Cyclic Pressure Research Articles

Structural phase purification of bulk HfO2:Y through pressure cycling

We combine synchrotron-based infrared absorption and Raman scattering spectroscopies with diamond anvil cell techniques and first-principles calculations to explore the properties of hafnia under compression. We find that pressure drives HfO[Formula: see text]:7%Y from the mixed monoclinic ([Formula: see text]) [Formula: see text] antipolar orthorhombic ([Formula: see text]) phase to pure antipolar orthorhombic ([Formula: see text]) phase at approximately 6.3 GPa. This transformation is irreversible, meaning that upon release, the material is kinetically trapped in the [Formula: see text] metastable state at 300 K. Compression also drives polar orthorhombic ([Formula: see text]) hafnia into the tetragonal ([Formula: see text]) phase, although the latter is not metastable upon release. These results are unified by an analysis of the energy landscape. The fact that pressure allows us to stabilize targeted metastable structures with less Y stabilizer is important to preserving the flat phonon band physics of pure HfO[Formula: see text].

Development of machine learning based classifier for the pressure test result prediction of type IV composite overwrapped pressure vessels

The stringent safety regulations of type IV composite overwrapped pressure vessels (COPVs) for commercial vehicles mandate a certification process involving pressurization up to 1050 bar, with the critical requirement of withstanding burst pressures of 1570 bar. Analyzing proof test data is crucial to enhance and ensure tank safety regarding burst pressure. In this study, we developed various machine learning classifiers for structure health monitoring and damage prediction of COPVs. The classifiers were trained using a substantial amount of acoustic emission data collected during burst and pressure cycling tests. The test results were employed as label inputs during the training process. Statistical features were extracted per time unit and trained using Naive Bayes, Logistic Regression, Decision Tree, XGBoost, and TabNet models. Upon training the data collected from the burst pressure test, TabNet, Decision Tree, and XGBoost achieved classification accuracies above 0.94. Notably, TabNet demonstrated also the best performance for the pressure cycling test with an accuracy of 0.98. Furthermore, TabNet provided visualizations of feature sensitivity in relation to classification results. This study marks the first development of a machine learning classifier for predicting the damage state of COPV tanks in commercial applications pertaining to required safety tests.

Fatigue Behaviour of Brazed Joints for Heat Exchangers.

The plate heat exchanger (PHE) is a component that provides heat to be transferred from hot water to domestic cold water without mixing them with high efficiency. Over the lifetime of the PHE, cyclic pressures act on the brazing points and the plates, and this may lead to fatigue failure. The fatigue behaviour of the PHE, designed using copper-brazed 316L stainless steel, was investigated in this study. First, the fatigue tests under the load ratio R = 0.1 were performed on the Vibrophore 100 testing machine to obtain the S-N curve of the analysed brazed joint. Based on the obtained experimental results, an appropriate material model of the analysed brazed joint has been created, which was validated with numerical calculation in the framework of a program code Ansys. A validated material model was then used for the subsequent numerical analysis of PHE. In order to carry out a numerical calculation using the finite element method (FEM), a three-dimensional model of the heat exchanger was created based on the previous scanning of PHE-geometry. Thereafter, the geometry was parameterised, which allowed us to perform parametric simulations (monitoring different responses depending on the input geometry). Numerical simulations were carried out in the framework of the Ansys 2023-R1 software, whereby the obtained results were analysed, and the responses were appropriately characterised according to previously determined load cases.

Pressure-cycling induced transition behaviors of MnBi2Te4.

MnBi2Te4 can generate a variety of exotic topological quantum states, which are closely related to its special structure. We conduct comprehensive multiple-cycle high-pressure research on MnBi2Te4 by using a diamond anvil cell to study its phase transition behaviors under high pressure. As observed, when the pressure does not exceed 15GPa, the material undergoes an irreversible metal-semiconductor-metal transition, whereas when the pressure exceeds 17GPa, the layered structure is damaged and becomes irreversibly amorphous due to the lattice distortion caused by compression, but it is not completely amorphous, which presents some nano-sized grains after decompression. Our investigation vividly reveals the phase transition behaviors of MnBi2Te4 under high pressure cycling and paves the experimental way to find topological phases under high pressure.

