923 resultados para implicit dynamic analysis


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This work proposes a extends a novel approach to compute tran sonic Limit Cycle Oscillations using high fidelity analysis. CFD based Harmonic Balance methods have proven to be efficient tools to predict periodic phenomena. This paper’s contribution is to present a methodology to determine the unknown frequency of oscillations using an implicit for- mulation of the HB method to accurately capture Limit Cycle Oscillations (LCOs); this is achieved by defining a frequency updating procedure based on a coupled CFD/CSD Harmonic Balance formulation to find the LCO condition. A pitch/plunge aerofoil and respective linear structural models is used to exercise the new method. Results show consistent agreement between the proposed and time-marching methods for both LCO amplitude and frequency.

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Hot compression tests were carried out on 9Cr–Nb–V heat resistant steels in the temperature range of 600–1200 °C and the strain rate range of 10−2–100 s−1 to study their deformation characteristics. The full recrystallization temperature and the carbon-free bainite phase transformation temperature were determined by the slope-change points in the curve of mean flow stress versus the inverse of temperature. The parameters of the constitutive equation for the experimental steels were calculated, including the stress exponent and the activation energy. The lower carbon content in steel would increase the fraction of precipitates by increasing the volume of dynamic strain-induced (DSIT) ferrite during deformation. The ln(εc) versus ln(Z) and the ln(σc) versus ln(Z) plots for both steels have similar trends. The efficiency of power dissipation maps with instability maps merged together show excellent workability from the strain of 0.05 to 0.6. The microstructure of the experimental steels was fully recrystallized upon deformation at low Z value owing to the dynamic recrystallization (DRX), and exhibited a necklace structure under the condition of 1050 °C/0.1 s−1 due to the suppression of the secondary flow of DRX. However, there were barely any DRX grains but elongated pancake grains under the condition of 1000 °C/1 s−1 because of the suppression of the metadynamic recrystallization (MDRX).

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Today there is a growing interest in the integration of health monitoring applications in portable devices necessitating the development of methods that improve the energy efficiency of such systems. In this paper, we present a systematic approach that enables energy-quality trade-offs in spectral analysis systems for bio-signals, which are useful in monitoring various health conditions as those associated with the heart-rate. To enable such trade-offs, the processed signals are expressed initially in a basis in which significant components that carry most of the relevant information can be easily distinguished from the parts that influence the output to a lesser extent. Such a classification allows the pruning of operations associated with the less significant signal components leading to power savings with minor quality loss since only less useful parts are pruned under the given requirements. To exploit the attributes of the modified spectral analysis system, thresholding rules are determined and adopted at design- and run-time, allowing the static or dynamic pruning of less-useful operations based on the accuracy and energy requirements. The proposed algorithm is implemented on a typical sensor node simulator and results show up-to 82% energy savings when static pruning is combined with voltage and frequency scaling, compared to the conventional algorithm in which such trade-offs were not available. In addition, experiments with numerous cardiac samples of various patients show that such energy savings come with a 4.9% average accuracy loss, which does not affect the system detection capability of sinus-arrhythmia which was used as a test case. 

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In this paper we make use of the first and second waves of the 2008 and 1998 cohorts of the Growing Up in Ireland study, to develop a multidimensional and dynamic approach to understanding the impact on families and children in Ireland of the Great Recession. Economic vulnerability is operationalised as involving a distinctive risk profile in relation to relative income, household joblessness and economic stress. We find that the recession was associated with a significant increase in levels of economic vulnerability and changing risk profiles involving a more prominent role for economic stress for both the 2008 and 1998 cohorts. The factors affecting vulnerability outcomes were broadly similar for both cohorts. Persistent economic vulnerability was significantly associated with lone parenthood, particularly for those with more than one child, lower levels of Primary Care Giver (PCG) education and to a lesser extent younger age of PCG at child’s birth, number of children and a parent leaving or dying. Similar factors were associated with transient vulnerability in the first wave but the magnitude of the effects was significantly weaker particularly in relation to lone parenthood and level of education of the PCG. For entry into vulnerability the impact of these factors was again substantially weaker than for persistent and transient vulnerability indicating a significantly greater degree of socio-economic heterogeneity among the group that became vulnerable during the recession. The findings raise policy and political problems that go beyond those associated with catering for groups that have tended to be characterized by high dependence on social welfare.

