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The newly formed UCL Institute for Digital Innovation in the Built Environment operates at the interface of digital engineering, computer science and human experience

Modelling the influence of the process inputs on the removal of surface contaminants from Ti-6Al-4V linear friction welds

The linear friction welding (LFW) process is finding increasing interest from industry for the fabrication of near-net-shape, titanium alloy Ti–6Al–4V, aerospace components. Currently, the removal of surface contaminants, such as oxides and foreign particles, from the weld interface into the flash is not fully understood. To address this problem, two-dimensional (2D) computational models were developed using the finite element analysis (FEA) software DEFORM and validated with experiments. The key findings showed that the welds made with higher applied forces required less burn-off to completely remove the surface contaminants from the interface into the flash; the interface temperature increased as the applied force was decreased or the rubbing velocity increased; and the boundary temperature between the rapid flash formation and negligible material flow was approximately 970 °C. An understanding of these phenomena is of particular interest for the industrialisation of near-net-shape titanium alloy aerospace components.EPSRC, Boeing Company, Welding Institut

Modelling of the workpiece geometry effects on Ti–6Al–4V linear friction welds

Linear friction welding (LFW) is a solid-state joining process that is finding increasing interest from industry for the fabrication of titanium alloy (Ti–6Al–4V) preforms. Currently, the effects of the workpiece geometry on the thermal fields, material flow and interface contaminant removal during processing are not fully understood. To address this problem, two-dimensional (2D) computational models were developed using the finite element analysis (FEA) software DEFORM and validated with experiments. A key finding was that the width of the workpieces in the direction of oscillation (in-plane width) had a much greater effect on the experimental weld outputs than the cross-sectional area. According to the validated models, a decrease of the in-plane width increased the burn-off rate whilst decreasing the interface temperature, TMAZ thickness and the burn-off required to remove the interface contaminants from the weld into the flash. Furthermore, the experimental weld interface consisted of a Widmanstätten microstructure, which became finer as the in-plane width was reduced. These findings have significant, practical benefits and may aid industrialisation of the LFW process.The authors would like to thank the Engineering and Physical Sciences Research Council (EPSRC), The Boeing Company and The Welding Institute (TWI) for funding the research presented in this paper

Robustness of spatial micronetworks

Power lines, roadways, pipelines, and other physical infrastructure are critical to modern society. These structures may be viewed as spatial networks where geographic distances play a role in the functionality and construction cost of links. Traditionally, studies of network robustness have primarily considered the connectedness of large, random networks. Yet for spatial infrastructure, physical distances must also play a role in network robustness. Understanding the robustness of small spatial networks is particularly important with the increasing interest in microgrids, i.e., small-area distributed power grids that are well suited to using renewable energy resources. We study the random failures of links in small networks where functionality depends on both spatial distance and topological connectedness. By introducing a percolation model where the failure of each link is proportional to its spatial length, we find that when failures depend on spatial distances, networks are more fragile than expected. Accounting for spatial effects in both construction and robustness is important for designing efficient microgrids and other network infrastructure

Identification of Air Traffic Management Principles Influential in the Development of an Airport Arrival Delay Prediction Model

Since the September 11, 2001 attacks, worldwide air traffic has steadily been increasing towards peak levels reported from 2000 to 2001 (Federal Aviation Administration [FAA], 2011). Although U.S. system-wide traffic is still around 10% less than the highest volumes, congestion at particular airports prone to delays, such as Newark, Philadelphia, New York LaGuardia, and New York Kennedy, is up nearly 10% from 2000 metrics. Other airports, such as Chicago O’Hare and Atlanta in the U.S. and London Heathrow, Madrid, and Istanbul in Europe, are seemingly continually plagued with flight delays regardless of variations in traffic (FAA, 2012). According to the Bureau of Transportation Statistics (2013), the best flight punctuality rate among the 29 largest primary U.S. airports in January 2012 was 89.7% with the worst being 77.2%. In Europe, 14 major airports reported arrival delays in excess of 15 minutes for more than 25% of flights (FAA, 2012). Air traffic forecasts through 2031 indicate that both the passenger volume and the number of transport aircraft will be double that of 2012 levels. Considering many of the aforementioned airports are operating near or beyond capacity, it is likely that air traffic delays will only get worse (Airbus, 2012). The importance of delay management is critical to a variety of stakeholders from passengers to air carrier operations management to air traffic control personnel. Reliable delay prediction can mitigate the snowball effects delays can have on the air traffic management system and air carrier structures (Xu, Sherry, & Laskey, 2008). A variety of studies have been implemented to study air traffic delays but generally focus on a system-wide approach that includes arrival, enroute, and departure delays (Brooker, 2009; Coy, 2006; Santos & Robin, 2011; Xu, Sherry, & Laskey, 2008). Alternatively, others have focused on individual airports and their potential influence on the whole air traffic management system (Nayak & Zhang, 2011). More research on the factors associated with and prediction of airport-related delays have been advocated (Brooker, 2009; Coy, 2006; Nayak & Zhang, 2011; Santos & Robin, 2011; Xu, Sherry, & Laskey, 2008). Ideally, an improved model with predictive capabilities would assist in planning for and potentially mitigating negative effects of airport-based arrival congestion. The goal of this pilot study is to begin the construction of an improved airport delay prediction model by exploring potentially influential air traffic management principles. Utilizing expert panel-based model and procedural improvement techniques similar to those used in medical and technical fields, this study aims to bolster existing airport arrival delay prediction models (Deason & Jefferson, 2010; Estes, 2008; Gisev, Bell, O’Reilly, Rosen, & Chen, 2010). In this Phase I pilot study, a purposeful sample of air traffic control instructors, college faculty, and air traffic controllers will be asked to generate a list of air traffic management principles that influence airport arrival efficiency. This data will be utilized to create subsequent phases which will include a Delphi Panel to rank the identified principles, confirmatory analysis, statistical modeling, and model testing

Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the U.S.

The characteristics of influenza seasons vary substantially from year to year, posing challenges for public health preparation and response. Influenza forecasting is used to inform seasonal outbreak response, which can in turn potentially reduce the impact of an epidemic. The United States Centers for Disease Control and Prevention, in collaboration with external researchers, has run an annual prospective influenza forecasting exercise, known as the FluSight challenge. Uniting theoretical results from the forecasting literature with domain-specific forecasts from influenza outbreaks, we applied parametric forecast combination methods that simultaneously optimize model weights and calibrate the ensemble via a beta transformation and made adjustments to the methods to reduce their complexity. We used the beta-transformed linear pool, the finite beta mixture model, and their equal weight adaptations to produce ensemble forecasts retrospectively for the 2016/2017, 2017/2018, and 2018/2019 influenza seasons in the U.S. We compared their performance to methods that were used in the FluSight challenge to produce the FluSight Network ensemble, namely the equally weighted linear pool and the linear pool. Ensemble forecasts produced from methods with a beta transformation were shown to outperform those from the equally weighted linear pool and the linear pool for all week-ahead targets across in the test seasons based on average log scores. We observed improvements in overall accuracy despite the beta-transformed linear pool or beta mixture methods\u27 modest under-prediction across all targets and seasons. Combination techniques that explicitly adjust for known calibration issues in linear pooling should be considered to improve probabilistic scores in outbreak settings