bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Hydrogen and Carbon Monoxide Addition to Methane on Laminar Burning Velocity
Authors: Morovatiyan Mohammadrasool , Shahsavan Martia , Baghirzade Mammadbaghir , Mack J. Hunter 
Published: December 2019

Abstract
Exhaust gas recirculation (EGR) in spark-ignited engines is a key technique to reduce in-cylinder NOx production by decreasing the combustion temperature. The major species of the exhaust gas in rich combustion of natural gas are hydrogen and carbon monoxide, which can subsequently be recirculated to the cylinders using EGR. In this study, the effect of hydrogen and carbon monoxide addition to methane on laminar burning velocity and flame morphological structure is investigated. Due to the broad flammability limit and high burning velocity of hydrogen compared to methane, this addition to the gaseous mixture leads to an increase in burning velocity, less emissions production, and a boost to the thermal efficiency of internal combustion engines. Premixed CH4-H2-CO-Air flames are experimentally investigated using an optically accessible constant volume combustion chamber (CVCC) accompanied with a high-speed Z-type Schlieren imaging system. Furthermore, a numerical code is applied to quantify the laminar burning velocity based on the pressure rise during flame propagation within the CVCC. According to the empirical and numerical results, the addition of hydrogen and carbon monoxide enhances laminar burning velocity while influencing the flame structure and development.
Screening Compounds for Fast Pyrolysis and Catalytic Biofuel Upgrading Using Artificial Neural Networks
Authors: Kessler Travis , Schwartz Thomas , Wong Hsi-Wu , Mack J. Hunter 
Published: December 2019

Abstract
There is significant interest among researchers in finding economically sustainable alternatives to fossil-derived drop-in fuels and fuel additives. Fast pyrolysis, a method for converting biomass into liquid hydrocarbons with the potential for use as fuels or fuel additives, is a promising technology that can be two to three times less expensive at scale when compared to alternative approaches such as gasification and fermentation. However, many bio-oils directly derived from fast pyrolysis have a high oxygen content and high acidity, indicating poor performance in diesel engines when used as fuels or fuel additives. Thus, a combination of selective fast pyrolysis and chemical catalysis could produce tuned bioblendstocks that perform optimally in diesel engines. The variance in performance for derived compounds introduces a feedback loop in researching acceptable fuels and fuel additives, as various combustion properties for these compounds must be determined after pyrolysis and catalytic upgrading occurs. The present work aims to reduce this feedback loop by utilizing artificial neural networks trained with quantitative structure-property relationship values to preemptively screen pure component compounds that will be produced from fast pyrolysis and catalytic upgrading. The quantitative structure-property relationship values selected as inputs for models are discussed, the cetane number and sooting propensity of compounds derived from the catalytic upgrading of phenol are predicted, and the viability of these compounds as fuels and fuel additives is analyzed. The model constructed to predict cetane number has a test set prediction root-mean-squared error of 9.874 cetane units, and the model constructed to predict yield sooting index has a test set prediction root-mean-squared error of 13.478 yield sooting index units (on the unified scale).
Novel Engine Cycle Enabling Partial Load Fuel Efficiency Beyond Full Load Conditions
Authors: de Jong Arjen 
Published: December 2019

Abstract
Fuel consumption reduction and emission reductions in internal combustion engines (ICE) is a hot topic nowadays. An adaption of cylinder de-activation technique called ECONAMIQ over-expansion can be applied to engines to improve fuel efficiency. Using the pressure from the exhaust gas from the active cylinders, the ‘idle’ cylinders could be expanded to extract more work out of the engine during partial load operation. Using the virtual simulation environment GT-Power, this cycle is applied to a 4-cylinder SI engine. This engine model is simulated for a part load operation point and compared with a standard 4-cylinder engine model and 4-cylinder engine model equipped with cylinder de-activation. From these simulations various variables for engine operation (valve timing etc.) are optimized to further reduce fuel consumption of the engine. A final brake specific fuel consumption reduction of over 10% is achieved using the overexpansion cycle, while improving engine performance on two burning cylinders over 10% as well. With this improvement it is shown that the over-expansion cycle has a significant benefit compared to a standard ICE and cylinder de-activation techniques. These simulations are being validated on an engine test dyno using a natural aspirated ICE.
Air Fuel Dilution in a Pilot Ignited Direct Injection Natural Gas Engine: Pollutants, Performance, and System Level Considerations
Authors: Singh Aditya Prakash , McTaggart-Cowan Gordon Patrick , Kirchen Patrick 
Published: December 2019

