Optimal algorithms for synthesis of reliable application-specificheterogeneous multiprocessors

Fast and optimally-reliable application-specific multiprocessor-synthesis is critical in system-level design, especially in medical, automotive, space, and military applications. Previous work in multiprocessor-synthesis and task-allocation for performance and reliability requires exponential time, and therefore, is useful only for small examples. We present the first deterministic and provably-optimal algorithm (RELSYN-OPT) to synthesize real-time, reliable multiprocessors using a heterogeneous library of N processors and L link types. We prove that for a series-parallel graph with M subtasks and nested-depth d, the worst-case computational complexity of RELSYN-OPT Is O(M·(L+N)·N^d). For tree-structured task graphs, RELSYN-OMT runs in O(M·(L+N)), and is asymptotically optimum, RELSYN-OPT, because of its speed, applies to static and dynamic task allocation for an ultra-reliable distributed processing environment for which, until now, research has produced only suboptimal heuristic solutions     

Fundamental studies of driven and self-propelled rotary tool cutting processes—II. Experimental investigation

An extensive experimental investigation has been carried out to verify the developed mechanics of cutting analyses for the fundamental driven and self-propelled rotary tool cutting processes. This involved testing the dynamic or perfect equivalence between the rotary tool and equivalent classical processes over a wide range of inclination angles, cut thickness and rake angles using statistical processing techniques. The collinearity conditions at the shear plane and rake face have also been tested as part of the model verification. It has been shown that all the force components, deformation and basic cutting parameter trends and quantities required for perfect equivalence have been satisfied as were the necessary collinearity conditions. The verified models provide a deeper understanding of the cutting mechanics and characteristics of these ingenious material removal operations and form the basis for the development of predictive cutting models for the fundamental and complex practical rotary tool operations.     

Hydrogen powered car: Two-stage modelling system

This paper presents a research work on intelligent two-stage modelling system to estimate a hydrogen internal combustion engine performances including: engine torque and oxides of nitrogen emissions. In the created models, the ignition timing is chosen as a local input, while the engine speed, throttle position, injection duration, injection end angle and lambda are chosen as global inputs. While previous papers [1], [2], [3] and [4] included tuning procedures and hydrogen engine performances, intelligent emissions prediction of hydrogen car, and two-stage modelling of torque, this paper carries on from those observations to develop a completed two-stage modelling system of the converted hydrogen engine. More details on individual two-stage models are provided based on data recorded during the fine tuning process on dynamometer. This work is a step towards establishing intelligent two-stage modelling of hydrogen powered car via application of response surface methodology with hydrogen engine in the loop simulation and testing.     

How would you integrate the equations of motion in dissipative particle dynamics simulations?

In this work we assess the quality and performance of several novel dissipative particle dynamics integration schemes that have not previously been tested independently. Based on a thorough comparison we identify the respective methods of Lowe and Shardlow as particularly promising candidates for future studies of large-scale properties of soft matter systems.     

Species segregation of binary mixtures and a continuous size distribution of Group B particles in riser flow

Experiments involving a gas–solid, pilot-scale circulating fluidized bed (CFB) have been carried out, with a focus on species segregation measurements in a riser. Three mixtures were considered: (i) a binary mixture with particles of different sizes (d_ave) but same material density (ρ_s), (ii) a binary mixture with particles of different material densities (ρ_s) but same size (d_ave), and (iii) a continuous particle size distribution (PSD). Local measurements of the composition (i.e., species segregation) of each mixture were obtained over a range of operating conditions. Similar to previous works, the results show that the more massive species (i.e., greater d_ave or ρ_s) preferentially segregates toward the wall in all cases. Several new trends were also observed. First, for the binary mixtures, composition of the more massive species increases with riser height at the wall under some operating conditions. The operating conditions that cause this phenomenon are mutually exclusive for the size-difference and density-difference systems. Second, for the continuous PSD, radial segregation is observed even when there is a net positive flux in the annular region, contrary to previous findings which indicated segregation only for conditions leading to a net downward flux in the annular region. Finally, two qualitative differences between the binary and continuous mixtures were noted: (i) a monotonic decrease in species segregation is observed for the binary mixtures with an increase in the solid loading (m), while a non-monotonic trend is observed for the continuous PSD, and (ii) while the shape of the radial segregation profile is flattest at the riser bottom for the binary mixtures, the flattest radial profile is at the riser top for the continuous PSD.     

ANN virtual sensors for emissions prediction and control

This paper demonstrates the use of artificial neural networks virtual sensors in emissions prediction and control for a gasoline engine. Tailpipe emissions and engine parameters were first measured experimentally to form a comprehensive database for network training and testing. Individual predictive models were constructed using the optimization layer-by-layer neural network. Simulation results demonstrated that the networks, as virtual sensors, can accurately predict the engine parameters and emissions quantitatively and qualitatively with RMS errors below 9%. The second part of this paper then presents a virtual sensor control model which is the combination of the two individual emissions and engine predictive models developed previously. The main objective of this part is to control the exhaust emissions within the desired limits by predicting optimum engine parameters with the use of artificial neural network virtual sensors. Results showed that the emissions levels were successfully controlled within the defined limits, with maximum tolerance of 6%. This first part of this paper demonstrated that with the use of artificial neural network virtual sensors, emissions and engine parameters can be accurately predicted. Hence with accurate virtual sensors, emissions were then controlled within the desired limits by optimizing the engine parameters. This proposed work demonstrated a viable and accurate methodology in emissions predictive and control. By applying virtual sensor models, the need additional, cumbersome and costly measuring and monitoring devices can be eliminated.     

ANALYSIS OF THE PERFORMANCE OF NON-UNIFORMLY ACTIVE CATALYST PELLETS FOR A NON-ISOTHERMAL SERIES REACTION

An analysis of the influence of the non-uniform distribution of the active species in a catalyst pellet on the effectiveness factor and selectivity for the case of a series non-isothermal reaction is carried out. The case in which the desired reaction has a lower activation energy than the undesired reaction is considered. The effect of different reaction parameters on the selectivity for a single pellet is studied, and the optimum activity distribution is determined for high values of Thiele modulus. It is shown that temperature gradients within the pellet have a detrimental effect on local and overall selectivity and that these gradients are a strong function of the distribution of catalytically active material within the pellet.