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Integrated maintenance and production planning with energy consumption and minimal repair
No full textIn this study, we analyze an integrated production-preventive maintenance planning problem where product processing times are affected by machine degradation. Preventive maintenance and repair can return the degraded machines/equipment to normal conditions, which can improve the job processing times and the amount of energy consumed. We propose a mathematical model for an integrated production and preventive maintenance planning problem in a multi-product, multi-period, single-machine manufacturing environment with minimal repair and energy consumption considerations in order to minimize the overall cost of production, inventory, energy, maintenance, and repair. The model investigates the impact of imperfect and “as-good-as-new” (AGAN) maintenance strategies on production plan and total cost. Experimental analyses provide insights about the proposed model: when the sensitivity of processing times to machine health status increases, the required number of maintenance actions increases. In addition, enforcing more number of maintenance actions into a production plan decreases energy cost of the system
A Numerical Study on Combustion and Emissions in a Dual Fuel Directly Injected Engine Using Biogas and Diesel
No full textA numerical study on the use of biogas and diesel in a dual-fueled directly-injected engine has been conducted. The objective of this study is to determine the effect of using biogas on engine performance, combustion, and emissions. The main fuel is biogas which is premixed with air in order to form a homogeneous mixture. The mixture is then compressed and ignited by injecting diesel fuel before TDC. The pilot fuel is expected to lead to multiple ignition points in the cylinder in order to achieve uniform combustion in the cylinder. The expected benefits are lower nitrogen oxides and soot compared to pure diesel combustion. Numerical simulations using CFD software were used to simulate fuel-air mixture, compression, fuel injection, combustion, and emissions. Different quantities of biogas and diesel were investigated to determine the optimum mixture ratio. Since biogas, which is natural gas produced from human waste, contains large quantities of carbon dioxide, the effect of carbon dioxide content in the fuel was investigated. The results of this study agree very well with results from other studies found in the literature
Dependence Structure of some Bivariate Distributions
No full textDependence in the world of uncertainty is a complex concept. However, it exists, is asymmetric, has magnitude and direction, and can be measured. We use some measures of dependence between random events to illustrate how to apply it in the study of dependence between non-numeric bivariate variables and numeric random variables. Graphics show what is the inner dependence structure in the Clayton Archimedean copula and the Bivariate Poisson distribution. We know this approach is valid for studying the local dependence structure for any pair of random variables determined by its empirical or theoretical distribution. And it can be used also to simulate dependent events and dependent r/v/’s, but some restrictions apply
Iterative Trapping of Gaseous Volatile Organic Compounds in a Capillary Column
No full textThe iterative trapping method has been developed for concentrating gaseous volatile organic compounds (VOCs) prior to gas chromatographic analysis. VOCs are trapped in a 50 cm x 0.53 mm metal capillary column coated with a 7 µm thick film of polydimethylsiloxane (PDMS). Iterative trapping does not employ the two-step thermal desorption approach used by most VOC concentrating techniques. Instead, a four-step cycle involving synchronized changes in flow direction and temperature is repeated throughout the sampling process. This iterative process causes VOCs to accumulate within the capillary well past the level where a standard two-step method reaches its saturation limit. Iterative trapping is capable of sampling and desorbing C5 through C11 n-alkanes with uniform efficiency. This new technique, in its current form, is most appropriate for focusing VOCs from gas volumes on the order of 10 mL. Iterative trapping increases the focusing power of a weak sorbent like PDMS and allows narrow chromatographic peaks to be generated without the use of high desorption temperatures or a secondary focusing stage
Erratum to: Accelerating Single Iteration Performance of CUDA-Based 3D Reaction-Diffusion Simulations
No full textThe most commonly used approach for solving reaction–diffusion systems relies upon stencil computations. Although stencil computations feature low compute intensity, they place high demands on memory bandwidth. Fortunately, GPU computing allows for the heavy reliance of stencil computations on neighboring data points to be exploited to significantly increase simulation speeds by reducing these memory bandwidth demands. Upon reviewing previously published works, a wide-variety of efforts have been made to optimize NVIDIA CUDA-based stencil computations. However, a critical aspect contributing to algorithm performance is commonly glossed over: the halo region loading technique utilized in conjunction with a given spatial blocking technique. This paper presents an in-depth examination of this aspect and the associated single iteration performance impacts when using symmetric, nearest neighbor 19-point stencils. This is accomplished by closely examining how the simulated space is partitioned into thread blocks and the balance between memory accesses, divergence, and computing threads. The resulting optimization strategy for accelerating 3-dimensional reaction–diffusion simulations offers up to 2.45 times speedup for single-precision floating point numbers in reference to GPU-based speedups found within the previously published work that this paper directly extends. In reference to our multithreaded CPU-based implementation, the resulting optimization strategy offers up to 8.69 times speedup for single-precision floating point numbers
Report of a Comprehensive Evaluation Visit
No full textThis document is the complete Report of a Comprehensive Evaluation Visit by HLC to Kettering in February 2014. The HLC’s recommendations are below.
-Monitoring: A monitoring report is required by December 1, 2015 (ADD LINK Kettering University Interim Report to the HLC 01Dec2015.pdf or Document 19) describing how capacity will be developed by Academic Affairs leadership for monitoring and improving assessment and other processes across academic programs.
-Rationale: Processes to ensure quality, rigor and consistency of teaching effectiveness, faculty workload and related expectations, and assessment of University Learning Outcomes, independent of mode of delivery, is needed.
-Monitoring: A monitoring report is required by June 1, 2016 describing a systematic program review process, including evidence of its implementation, initial data collection, and institutional reflection on the initial findings. NOTE: In a follow-up letter (ADD LINK ) this monitoring report was not required by HLC. Instead this issue will be carefully examined in the 4th year self-study and visit.
-Rationale: A robust and reoccurring process of program review that uses standard measures or performance indicators across academic programs and that generates data and information for use in resource allocation and decision making is essential for institutional planning
Flint International Statistics Conference Agenda
Get PDFConference Agenda—Keynote Speakers, Invited & Contributed Talks, Posters, Field Trips Tuesday, June 24 Wednesday, June 25 Thursday, June 26 Friday, June 27 Saturday, June 28
Select Sessions: Elart von Collani “Statistics as a general tool for all sciences.” Francesca Greselin “Measuring inequality at the time of the Great Divergence.” Ernest Fokoue “Recent Applications of Statistical Data Mining for Big Data Predictive Analysis.” Vladimir Kaishev “Probability and statistics in actuarial applications.” Galia Novikova “Data Mining for Software Development Quality Management.” Leda Minkova “Stochastic Models and Statistical Applications.” Krzysztof Podgorski “Non-Gaussian stochastic models: theory and applications.” Kristina Sendova “Risk measures, probability measures and mortality.