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Collective wind farm flow control, where wind turbines are operated in an individually suboptimal strategy to benefit the aggregate farm, has demonstrated potential to reduce wake interactions and increase farm energy production. However, existing wake models used for flow control often estimate the thrust and power of yaw-misaligned turbines using simplified empirical expressions that require expensive calibration data and do not extrapolate accurately between turbine models. The thrust, wake velocity deficit, wake deflection and power of a yawed wind turbine depend on its induced velocity. Here, we extend classical one-dimensional momentum theory to model the induction of a yaw-misaligned actuator disk. Analytical expressions for the induction, thrust, initial wake velocities and power are developed as a function of the yaw angle ( $$\gamma$$ ) and thrust coefficient. The analytical model is validated against large eddy simulations of a yawed actuator disk. Because the induction depends on the yaw and thrust coefficient, the power generated by a yawed actuator disk will always be greater than a $$\cos ^3(\gamma )$$ model suggests. The power lost due to yaw misalignment depends on the thrust coefficient. An analytical expression for the thrust coefficient that maximizes power, depending on the yaw, is developed and validated. Finally, using the developed induction model as an initial condition for a turbulent far-wake model, we demonstrate how combining wake steering and thrust (induction) control can increase array power, compared to either independent steering or induction control, due to the joint dependence of the induction on the thrust coefficient and yaw angle. 

The reactive force field (ReaxFF) interatomic potential is a powerful tool for simulating the behavior of molecules in a wide range of chemical and physical systems at the atomic level. Unlike traditional classical force fields, ReaxFF employs dynamic bonding and polarizability to enable the study of reactive systems. Over the past couple decades, highly optimized parallel implementations have been developed for ReaxFF to efficiently utilize modern hardware such as multi-core processors and graphics processing units (GPUs). However, the complexity of the ReaxFF potential poses challenges in terms of portability to new architectures (AMD and Intel GPUs, RISC-V processors, etc.), and limits the ability of computational scientists to tailor its functional form to their target systems. In this regard, the convergence of cyber-infrastructure for high performance computing (HPC) and machine learning (ML) presents new opportunities for customization, programmer productivity and performance portability. In this paper, we explore the benefits and limitations of JAX, a modern ML library in Python representing a prime example of the convergence of HPC and ML software, for implementing ReaxFF. We demonstrate that by leveraging auto-differentiation, just-in-time compilation, and vectorization capabilities of JAX, one can attain a portable, performant, and easy to maintain ReaxFF software. Beyond enabling MD simulations, end-to-end differentiability of trajectories produced by ReaxFF implemented with JAX makes it possible to perform related tasks such as force field parameter optimization and meta-analysis without requiring any significant software developments. We also discuss scalability limitations using the current version of JAX for ReaxFF simulations. 

Studies have shown that the graduation rate for underrepresented minorities (URM) students enrolled in engineering doctorates is significantly lower than their peers. In response, we created the “Rising Doctoral Institute (RDI)”. This project aims to address issues that URM students encounter when transitioning into a Ph.D. in engineering and their decision to persist in the program. To suggest institutional policies that increase the likelihood of URM students to persist in their doctorate, we identify and analyze some factors in the academic system that reinforce or hinder the retention of URM students in doctoral education. Although the factors that influence persistence in URM students have been largely studied as direct causes of attrition or retention, there is a need for a system perspective that takes into account the complexity and dynamic interaction that exists between those factors. The academic system is a complex system that, by nature, is policy resistant. This means that a positive variation of a factor can incur unintended consequences that could lead to a negative variation in other factors and ultimately hinder the positive outcomes of that policy. In this work-in-progress article, we analyze the dynamics of the factors in the academic system that reinforce or hinder the retention of URM graduate students in engineering. The purpose is to build some of the causal loops that involve those factors, to improve the understanding of how the complex system works, and prevent unintended consequences of institutional policies. We used Causal Loop Diagrams (CLD) to model the feedback loops of the system based on initial hypotheses of causal relationships between the factors. We followed a process that started with establishing hypotheses from a previous literature review, then using a different set of articles we identified the factors related to the hypotheses and the causal links between them. Next, we did axial coding to group the concepts into smaller categories and established the causal relations between categories. With these categories and relations, we created the CLDs for each hypothesis. For the CLDs that have connections missing to close the loop, we went to find additional literature to close them. Finally, we analyzed the implications of each CLD. In this article, we analyze and describe three major CLDs found in literature. The first one was built around the factor of having a positive relationship with the supervisor. The second centered on the student’s experience. The third focused on factors that relate to university initiatives