Variable-speed Compression Research Articles

Use of Emotional and Neutral Speech in Evaluating Compression Speeds.

Emotional speech differs from neutral speech in its envelope characteristics. Use of emotional speech materials may be more sensitive for evaluating signal processing algorithms that affect the temporal envelope. Subjective listener preference was compared between variable speed compression (VSC) and fast acting compression (FAC) amplitude compression algorithms using neutral and emotional speech. The study used a single-blinded, repeated measures design. Twenty hearing-impaired (HI) listeners with a bilaterally symmetrical, mild- to-moderately severe sensorineural hearing loss and 21 listeners with normal hearing (NH) participated. Speech was processed using FAC and VSC algorithms. A paired-comparison paradigm assessed subjective preference for FAC versus VSC using emotional and neutral speech materials. The significance of subjective preference for compression algorithm (FAC or VSC) was evaluated using a linear mixed effects model at each combination of stimulus type (emotional or neutral speech) and hearing group (NH or HI). HI listeners showed a preference for VSC over FAC when listening to emotional speech. The same listeners showed a nonsignificant, preference for VSC over FAC when listening to neutral speech. NH listeners showed preference for VSC over FAC for both neutral and emotional speech materials. These results suggest that the subjective sound quality of emotional speech is more susceptible than neutral speech to changes in the signal introduced by FAC. Clinicians should consider including emotional speech materials when evaluating listener preference for different compression speeds in the clinic.

Engine characteristics analysis of chaulmoogra oil blends and corrosion analysis of injector nozzle using scanning electron microscopy/energy dispersive spectroscopy

This work describes the performance, combustion and emission behavior of chaulmoogra oil blend and neat diesel in the variable compression ratio, variable speed compression ignition engine with exhaust gas recirculation (EGR). Exergy analysis was employed to investigate availability shares involved in the research engine. Artificial neural network (ANN) modeling and particle swarm optimization (PSO) are adopted with response surface methodology (RSM) in order to investigate the engine performance fuelled with ternary blend (65% diesel+25% chaulmoogra oil+10% diethyl ether) and neat diesel. In this work, compression ratio (CR) is varied from 14.5 to 20.6, engine speed varied from 1500 to 2400 revolution per minute (rpm) and EGR varied from 0 to 30%. The optimized condition was observed as 10% EGR, 18.1CR and 1672 rpm with respect to lesser exhaust emissions and enhanced thermal efficiency. Maximum brake thermal efficiency of 29.12% was observed 10% EGR rate and maximum exergy efficiency of 52.64% was observed for the ternary blend at the optimized engine condition. The results conclude that the RSM-ANN-PSO provide better the engine performance modeling with acceptable accuracy. The corrosion and wear analyses were done on the fuel injector nozzle and cylinder gaskets using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS).

Experimental investigation and optimization of exhaust gas recirculation on a Euro‐1 variable speed compression ignition engine

Automobile emissions are the main source of greenhouse gases (GHG) accumulation in environment. As old engines (Euro III or earlier) produce more emissions and it will be difficult to entirely stop their usage especially in developing and underdeveloped nations, it is desired that appropriate emission reduction technologies are tested on such engines to analyze their feasibility and economical acceptability. While most such studies have been conducted on constant speed stationary engines and modern engines, this study tried to analyze the effectiveness of exhaust gas recirculation (EGR) system on a Euro‐1, water‐cooled, direct injection, variable speed, compression ignition (C.I.) engine in a laboratory setup in India. This was done by testing an electronic control unit (ECU)‐controlled EGR valve running the engine on different speeds; with different torques, as also with varying EGR percentages. Then optimum value of EGR valve opening for each set of engine speed and torque was determined, that is, corresponding to minimum oxides of nitrogen (NOx). Optimum EGR valve position was programmatically fed, for each set of torque and engine speed (in RPM), into EGR ECU and then the engine was run at varying speeds and torques and the emissions were verified in the automated mode. It was found that using EGR, NOx emissions were substantially reduced (average 45% reduction), without significantly impacting the engine performance. However, smoke emission increased with EGR. In order to counter the adverse effects, additional emission reduction technologies should be used in tandem with EGR. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1685–1693, 2017

