Compressibility Factor Research Articles

Theoretical porosity design, mechanical properties, and durability of large-pore sandy recycled concrete

This study used recycled aggregate (RA) and mineral additions to design large-pore sandy recycled concrete (LPSRC) using a design porosity (DP) of 25 %, 20 %, and 15 %. Thus, the related effects of recycled coarse aggregate (RCA), recycled fine aggregate (RFA) and fly ash (FA) on the compressive strength, frost resistance (using slow and fast freezing methods), and sulfate attack properties of LPSRC were systematically studied. Moreover, to reduce the error between the design and theoretical porosity values, the bulk density ratio adjustment method was employed to design the theoretical porosity. The results of binarization analysis demonstrated that the deviation of the actual porosity caused by the sidewall effect was ∼2 %, which should be considered during DP design and performance evaluation. The compressive strength of the LPSRC after 56 curing days increased by 35.9 % on average as the DP was reduced from 25 % to 15 %. In a freeze–thaw cycle test, the slow freezing method was more accurate for evaluating the frost resistance of the LPSRC. Under the same conditions, the mass loss rate of the LPSRC calculated using the slow and fast freezing methods was 3.2 % and 7.62 %, respectively. This distinction is pivotal for establishing reliable frost resistance criteria for LPSRC in cold-weather applications, complementing previous research in the field. In terms of sulfate deterioration resistance, compressive strength corrosion factors decreased with increasing DP. Additionally, damage was mainly concentrated at the contact interface between RCA and the cement matrix, while the aggregate itself showed relatively little damage, suggesting RCA application has a positive effect on improving preparation of the LPSRC. Specifically, as DP increased from 15 % to 25 %, the average reduction in the compressive strength corrosion factors for LPSRC after 30 and 60 times of dry–wet cycle were 9.64 % and 8.37 %, respectively, illustrating a clear correlation between higher DP and reduced sulfate resistance. This study contributed to the LPSRC design and production using RCA, RCF and FA, that positively impacts to the sustainable development of the construction industry and helps to evaluate the effectiveness of RA in macroporous concrete. Moreover, the strategic implementation of design porosity has been shown to optimize the structural integrity and performance of the concrete, achieving a balance between strength and workability, which is crucial for the concrete's durability and its ability to adapt to various environmental conditions.

Evaluating concrete material models for blast analysis using 3D finite element analysis

Blast loads are extremely detrimental to structural elements of a building and can result in minor damage to complete failure of a building. Finite element analysis (FEA) is an effective tool to study the behavior of structural elements exposed to blast loading. The focus of this study was to evaluate the performance of three concrete material models available in LS-DYNA and compare the results to experimental results from blast loaded reinforced concrete (RC) panel supported on four sides (two-way bending). Concrete material models chosen for this study were Winfrith (MAT_84-85), Karagozian & Case (MAT_072R3), and Continuous Surface Cap Model (MAT_159). These are widely used in finite element modeling related to civil structures and can account for strain rate dependent properties. Each concrete material model was implemented with and without strain rate effects. The finite element model had concrete material as solid elements and steel reinforcement material as beam elements with simply supported boundary conditions. Results from two different blast loading methods are presented: (1) blast pulse load curve from experimental data (2) blast input by defining type, location, and amount of explosive in terms of TNT. The finite element models were evaluated by comparing fundamental period, peak displacement, and residual displacement to experimental results. In this study, the model using MAT_072R3 most closely matched experimental behavior in comparison to MAT_84-85 and MAT_159. Additional discussion is presented about: (1) single degree of freedom (SDOF) analysis, (2) comparison of numerical and experimental crack patterns, and (3) behavior of concrete material models accounting for compressive and tensile dynamic increase factors (DIFs).