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The efficiency of generation plants is an important measure for evaluating the operating performance. The objective of this paper is to evaluate electricity power generation by conducting an All-Island-Generator-Efficiency-Study (AIGES) for the Republic of Ireland and Northern Ireland by utilising a Data Envelopment Analysis (DEA) approach. An operational performance efficiency index is defined and pursued for the year 2008. The economic activities of electricity generation units/plants examined in this paper are characterized by numerous input and output indicators. Constant returns to scale (CRS) and variable returns to scale (VRS) type DEA models are employed in the analysis. Also a slacks based analysis indicates the level of inefficiency for each variable examined. The findings from this study provide a general ranking and evaluation but also facilitate various interesting efficiency comparisons between generators by fuel type.

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Power has become a key constraint in nanoscale inte-grated circuit design due to the increasing demands for mobile computing and higher integration density. As an emerging compu-tational paradigm, an inexact circuit offers a promising approach to significantly reduce both dynamic and static power dissipation for error-tolerant applications. In this paper, an inexact floating-point adder is proposed by approximately designing an exponent sub-tractor and mantissa adder. Related operations such as normaliza-tion and rounding are also dealt with in terms of inexact computing. An upper bound error analysis for the average case is presented to guide the inexact design; it shows that the inexact floating-point adder design is dependent on the application data range. High dynamic range images are then processed using the proposed inexact floating-point adders to show the validity of the inexact design; comparison results show that the proposed inexact floating-point adders can improve the power consumption and power-delay product by 29.98% and 39.60%, respectively.

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Complex collaboration in rapidly changing business environments create challenges for management capability in Utility Horizontal Supply Chains (UHSCs) involving the deploying and evolving of performance measures. The aim of the study is twofold. First, there is a need to explore how management capability can be developed and used to deploy and evolve Performance Measurement (PM), both across a UHSC and within its constituent organisations, drawing upon a theoretical nexus of Dynamic Capability (DC) theory and complementary Goal Theory. Second, to make a contribution to knowledge by empirically building theory using these constructs to show the management motivations and behaviours within PM-based DCs. The methodology uses an interpretive theory building, multiple case based approach (n=3) as part of a USHC. The data collection methods include, interviews (n=54), focus groups (n=10), document analysis and participant observation (reflective learning logs) over a five-year period giving longitudinal data. The empirical findings lead to the development of a conceptual framework showing that management capabilities in driving PM deployment and evolution can be represented as multilevel renewal and incremental Dynamic Capabilities, which can be further understood in terms of motivation and behaviour by Goal-Theoretic constructs. In addition three interrelated cross cutting themes of management capabilities in consensus building, goal setting and resource change were identified. These management capabilities require carefully planned development and nurturing within the UHSC. 

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Natural gas (NG) network and electric network are becoming tightly integrated by microturbines in the microgrid. Interactions between these two networks are not well captured by the traditional microturbine (MT) models. To address this issue, two improved models for single-shaft MT and split-shaft MT are proposed in this paper. In addition, dynamic models of the hybrid natural gas and electricity system (HGES) are developed for the analysis of their interactions. Dynamic behaviors of natural gas in pipes are described by partial differential equations (PDEs), while the electric network is described by differential algebraic equations (DAEs). So the overall network is a typical two-time scale dynamic system. Numerical studies indicate that the two-time scale algorithm is faster and can capture the interactions between the two networks. The results also show the HGES with a single-shaft MT is a weakly coupled system in which disturbances in the two networks mainly influence the dc link voltage of the MT, while the split-shaft MT is a strongly coupled system where the impact of an event will affect both networks.

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Milling is an important operation in many industries, such as mining and pharmaceutical. Although the comminution process during milling has been extensively studied, the material fragmentation mechanisms in a mill are still not well understood partly because of the lack of an understanding on the local stressing and dynamic information under operational conditions in mills. This paper presents a DEM simulation of particle dynamics and impact events in a centrifugal impact pin mill. The main focus is the statistical characteristics of the dominant stressing modes during the milling process. The frequency, velocity and force of the different impact events between particles and mill components, or between particles, are analysed. © 2013 AIP Publishing LLC.

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Strengthening reinforced concrete (RC) structures by externally bonded FRP composites has been widely used for static loading and seismic retrofitting since 1990s. More recently many studies on strengthening concrete and masonry structures with externally bonded FRP for improved blast and impact resistance in protective engineering have also been conducted. The bond behaviour between the FRP and concrete plays a critical role in a strengthening system with externally bonded FRP. However, the understanding of how the bond between FRP and concrete performs under high strain rate is severely limited. Due to the dynamic characteristics of blast and impact loading, the bond behaviour between FRP and concrete under such loading is very different from that under static loading. This paper presents a study on the dynamic bond-slip behaviour based on both the numerical analysis and test results. A dynamic bond-slip model is proposed in this paper.