Abstract
Dilution of natural gas fuel with air for use in a pilot ignited direct injection natural gas engine was investigated to evaluate the impact of this strategy on emissions and engine performance. A representative heavy-duty mode (mid to high-load at medium speed) was considered and the equivalence ratio (Φ) and exhaust gas recirculation (EGR) rates were varied from this representative mode. Air dilution resulted in a significant reduction in several pollutants: 90 to 97% reductions in black carbon particulate matter, 45 to 95% reductions in carbon monoxide, 68 to 85% reductions in total unburnt hydrocarbons. NOx emissions were found to increase by between 1.5 and 2.5x, depending on Φ and EGR, for a fixed combustion phasing. Beyond the emissions improvements, the gross indicated thermal efficiency increased by 2.5 percentage points at both high and low EGR rates. At higher EGR rates, this improvement was due to improved combustion efficiency, while the mechanism for efficiency improvement at lower EGR rates was unclear. The application of air-fuel dilution requires compressed air (&gt; 300 bar) to mix with natural gas at high pressures. A system level analysis considered the compression power required by an industrial 3-stage reciprocating compressor and indicated that the gross indicated thermal efficiency improvements could compensate for the compression requirements for engine operation at high Φ.
Direct Measurement of Ammonia Storage on Passive SCR Systems for Lean Gasoline NOx Reduction Using Radio Frequency Sensing
Authors: Ragaller Paul , Mandelbaum Josh , Lapenta Luc , Sappok Alexander , Pihl Josh , Prikhodko Vitaly , Parks James 
Published: December 2019

Abstract
Lean gasoline engine operation provides clear efficiency benefits relative to conventional stoichiometric combustion approaches. One of the key hurdles to the widespread, practical implementation of lean gasoline combustion remains the challenge of lean NOx control. One of the potential approaches for controlling NOx emission from lean gasoline engines is the so-called passive selective catalytic reduction (SCR) system. In such systems, periods of rich operation generate ammonia over a three-way catalyst (TWC), which is then adsorbed on the downstream SCR and consumed during lean operation. Brief periods of rich operation must occur in response to the depletion of stored ammonia on the SCR, which requires reliable measurements of the SCR ammonia inventory. Presently, lean exhaust system controls rely on a variety of gas sensors mounted up- and downstream of the catalysts, and which only provide an indirect inference of the operation state.
In this study, a radio frequency (RF) sensor was used to provide a direction measurement of the amount of ammonia adsorbed on the SCR in real-time. The RF sensor was calibrated and deployed on a BMW N43B20 4-cylinder lean gasoline engine equipped with a passive SCR system. Brief periods of rich operation performed at lambda values between 0.98 and 0.99 generated the ammonia, subsequently stored on the SCR for consumption during periods of lean operation. The experiments compared real-time measurements of SCR ammonia inventory from the RF sensor with estimates of ammonia coverage derived from exhaust gas composition measurements upstream and downstream of the catalyst. The results showed a high degree of correlation between the RF measurements and SCR ammonia storage inventory, and demonstrated NOx conversion efficiencies above 98%, confirming the feasibility of the concept. Relative to stoichiometric operation, lean-gasoline operation resulted in fuel efficiency gains of up to 10%, which may be further improved through direct feedback control from the RF sensor to optimize lean–rich cycling based on actual, measured SCR ammonia levels.
Optimization of High Pressure Direct Injection Micro-Pilot As a Retrofit Technology for Conventional Dual Fuel Engines
Authors: Beshouri Greg , Fischer Gerry 
Published: December 2019