Artificial neural network aided vapor–liquid equilibrium model for multi-component high-pressure transcritical flows with phase change

In this work, an artificial neural network (ANN) aided vapor–liquid equilibrium (VLE) model is developed and coupled with a fully compressible computational fluid dynamics (CFD) solver to simulate the transcritical processes occurring in high-pressure liquid-fueled propulsion systems. The ANN is trained in Python using TensorFlow, optimized for inference using Open Neural Network Exchange Runtime, and coupled with a C++ based CFD solver. This plug-and-play model/methodology can be used to convert any multi-component CFD solver to simulate transcritical processes using only open-source packages, without the need of in-house VLE model development. The solver is then used to study high-pressure transcritical shock-droplet interaction in both two- and four-component systems and a turbulent temporal mixing layer (TML), where both qualitative and quantitative agreement (maximum relative error less than 5%) is shown with respect to results based on both direct evaluation and the state-of-the-art in situ adaptive tabulation (ISAT) method. The ANN method showed a 6 times speed-up over the direct evaluation and a 2.2-time speed-up over the ISAT method for the two-component shock-droplet interaction case. The ANN method is faster than the ISAT method by 12 times for the four-component shock-droplet interaction. A 7 times speed-up is observed for the TML case for the ANN method compared to the ISAT method while achieving a data compression factor of 2881. The ANN method also shows intrinsic load balancing, unlike traditional VLE solvers. A strong parallel scalability of this ANN method with the number of processors was observed for all the three test cases. Code repository for 0D VLE solvers, and C++ ANN interface—https://github.com/UMN-CRFEL/ANN_VLE.git.

Plasma-grating-based laser pulse compressor.

To avoid damage in high-power laser systems, a chirped plasma-based grating is proposed for compressing laser pulses that have been previously stretched and amplified. This chirped grating is generated through the interaction of chirped pump laser pulses in a plasma slab. Particle-in-cell (PIC) simulations demonstrate that the grating exists for a duration sufficient to be utilized in the final chirped pulse amplification (CPA) stage. The generation of the grating is quite flexible, as several parameters can be adjusted, such as plasma density, chirp, length, and intensity of the pump laser. To begin, the structure of the grating is analyzed in terms of ponderomotive effects of the pump laser pulses. The primary application of the chirped plasma-based grating lies in compressing laser pulses to large amplitudes and short durations after they have been stretched and amplified beforehand. The compression factor is explored in connection with potential grating parameters. Reflectivity and effective bandwidth of chirped plasma gratings are parameters to be optimized. However, the grating spectral bandwidth can only be increased at the expense of reflectivity. The PIC results are made understandable through model calculations based on coupled mode equations.

Porosity Prediction Based on Ensemble Learning for Feature Selection and an Optimized GRU Improved by the PSO Algorithm

Accurate and reliable prediction of porosity forms the foundational basis for evaluating reservoir quality, which is essential for the systematic deployment of oil and gas exploration and development plans. When data quality of samples is low, and critical model parameters are typically determined through subjective experience, resulting in diminished accuracy and reliability of porosity prediction methods utilizing gated recurrent units (GRU), a committee-voting ensemble learning (EL) method, and an enhanced particle swarm optimization (PSO) algorithm are proposed to optimize the GRU-based porosity prediction model. Initially, outliers are eliminated through box plots and the min–max normalization is applied to enhance data quality. To address issues related to model accuracy and high training costs arising from dimensional complexity, substantial noise, and redundant information in logging data, a committee-voting EL strategy based on four feature selection algorithms is introduced. Following data preprocessing, this approach is employed to identify logging parameters highly correlated with porosity, thereby furnishing the most pertinent data samples for the GRU model, mitigating constraints imposed by single-feature selection methods. Second, an improved PSO algorithm is suggested to tackle challenges associated with low convergence accuracy stemming from random population initialization, alongside the absence of global optimal solutions due to overly rapid particle movement during iteration. This algorithm uses a good-point set for population initialization and incorporates a compression factor to devise an adaptive velocity updating strategy, thereby enhancing search efficacy. The enhanced PSO algorithm’s superiority is substantiated through comparison with four alternative swarm intelligent algorithms across 10 benchmark test functions. Ultimately, optimal hyper-parameters for the GRU model are determined using the improved PSO algorithm, thereby minimizing the influence of human factors. Experimental findings based on approximately 15,000 logging data points from well A01 in an operational field validate that, relative to three other deep learning methodologies, the proposed model proficiently extracts spatiotemporal features from logging data, yielding enhanced accuracy in porosity prediction. The mean squared error on the test set was 7.19 × 10–6, the mean absolute error stood at 0.0082, and coefficient of determination reached 0.99, offering novel insights for predicting reservoir porosity.