Combined deterioration effects of freeze–thaw and corrosion on the cyclic flexural behavior of RC beams

Reinforced concrete (RC) stands as the predominant construction material, and with the growing prevalence of aging infrastructure, the safety assessment of deteriorating structures has emerged as a pressing and critical concern. This study presents an experimental investigation into the combined effects of freeze–thaw cycles (100, 200, and 300 cycles) and corrosion (corrosion ratio: 3% and 10%) on the cyclic behavior of RC beams. Seven RC beam specimens with varying types and levels of deterioration were fabricated, and static tests were conducted using a four-point bending method to assess the behavior of RC beams under each degradation factor. The experimental results revealed that increasing freeze–thaw cycles significantly impact RC beam behavior, leading to intensified crack formation due to cyclical frost-heaving pressure. This ultimately results in reduced yield loads ranging from 3% to 11% and peak loads reduced by 1–2% compared to the reference specimen A-0-0. Corrosion of steel bars in RC beams alters failure modes, with corroded specimens (C-0-3 and C-0-10) experiencing substantial decreases in yield load, a 15% reduction in C-0-3, and a 26% decrease in C-0-10, compared to A-0-0. Displacement at yield load and peak load also decreased, highlighting the detrimental impact of corrosion on beam mechanical properties. The combined deterioration from freeze–thaw cycles and corrosion further exacerbates these reductions, resulting in a substantial decrease in yield load (32%) and peak load (24%) in D-3-10 compared to the reference specimen A-0-0. Additionally, energy dissipation capacity and cumulative energy dissipation capacity exhibit dynamic changes influenced by the progression of freeze–thaw cycles, corrosion, and their combined effects.

Simulation of Pressure Swing Adsorption for Oxygen Concentrator Using LiX Zeolite

The existence of oxygen is needed in various fields such as health, industry, aerospace, and energy. Pressure swing adsorption method using zeolite A and X is commonly used in the oxygen separation process. Zeolite LiX is widely used especially in oxygen purification systems from air for medical needs until the purity reach >90%. In the present work the simulation studies of oxygen separation from air using zeolite LiX were investigated by pressure swing adsorption method. This simulation uses 2 beds/4 steps Skarstrom cycle which generally consists of bed pressurization, adsorption, depressurization and desorption. Several variables were studied such as the effect of pressure, cycle time, and temperature to determine the level of product purity. The simulation shows that the increase in oxygen concentration is caused by an increase in operating pressure and total cycle time to obtain maximum results. Meanwhile, large pressures can reduce the efficiency of the adsorbent. The maximum oxygen concentration that can be obtained up to 99%. However, the temperature variation has no significant change. In general, pressure plays the most significant role in oxygen purification.

Improving functional properties of lupin protein by physical modification: high pressure homogenisation

SummaryAqueous dispersions of commercially produced lupin protein isolate with pH 5 and 9 were treated using high pressure homogenisation (HPH) at 25–200 MPa with one to ten homogenisation cycles. Particle size, zeta potential, solubility, molecular weight and rheological properties were analysed. HPH reduced the particle size for lupin protein at pH 5 from 54 to 5 μm and from 14 to <2 μm at pH 9 at 25–100 MPa. However, at 200 MPa, the particle size had less reduction. HPH did not have any significant impact on the zeta potential where it was maintained at −13 mV for pH 5 and −25 mV for pH 9. After HPH, solubility increased significantly for both pH 5 and 9 lupin protein samples. However, prolonged homogenisation decreased solubility. HPH impacted the viscosity of lupin protein at pH 5 and 9, depending on pressure and homogenisation cycles. The highest viscosity (~10 Pa.s) was achieved at 200 MPa with 10 passes. HPH increased the loss modulus where the viscoelastic properties were improved for both pH 5 and 9 at 200 MPa. The storage modulus profile indicated that HPH improved the gelling properties of lupin protein both at pH 5 and 9 at 200 MPa.