Application of artificial neural networks for generation of energetic maps of a variable speed compression system working with R1234yf

This paper proposes a new tool that uses artificial neural networks to build energetic maps for a vapor compression system working with R1234yf. From these energetic maps, it is possible to visualize and identify the zones with the best performance. Additionally, it was concluded that the temperature of the condensing agent and the brine has a greater influence on the COP than their volumetric flows.Several computer simulations were performed to analyze the impact of changing the configuration of the artificial neural network. A hybrid method to train the artificial neural network was used; this method was a combination of: simulated annealing, regression, and the conjugate gradient in multi-dimensions.The results show that artificial neural networks can be used to predict the COP of a vapor compression system, and to analyze how several input parameters of the system may affect its energetic performance. Additionally and for energetic comparison purposes, one artificial neural network was trained using data from a compression system operating with R134a, and another artificial neural network was trained with R1234yf.

Development of 20 integrated energy efficiency ratio rooftop units—system modeling and building energy simulations

Based on a detailed steady-state system and component modeling, a rooftop unit system design was developed that is can achieve an integrated energy efficiency rating higher than 20. Fin-and-tube and microchannel heat exchangers were modeled using a segment-to-segment approach, and an AHRI 10-coefficient compressor map used to simulate compressor performance. The system modeling is based on a component-based modeling approach, which facilitates flexible simulation of complicated system configurations. Starting with a baseline system having integrated energy efficiency rating of 16.6, numerous technical options were extensively investigated, i.e., varying compressor sizes, heat exchanger fin densities, fin-and-tube or microchannel heat exchanger, suction line heat exchanger, desiccant wheel, tandem compressor (TD), variable-speed compressor (VS), and condenser evaporative pre-cooling; an innovative system configuration was developed by combining a tandem compression system with a variable-speed compression system. The combined system can achieve a high integrated energy efficiency ratio as well as process the outdoor ventilation air over an extensive range. The design concept for a 20-ton (70.4-kW) unit, as well as a 10-ton (35.2-kW) unit was successfully evaluated. All selected components are readily accessible on the market, and performance predictions were validated against existing rooftop unit products at the rating condition. This article illustrates a potentially cost-effective high integrated energy efficiency ratio rooftop unit design. In addtion, extensive building energy simulations were conducted using EnergyPlus to predict seasonal energy saving potentials and peak power reductions using the high integrated energy efficiency ratio rooftop unit in 16 U.S. cities in comparison to a rooftop unit with a minimum efficiency.

Uniaxial strain rate effects in pharmaceutical powders during cold compaction

The results of uniaxial compression tests on some pharmaceutical powders subjected to strain rates of between 10−3 and 105 s−1 are given. The tests fall into three main categories: low-strain-rate tests (10−3–10 s−1) performed on a servohydraulic variable speed-compression machine at constant compression rate; medium-strain-rate tests (102–103 s−1) carried out on a drop hammer; and high strain rate tests (103–105 s−1) performed on a high-pressure air projectile launcher compaction apparatus. Axial and radial pressures, as well as displacement-time measurements, are made. Powders tested include: Dipac sugar; sodium chloride; potassium bromide; lactose; paracetamol d.c.; avicel; calcium phosphate; and copper sulphate. The influence of compression rate on the form of the characteristic pressure-density and radial-axial pressure relationships during uniaxial straining is presented. The investigation showed that the general tendency for all powders tested, except for paracetamol d.c., is to exhibit increased compaction pressure with strain rate up to 105 s−1. Due to morphological and compositional effects, paracetamol d.c. softens with the rate of straining up to about 102 s−1 and at higher rates it behaves like other powders. Also the mean radial pressure at the die wall (obtained by a pin-type transducer) shows that the friction conditions are variable during the process, and their effect tends to decrease as the speed of compaction increases, resulting in more uniform density compacts. Finally, by observing the decay of both axial and radial pressures with time under constant volume conditions, a reasonably linear behaviour is obtained for all materials tested, particularly the axial relaxation curves, over the period recorded.