Abstract
In the late 1980’s Enterprise Engine Company performed a single cylinder test of micro-pilot high pressure direct injection as a retrofit technology for conventional dual fuel engines. While that testing demonstrated a number of benefits for this technology, non-technical considerations led to the use of low pressure Pre-Combustion Chamber (PCC) micro-pilot technology as the retrofit technology instead. Thirty years later, when the automotive components of the PCC micro-pilot system were no longer available, the opportunity again arose to test the capabilities of an off the shelf high pressure direct injection micro-pilot system as a retrofit technology for a conventional dual fuel engine.
Single cylinder and full engine testing of the high pressure direct injection micro-pilot injection confirmed the results of the 1980’s testing. The test results also corroborated modern analytical and experimental testing of high pressure pilot technology. In particular, the interaction between the diesel pilot and primary fuel gas charge is very complex and sometimes counterintuitive. Likewise performance optimization requires careful balance of injection timing, injection quantity and fuel gas air/fuel ratio. Even then, exhaust gas methane emissions remain counterintuitive.
This paper reviews modern single cylinder and full engine test results focusing on optimization parameters for high pressure direct injection micro-pilot for retrofit and new engine applications.
A Statistical Study to Evaluate the Performance of Liquid Cooling Garments Considering Thermal Comfort
Authors: Shu Weicheng , Wang Jiawen , Zhang Xinfeng , Luo Xiaobing 
Published: December 2019

Abstract
Liquid cooling garments (LCGs) are considered a feasible cooling equipment to protect individuals from hyperthermia and heat-related illness when working in extremely hot and stressful environments. So far, the goals for optimization design of LCGs are mostly from the perspective of enhancing its efficiency and duration working time. However, thermal comfort is the key factor which is often not considered. In fact, there are many situations that may cause discomfort. For example, as the ice melts, the inlet temperature of the liquid cooling vest changes constantly resulting in the change of thermal states of the human body, which lead to discomfort of human. So, it is very significative to develop a method to evaluate the performance of LCGs considering thermal comfort. In this paper, an uncomfortable time ratio was proposed to evaluate the performance of LCGs considering thermal comfort. It defined the proportion of uncomfortable time including overcooling and overheating in the entire working time. Series of tests were conducted by a modified thermal manikin method to evaluate the thermal properties. According to the analyses, the duration working time was 82.77 min, while the uncomfortable time ratio was too large, up to 57.6 %. It showed that the requirement of comfort should be considered when optimizing the performance of LCGs. The influences of different parameters such as the volume of ice, flowrate, inlet temperature on the performance of LCGs were investigated through orthogonal experimental design. The statistical analysis illustrated that the influence of the volume of ice on the uncomfortable time ratio is greater than that of flowrate and ambient temperature. It is concluded that this method is useful for the control and design of LCGs considering thermal comfort.
Optimal Arrangement of Multiple Heat Sources in Vertically Stacked Two-Layer 3-D IC Using Genetic Algorithm
Authors: Rangarajan Srikanth , Hadad Yaser , Choobineh Leila , Sammakia Bahgat 
Published: December 2019

