The Microenvironmental Impact Of Roadways And Distributed Generation On Local Air Quality

Microscale air quality in highly populated urban areas has gained increasing attention in recent years. Various emission sources are present, and their contributions need to be quantified for assessing human exposure and developing effective emission control strategies. This dissertation presents the effort towards establishing a better understanding of the spatial variation of multiple air pollutants in complex urban microenvironments through numerical modeling and experimental evaluation. In the first part, I investigated the transport of Black Carbon (BC) in a typical highwaybuilding environment next to an urban high school in South Bronx, NYC. Two generalized configurations i.e., highway-building canyon and highway viaductbuilding are discovered, which is critical to the spatial variation of BC. The second part focuses on roadside barrier designs with the objective to mitigate near-road air pollution. Our analysis revealed two potentially viable design options: a) wide vegetation barriers with high leaf area density which reduces downwind particle concentrations significantly, while resulting in a moderate increase in on-road concentrations, and b) vegetation-solid barrier combinations lead to the greatest reduction in downwind particle concentrations among all configurations and a large increase in on-road concentrations at the same time. The third part investigates the near-source environmental impact of diesel backup generators that participate in demand response programs. The micro-environmental air quality simulation is improved by coupling with a meteorology processor to provide realistic boundary conditions. The study found the near-ground PM2.5 concentration for the worst scenarios could well exceed 100 [MICRO SIGN]g m-3, posing a potential health risk to people living and working nearby. Our analysis also implies that the siting of diesel backup generators stacks should consider not only the interactions of fresh air intake and exhaust outlet, but also the dispersion of exhaust plumes in the surrounding environment. The last part studies the environmental impact of a biomass boiler with and without PM emission control. A micro-environmental model was applied to simulate the experimental conditions, and a good agreement between predicted and on-site measurement is observed. Our analysis shows that the absence of ESP could lead to an almost 7 times increase of the near-ground PM2.5 concentrations in the surrounding environment

Preliminary Design of Multistage Radial Turbines Based on Rotor Loss Characteristics under Variable Operating Conditions

The loading-to-flow diagram is a widely used classical method for the preliminary design of radial turbines. This study improves this method to optimize the design of radial turbines in the early design phase under variable operating conditions. The guide vane outlet flow angle is a key factor affecting the off-design performance of the radial turbine. To optimize the off-design performance of radial turbines in the early design phase, we propose a hypothesis that uses the ratio of the mean velocity of the fluid relative to the rotor passage with respect to the circumferential velocity of the rotor as an indicator to indirectly and qualitatively estimate the rotor loss, as it plays a key role in the off-design efficiency. Theoretical analysis of rotor loss characteristics under different types of variable operating conditions shows that a smaller design value of guide vane outlet flow angle results in a better off-design performance in the case of a reduced mass flow. In contrast, radial turbines with a larger design value of guide vane outlet flow angle can obtain a better off-design performance with increased mass flow. The above findings were validated with a mean-line model method. Furthermore, this study discusses the optimization of the design value of guide vane outlet flow angle based on the matching of rotor loss characteristics with specified variable operating conditions. It provides important guidance for the design optimization of multistage radial turbines with variable operating conditions in compressed air energy storage (CAES) systems

Modeling In-Vehicle VOCs Distribution from Cabin Interior Surfaces under Solar Radiation

In-vehicle air pollution has become a public health priority worldwide, especially for volatile organic compounds (VOCs) emitted from the vehicle interiors. Although existing literature shows VOCs emission is temperature-dependent, the impact of solar radiation on VOCs distribution in enclosed cabin space is not well understood. Here we made an early effort to investigate the VOCs levels in vehicle microenvironments using numerical modeling. We evaluated the model performance using a number of turbulence and radiation model combinations to predict heat transfer coupled with natural convection, heat conduction and radiation with a laboratory airship. The Shear–Stress Transport (SST) k-ω model, Surface-to-surface (S2S) model and solar load model were employed to investigate the thermal environment of a closed automobile cabin under solar radiation in the summer. A VOCs emission model was employed to simulate the spatial distribution of VOCs. Our finding shows that solar radiation plays a critical role in determining the temperature distribution in the cabin, which can increase by 30 °C for directly exposed cabin surfaces and 10 °C for shaded ones, respectively. Ignoring the thermal radiation reduced the accuracy of temperature and airflow prediction. Due to the strong temperature dependence, the hotter interiors such as the dashboard and rear board released more VOCs per unit time and area. A VOC plume rose from the interior sources as a result of the thermal buoyancy flow. A total of 19 mg of VOCs was released from the interiors within two simulated hours from 10:00 am to noon. The findings, such as modeled spatial distributions of VOCs, provide a key reference to automakers, who are paying increasing attention to cabin environment and the health of drivers and passengers