In-depth metaproteomics analysis of tongue coating for gastric cancer: a multicenter diagnostic research study

BackgroundOur previous study revealed marked differences in tongue images between individuals with gastric cancer and those without gastric cancer. However, the biological mechanism of tongue images as a disease indicator remains unclear. Tongue coating, a major factor in tongue appearance, is the visible layer on the tongue dorsum that provides a vital environment for oral microorganisms. While oral microorganisms are associated with gastric and intestinal diseases, the comprehensive function profiles of oral microbiota remain incompletely understood. Metaproteomics has unique strength in revealing functional profiles of microbiota that aid in comprehending the mechanism behind specific tongue coating formation and its role as an indicator of gastric cancer.MethodsWe employed pressure cycling technology and data-independent acquisition (PCT-DIA) mass spectrometry to extract and identify tongue-coating proteins from 180 gastric cancer patients and 185 non-gastric cancer patients across 5 independent research centers in China. Additionally, we investigated the temporal stability of tongue-coating proteins based on a time-series cohort. Finally, we constructed a machine learning model using the stochastic gradient boosting algorithm to identify individuals at high risk of gastric cancer based on tongue-coating microbial proteins.ResultsWe measured 1432 human-derived proteins and 13,780 microbial proteins from 345 tongue-coating samples. The abundance of tongue-coating proteins exhibited high temporal stability within an individual. Notably, we observed the downregulation of human keratins KRT2 and KRT9 on the tongue surface, as well as the downregulation of ABC transporter COG1136 in microbiota, in gastric cancer patients. This suggests a decline in the defense capacity of the lingual mucosa. Finally, we established a machine learning model that employs 50 microbial proteins of tongue coating to identify individuals at a high risk of gastric cancer, achieving an area under the curve (AUC) of 0.91 in the independent validation cohort.ConclusionsWe characterized the alterations in tongue-coating proteins among gastric cancer patients and constructed a gastric cancer screening model based on microbial-derived tongue-coating proteins. Tongue-coating proteins are shown as a promising indicator for identifying high-risk groups for gastric cancer.1cCPG45EGUX3nZjXKAFgvfVideo

Mechanical Stimulation of Adipose-DerivedStromal/Stem Cells for Functional Tissue Engineering of the Musculoskeletal System via Cyclic Hydrostatic Pressure, Simulated Microgravity, and Cyclic Tensile Strain.

It is critical that human adipose-derived stromal/stem cell (hASC) tissue engineering therapies possess appropriate mechanical properties in order to restore the function of the load-bearing tissues of the musculoskeletal system. In an effort to elucidate hASC response to mechanical stimulation and develop mechanically robust tissue-engineered constructs, recent research has utilized a variety of mechanical loading paradigms, including cyclic tensile strain, cyclic hydrostatic pressure, and mechanical unloading in simulated microgravity. This chapter will describe the methods for applying these mechanical stimuli to hASC to direct differentiation for functional tissue engineering of the musculoskeletal system.

Reliability and variability of pressure reactivity index (prx) during oscillatory pattern in arterial blood pressure and intracranial pressure in traumatic brain injured patients