Abstract
The semiconductor packaging technologies have seen its growth from MCM (Multi-chip module), SiP (System in Package), SoC (System on Chip) to the heterogeneous integration of the MCM. Thermal management of multi-chip vertically integrated systems poses additional constraints and limitations beyond those for single chip modules. 3-D integrated circuits (3-D ICs) technology is a potential approach for next-generation semiconductor packaging technologies. A 3-D IC is formed by vertical interconnection of multiple substrates containing active devices which offer reduced die footprint and interconnect length. This manuscript discusses the optimal arrangement of two hotspots on each layer of a two-die stacked 3-D IC. An analytical heat transfer model for prediction of three-dimensional temperature field of a 3-D IC based on the solution of governing energy equations has been developed and used for this study. The model is subject to adiabatic boundary conditions at the walls except for the bottom wall which is subject to convective boundary condition. Genetic Algorithm is employed for solving two non-conflicting objectives constrained optimization problem. The first objective aims to minimize the maximum temperature on both layers, the second objective aims to achieve temperature uniformity in the layers. A feed-forward back propagation Artificial Neural Network (ANN) is employed for obtaining the functional relationship between the location of the hotspots and the objectives. The results of the optimization study are expected to provide recommendations on the design guidelines for arranging hotspots on vertically stacked substrates.
Evaluation of Low Order Stress Models for Use in Co-Design Analysis of Electronics Packaging
Authors: Boteler L. M. , Miner S. M. 
Published: December 2019

Abstract
Co-design and co-engineering have the potential to improve the design of electronics packaging significantly. A co-designed approach moves away from the sequential approach of an electrical layout followed by a mechanical module design, and then the addition of a heat sink. Replacing it with an approach that addresses the electrical, thermal, and mechanical design simultaneously during the initial design. The goal is to evaluate the design space quickly, considering both the thermal and mechanical stress aspects together. ParaPower is a low order fast running parametric analysis tool, developed by the Army Research Laboratory (ARL), that allows rapid evaluation of package temperatures and coefficient of thermal expansion (CTE) induced stresses throughout the design space. The model uses a 3D nodal network to calculate device temperatures and thermal stresses. In order to rapidly evaluate the design space both the thermal and stress models must be reduced order and provide reasonable results on coarse grids. In the case of the stress model, the goal is a low order relationship between the temperatures and the CTE induced stresses. This paper compares three different low order models for stress. The first uses a more traditional planar module design. This assumes a substantial substrate or heat spreader as the base for the module assembly. The second model is less restrictive, eliminating the requirement for a substrate. The third model also eliminates the substrate requirement, but also allows for in-plane distribution of the stresses. The first two models do not account for the in-plane distribution. Two geometries are considered, a standard power module with a substantial substrate and a stacked novel module with no clear substrate layer. Results for both geometries and the three stress models are compared to finite element analysis (FEA) using SolidWorks, beginning with a thermal analysis followed by a stress analysis based on the temperature solution. All three models run roughly two orders of magnitude faster than the FEA and they correctly predict the trends in the CTE induced stresses.
Evaluation of a Lead Glass for Encapsulating High-Temperature Power Modules for Aerospace Application
Authors: Liu Lanbing , Nam David , Guo Ben , Burgos Rolando , Lu Guo-quan 
Published: December 2019

Abstract
Encapsulation is a big challenge for packaging high-temperature power modules due to limited choices of insulation materials that can be easily processed and have high reliable working temperature of over 250°C. In this work, we evaluated a lead glass as a potential high-temperature encapsulant for protecting SiC power chips interconnected on a common Al2O3 direct-bond-copper (DBC) substrate. To avoid glass cracking due to its high elastic modulus and mismatched coefficient of thermal expansion (CTE) with that of the DBC substrate, we added a polyimide buffer layer between the glass and the substrate to reduce thermomechanical stresses. We found that the buffer layer was effective in reducing cracks in the glass, but it also lowered the breakdown and partial discharge inception field strengths. Single-chip SiC MOSFET packages were fabricated using the glass encapsulant to demonstrate its feasibility for high-temperature encapsulation.
Correlated Effects of Self-Heating, Light Output, and Efficiency of GaN Light-Emitting Diodes on Junction Temperature
Authors: Chatterjee Bikramjit , Lundh James Spencer , Shoemaker Daniel , Kim Tae Kyoung , Kwak Joon Seop , Cho Jaehee , Choi Sukwon 
Published: December 2019