Signal Identification of Gear Vibration in Engine-Gearbox Systems Based on Auto-Regression and Optimized Resonance-Based Signal Sparse Decomposition

As an essential part of the transmission system, gearboxes are considered as a major source of vibration. Signal identification of gear vibration is necessary for online monitoring of the mechanical systems. However, in engine-gearbox systems, the ignition impact of the engine is strong, so that the gear vibration is generally submerged. To overcome this issue, the resonance-based signal sparse decomposition (RSSD) method is used in this paper based on different oscillatory behaviors of the gear meshing impact and the engine ignition impact. To improve the accuracy of RSSD under interferences, the meshing frequency energy ratio (MF–ER) index is introduced into RSSD to adaptively choose the decomposition parameters. Before applying the RSSD method, the auto-regression (AR) model is used as a pre-whitening step to eliminate the normal gear meshing vibration, which improves the decomposition performance of RSSD. The effectiveness of the proposed AR-ORSSD (AR-based optimized RSSD) algorithm is tested using both simulated signals and measured vibration signals from an engine-gearbox system in a forklift. Comparisons were made with the RSSD algorithm based on a genetic algorithm. Experimental results indicate that the AR-ORSSD algorithm is superior at identifying gear vibration signals especially when under strong interferences

Residual Stress Distribution Design for Gear Surfaces Based on Genetic Algorithm Optimization

The rolling contact fatigue of gear surfaces in a heavy loader gearbox is investigated under various working conditions using the critical plane-based multiaxial Fatemi–Socie criterion. The mechanism for residual stress to increase the fatigue initiation life is that the compressive residual stress has a negative normal component on the critical plane. Based on this mechanism, the genetic algorithm is used to search the optimum residual stress distribution that can maximize the fatigue initiation life for a wide range of working conditions. The optimum residual stress distribution is more effective in increasing the fatigue initiation life when the friction coefficient is larger than its critical value, above which the fatigue initiation moves from the subsurface to the surface. Finally, the effect on the fatigue initiation life when the residual stress distribution deviates from the optimum distribution is analyzed. A sound physical explanation for this effect is provided. This yields a useful guideline to design the residual stress distribution

Cavitation Diagnostics Based on Self-Tuning VMD for Fluid Machinery with Low-SNR Conditions

Abstract Variational mode decomposition (VMD) is a suitable tool for processing cavitation-induced vibration signals and is greatly affected by two parameters: the decomposed number K and penalty factor α under strong noise interference. To solve this issue, this study proposed self-tuning VMD (SVMD) for cavitation diagnostics in fluid machinery, with a special focus on low signal-to-noise ratio conditions. A two-stage progressive refinement of the coarsely located target penalty factor for SVMD was conducted to narrow down the search space for accelerated decomposition. A hybrid optimized sparrow search algorithm (HOSSA) was developed for optimal α fine-tuning in a refined space based on fault-type-guided objective functions. Based on the submodes obtained using exclusive penalty factors in each iteration, the cavitation-related characteristic frequencies (CCFs) were extracted for diagnostics. The power spectrum correlation coefficient between the SVMD reconstruction and original signals was employed as a stop criterion to determine whether to stop further decomposition. The proposed SVMD overcomes the blindness of setting the mode number K in advance and the drawback of sharing penalty factors for all submodes in fixed-parameter and parameter-optimized VMDs. Comparisons with other existing methods in simulation signal decomposition and in-lab experimental data demonstrated the advantages of the proposed method in accurately extracting CCFs with lower computational cost. SVMD especially enhances the denoising capability of the VMD-based method

Signal Identification of Gear Vibration in Engine-Gearbox Systems Based on Auto-Regression and Optimized Resonance-Based Signal Sparse Decomposition