IntroductionPressure reactivity index (PRx) is used for continuous monitoring of cerebrovascular reactivity in traumatic brain injury (TBI). However, PRx has a noisy character. Oscillations in arterial blood pressure (ABP) introduced by cyclic positive end-expiratory pressure adjustment, can make PRx more reliable. However, if oscillations are introduced by the cycling process of an anti-decubitus-mattress the effect on PRx is confounding, as they affect directly also intracranial pressure (ICP). In our routine monitoring in TBI patients we noticed periods of highly regular, slow, spontaneous oscillations in ABP and ICP signals. Research questionWe set out to explore the nature of these oscillations and establish if PRx remains reliable during the oscillations. Materials and methods10 TBI patients’ recordings with oscillations in ICP and ABP were analysed. We computed PRx, PRx variability (hourly-average of standard-deviation, SD), phase-shift and coherence between ABP and ICP in the slow frequency range. Metrics were compared between oscillation and peri-oscillation periods. ResultsDuring oscillations (frequency 0.006± 0.002Hz), a significantly lower variability of PRx (SD 0.185vs0.242) and higher coherence ABP-ICP (0.618±0.09 vs 0.534 ±0.09) were observed. No external oscillations sources could be identified. 34 out of 48 events showed signs of 'active' transmission associated with negative PRx, indicating a potential positive impact on PRx reliability. Discussion and conclusionsSpontaneous oscillations observed in ABP and ICP signals were found to enhance rather than confound PRx reliability. Further research is warranted to elucidate the nature of these oscillations and develop strategies to leverage them for enhancing PRx reliability in TBI monitoring.

A refined deviatoric hardening plastic model for sand

The classical deviatoric hardening models are capable of characterizing the mechanical response of granular materials for a broad range of degrees of compaction. This work finds that it has limitations in accurately predicting the volumetric deformation characteristics under a wide range of confining/consolidation pressures. The issue stems from the pressure independent hardening law in the classical deviatoric hardening model. To overcome this problem, we propose a refined deviatoric hardening model in which a pressure-dependent hardening law is developed based on experimental observations. Comparisons between numerical results and laboratory triaxial tests indicate that the improved model succeeds in capturing the volumetric deformation behavior under various confining/consolidation pressure conditions for both dense and loose sands. Furthermore, to examine the importance of the improved deviatoric hardening model, it is combined with the bounding surface plasticity theory to investigate the mechanical response of loose sand under complex cyclic loadings and different initial consolidation pressures. It is proved that the proposed pressure-dependent deviatoric hardening law is capable of predicting the volumetric deformation characteristics to a satisfactory degree and plays an important role in the simulation of complex deformations for granular geomaterials.

Machine Learning Aided Optimization of Non-Metallic Seals in Downhole Tools

Machine learning-based optimization analysis is conducted to improve the performance and reliability of non-metallic seals that undergo dynamic loading. Non-metallic seals, mostly made from elastomeric materials such as rubber, are widely used in the oil and gas industry due to their combination of hyper-elastic behavior and lack of plastic deformation under pressure and temperature cycles. However, these elastomeric materials could soften at elevated temperatures or during cyclic loading which makes them more prone to extrude from the seal region. For simple seals such as O-rings, thermoplastic anti-extrusion backup rings can be used to resist rubber extrusion. For more complex molded or bonded seals subject to cyclic loads and temperature cycles, minimizing the rubber damage and maintaining the seal integrity becomes more challenging as there may be inadequate room for backup rings. Additionally, the rubber may also experience repeated long stroke lengths during service which can cause surface abrasion. Non-metallic seals are extensively used in Sand Control Tools, a class of completion equipment. In the development of these molded and bonded seals, advanced optimization analysis techniques are important to make the seal design sufficiently robust to meet the stringent requirements of industry codes and regulations. Machine learning-based optimization analysis allows for increasing the seal performance and reliability. The Mullins effect is included in the elastomeric material model to capture the material softening effects under cyclic loading. Machine learning-based optimization starts with a feasible design space that is defined based on the controlling design parameters related to the geometry of the seal. Comprehensive Finite Element Analysis is performed on each design sample under the target cyclic load and temperature cycles. The key response parameters of the non-metallic seal are selected as the principal strain and the contact stress in the rubber which together indicate the damage and seal-ability states of the design. A Performance Objective Index is defined as a function of the key response parameters. The FEA results covering the entire design space provide the simulation-driven training data to build the surrogate model using multi-layer neural networks. The optimum design solution is found from the surrogate model which correlates the Performance Objective Index to the design parameters. The results of this study reveal that the performance of the seal is greatly affected by the ratio of the elastomeric seal volume to the gland volume into which the seal is molded. As opposed to the base design, the optimum design shows a solid performance with no signs of extrusion when the principal strain of the rubber is kept below the elongation limit throughout the load cycles, and when sufficient contact stress is developed to maintain the seal.

