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The state of Iowa is known for its high-yield agriculture, supporting rising demands for food and fuel production. But this productivity is also a significant contributor of nitrogen loading to the Mississippi River basin causing the hypoxic zone in the Gulf of Mexico. The delivery of nutrients, especially nitrogen, from the upper Mississippi River basin, is a function, not only of agricultural activity, but also of hydrology. Thus, it is important to consider extreme weather conditions, such as drought and flooding, and understand the effects of weather variability on Iowa’s food-energy-water (IFEW) system and nitrogen loading to the Mississippi River from Iowa. In this work, the simulation decomposition approach is implemented using the extended IFEW model with a crop-weather model to better understand the cause-and-effect relationships of weather parameters on the nitrogen export from the state of Iowa. July temperature and precipitation are used as varying input weather parameters with normal and log normal distributions, respectively, and subdivided to generate regular and dry weather conditions. It is observed that most variation in the soil nitrogen surplus lies in the regular condition, while the dry condition produces the highest soil nitrogen surplus for the state of Iowa. 

Purpose The purpose of this work is to investigate the similarity requirements for the application of multifidelity modeling (MFM) for the prediction of airfoil dynamic stall using computational fluid dynamics (CFD) simulations. Design/methodology/approach Dynamic stall is modeled using the unsteady Reynolds-averaged Navier–Stokes equations and Menter's shear stress transport turbulence model. Multifidelity models are created by varying the spatial and temporal discretizations. The effectiveness of the MFM method depends on the similarity between the high- (HF) and low-fidelity (LF) models. Their similarity is tested by computing the prediction error with respect to the HF model evaluations. The proposed approach is demonstrated on three airfoil shapes under deep dynamic stall at a Mach number 0.1 and Reynolds number 135,000. Findings The results show that varying the trust-region (TR) radius (λ) significantly affects the prediction accuracy of the MFM. The HF and LF simulation models hold similarity within small (λ ≤ 0.12) to medium (0.12 ≤ λ ≤ 0.23) TR radii producing a prediction error less than 5%, whereas for large TR radii (0.23 ≤ λ ≤ 0.41), the similarity is strongly affected by the time discretization and minimally by the spatial discretization. Originality/value The findings of this work present new knowledge for the construction of accurate MFMs for dynamic stall performance prediction using LF model spatial- and temporal discretization setup and the TR radius size. The approach used in this work is general and can be used for other unsteady applications involving CFD-based MFM and optimization. 

The Midwest state of Iowa in the US is one of the major producers of corn, soybean, ethanol, and animal products, and has long been known as a significant contributor of nitrogen loads to the Mississippi river basin, supplying the nutrient-rich water to the Gulf of Mexico. Nitrogen is the principal contributor to the formation of the hypoxic zone in the northern Gulf of Mexico with a significant detrimental environmental impact. Agriculture, animal agriculture, and ethanol production are deeply connected to Iowa’s economy. Thus, with increasing ethanol production, high yield agriculture practices, growing animal agriculture, and the related economy, there is a need to understand the interrelationship of Iowa’s food-energy-water system to alleviate its impact on the environment and economy through improved policy and decision making. In this work, the Iowa food-energy-water (IFEW) system model is proposed that describes its interrelationship. Further, a macro-scale nitrogen export model of the agriculture and animal agriculture systems is developed. Global sensitivity analysis of the nitrogen export model reveals that the commercial nitrogen-based fertilizer application rate for corn production and corn yield are the two most influential factors affecting the surplus nitrogen in the soil. 