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Highway structures such as bridges are subject to continuous degradation primarily due to ageing, loading and environmental factors. A rational transport policy must monitor and provide adequate maintenance to this infrastructure to guarantee the required levels of transport service and safety. Increasingly in recent years, bridges are being instrumented and monitored on an ongoing basis due to the implementation of Bridge Management Systems. This is very effective and provides a high level of protection to the public and early warning if the bridge becomes unsafe. However, the process can be expensive and time consuming, requiring the installation of sensors and data acquisition electronics on the bridge. This paper investigates the use of an instrumented 2-axle vehicle fitted with accelerometers to monitor the dynamic behaviour of a bridge network in a simple and cost-effective manner. A simplified half car-beam interaction model is used to simulate the passage of a vehicle over a bridge. This investigation involves the frequency domain analysis of the axle accelerations as the vehicle crosses the bridge. The spectrum of the acceleration record contains noise, vehicle, bridge and road frequency components. Therefore, the bridge dynamic behaviour is monitored in simulations for both smooth and rough road surfaces. The vehicle mass and axle spacing are varied in simulations along with bridge structural damping in order to analyse the sensitivity of the vehicle accelerations to a change in bridge properties. These vehicle accelerations can be obtained for different periods of time and serve as a useful tool to monitor the variation of bridge frequency and damping with time.

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A three-dimensional continuum damage mechanics-based material model has been implemented in an implicit Finite Element code to simulate the progressive degradation of advanced composite materials. The damage model uses seven damage variables assigned to tensile, compressive and non-linear shear damage at a laminae level. The objectivity of the numerical discretization is assured using a smeared formulation. The material model was benchmarked against experimental uniaxial coupon tests and it is shown to reproduce key aspects observable during failure, such as the inclined fracture plane in matrix compression and the shear band in a ±45° tension specimen.

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Static timing analysis provides the basis for setting the clock period of a microprocessor core, based on its worst-case critical path. However, depending on the design, this critical path is not always excited and therefore dynamic timing margins exist that can theoretically be exploited for the benefit of better speed or lower power consumption (through voltage scaling). This paper introduces predictive instruction-based dynamic clock adjustment as a technique to trim dynamic timing margins in pipelined microprocessors. To this end, we exploit the different timing requirements for individual instructions during the dynamically varying program execution flow without the need for complex circuit-level measures to detect and correct timing violations. We provide a design flow to extract the dynamic timing information for the design using post-layout dynamic timing analysis and we integrate the results into a custom cycle-accurate simulator. This simulator allows annotation of individual instructions with their impact on timing (in each pipeline stage) and rapidly derives the overall code execution time for complex benchmarks. The design methodology is illustrated at the microarchitecture level, demonstrating the performance and power gains possible on a 6-stage OpenRISC in-order general purpose processor core in a 28nm CMOS technology. We show that employing instruction-dependent dynamic clock adjustment leads on average to an increase in operating speed by 38% or to a reduction in power consumption by 24%, compared to traditional synchronous clocking, which at all times has to respect the worst-case timing identified through static timing analysis.

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The X-parameter based nonlinear modelling tools have been adopted as the foundation for the advanced methodology
of experimental characterisation and design of passive nonlinear devices. Based upon the formalism of the Xparameters,
it provides a unified framework for co-design of antenna beamforming networks, filters, phase shifters and
other passive and active devices of RF front-end, taking into account the effect of their nonlinearities. The equivalent
circuits of the canonical elements are readily incorporated in the models, thus enabling evaluation of PIM effect on the
performance of individual devices and their assemblies. An important advantage of the presented methodology is its
compatibility with the industry-standard established commercial RF circuit simulator Agilent ADS.
The major challenge in practical implementation of the proposed approach is concerned with experimental retrieval of the X-parameters for canonical passive circuit elements. To our best knowledge commercial PIM testers and practical laboratory test instruments are inherently narrowband and do not allow for simultaneous vector measurements at the PIM and harmonic frequencies. Alternatively, existing nonlinear vector analysers (NVNA) support X-parameter measurements in a broad frequency bands with a range of stimuli, but their dynamic range is insufficient for the PIM characterisation in practical circuits. Further opportunities for adaptation of the X-parameters methodology to the PIM
characterisation of passive devices using the existing test instruments are explored.