Numerical INJection Analysis Research Articles

Numerical analysis of injection and spray characteristics of diesel fuel and rapeseed oil in a diesel engine

Vegetable oil-based biofuels are promising carbon-neutral fuels to replace fossil fuels. The study focuses on a comparative analysis of injection and spray characteristics of DF and RO and the relationship between spray and emission formation. Steady nozzle flows were simulated in Ansys Fluent. Free sprays were modeled with 0D spray models, and the relationships between atomization parameters and TKE at the orifice exit were analyzed. Sprays of DF/RO blends in real combustion chamber conditions were simulated with a multi-zone spray model in DIESEL-RK. The influence of RO content on fuel atomization and fuel-air mixture formation were analyzed. Compared with DF, RO has lower injection velocity and TKE at the orifice exit. The empirical correlations of mean droplet diameters and spray angle with TKE were developed by using a complex TKE/ν 0.5 with correlation coefficients above 0.988. The increase of the RO content from 0 to 100% leads to a reduction in spray angle by 24% and an increase in spray tip penetration by 15.5%, SMD by 62.1%, and the mass fraction of fuel injected onto the combustion chamber walls from 17.1 to 42%. A correlation between NOx emissions and SMD was fitted with a correlation coefficient of 0.9968.

Experimental and Numerical Analysis of Injection Molding of Ti-6Al-4V Powders for High-Performance Titanium Parts

Both experimental and numerical analysis of powder injection molding (PIM) of Ti-6Al-4V alloy were performed to prepare a defect-free high-performance Ti-6Al-4V part with low carbon/oxygen contents. The prepared feedstock was characterized with specific experiments to identify its viscosity, pressure–volume–temperature and thermal properties to simulate its injection molding process. A finite-element-based numerical scheme was employed to simulate the thermomechanical process during the injection molding. In addition, the injection molding, debinding, sintering and hot isostatic pressing processes were performed in sequence to prepare the PIMed parts. With optimized processing conditions, the PIMed Ti-6Al-4V part exhibits excellent physical and mechanical properties, showing a final density of 99.8%, tensile strength of 973 MPa and elongation of 16%.

Numerical analysis of injection molding for the Syringe Barrel with optimum design and processing condition

The numerical analysis was performed to predict the potential problem, often occurring during the manufacturing process of the disposable medical device of a great volume. The cavity filling analyses were performed for the new design of the 3cc Syringe Barrel using polypropylene(PP1), and a new nucleated polypropylene(PP2) material for better clarity. These analyses have been performed for different processing conditions as well as various wall thickness designs for both materials. With the nucleated material, only the original wall thickness design has been studied at two different processing conditions for comparison purpose. This study was conducted to investigate the optimum part design and processing condition for two different materials. The most desirable design was selected with Design 3 for material utilization and reduced flow stresses by comparing the field results. The new nucleated polypropylene provided slightly better product quality and processing.

The NINJA-2 project: detecting and characterizing gravitational waveforms modelled using numerical binary black hole simulations

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave (GW) astrophysics communities. The purpose of NINJA is to study the ability to detect GWs emitted from merging binary black holes (BBH) and recover their parameters with next-generation GW observatories. We report here on the results of the second NINJA project, NINJA-2, which employs 60 complete BBH hybrid waveforms consisting of a numerical portion modelling the late inspiral, merger, and ringdown stitched to a post-Newtonian portion modelling the early inspiral. In a ‘blind injection challenge’ similar to that conducted in recent Laser Interferometer Gravitational Wave Observatory (LIGO) and Virgo science runs, we added seven hybrid waveforms to two months of data recoloured to predictions of Advanced LIGO (aLIGO) and Advanced Virgo (AdV) sensitivity curves during their first observing runs. The resulting data was analysed by GW detection algorithms and 6 of the waveforms were recovered with false alarm rates smaller than 1 in a thousand years. Parameter-estimation algorithms were run on each of these waveforms to explore the ability to constrain the masses, component angular momenta and sky position of these waveforms. We find that the strong degeneracy between the mass ratio and the BHs’ angular momenta will make it difficult to precisely estimate these parameters with aLIGO and AdV. We also perform a large-scale Monte Carlo study to assess the ability to recover each of the 60 hybrid waveforms with early aLIGO and AdV sensitivity curves. Our results predict that early aLIGO and AdV will have a volume-weighted average sensitive distance of 300 Mpc (1 Gpc) for 10M⊙ + 10M⊙ (50M⊙ + 50M⊙) BBH coalescences. We demonstrate that neglecting the component angular momenta in the waveform models used in matched-filtering will result in a reduction in sensitivity for systems with large component angular momenta. This reduction is estimated to be up to ∼15% for 50M⊙ + 50M⊙ BBH coalescences with almost maximal angular momenta aligned with the orbit when using early aLIGO and AdV sensitivity curves.