Abstract
With the advent of GaN as the major material system in the solid-state lighting industry — high power, high brightness LEDs with wavelength ranging from near UV to white are getting fabricated and part of a tremendously large and ever-increasing market. However, device self-heating and environment temperature significantly deteriorates the LED’s optical performance. Hence, it is extremely important to quantify the device self-heating and its impact on optical performance. In this work, three different characterization techniques were used to calculate temperature rise due to self-heating for an InGaN/GaN LED with 5 pairs of multiple quantum wells. The impact of self-heating and increased environment temperature on the device optical performance were also studied. Nanoparticle assisted Raman thermometry was used for the first time to measure the LED mesa surface temperature. The temperature measured using this technique was compared with temperature data obtained by using the forward voltage method and infrared (IR) thermography. The IR and Raman measurement results were in close agreement while the temperature data obtained from forward voltage method underestimated the temperature by 510%. It was also observed that due to environment temperature increase from 25°C to 100°C, LED optical power output drops by 12%.
Comparative Study on Power Module Architectures for Modularity and Scalability
Authors: Lu Mei-Chien 
Published: December 2019

Abstract
Silicon carbide wide bandgap power electronics have gained application spaces in hybrid electric vehicle and electrical vehicles. The Department of Energy has set target performance goals for 2025 to promote electric vehicles and hybrid electric vehicles as a means of carbon emission reduction and long term sustainability. Silicon carbide technology is well suited to reach these goals. Challenges include higher expectations on power density, performance, efficiency, thermal management, compactness, cost, and reliability. This study will benchmark state of the art silicon and silicon carbide technologies. Power modules of commercial traction inverters are analyzed for their within-package interconnect scheme, module architecture, and cooling methods. A few power module package architectures from both industry adopted standards and proposed patented technologies are compared for modularity and scalability for integration into inverters. The within package interconnect schemes are crucial elements to support power module design. Current trends of power module architectures and their integration into inverter are discussed. The development of an eco-system to support the transition from silicon-based to silicon carbide-based power electronics is additionally discussed as an ongoing challenge.
Comparative Evaluation of Algorithms for Achieving Ultrapacked Thermal Greases: Microstructural Models and Effective Behavior
Authors: Achar P. L. Sukshitha , Liao Huanyu , Subbarayan Ganesh 
Published: December 2019

Abstract
As device power density increases, there is a need to dissipate generated heat by increasing particle volume loading in thermal interface materials. In this work, we develop and evaluate algorithms for generating ultrapacked microstructures of particles. Simulated microstructures reported in the literature rarely contain particle volume fractions greater than 60%. However, commercially available thermal greases claim to achieve volume fractions in the range of 60–80%. Therefore, to analyze effectiveness of commercially available particle-filled thermal interface materials, there is a need to develop algorithms capable of generating ultrapacked microstructures. The particle packing problem is initially posed as a nonlinear programming problem (NLP), and formal optimization algorithms are applied to generate microstructures that are maximally packed. Since accuracy of the simulated behavior is dependent on the number of particles in the simulation cell, efficiently simulating large number of particles is imperative. However, the packing simulation is computationally expensive. Therefore, various optimization algorithms are systematically evaluated to assess the computational efficiency as measured by the time to generate the microstructures for a system containing a large number of particles. The evaluated algorithms include the penalty function methods, best-in-class sequential programming method, matrix-less conjugate gradient method as well as the augmented Lagrangian method. In addition, heuristic algorithms are also evaluated to achieve computationally efficient packing. The evaluated heuristic algorithms are mainly based on the Drop-Fall-Shake method, but modified to more effectively simulate the mixing process in commercial planetary mixers. With the developed procedures, Representative Volume Elements (RVE) with volume fraction as high as 74% are demonstrated. The simulated microstructures are analyzed using our previously developed random network model to estimate the effective thermal and mechanical behavior given a particle arrangement.