Experimental study on impact behaviour of normal strength mortar at cryogenic temperatures and after freeze-thaw cycles

Concrete structures employed for the storage of Liquefied Natural Gases (LNG) currently undergo temperature as low as −170 °C. These critical engineering structures may also experience dynamic loads such as impact or explosion during service life. Therefore, it is crucial to explore the mechanical response of concrete at intermediate to high strain rates together with low temperatures or cryogenic freeze-thaw (FT) cycles. This study presents experimental research into the combined effects of low temperatures and strain rate on normal strength mortar, providing a preliminary analysis of the dynamic performance of concrete at low and cryogenic temperatures. A Split Hopkinson Pressure Bar (SHPB) device was used to investigate the characteristics of dynamic compression (at strain rates of 40, 80, 120 and 160 s−1) and dynamic split tension (at strain rates of 20, 40, 60 and 80 s−1) of Normal Strength Mortar (NSM) at different low temperatures. In addition, the dynamic compression (at strain rates of 80, 130 and 180 s−1) after cryogenic FT cycles was also explored. The findings revealed that the failure pattern of NSM samples exposed to coupled low temperature or cryogenic FT cycles and high strain rate loading notably varied from that observed under room temperature condition. Both the dynamic compressive and split tensile strengths of NSM increased with the strain rates at all temperatures. At −160 °C, the dynamic compressive and splitting tensile strengths of NSM specimens were greater than those at room temperature (by 10.94 % and 28.29 %, respectively) and −70 °C (by 3.13 % and 4.12 %, respectively). Cryogenic freeze-thaw cycles evidently impacted the material static and dynamic performance. An empirical model was developed to predict the dynamic increase factors (DIFs) for both dynamic compression and splitting tensile strengths of NSM at low/cryogenic temperatures and after freeze-thaw cycles. Scanning electron microscopy (SEM) technique was performed to study and comprehend the microscopic processes and microstructural changes after FT cycles.

A Review of the Utilization of CO2 as a Cushion Gas in Underground Natural Gas Storage

A cushion gas is an indispensable and the most expensive part of underground natural gas storage. Using CO2 injection to provide a cushion gas, not only can the investment in natural gas storage construction be reduced but the greenhouse effect can also be reduced. Currently, the related research about the mechanism and laws of CO2 as a cushion gas in gas storage is not sufficient. Consequently, the difference in the physical properties of CO2 and CH4, and the mixing factors between CO2 and natural gas, including the geological conditions and injection–production parameters, are comprehensively discussed. Additionally, the impact of CO2 as a cushion gas on the reservoir stability and gas storage capacity is also analyzed by comparing the current research findings. The difference in the viscosity, density, and compressibility factor between CO2 and CH4 ensures a low degree of mixing between CO2 and natural gas underground, thereby improving the recovery of CH4 in the operation process of gas storage. In the pressure range of 5 MPa–13 MPa and temperature range of 303.15 K–323.15 K, the density of CO2 increases five to eight times, while the density of natural gas only increases two to three times, and the viscosity of CO2 is more than 10 times that of CH4. The operation temperature and pressure in gas storage should be higher than the temperature and pressure in the supercritical conditions of CO2 because the diffusion ability between the gas molecules is increased in these conditions. However, the temperature and pressure have little effect on the mixing degree of CO2 and CH4 when the pressure is over the limited pressure of supercritical CO2. The CO2, with higher compressibility, can quickly replenish the energy of the gas storage facility and provide sufficient elastic energy during the natural gas production process. In addition, the physical properties of the reservoir also have a significant impact on the mixing and production of gases in gas storage facilities. The higher porosity reduces the migration speed of CO2 and CH4. However, the higher permeability promotes diffusion between gases, resulting in a higher degree of gas mixing. For a large inclination angle or thick reservoir structure, the mixed zone width of CO2 and CH4 is small under the action of gravity. An increase in the injection–production rate intensifies the mixing of CO2 and CH4. The injection of CO2 into reservoirs also induces the CO2–water–rock reactions, which improves the porosity and is beneficial in increasing the storage capacity of natural gas.

