INITIAL RESULTS ON A NEW LIGHT-DUTY 2.7L OPPOSED-PISTON GASOLINE COMPRESSION IGNITION MULTI-CYLINDER ENGINE

Gasoline compression ignition (GCI) is a cost-effective approach to achieving diesel-like efficiencies with low emissions. The fundamental architecture of the two-stroke Achates Power Opposed-Piston Engine (OP Engine) enables GCI by decoupling piston motion from cylinder scavenging, allowing for flexible and independent control of cylinder residual fraction and temperature leading to improved low load combustion. In addition, the high peak cylinder pressure and noise challenges at high-load operation are mitigated by the lower BMEP operation and faster heat release for the same pressure rise rate of the OP Engine. These advantages further solidify the performance benefits of the OP Engine and emonstrate the near-term feasibility of advanced combustion technologies, enabled by the opposed-piston architecture. This paper presents initial results from a steady state testing on a brand new 2.7L OP GCI multi-cylinder engine designed for light-duty truck applications. Successful GCI operation calls for high compression ratio, leading to higher combustion stability at low-loads, higher efficiencies, and lower cycle HC+NOX emissions. Initial results show a cycle average brake thermal efficiency of 31.7%, which is already greater than 11% conventional engines, after only ten weeks of testing. Emissions results suggest that Tier 3 Bin 160 levels can be achieved using a traditional diesel after-treatment system. Combustion noise was well controlled at or below the USCAR limits. In addition, initial results on catalyst light-off mode with GCI are also presented.

The Effects of Backpressure on the Performance of Internal Combustion Engine and Automobile Exhaust Thermoelectric Generator

The maximum generated power of automobile exhaust thermoelectric generator (AETEG) can be enhanced by applying inserted fins to its heat exchanger, for the temperature difference of thermoelectric modules (TEMs) is increased. However, the heat exchanger will result in undesired backpressure, which may deteriorate the performance of the internal combustion engine (ICE). To evaluate the backpressure on the performance of both the ICE and the AETEG, the model of ICE integrated with AETEG was established with the GT-power software and validated with the AETEG test bench. The heat exchangers with chaos shape and fishbone shape were proposed, their pressure drop with different engine speeds was studied, and their effects on the performance of both the AETEG and the ICE were analyzed. The results showed that compared with the fishbone-shaped structure, the pressure drop of chaos-shaped heat exchanger is larger at the same engine speed, which contributes to the increased maximum power and hot side temperature of the AETEG. Moreover, compared with the ICE without heat exchanger, the brake torque, brake power, volumetric efficiency and pumping mean effective pressure of the ICE assembled with chaos-shape and fishbone-shape heat exchanger reduce, and the corresponding brake specific fuel consumption, CO emission and CO2 emission increase because of the raised backpressure caused by the heat exchanger.

Analysis of Low and High Temperature Heat Release in Dual-Fuel RCCI Engine and its Relationship with Particle Emissions

This study presents the influence of low-temperature heat release (LTHR) and high-temperature heat release (HTHR) on the combustion and particle number characteristics of the RCCI engine. The study investigates the relationship between the amount of LTHR, HTHR, and particle number emission characteristics. In this study, gasoline and methanol are used as low reactivity fuel (LRF), and diesel is used as a high reactivity fuel (HRF). The LRF is injected into the intake manifold using a port-fuel injection (PFI) strategy, and HRF is directly injected into the cylinder using a direct injection strategy. A particle sizer is used to measure particle emission in size ranging from 5 to 1000 nm. Firstly, the LTHR and HTHR are analyzed for different diesel injection timing (SOI) for RCCI operation. Later, the variation of particle emissions with LTHR and HTHR is characterized. Additionally, empirical correlations are developed to understand the relation between the LTHR and HTHR with particle emission. Two-staged auto-ignition of charge has been observed in RCCI combustion. Results depict that LTHR varies with diesel injection timing and the phasing of HTHR depends on the amount and location of LTHR. Results also showed that HTHR and LTHR significantly influence the formation of particle number concentration in RCCI combustion. The developed empirical correlation depicts a good correlation between diesel SOI and the ratio of HTHR to LTHR to estimate total particle number concentration.

Statistical Methods to Improve the Quality of Real-Time Drilling Data

The age of easy oil is ending, the industry started drilling in remote unconventional conditions. To help produce safer, faster, and most effective operations, the utilization of artificial intelligence and machine learning (AI/ML) has become essential. Unfortunately, due to the harsh environments of drilling and the data-transmission setup, a significant amount of the real-time data could defect. The quality and effectiveness of AI/ML models are directly related to the quality of the input data; only if the input data are good, the AI/ML generated analytical and prediction models will be good. Improving the real-time data is therefore critical to the drilling industry. The objective of this paper is to propose an automated approach using eight statistical data-quality improvement algorithms on real-time drilling data. These techniques are Kalman filtering, moving average, kernel regression, median filter, exponential smoothing, lowess, wavelet filtering, and polynomial. A dataset of +150,000 rows is fed into the algorithms, and their customizable parameters are calibrated to achieve the best improvement result. An evaluation methodology is developed based on real-time drilling data characteristics to analyze the strengths and weaknesses of each algorithm were highlighted. Based on the evaluation criteria, the best results were achieved using the exponential smoothing, median filter, and moving average. Exponential smoothing and median filter techniques improved the quality of data by removing most of the invalid data points, the moving average removed more invalid data-points but trimmed the data range.