Global sensitivity analysis (GSA) is a method to quantify the effect of the input parameters on outputs of physics-based systems. Performing GSA can be challenging due to the combined effect of the high computational cost of each individual physics-based model, a large number of input parameters, and the need to perform repetitive model evaluations. To reduce this cost, neural networks (NNs) are used to replace the expensive physics-based model in this work. This introduces the additional challenge of finding the minimum number of training data samples required to train the NNs accurately. In this work, a new method is introduced to accurately quantify the GSA values by iterating over both the number of samples required to train the NNs, terminated using an outer-loop sensitivity convergence criteria, and the number of model responses required to calculate the GSA, terminated with an inner-loop sensitivity convergence criteria. The iterative surrogate-based GSA guarantees converged values for the Sobol’ indices and, at the same time, alleviates the specification of arbitrary accuracy metrics for the surrogate model. The proposed method is demonstrated in two cases, namely, an eight-variable borehole function and a three-variable nondestructive testing (NDT) case. For the borehole function, both the first- and total-order Sobol’ indices required 200 and 105 data points to terminate on the outer- and inner-loop sensitivity convergence criteria, respectively. For the NDT case, these values were 100 for both first- and total-order indices for the outer-loop sensitivity convergence, and 106 and 103 for the inner-loop sensitivity convergence, respectively, for the first- and total-order indices, on the inner-loop sensitivity convergence. The differences of the proposed method with GSA on the true functions are less than 3% in the analytical case and less than 10% in the physics-based case (where the large error comes from small Sobol’ indices). 

The dynamic stall phenomenon produces adverse aerodynamic loading, which negatively affects the structural strength and life of aerodynamic systems. Aerodynamic shape optimization (ASO) provides a practical approach for delaying and mitigating dynamic stall characteristics without the addition of an auxiliary system. A typical ASO investigation requires multiple evaluations of accurate but time-consuming computational fluid dynamics (CFD) simulations. In the case of dynamic stall, unsteady CFD simulations are required for airfoil shape evaluation; combining it with high-dimensions of airfoil shape parameterization renders the ASO investigation computationally costly. In this study, metamodel-based optimization (MBO) is proposed using the multifidelity modeling (MFM) technique to efficiently conduct ASO investigation for computationally expensive dynamic stall cases. MFM methods combine data from accurate high-fidelity (HF) simulations and fast low-fidelity (LF) simulations to provide accurate and fast predictions. In particular, Cokriging regression is used for approximating the objective and constraint functions. The airfoil shape is parameterized using six PARSEC parameters. The objective and constraint functions are evaluated for a sinusoidally oscillating airfoil with the unsteady Reynolds-averaged Navier-Stokes equations at a Reynolds number of 135,000, Mach number of 0.1, and reduced frequency of 0.05. The initial metamodel is generated using 220 LF and 20 HF samples. The metamodel is then sequentially refined using the expected improvement infill criteria and validated with the normalized root mean square error. The refined metamodel is utilized for finding the optimal design. The optimal airfoil shape shows higher thickness, larger leading-edge radius, and an aft camber compared to baseline (NACA 0012). The optimal shape delays the dynamic stall occurrence by 3 degrees and reduces the peak aerodynamic coefficients. The performance of the MFM method is also compared with the single-fidelity metamodeling method using HF samples. Both the approaches produced similar optimal shapes; however, the optimal shape from MFM achieved a minimum objective function value while more closely satisfying the constraint at a computational cost saving of around 41%. 

Abstract Regulatory agencies aim to protect the public by moderating risks associated with innovation, but a good regulatory regime should also promote justified public trust. After introducing the USDA 2020 SECURE Rule for regulation of biotech innovation as a case study, this essay develops a theory of justified public trust in regulation. On the theory advanced here, to be trustworthy, a regulatory regime must (1) fairly and effectively manage risk, must be (2) “science based” in the relevant sense, and must in addition be (3) truthful, (4) transparent, and (5) responsive to public input. Evaluated with these norms, the USDA SECURE Rule is shown to be deeply flawed, since it fails appropriately to manage risk, and similarly fails to satisfy other normative requirements for justified trust. The argument identifies ways in which the SECURE Rule itself might be improved, but more broadly provides a normative framework for the evaluation of trustworthy regulatory policy-making.