High-order compact gas-kinetic scheme in arbitrary Lagrangian-Eulerian formulation

This study proposes an extension of the high-order compact gas-kinetic scheme (CGKS) to compressible flow simulation in an arbitrary Lagrangian-Eulerian (ALE) formulation in unstructured mesh. The ALE method is achieved by subdividing arbitrary mesh into tetrahedrons and integrating flux function in a local coordinate system at the cell interface to ensure geometric conservation law. The scheme incorporates a compact reconstruction with third-order accuracy for updating both cell-averaged conservative flow variables and their gradients. HWENO-type nonlinear reconstruction and gradient compression factors are adopted to improve the accuracy and robustness of the scheme. A multi-stage multi-derivative (MSMD) time-stepping method is also implemented to achieve high-order time accuracy with fewer middle stages. The scheme is used to study problems involving moving boundaries. The numerical experiments demonstrate the effectiveness of the scheme in capturing the accurate solutions of both low-speed smooth flow and highly compressible ones with strong shock waves.

Multi-domain awareness for compressed deepfake videos detection over social networks guided by common mechanisms between artifacts

The viral spread of massive deepfake videos over social networks has caused serious security problems. Despite the remarkable advancements achieved by existing deepfake detection algorithms, deepfake videos over social networks are inevitably influenced by compression factors. This causes deepfake detection performance to be limited by the following challenging issues: (a) interfering with compression artifacts, (b) loss of feature information, and (c) aliasing of feature distributions. In this paper, we analyze the common mechanism between compression artifacts and deepfake artifacts, revealing the structural similarity between them and providing a reliable theoretical basis for enhancing the robustness of deepfake detection models against compression. Firstly, based on the common mechanism between artifacts, we design a frequency domain adaptive notch filter to eliminate the interference of compression artifacts on specific frequency bands. Secondly, to reduce the sensitivity of deepfake detection models to unknown noise, we propose a spatial residual denoising strategy. Thirdly, to exploit the intrinsic correlation between feature vectors in the frequency domain branch and the spatial domain branch, we enhance deepfake features using an attention-based feature fusion method. Finally, we adopt a multi-task decision approach to enhance the discriminative power of the latent space representation of deepfakes, achieving deepfake detection with robustness against compression. Extensive experiments show that compared with the baseline methods, the detection performance of the proposed algorithm on compressed deepfake videos has been significantly improved. In particular, our model is resistant to various types of noise disturbances and can be easily combined with baseline detection models to improve their robustness.

Electrically Controlled High Sensitivity Strain Modulation in MoS2 Field-Effect Transistors via a Piezoelectric Thin Film on Silicon Substrates.

Strain can modulate bandgap and carrier mobilities in two-dimensional (2D) materials. Conventional strain-application methodologies relying on flexible/patterned/nanoindented substrates are limited by low thermal tolerance, poor tunability, and/or scalability. Here, we leverage the converse piezoelectric effect to electrically generate and control strain transfer from a piezoelectric thin film to electromechanically coupled 2D MoS2. Electrical bias polarity change across the piezo film tunes the nature of strain transferred to MoS2 from compressive (∼0.23%) to tensile (∼0.14%) as verified through Raman and photoluminescence spectroscopies and substantiated by density functional theory calculations. The device architecture, on silicon substrate, integrates an MoS2 field-effect transistor on a metal-piezoelectric-metal stack enabling strain modulation of transistor drain current (130×), on/off ratio (150×), and mobility (1.19×) with high precision, reversibility, and resolution. Large, tunable tensile (1056) and compressive (-1498) strain gauge factors, electrical strain modulation, and high thermal tolerance promise facile integration with silicon-based CMOS and micro-electromechanical systems.

