Running-In of Systems Protected by Additive-Rich Oils

Recent research on mild wearing systems running under boundary lubrication conditions focus more and more on the role of the nano-crystalline layer present at the surface of the components in contact. This layer has a typical thickness of a few tenths of nano-meters up to a few microns depending on the operational conditions. The role of this layer with respect to wear is, however, still unclear as well as its mechanical behavior. In this study, a first step is made in incorporating this type of layer into a wear model. Using an elasto-plastic semi-analytical-method the effect of different material behaviors reported through out current literature for the nano-crystalline layer on wear is studied. From the results it can be concluded that the effect of this mechanically altered layer has an important influence on the wear of the system, especially during the initial phase of running

Mild wear prediction of boundary lubricated contacts

In this paper, a wear model is introduced for the mild wear present in boundary-lubricated systems protected by additive-rich lubricants. The model is based on the hypothesis that the mild wear is mainly originating from the removal of the sacrificial layer formed by a chemical reaction between the base material and the additive packages present in the lubricant. By removing a part of this layer, the chemical balance of the system is disturbed and the system will try to restore the balance for which it uses base material. In this study, mechanical properties reported throughout literature are included into the wear model based on observed phenomena for this type of systems. The model is validated by model experiments and the results are in very good agreement, suggesting that the model is able to simulate wear having a predictive nature rather than on empirical-based relationships as Archard’s linear wear model. Also a proposal is made to include the transition from mild to severe wear into the model creating a complete wear map

Rail Wear Estimation for Predictive Maintenance:a strategic approach

Since the very beginning of rail transport, wear has been identified as one of the dominant damage mechanisms that influence the Remaining Useful Life (RUL) of rail tracks. Whereas maintenance of the track is now predominantly executed at fixed intervals or based on yearly inspections, the accurate prediction of rail wear could considerably improve the maintenance process. The present work proposes a method for long-term rail wear prediction using measurements of actual rail and wheel profiles as starting point. By doing so, the computational expensive step of updating the rail profile in a wear calculation, as is done in presently used methods, can be omitted. The proposed method is used to study a number of generic trends, varying curve radius and rail or wheel profile. Further, the method is validated against measured wear on actual track sections for moderate curves. Finally, it can easily be extended to include variations in operational usage of the track (type / weight of trains, geometric details, slip conditions) in the future. The method presented in this paper can therefore assist in improving the track maintenance process by maximizing the utilization of the track service life, and minimizing maintenance costs

Control of a tandem queue with a startup cost for the second server

Various systems across a broad range of applications contain tandem queues. Strong dependence between the servers has proven to make such networks complicated and difficult to study. Exact analysis is rarely computationally tractable and sometimes not even possible. Nevertheless, as it is most often the case in reality, there are costs associated with running such systems, and therefore, optimizing the control of tandem queues is of main interest from both a theoretical and a practical point of view. Motivated by this, the present paper considers a tandem queueing network with linear holding costs and a startup cost for the second server. In our work, we present a rather intuitive, easy to understand, and at the same time very accurate technique to approximate the optimal decision policy. Extensive numerical experimentation shows that the approximation works extremely well for a wide range of parameter combinations

Flow termination signaling in the centralized pre-congestion notification architecture

Pre-congestion notification (PCN) protects inelastic traffic by using feedback on network link loads on and acting upon this accordingly. These actions comprise to admission control and termination of flows. Two PCN architectures have been defined by IETF: the centralized and decentralized PCN architecture. The decentralized PCN architecture has received much attention in the literature whereas the centralized PCN architecture has not. In the decentralized architecture, feedback is sent from the egress nodes to ingress nodes, which then take and apply decisions regarding admission of new flows and/or termination of ongoing flows. Signaling occurs only between ingress and egress nodes. In the centralized architecture these decisions are made at a central node, which requires proper signaling for action and information exchange between the central node and the egress and ingress nodes. This signaling has been suggested by other authors, but is not fully defined yet. Our contribution is twofold. We define signaling in the centralized PCN architecture focussing on flow termination, which completes the definition of the signaling in the centralized PCN architecture. Secondly, we run extensive simulations showing that the proposed signaling works well and that the performances of the centralized PCN and the decentralized PCN architectures are similar. Hence, it is expected that results from existing research on the effectiveness of decentralized PCN are also valid when the centralized PCN architecture is used

Training in data definitions improves quality of intensive care data

BACKGROUND: Our aim was to assess the contribution of training in data definitions and data extraction guidelines to improving quality of data for use in intensive care scoring systems such as the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II in the Dutch National Intensive Care Evaluation (NICE) registry. METHODS: Before and after attending a central training programme, a training group of 31 intensive care physicians from Dutch hospitals who were newly participating in the NICE registry extracted data from three sample patient records. The 5-hour training programme provided participants with guidelines for data extraction and strict data definitions. A control group of 10 intensive care physicians, who were trained according the to train-the-trainer principle at least 6 months before the study, extracted the data twice, without specific training in between. RESULTS: In the training group the mean percentage of accurate data increased significantly after training for all NICE variables (+7%, 95% confidence interval 5%–10%), for APACHE II variables (+6%, 95% confidence interval 4%–9%) and for SAPS II variables (+4%, 95% confidence interval 1%–6%). The percentage data error due to nonadherence to data definitions decreased by 3.5% after training. Deviations from 'gold standard' SAPS II scores and predicted mortalities decreased significantly after training. Data accuracy in the control group did not change between the two data extractions and was equal to post-training data accuracy in the training group. CONCLUSION: Training in data definitions and data extraction guidelines is an effective way to improve quality of intensive care scoring data