. The sparsity of the dataset availability for effectively training deep-learning network is addressed through the application of data augmentation process that is able to accurately recreate the conditions faced by the farmers. The overall accuracy of the proposed training solution achieved is 73% on the validation dataset and 76% for the testing data. The application of the augmentation transformation function leads to an improvement of 13.43% of accuracy on the testing data.

Ensuring the performance of equipment during the warranty period of operation

Annotation. The problem of increasing the reliability of domestic technological machines and equipment remains relevant and requires a speedy solution in the current conditions of acute competition in the market of equipment and technologies. The low level of reliability of equipment reduces the efficiency of using machines and the quality of their technological processes, increases operating costs. The solution of this problem, as the results of research show, is impossible without participation in ensuring the operability of technological machines and equipment of manufacturers of modern domestic equipment. Providing engineering and technical support for manufactured machines, taking part in technical service, equipment manufacturers have the opportunity to study the reasons for the low reliability of units, units, connections and take measures to exclude their appearance. By setting the wear rate of the working surfaces of the critical interfaces in real-world operating conditions of the machine, it becomes possible to control its reliability and efficiency

Development of the cycling life model of Ni-MH power batteries for hybrid electric vehicles based on real-world operating conditions

For the hybrid electric vehicle (HEV), accurate prediction of the remaining use life for the power batteries under actual operating conditions is of great significance to the battery power state of charge (SOC) estimation, as well as to the reliability and safety. However, there is little study available to evaluate the battery degradation behavior in real applications. Current battery degradation model is mostly established based on laboratory condition. In such case, charging/discharging scenarios are mostly constant across the battery cycle, and thus, they may be not a good representative of the working condition of the Ni-MH in real application where charging/discharging are frequently altered. Therefore, in this paper, the cycle life degradation characteristics of Ni-MH batteries for HEVs were studied, and an experienced model of cycle life is established based on the data from the real-world driving settings. The cycle life results indicated that the capacity loss is strongly impacted by the charge and discharge rate (C-rate), temperature, and depth of discharge (DOD). A large cycle test matrix is used to collect battery life data to build the model. The test matrix parameters include temperature (25 °C, 45 °C, 55 °C), DOD (0.5, 0.8, 1) and C-rate (1C, 2C, 5C). To verify the reliability of the life model applied to the HEV, the equivalent cycle life test verification of the real vehicle road spectrum was carried out. The test results show that the model can characterize the battery's decay process well.

Biodeterioration Affecting Efficiency and Lifetime of Plastic-Based Photovoltaics

Summary The low environmental impact of electricity generation using solar cells crucially depends on high energy-conversion efficiencies, long lifetimes, and a minimal energy and material demand during production. Emerging thin-film photovoltaics such as perovskites on plastic substrates could hold promises to fulfill all these requirements. Under real-world operating conditions, photovoltaic operation is challenged by biological stressors, which have not been incorporated for evaluation in any test. Such stressors cause biodeterioration, which impairs diverse, apparently inert materials such as rock, glass, and steel and therefore could significantly affect the function and stability of plastic-based solar cells. Given that different photovoltaic technologies commonly use similar materials, the biodeterioration mechanisms reviewed here may possibly affect the efficiency and lifetimes of several technologies if they occur sufficiently faster (during the expected lifetime of photovoltaics). Once the physical integrity of uppermost module layers is challenged by biofilm growth, microbially mediated dissolution and precipitation reactions of photovoltaic functional materials are very likely to occur. The biodeterioration of substrates and seals also represents emission points for the release of potentially harmful photovoltaic constituents to the environment.