Sub-Nanosecond Microwave Passive Pulse Generation With High Power Compression Factor Using Structural Reflection

Sub-Nanosecond Microwave Passive Pulse Generation With High Power Compression Factor Using Structural Reflection

Effectiveness and safety of coils plus glue in slope embankment technology versus coils plus sclerosant in embolization therapy for reflux-type pelvic venous disorders

ObjectiveThis study aimed to evaluate the effectiveness and safety of coils plus glue (CPG) in slope embankment technology vs coils plus sclerosant (CPS) in treating reflux-type pelvic venous disorders. MethodsThe analysis included patients diagnosed with reflux-type pelvic venous disorders who were treated with CPG or CPS from 2019 to 2021. The inclusion criteria were noncyclic pain lasting more than 6 months, atypical varicose, and transvaginal Doppler ultrasound (TVDUS) and computed tomographic venography confirming the diagnosis and excluding compression factors and other diseases. Propensity score matching was performed at a 1:1.1 ratio based on the following covariates: age, pregnancy, body mass index, pretreatment visual analog scale (VAS), dysmenorrhea, dyspareunia, urinary urgency, tenesmus, low back pain, vulvar varicosities, vaginal varicosities, and lower limb varices. The pain was relieved by embolizing the target lesions with different embolic materials. The efficacy and safety of the different embolization materials were compared by VAS and TVDUS examinations at 1, 3, 6, 12, 24, and 36 months. ResultsFrom a total of 495 patients, 88 patients were selected from the CPG group and 77 patients from the CPS group by propensity score matching. The patients were followed up for 36 months. The preoperative VAS score of the CPG group was 8 (range, 6-8), and the CPS score was 8 (range, 7-8; P = .64). The postembolization VAS score of the CPG group was 2.05 ± 0.37, and the CPS score was 2.14 ± 0.35 (P = .55). A total of 28 cases (16.9%) showed complications, most of which were transient pain after embolization. No serious complications such as coil embolization to the lungs occurred. In addition, the CPG group used fewer coils than the CPS group by using the slope embankment technique. The mean coil length of the CPG group was 77.18 ± 33.82 cm, and the CPS group was 105.29 ± 71 cm (P = .001). The CPG group had an average operative time of 44.49 ± 5.72 minutes, whereas the CPS group took 43.45 ± 4.18 minutes on average (P = .19). The radiation dose in the CPG group was 398.40 ± 76.16 mGy, and the radiation dose in the CPS group was 388 ± 44.23 mGy (P = .30). The median recurrence-free survival in the CPG group was 34.23 months (95% confidence interval, 33.2-35.2), and the median recurrence-free survival in the CPS group was 30.39 months (95% confidence interval, 28.2-32.6; log rank P = .018). ConclusionsEmbolization therapy for refluxing PeVD was safe and effective, and proficient use of slope embankment technique with CPG increased efficacy and reduced complications.

Thermochemical and physical characteristics of isomeric tetracycloundecanes obtained from endo- and exo-dicyclopentadienes

By combining the cyclopropanation and hydrogenation processes of endo- and exo-isomers of dicyclopentadiene, two pairs of tetracycloundecane stereoisomers were obtained, differing in the position of a three-membered cycle. For the first time, under the same conditions for the exo,endo- and exo,exo-tetracyclo[5.3.1.02,6.08,10]undecanes and endo,anti- and exo,anti-tetracyclo[6.2.1.02,7.03,5]undecanes obtained the enthalpy of combustion and formation, saturated vapor pressure and enthalpy and entropy of vaporization were determined, and critical temperatures, pressures, volumes, Pitzer's acentric factors and critical compressibility factors were calculated. The isomers obtained have a higher energy density than JP-10.

Theoretical Expression and Screening of Real Gas Effect of Spiral Groove Dry Gas Seal

The emergence of dry gas seals has revolutionized the form of fluid sealing. The traditional research and analysis of dry gas seals is carried out by considering the lubricating medium gas as an ideal gas, but at this stage, the sealing application environment is complicated, so it is necessary to consider the real gas effect of the lubricating medium gas to expand and break through the design system of dry gas seals. We choose seven common lubricating media in dry gas seal applications and screen the optimal density expression of the real gas using different real gas equations of state. Then, we study the extent to which the compression factors of different lubricating gases deviate from the ideal gas and analyze the errors of different real gas equations of state. These results can provide an optimal expression to clarify the mechanism by which the real gas effect affects the dry gas seal performance, which helps to grasp the nature of dry gas seals, predict the dry gas seal behavior, and guide the dry gas seal application.