Assessment on structural integrity of aluminium alloy AA6061 water tank used in satellite launch vehicles

AA 6061 is an age-hardenable aluminium alloy with 1wt% magnesium and 0.6wt% silicon as major alloying constituents and is used for manufacture of toroidal water tanks of earth storable liquid rocket engines of ISRO’s launch vehicles. These water tanks are used for storing water to generate steam in gas generator as well as for cooling various components of liquid rocket engine. As a part of the developmental efforts of a new source for fabrication of water tanks, a qualification test procedure was laid down, wherein a quarter portion of the water tank, namely the test article, is designed, fabricated and subjected to series of acceptance and qualification tests, including proof pressure test, cyclic pressure tests with intermediate non-destructive tests. These were finally followed by burst testing of the test article for conforming structural design margins and quality standards adopted for realizing the hardware. A burst test is a destructive test, performed to determine the overload capacity of the pressure vessels depicting margin of safety with respect to its mechanical strength limit. This work highlights the detailed manufacturing processes viz. forming, machining and welding including heat treatment of shells, metallurgical analysis carried out on the parent and weld specimens as well as results from proof pressure and pressure cyclic tests. Further, fractographic studies were carried out on the burst tested hardware, wherein specimens collected from different locations of the burst tested hardware were analyzed using optical scanning electron microscopy, to locate the origin of crack initiation and nature of the failure.

Structural Strength Analysis of Automobile Fuel Tanks Based on Positive and Negative Pressure Cycle Tests

Structural Strength Analysis of Automobile Fuel Tanks Based on Positive and Negative Pressure Cycle Tests

Investigating the Impact of Cumulative Pressure-Induced Stress on Machine Learning Models for Pipe Breaks

Significant financial resources are needed for the maintenance and rehabilitation of water supply networks (WSNs) to prevent pipe breaks. The causes and mechanisms for pipe breaks vary between different WSNs. However, it is commonly acknowledged that the operational management and water pressure influence significantly the frequency of pipe breaks. Pipe breaks occur when the water pressure exceeds the tensile strength of a pipe, or due to repetitive pressure cycles that result in fatigue-related failures. Considering these pipe failure modes, a new metric known as cumulative pressure-induced stress has been introduced. This metric takes into account both static and dynamic pressure components that contribute to pipe breaks, including mean pressure and the magnitude and frequency of pressure fluctuations, respectively. The impact of CPIS on pipe breaks has not been extensively investigated. Consequently, this study investigates and evaluates the impact of this metric when incorporated as an explanatory variable in Random Forest (RF) models that analyse the key causes of pipe breaks in two WSNs. Different RF models were developed both with and without incorporating pressure components. Subsequently, the performance of these models and the significance of each input variable were assessed. The results of this study suggest that CPIS is an important variable, especially in cases where pressure-related factors play a significant role in pipe breaks. Consequently, incorporating CPIS has shown a notable improvement in the accuracy of pipe break models.