박형 도광판의 고속사출성형을 위한 수지 점도 측정 및 수치해석

The light guide plate has become the major component for the backlight module in general information technology products (e.g. mobile phones, monitors, etc.). High speed injection molding has been adopted for thin walled LGP giving advantages such as weight, shape, size, and reduction in production costs. In the current study, the rheological characteristics of high liquidity plastic resin PC(HL8000) were measured using a capillary rheometer to improve the reliability of the numerical analysis for high speed injection molding. With the measured viscosity and PVT of PC(HL8000), numerical analysis of injection molding was conducted using the simulation software(Moldflow). Filling time and deflection were predicted and compared with those of traditional PC resins(H3000, H4000). The results show that PC(HL8000) has significantly different rheological characteristics during high speed injection molding. Hence proper properties of the resin should be used to improve the accuracy of numerical predictions.

The NINJA-2 catalog of hybrid post-Newtonian/numerical-relativity waveforms for non-precessing black-hole binaries

The numerical injection analysis (NINJA) project is a collaborative effort between members of the numerical-relativity and gravitational wave data-analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search and parameter-estimation algorithms using numerically generated waveforms and to foster closer collaboration between the numerical-relativity and data-analysis communities. The first NINJA project used only a small number of injections of short numerical-relativity waveforms, which limited its ability to draw quantitative conclusions. The goal of the NINJA-2 project is to overcome these limitations with long post-Newtonian—numerical-relativity hybrid waveforms, large numbers of injections and the use of real detector data. We report on the submission requirements for the NINJA-2 project and the construction of the waveform catalog. Eight numerical-relativity groups have contributed 56 hybrid waveforms consisting of a numerical portion modeling the late inspiral, merger and ringdown stitched to a post-Newtonian portion modeling the early inspiral. We summarize the techniques used by each group in constructing their submissions. We also report on the procedures used to validate these submissions, including examination in the time and frequency domains and comparisons of waveforms from different groups against each other. These procedures have so far considered only the (ℓ, m) = (2, 2) mode. Based on these studies, we judge that the hybrid waveforms are suitable for NINJA-2 studies. We note some of the plans for these investigations.

Unmodeled search for black hole binary systems in the NINJA project

The gravitational-wave signature from binary black hole coalescences is an important target for ground-based interferometric detectors such as LIGO and Virgo. The Numerical INJection Analysis (NINJA) project brought together the numerical relativity and gravitational wave data analysis communities, with the goal to optimize the detectability of these events. In its first instantiation, the NINJA project produced a simulated data set with numerical waveforms from binary black hole coalescences of various morphologies (spin, mass ratio, initial conditions), superimposed to Gaussian colored noise at the design sensitivity for initial LIGO and Virgo. We analyzed the NINJA simulated data set with the Q-pipeline algorithm, designed for the all-sky detection of gravitational-wave bursts with minimal assumptions on the shape of the waveform. The algorithm filters the data with a bank of sine-Gaussians, sinusoids with Gaussian envelope, to identify significant excess power in the time-frequency domain. We compared the performance of this burst search algorithm with lalapps_ring, which match-filters data with a bank of ring-down templates to specifically target the final stage of a coalescence of black holes. A comparison of the output of the two algorithms on NINJA data in a single detector analysis yielded qualitatively consistent results; however, due to the low simulation statistics in the first NINJA project, it is premature to draw quantitative conclusions at this stage, and further studies with higher statistics and real detector noise will be needed.

Testing gravitational-wave searches with numerical relativity waveforms: results from the first Numerical INJection Analysis (NINJA) project

The Numerical INJection Analysis (NINJA) project is a collaborative effort between members of the numerical relativity and gravitational-wave data analysis communities. The purpose of NINJA is to study the sensitivity of existing gravitational-wave search algorithms using numerically generated waveforms and to foster closer collaboration between the numerical relativity and data analysis communities. We describe the results of the first NINJA analysis which focused on gravitational waveforms from binary black hole coalescence. Ten numerical relativity groups contributed numerical data which were used to generate a set of gravitational-wave signals. These signals were injected into a simulated data set, designed to mimic the response of the initial LIGO and Virgo gravitational-wave detectors. Nine groups analysed this data using search and parameter-estimation pipelines. Matched filter algorithms, un-modelled-burst searches and Bayesian parameter estimation and model-selection algorithms were applied to the data. We report the efficiency of these search methods in detecting the numerical waveforms and measuring their parameters. We describe preliminary comparisons between the different search methods and suggest improvements for future NINJA analyses.