On the equation of state of the hard disk system: the fluid-hexatic phase transition

The accuracy of four approximations proposed along time as equations of state for a homogeneous system of hard disks is compared against molecular dynamics results of 10 5 disks in equilibrium [L. Mier-y-Terán, J. Munguía and J. Antonio Moreno-Razo, Mol. Phys. (2023). doi:10.1080/00268976.2023.2288702]. Three of the equations studied here are Padé approximants. Only one of the approximations studied, based on the virial expansion of the compressibility factor of the homogeneous fluid, is considered to have sound physical grounds. Assuming that the total pressure predicted generates the positional part of the Helmholtz free energy, only, the four approximations, combined with the molecular dynamics simulation results referred above, allow identifying four linear regions, where different orientational processes can be associated with the well known steps of the melting transition. Results for a finite system are consistent with the KTHNY theory. In particular, under this assumption, the four approximations, with small quantitative differences, show the ordering effects present in the fluid at the low-density-end of the fluid-hexatic first-order phase transition. It is shown that the latter region continues to exist under the assumption proposed. In addition, a linear combination of the virial and compressibility coefficients is used to obtain a more accurate version of the Carnahan-Starling type approximation for the hard-disk system.

МЕХАНИЧЕСКИЕ СВОЙСТВА СВЕРХВЫСОКОПРОЧНОГО ФИБРОБЕТОНА С РАЗЛИЧНЫМ ВИДОМ СТАЛЬНОЙ ФИБРЫ

The article examines the influence of steel fiber type and volumetric content on the mechanical properties of UHPFRC. Five fiber types were used: corrugated 15/0.3 and 22/0.3 mm, straight 13/0.3 and 13/0.2 mm, and hooked-end 30/0.5 mm. The fiber volume content ranged from 0 to 3%. During the experimental studies, compressive strength, flexural strength, as well as the flexural fracture energy were determined. It has been discovered that an increase in the volumetric content of steel fiber results in an enhancement of all the properties under consideration, irrespective of the type of fiber. The fiber has the biggest effect on the bending fracture energy and the least on the compressive strength. Linear relationships between the mechanical characteristics of UHPFRC and the fiber factor were established, which reflects the volumetric content, length, and diameter of an individual fiber. The slope angle of the approximating lines of the “compressive strength-fiber factor” and “flexural strength-fiber factor” relationships takes on different values depending on the type of fiber used. The highest slope angle is found for corrugated fiber. It was discovered that there is a threshold value of the fiber factor, upon reaching which the steel fiber leads to an increase in the flexural strength of the UHPFRC. According to a generalized quality criterion it has been determined that corrugated fiber with dimensions of 22/0.3 mm is the optimal substitute for straight fiber 13/0.2 mm, which is widely utilized for the production of Ultra-High Performance Fiber-Reinforced Concrete

Adaptive multi-band modulation for robust and low-complexity faster-than-Nyquist non-orthogonal FDM IM-DD systems.

Faster-than-Nyquist non-orthogonal frequency-division multiplexing (FTN-NOFDM) has been shown to have good robustness against the steep frequency roll-off in a bandwidth-limited optical communication system. Among various FTN-NOFDM techniques, the non-orthogonal matrix precoding (NOM-p) based FTN has relatively high compatibility, which can easily utilize the existing advanced digital signal processing (DSP) techniques in the conventional OFDM. In this work, we propose what we believe to be a novel FTN-NOFDM scheme with adaptive multi-band modulation. By dividing the single-band NOM-p into multiple-band NOM-p, the proposed scheme is able to assign different quadrature amplitude modulation (QAM) levels to different sub-bands, leading to better utilization of the low-pass-like channel as well as reduced computational complexity. The impacts of sub-band number and bandwidth compression factor on the bit-error-rate (BER) performance and complexity are experimentally analyzed in a 32.23-Gb/s and 20-km intensity modulation-direct detection (IM-DD) optical transmission system. Results show that the proposed scheme with a proper sub-band number can lower the BER and greatly reduce the complexity compared to the conventional single-band scheme.