Nationwide diurnal pattern among intracerebral hemorrhage incidence and volume

IntroductionIntracerebral hemorrhage (ICH) incidence follows both seasonal and diurnal patterns with greater severity reported in nighttime hemorrhages. These differences have been attributed to variations in the coagulation cascade, blood pressure, and sleep-wake cycle that all have their own rhythmicity. The purpose of this analysis was to validate these trends in a large nationwide database of automated ICH detection scans and evaluate for differences in hematoma volume by image acquisition time. MethodsSerial non-contrast head CT (NCHCT) data, processed with an automated imaging software (iSchemaView), was acquired from U.S. hospitals between 1/1/2020 and 12/31/2021. Final exclusion criteria included: (1) patient age ≤ 25, (2) hematoma volume ≥ 100 ml, (3) hematoma volume ≤ 0.4 ml. Imaging time was subdivided into three epochs: (1) Night: 23:00h-06:59h, (2) Day: 07:00h-14:59h, and (3) Evening: 15:00h-22:59h. ResultsA total of 19,397 scans were included in the final analysis with a median ICH volume of 2.9 ml and mean volume of 13.23 mL; 15.6% of scans had volumes above 30 ml. Peak imaging occurred around noon. Hematoma volume was significantly different across timepoints (p = 0.003), with ICHs presenting at night (average volume 14.2 ml) larger than those presenting during the day (12.9 ml, p = 0.002) or evening (13.0 ml, p = 0.012). ConclusionIn this real world, multi-site data set, we show similar diurnal trends in ICH incidence as previously reported and detected subtle differences in volume based on time of imaging. Further research is required to elucidate the potential underlying mechanisms for these differences.

Vocal Demand Response to the Educational Setting Based Communication Scenarios: A Single Subject Study

Objectives. This was a single-subject study, aimed to demonstrate different vocal demand situations that are typical for primary school and teacher's vocal demand response under two acoustical conditions, with and without voice amplification, during five working days. Methods. The long-term voice dosimetry with Vocal Holter Med (PR.O. Voice Srl) was carried out on a 49-year-old female teacher with voice disorders during daily teaching activities. A sound field amplification system (SFAS) PentaClass Runa was installed in the classroom. Voice dosimetry was provided under two different acoustical conditions: without SFAS (2 days) and with SFAS (3 days). Results. Phonation time percentage, sound pressure level (SPL), SPL SD, fundamental frequency (F0), F0 SD, cycle, and distance doses were investigated in seven communication scenarios (lessons, group/individual classes, sports lessons in the gym and schoolyard, breaks, lunch breaks, and other activities). The median scores of all voice parameters differed significantly between different vocal demand contexts. The significant statistical difference in the vocal demand response was in the communication situations with and without SFAS. In addition, the number of children, reverberation time, and ambient air relative humidity impacted voice SPL and the cycle dose. Conclusions. Lessons, sports lessons held in the gym or schoolyard, breaks, and lunch breaks were considered as high vocal demand communication situations requiring higher voice intensity and fundamental frequency, higher phonation time percentage, cycle, and distance doses. Group/individual work and other teacher activities during the day, unrelated to direct work with students, were categorized as low vocal demand communication scenarios.

Numerical simulation study of enhanced geothermal system modification and heat mining performance under cyclic injection

Enhanced geothermal systems (EGS) involve complex thermal–hydraulic-mechanical (THM) coupling. The reservoir's permeability evolution and heat extraction characteristics under cyclic variable pressure injection conditions are unknown. In this paper, a THM model that considered the dynamic changes of rock matrix and fluid properties was developed based on the theory of local heat balance. The THM coupling model investigated the effects of the number of fractures, initial permeability of fractures (kf), maximum injection pressure (P), frequency (td), and working fluid on the heat recovery performance of the EGS under cyclic injection for 20 years. Finally, the sensitivity of each parameter was discussed. When the number of fractures increased from 100 to 200, the temperature of the reservoir decreased by 68.5 K, and the cumulative heat energy extracted increased by 6.6 × 1014 J. With the increase of the initial permeability of the fractures, the irregularities in the temperature and permeability fields of the reservoir were significantly enhanced. When the maximum injection pressure increased from 34 MPa to 40 MPa, the permeability ratio of the reservoir increased by about 13.45 %, and the cumulative extracted thermal energy increased by 3.5 × 1014 J. The frequency of cyclic injection increased from 5 d to 45 d, the production temperature decreased by 26.78 K, and the permeability ratio of the reservoir and the cumulative extracted thermal energy increased. The low-temperature range of CO2-EGS is significantly larger than that of H2O-EGS, and the reservoir temperature decreases faster. The permeability of CO2-EGS increases by 29.32 %, and the cumulative thermal energy extraction increases by 17.5 × 1014 J compared with H2O-EGS. Parameter sensitivities were found for cyclic injection. Factors affecting production temperature and reservoir permeability in that order: P > td > kf. Injection frequency had a slightly greater impression on cumulative extracted thermal energy than injection pressure.