Localization of Thermal Wellbore Defects Using Machine Learning

Defect detection and localization are key to preventing environmentally damaging wellbore leakages in both geothermal and oil/gas applications. In this work, a multi-step, machine learning approach is used to localize two types of thermal defects within a wellbore model. This approach includes a COMSOL heat transfer simulation to generate base data, a neural network to classify defect orientations, and a localization algorithm to synthesize sensor estimations into a predicted location. A small-scale physical wellbore test bed was created to verify the approach using experimental data. The classification and localization results were quantified using this experimental data. The classification predicted all experimental defect orientations correctly. The localization algorithm predicted the defect location with an average root mean square error of 1.49 in. The core contributions of this work are 1) the overall localization architecture, 2) the use of centroid-guided mean-shift clustering for localization, and 3) the experimental validation and quantification of performance.

Optimum Design and Performance Simulation of a Multi-device Integrated System Based on a Proton Exchange Membrane Fuel Cell

Proton exchange membrane fuel cells (PEMFCs) based on syngas are a promising technology for electric vehicle applications. To increase the fuel conversion efficiency, the low-temperature waste heat from the PEMFC is absorbed by a refrigerator. The absorption refrigerator provides cool air for the interior space of the vehicle. Between finishing the steam reforming reaction and flowing into the fuel cell, the gases release heat continuously. A Brayton engine is introduced to absorb heat and provide a useful power output. A novel thermodynamic model of the integrated system of the PEMFC, refrigerator, and Brayton engine is established. Expressions for the power output and efficiency of the integrated system are derived. The effects of some key parameters are discussed in detail to attain optimum performance of the integrated system. The simulation results show that when the syngas consumption rate is 4.0 × 10−5 mol s−1cm−2, the integrated system operates in an optimum state, and the product of the efficiency and power density reaches a maximum. In this case, the efficiency and power density of the integrated system are 0.28 and 0.96 J s−1 cm−2, respectively, which are 46% higher than those of a PEMFC.

Numerical Study on Fragmentation Characteristics of Granite Under a Single Polycrystalline Diamond Compact Cutter in Rotary-Percussive Drilling

Fragmentation characteristics of granite in rotary-percussive drilling are studied using the distinct element method. We developed a model to investigate the interaction between the rock and a Polycrystalline Diamond Compact cutter. The micro contact parameters in the model are calibrated by conducting a series of simulated mechanical tests of the rock. Sensitivity analyses are then conducted according the drilling performances which are quantified as the penetration displacement, the fragmentation volume and the specific energy, as well as the lateral force and the particle size distribution. Results show that the model can well represent the typical fracture system under indentation of the cutter, the torque fluctuation phenomenon in drilling and the formation of lateral chips, which verify the reliability of the model. The cutter with a back rake angle of 55°and impact frequency of 30Hz has the best penetration performance in evaluated parameters. Increasing the frequency has a great effect on the rock breaking speed under the coupling effect of impact and cutting in the low frequency range. Considering crushing efficiency, 50 Hz is the recommended impact frequency. This paper provides a useful tool to represent the fragmentation performance of rotary-percussive drilling and sensitivity analyses shed light on the potential ways to improve the performance.

Evaluating the Effectiveness of Machine Learning Technologies in Improving Real-Time Drilling Data-Quality

Due to high demand for energy, oil and gas companies started to drill wells in remote environments conducting unconventional operations. In order to maintain safe, fast and more cost-effective operations, utilizing machine learning (ML) technologies has become a must. The harsh environments of drilling sites and the transmission setups, are negatively affecting the drilling data, leading to less than acceptable ML results. For that reason, big portion of ML development projects were actually spent on improving the data by data-quality experts. The objective of this paper is to evaluate the effectiveness of ML on improving the real-time drilling-data-quality and compare it to a human expert knowledge. To achieve that, two large real-time drilling datasets were used; one dataset was used to train three different ML techniques: artificial neural network (ANN), support vector machine (SVM) and decision tree (DT), the second dataset was used to evaluate it. The ML results were compared with the results of a real- time drilling data quality expert. Despite the complexity of ANN and good results in general, it achieved a relative root mean square error (RRMSE) of 2.83%, which was lower than DT and SVM technologies that achieved RRMSE of 0.35% and 0.48% respectively. The uniqueness of this work is in developing ML that simulates the improvement of drilling-data- quality by an expert. This research provides a guide for improving the quality of real-time drilling data.

Effects of mustard husk, wheat straw and flaxseed residue blending on combustion behavior of high ash coal and petroleum coke blends

This present work reports the combustion studies of coal, petroleum coke (PC) and biomass blends to assess the effects of the mustard husk (MH), wheat straw (WS) and flaxseed residue (FR) blending towards improvement of coal combustion characteristics. Ignition temperature (TS), maximum temperature (TP), burnout temperature (TC), activation energy (AE) and thermodynamic parameters (ΔH, ΔG and ΔS) were analyzed to evaluate the impact of biomass and PC blending on coal combustion. Experimental results indicate that coal and PC have inferior combustion characteristics compared to MH, WS and FR. With the increase in WS content in blends from 10 to 30%, TS reduced from 371 to 258OC, TP decreased from 487 to 481OC, inferring substantial enhancements in combustion properties. Kinetic analysis inferred that blended fuel combustion could be explained mostly using reaction models, followed by diffusion-controlled and contracting sphere models. Overall, with the increase in FR mass in blends from 10 to 30%, AE decreased from 108.97 kJ/mol to 70.15 kJ/mol signifying ease of combustion. Analysis of synergistic effects infers that higher biomass addition improves coal and PC blends' combustion behavior through catalytic effects of alkali mineral matters present in biomass. Calculation of thermodynamic parameters signified that combustion of coal and PC is challenging than biomasses, however, blending of biomass makes the combustion process easier.