As an essential part of the transmission system, gearboxes are considered as a major source of vibration. Signal identification of gear vibration is necessary for online monitoring of the mechanical systems. However, in engine-gearbox systems, the ignition impact of the engine is strong, so that the gear vibration is generally submerged. To overcome this issue, the resonance-based signal sparse decomposition (RSSD) method is used in this paper based on different oscillatory behaviors of the gear meshing impact and the engine ignition impact. To improve the accuracy of RSSD under interferences, the meshing frequency energy ratio (MF–ER) index is introduced into RSSD to adaptively choose the decomposition parameters. Before applying the RSSD method, the auto-regression (AR) model is used as a pre-whitening step to eliminate the normal gear meshing vibration, which improves the decomposition performance of RSSD. The effectiveness of the proposed AR-ORSSD (AR-based optimized RSSD) algorithm is tested using both simulated signals and measured vibration signals from an engine-gearbox system in a forklift. Comparisons were made with the RSSD algorithm based on a genetic algorithm. Experimental results indicate that the AR-ORSSD algorithm is superior at identifying gear vibration signals especially when under strong interferences

Residual Stress Distribution Design for Gear Surfaces Based on Genetic Algorithm Optimization

The rolling contact fatigue of gear surfaces in a heavy loader gearbox is investigated under various working conditions using the critical plane-based multiaxial Fatemi–Socie criterion. The mechanism for residual stress to increase the fatigue initiation life is that the compressive residual stress has a negative normal component on the critical plane. Based on this mechanism, the genetic algorithm is used to search the optimum residual stress distribution that can maximize the fatigue initiation life for a wide range of working conditions. The optimum residual stress distribution is more effective in increasing the fatigue initiation life when the friction coefficient is larger than its critical value, above which the fatigue initiation moves from the subsurface to the surface. Finally, the effect on the fatigue initiation life when the residual stress distribution deviates from the optimum distribution is analyzed. A sound physical explanation for this effect is provided. This yields a useful guideline to design the residual stress distribution

Fatigue Damage of an Asperity in Frictionless Normal Contact with a Rigid Flat

Surface fatigue wear widely exists, and it occurs as long as a sufficient number of loading–unloading cycles are applied. Slowing down surface fatigue wear requires understanding the evolution of fatigue damage in the surface. Real surfaces are composed of many asperities; therefore, it is important to study the fatigue damage of a single asperity. A finite element model of an asperity subjected to cyclic elastic–plastic normal loading was developed under frictionless contact condition. The asperity can be either completely or partially unloaded in a loading cycle. For the sake of completeness, both cases were investigated in the present study. The multiaxial Fatemi-Socie fatigue criterion was adopted to evaluate the fatigue damage of the asperity in elastic shakedown state, which was achieved after several loading cycles. For the case of complete unloading, severe fatigue damage was confined in a subsurface ridge starting from the edge of the maximum loaded contact area. The shape and volume of the wear particles were predicted based on a fundamentally valid assumption. For the case of partial unloading, the fatigue damage was much milder. Finally, potential research directions to expand the current study are suggested

Vibration Separation Methodology Compensated by Time-Varying Transfer Function for Fault Diagnosis of Non-Hunting Tooth Planetary Gearbox

Due to planetary movement of planet gears, the vibration signal perceived by a stationary sensor is modulated and difficult to diagnose. This paper proposed a vibration separation methodology compensated by a time-varying transfer function (TVTF-VS), which is a further development of the vibration separation (VS) method in the diagnosis of non-hunting tooth planetary gearboxes. On the basis of VS, multi-teeth VS is proposed to extract and synthesize the meshing signal of a planet gear using a single transducer. Considering the movement regularity of a planetary gearbox, the time-varying transfer function (TVTF) is represented by a generalized expression. The TVTF is constructed using a segment of healthy signal and an evaluation indicator is established to optimize the parameters of the TVTF. The constructed TVTF is applied to overcome the amplitude modulation effect and highlight fault characteristics. After that, experiments with baseline, pitting, and compound localized faults planet gears were conducted on a non-hunting tooth planetary gearbox test rig, respectively. The results demonstrate that incipient failure on a planet gear can be detected effectively, and relative location of the local faults can be determined accurately