Effect of Nb and V doped elements on the mechanical and tribological properties of CrYN coatings

One of the most promising approaches to enhancing the tribological properties of engineering coatings is to add transition elements to the structure. In this study, Nb-doped CrYN and V-doped CrYN thin films were deposited by pulsed DC reactive sputtering in a closed-field unbalanced magnetron sputtering (CFUBMS) system. The deposition parameters examined were target current (1, 1.5 and 2 A), deposition pressure (0.15, 0.25 and 0.35 Pa), pulse frequency (100, 200 and 350 kHz) and duty cycle (85 %, 70 % and 50 %). A Taguchi L9 orthogonal design was used to define the deposition process parameters for each doped film. The Nb and V-doped CrYN thin films were characterized in terms of their microstructure, thickness, composition, hardness and tribological properties by X-ray diffraction (XRD), scanning electron microscopy (SEM), Energy dispersive spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), nanohardness and pin-on-disc testing, respectively. The bond strength between the substrate and the films (adhesion) was analyzed by scratch testing. For the Nb-doped thin films, a maximum hardness value of 21.4 GPa and the lowest friction coefficient of 0.36 were obtained. On the other hand, in the V-doped thin films, the maximum hardness value was 16.1 GPa, while the lowest friction coefficient obtained was 0.11. In addition, Nb-doped and V-doped CrYN thin films exhibited extraordinary adhesion properties. The effect of the selected deposition parameters (target current, pulse frequency, and duty cycle) in relation to the film thickness, hardness, and coefficient of friction properties of the Nb and V-doped CrYN thin films were investigated using the Taguchi approach and optimum operating conditions were identified and confirmed.

Identification of maximal cyclic pressure with the non-motorized method based on specific refraction

ВACKGROUND: Russian scientists constantly deal with the issues of the engine performance evaluation with non-standard methods, as they are commonly called “express methods”. In many cases, if the initial components retain the relative constancy of their physicochemical properties, the determination of performance indicators can be carried out with simple classical methods of refractometry, magneto-optics, densimetry, interfacial tensiometry or their combination (aggregation).
 AIM: Identification of the maximal cycle pressure of an operating diesel engine with the non-motorized method based on the values of specific refraction for each type of biofuel.
 METHODS: The object of research was the D-245.5S2 turbocharged four-stroke engine with intercooler. Mixtures of DT with ethanol, rapeseed oil and surep oil were prepared for the study. The mass fraction of oils and ethanol in the mixture varied from 0% to 50%. Density d and refractive index were measured for each sample. The measurements were carried out at an ambient temperature of 20˚C. The refractive index of the samples was measured using the IRF – 454b refractometer. The density was determined using the PZh-2-25 pycnometer and the VIBRAAJH-620CE laboratory scales according to GOST 3900-85 “Oil and petroleum products. Methods for determining density”.
 RESULTS: Data analysis showed that there is a strong correlation between the specific refraction sR and the maximal pressure in a cylinder Pz (MPa). Linear regression models for various alternative fuel mixtures have been developed to determine the maximal cycle pressure during diesel operation.
 CONCLUSION: The application of the proposed dependencies makes it possible to identify the maximal cycle pressure of an operating a diesel engine with sufficient accuracy in a non-motorized way.