Bayesian inference on the Numerical INJection Analysis (NINJA) data set using a nested sampling algorithm

We present the results of an analysis of the first data release of the Numerical INJection Analysis (NINJA) project to search for gravitational waves generated by the coalescence of black hole binaries. We adopt a Bayesian approach implemented through a nested sampling algorithm to explore how different waveform families affect the confidence of detection of signals generated by numerically evolving a two-body system in full general relativity. In particular we consider the standard second-order post-Newtoninan approximation to the inspiral phase (TaylorF2 approximant) and the inspiral-merger-rigdown phenomenological waveforms suggested by Ajith and collaborators (2008 Phys. Rev. D 77 104017) (IMRPhenomA approximant). We show that the latter family consistently out-performs TaylorF2 waveforms, and 112 of the 126 signals present in the data set are recovered with a log Bayes factor larger than 3. We also carry out a study of the recovered source parameters, and in particular we discuss the errors affecting the determination of the total mass and sky position.

Status of NINJA: the Numerical INJection Analysis project

The 2008 NRDA conference introduced the Numerical INJection Analysis project (NINJA), a new collaborative effort between the numerical relativity community and the data analysis community. NINJA focuses on modeling and searching for gravitational wave signatures from the coalescence of binary system of compact objects. We review the scope of this collaboration and the components of the first NINJA project, where numerical relativity groups, shared waveforms and data analysis teams applied various techniques to detect them when embedded in colored Gaussian noise.

Numerical modeling of injection/compression molding for center‐gated disk: Part I. Injection molding with viscoelastic compressible fluid model

AbstractThe present study attempted to numerically simulate the process in detail by developing an appropriate physical modeling and the corresponding numerical analysis for injection molding and injection/compression molding processes of centergated disks. In Part I, a physical modeling and associated numerical analysis of injection molding with a compressible viscoelastic fluid model are presented. In the distribution of birefringence, the packing procedure results in the inner peaks in addition to the outer peaks near the mold surface, and values of the inner peaks increase with the packing time. Also, values of the density in the core region increase with the packing time. From the numerical results, we also found that birefringence becomes smaller as the melt temperature gets higher and that it is insignificantly affected by the flow rate and the mold temperature. As far as the density distribution is concerned, mold temperature affects the distribution of density especially near the wall. But it was not significantly affected by flow rate and melt temperature. Numerical results of birefringence coincided with experimental data qualitatively, but not quantitatively.

Numerical analysis of injection molding of glass fiber reinforced thermoplastics. Part 2: Fiber orientation

AbstractNumerical predictions of fiber orientation during injection molding of fiber‐filled thermoplastics are compared to measurements. The numerical work successfully describes the flow of fiber‐filled plastic during injection molding, using finite‐difference solutions for the transport equations and marker particles to track the flow front. The flow is modeled as a 2‐D, non‐isothermal, free‐surface flow with a new viscosity model dependent upon temperature, pressure, and fiber concentration. The fiber orientation is based upon solution to the Fokker‐Planke equation. The comparison demonstrates fair agreement between predicted fiber orientation and experimental results for slow and fast injection speeds. For the slow speed case at 10 and 20 wt% fibers, the numerical and experimental works show that the fibers are more random at the flow front than at the centerline, and that the fibers become more aligned as they flow from the gate to the midstream region. At fast injection speeds, the agreement between the numerical and experimental works is not as good as at slow injection speed. Possible explanations for the discrepancies are that the flow is assumed to be simple shear when injection molding is known to be a pressure‐driven flow, the fibers have an initial orientation for the runner rather than the assumed random orientation, the fibers that were displayed from the camera were more oriented just behind the flow front (owing to the fast injection speed), and the orientation requires more than a 2‐D video image to represent a 3‐D fiber orientation.

Numerical analysis of injection molding of glass fiber reinforced thermoplastics. Part 1: Injection pressures and flow

AbstractNumerical calculations of flow and injection pressures during injection molding of fiber‐filled thermoplastics are compared to experimental measurements. The flow is modeled as a 2–D, nonisothermal, free‐surface flow with a new viscosity model dependent upon temperature, pressure, and fiber concentration. The steady‐state viscosity model is developed to account for the fiber‐concentration and shear‐thinning viscosities of the polymer based upon combining the Dinh‐Armstrong fiber model with the Carreau viscosity model. The new model has four parameters, three from the Carreau model and one from Dinh‐Armstrong for fiber concentrations. The new model calculates reasonably well the steady‐state viscosity of fiber‐filled polypropylene over the shear rate range of 0.01 s−1to 20 s−1. The numerical work successfully describes the flow of fiber‐filled polymers during injection molding using finite‐difference solutions for the transport equations and marker particles to track the flow front. The comparisons between the calculated and measured pressure drops for an injection molded part were reasonable for the unfilled and fiber‐filled polypropylene materials. The pressure drop comparison is very good for slow fill of a base case resin, Himont polypropylene, but not as good for fast fill of the resin. The pressure drop comparison is very good for fast fill of glass‐filled resin, DSM polypropylene with 10% and 20% short fibers, but not as good for slow fill of the resin and resin plus fibers.