MULTILATERATION UNDER FLIP AMBIGUITY FOR UAV POSITIONING USING ULTRAWIDE-BAND

Abstract. Ultrawide-band (UWB) ranging technology and multilateration techniques have recently been emerging solutions for positioning unmanned aerial vehicles (UAVs) in GNSS-denied environments. This solution offers cm-level ranging accuracy and considerable robustness to multipath receptions. UWB modules are commonly used in an anchor-based configuration; i.e., one UWB tag is mounted on the UAV, and several UWB anchors are installed on the ground. In real-world operational conditions, anchors can form a planar or a near-planar surface. This causes a geometric ambiguity, called flip ambiguity, in position estimation. Flip ambiguity can lead to considerable errors in the estimated position by multilateration. In this paper, we present a multilateration approach, which automatically resolves the flip ambiguity for UAV-positioning using UWB ranging. The proposed multilateration method first computes an algebraic solution through recursive least squares. If the initially estimated position is found to be flipped, then it is corrected by a symmetric reflection with respect to the anchor plane. Finally, the estimated position is refined by non-linear optimization. Extensive experiments in a real environment show that the proposed algorithm can effectively tackle the issue of flip ambiguity in multilateration, leading to a significant improvement in positioning accuracy.

Analytical derivation and analysis of parameter sensitivity for battery electrochemical dynamics

Model state and parameter estimation is a critical issue in battery research, and it has been recognized that the quality of data plays an important role in determining the accuracy of estimation. Consequently, data optimization for estimation has received increasing attention recently. The key is to evaluate the sensitivity of data to the target variables and design/select the most sensitive data for estimation. However, due to the lack of efficient ways to compute sensitivity and reliance on simulation, data optimization is mostly intractable because of the computational complexity, especially for the first-principle electrochemical battery models. To address this challenge, this paper proposes a methodology to derive the analytic expressions for the sensitivity of battery electrochemical parameters by leveraging reasonable model reformulation, approximation, and simplification techniques. The analytic expressions enable theoretic parameter sensitivity analysis under generic current input, which reveals the fundamental features of the sensitivity and sensitive data for different parameters. The compact expressions also facilitate sensitivity computation for data optimization. The results have been verified by comparing with the exact numerical simulation based on the full order electrochemical model, showing satisfactory fidelity over adequate range of real-world operating conditions.

DSP enabled next generation 50G TDM-PON

With 10G time division multiplexed passive optical network (TDM-PON) systems ready for massive deployment worldwide, the next generation PON standards are being intensively discussed in both IEEE and ITU-T. Compared with the IEEE’s wavelength stacked 2 ∗ 25 G solution, the ITU-T’s single carrier non-return-to-zero-based 50G TDM-PON has the advantages of low cost, easy operation, and convenient management. However, the 50G TDM-PON will also have to face great challenges in real-world operating conditions. In this paper, we will illustrate the unique role for digital signal processing (DSP) in solving the challenges for the next generation 50G TDM-PON systems, such as device bandwidth limitation, dispersion induced power penalty, and high link budget. To analyze DSP’s feasibility in this application, potential issues such as cost and interoperability are analyzed. Through offline experiments and prototype demonstrations, DSP’s role in enabling next generation single carrier 50G TDM-PON is highlighted.

Stability of Proton-Conducting Solid Oxide Electrolyzers for Hydrogen Production and Energy Storage

Proton-conducting solid oxide electrolyzers (H-SOEs) provide promising opportunity to produce pure and dry hydrogen in steam electrolysis at relatively low operating temperatures (550-700˚C) utilizing electricity and heat generated from renewable energy sources. Compared to traditional high temperature (750-1000˚C) oxygen-conducting solid oxide electrolyzers (O-SOEs), lower operating temperature of H-SOE offers ease of thermal management, active stack and BOP materials cost reduction and reduction in chromium evaporation from metallic components. Like O-SOEs, preserving the long-term stability of H-SOEs is one of the technical challenges for large-scale hydrogen production. In this technical contribution, results of experimental evaluation of H-SOEs under real-world operating conditions are presented. As fabricated and posttest cells have been characterized using operando electrochemical impedance spectroscopy, X-ray diffraction, focused ion beam-transmission electron microscopy and other bulk and surface characterization techniques to examine bulk, surface and interface stability of electrochemically active components. Phase and morphological changes, compositional uniformity and interfacial reaction products formation have been examined. Electrolyte/electrode materials stability, cell and gas seal fabrication processes, and gaseous impurities affecting long-term electrochemical performance will be discussed. H-SOE electrochemical performance model based on cell materials and operating conditions has been proposed and validated based on single cell testing data.