SHM data compression and reconstruction based on IGWO-OMP algorithm

The long-term operating structural health monitoring (SHM) system generates massive monitoring data, whose transmission and storage are challenging tasks. To address this issue, this study proposed an improved grey wolf optimizer-orthogonal matching pursuit (IGWO-OMP) algorithm for SHM data compression and reconstruction. The effectiveness of the proposed algorithm was validated using four SHM datasets including ultrasonic guided wave (UGW), acceleration, and audio signals. Firstly, the optimization results of IGWO were compared with those of other classical algorithms to verify its superiority in reconstruction accuracy. Secondly, the reconstruction accuracy under various compression factors was investigated and the optimal compression factor was determined. Finally, the relationship between reconstruction accuracy and signal sparsity as well as the performance of IGWO-OMP in dealing with noise-containing signals were discussed. The results demonstrate the generalizability and excellent noise robustness of IGWO-OMP in SHM data compression and reconstruction. Compared with other optimization algorithms, IGWO exhibits the best global optimal solution searching ability and obtains the reconstructed signal closest to the original one. Comprehensively considering the compressed signal length, reconstruction accuracy and computational efficiency, the optimal compression factor is suggested to be 0.2. In IGWO-OMP, the signal reconstruction accuracy is related to its sparsity, the variation of the CCD index for the reconstructed signal with relative sparsity follows an exponential growth relationship.

Research on the design of hydraulic heater and optimization of energy matching of internal combustion engine heated steam generator

A novel internal combustion engine heated steam generator (ICEHSG) and the energy-matching optimization method are proposed in this paper. In the ICEHSG, the energy generated by the internal combustion engine (ICE) of the vehicle is converted to thermal energy as the heating source of the steam generator. Firstly, the prototype model of the hydraulic heater (HH) of the ICEHSG is established, and the relevant structural parameters are optimized based on computational fluid dynamics (CFD). Secondly, the energy matching between HH and ICE is optimized based on the particle swarm algorithm with compression factors (CFPSO). Finally, the energy provided by the ICEHSG and the required energy of the steam generation process is optimized, and the analysis is combined with the actual engineering case. The simulation results show that when the inclination angle of the blade is 45°, the number of rotor and stator blades is 31–33, and the diameter of the circular circle is 386 mm, the torque performance and heating performance of the HH are optimal. Meanwhile, the maximum thermal power can be obtained when the liquid filling rate of the HH is 100%. After optimization, the maximum torque of the HH working with the ICE is increased by 8.07%. Compared with the traditional boiler steam generator, the ICEHSG system proposed in this paper can increase the thermal efficiency by 1.3%–2.7% and reduce the cost by 37%.

Blitzcrank: Fast Semantic Compression for In-Memory Online Transaction Processing

We present Blitzcrank, a high-speed semantic compressor designed for OLTP databases. Previous solutions are inadequate for compressing row-stores: they suffer from either low compression factor due to a coarse compression granularity or suboptimal performance due to the inefficiency in handling dynamic data sets. To solve these problems, we first propose novel semantic models that support fast inferences and dynamic value set for both discrete and continuous data types. We then introduce a new entropy encoding algorithm, called delayed coding, that achieves significant improvement in the decoding speed compared to modern arithmetic coding implementations. We evaluate Blitzcrank in both standalone microbenchmarks and a multicore in-memory row-store using the TPC-C benchmark. Our results show that Blitzcrank achieves a sub-microsecond latency for decompressing a random tuple while obtaining high compression factors. This leads to an 85% memory reduction in the TPC-C evaluation with a moderate (19%) throughput degradation. For data sets larger than the available physical memory, Blitzcrank help the database sustain a high throughput for more transactions before the I/O overhead dominates.