Publications

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Applying extreme temperature events for future conditions is not straightforward for infrastructure resilience analyses. This work introduces a stochastic model that fills this gap. The model uses at least 50 years of daily extreme temperature records, climate normals with 10%–90% confidence intervals, and shifts/offsets for increased frequency and intensity of heat wave events. Intensity and frequency are shifted based on surface temperature anomaly from 1850–1900 for 32 models from CMIP6. A case study for Worcester, Massachusetts passed 85% of cases using the two-sided Kolmogorov–Smirnov p-value test with 95% confidence for both temperature and duration. Future shifts for several climate scenarios to 2020, 2040, 2060, and 2080 had acceptable errors between the shifted model and 10- and 50-year extreme temperature event thresholds with the largest error being 2.67°C. The model is likely to be flexible enough for other patterns of extreme weather such as extreme precipitation and hurricanes.

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Interval Assignment (IA) is the problem of selecting the number of mesh edges (intervals) for each curve for conforming quad and hex meshing. The intervals x is fundamentally integer-valued. Many other approaches perform numerical optimization then convert a floating-point solution into an integer solution, which is slow and error prone. We avoid such steps: we start integer, and stay integer. Incremental Interval Assignment (IIA) uses integer linear algebra (Hermite normal form) to find an initial solution to the meshing constraints, satisfying the integer matrix equation Ax=b. Solving for reduced row echelon form provides integer vectors spanning the nullspace of A. We add vectors from the nullspace to improve the initial solution, maintaining Ax=b. Heuristics find good integer linear combinations of nullspace vectors that provide strict improvement towards variable bounds or goals. IIA always produces an integer solution if one exists. In practice we usually achieve solutions close to the user goals, but there is no guarantee that the solution is optimal, nor even satisfies variable bounds, e.g. has positive intervals. We describe several algorithmic changes since first publication that tend to improve the final solution. The software is freely available.

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Fluid–structure interactions were measured between a representative control surface and the hypersonic flow deflected by it. The control surface is simplified as a spanwise finite ramp placed on a longitudinal slice of a cone. The front surface of the ramp contains a thin panel designed to respond to the unsteady fluid loading arising from the shock-wave/boundary-layer interactions. Experiments were conducted at Mach 5 and Mach 8 with ramps of different angles. High-speed schlieren captured the unsteady flow dynamics and accelerometers behind the thin panel measured its structural response. Panel vibrations were dominated by natural modes that were excited by the broadband aerodynamic fluctuations arising in the flowfield. However, increased structural response was observed in two distinct flow regimes: 1) attached or small separation interactions, where the transitional regime induced the strongest panel fluctuations. This was in agreement with the observation of increased convective undulations or bulges in the separation shock generated by the passage of turbulent spots, and 2) large separated interactions, where shear layer flapping in the laminar regime produced strong panel response at the flapping frequency. In addition, panel heating during the experiment caused a downward shift in its natural mode frequencies.

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Deploying Tidal Energy Converters for electricity generation requires prior-knowledge of the potential Annual Energy Production (AEP) at the site, Ideally using a year-long tidal current record at the proposed site to minimize uncertainty. However, such records are often unavailable. Fortunately, using the periodic nature of tidal variability, the International Electrotechnical Commission Technical Specification for tidal energy resource assessment requires AEP calculation using at least 90 days of tidal current records at each turbine location. The sensitivity of AEP to different record durations has not been fully assessed. This is the goal of our study. The study utilized the U.S. tidal energy geodatabase to simulate tidal currents with various lengths, during 100 years of the 21st century. We then consider two frameworks for evaluating AEP: (a) The long-term (months) fixed instrument (FI) measurement at each proposed tidal turbine location, and (b) one FI measurement and short-term (hours) boat-based moving vessel measurements. Under the two scenarios, we examine the AEP assessed from short tidal current records, including how the AEP uncertainties vary spatially and temporally, and how they are associated with various astronomical factors. This helps provide guidance on choosing the appropriate assessment methodologies to reduce the AEP uncertainties and project cost.

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