Search in the Product Market and the Real Business Cycle

Empirical evidence suggests that most firms operate in imperfectly competitivemarkets. We develop a search-matching model between wholesalers and retailers. Firms face search costs and form long-term relationships. Price bargain results in both wholesaler and retailer markups, depending on firms’ relative bargaining power. We simulate the model to explore the role of product market search frictions in business cycles. We show that the way search costs are modelled is crucial to provide a realistic picture of firms’ business environment and improve the cyclical properties of an otherwise standard real business cycle model.Business cycle, Frictions, Product market, Price bargain

On the Benefits of Edge Caching for MIMO Interference Alignment

In this contribution, we jointly investigate the benefits of caching and interference alignment (IA) in multiple-input multiple-output (MIMO) interference channel under limited backhaul capacity. In particular, total average transmission rate is derived as a function of various system parameters such as backhaul link capacity, cache size, number of active transmitter-receiver pairs as well as the quantization bits for channel state information (CSI). Given the fact that base stations are equipped both with caching and IA capabilities and have knowledge of content popularity profile, we then characterize an operational regime where the caching is beneficial. Subsequently, we find the optimal number of transmitter-receiver pairs that maximizes the total average transmission rate. When the popularity profile of requested contents falls into the operational regime, it turns out that caching substantially improves the throughput as it mitigates the backhaul usage and allows IA methods to take benefit of such limited backhaul.Comment: 20 pages, 5 figures. A shorter version is to be presented at 16th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC'2015), Stockholm, Swede

NCAA Enforcement Staff and Member Institutions Protecting Mutual Interests: Binding Arbitration in Coaching Contracts

Article published in the Michigan State University School of Law Student Scholarship Collection

Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation

To achieve virtual certification for industrial design, quantifying the uncertainties in simulation-driven processes is crucial. We discuss a physics-constrained approach to account for epistemic uncertainty of turbulence models. In order to eliminate user input, we incorporate a data-driven machine learning strategy. In addition to it, our study focuses on developing an a priori estimation of prediction confidence when accurate data is scarce.Comment: Workshop on Synergy of Scientific and Machine Learning Modeling, SynS & ML ICM

Physically constrained eigenspace perturbation for turbulence model uncertainty estimation

Aerospace design is increasingly incorporating Design Under Uncertainty based approaches to lead to more robust and reliable optimal designs. These approaches require dependable estimates of uncertainty in simulations for their success. The key contributor of predictive uncertainty in Computational Fluid Dynamics (CFD) simulations of turbulent flows are the structural limitations of Reynolds-averaged Navier-Stokes models, termed model-form uncertainty. Currently, the common procedure to estimate turbulence model-form uncertainty is the Eigenspace Perturbation Framework (EPF), involving perturbations to the modeled Reynolds Stress tensor within physical limits. The EPF has been applied with success in design and analysis tasks in numerous prior works from the industry and academia. Owing to its rapid success and adoption in several commercial and open-source CFD solvers, in depth Verification and Validation of the EPF is critical. In this work, we show that under certain conditions, the perturbations in the EPF can lead to Reynolds stress dynamics that are not physically realizable. This analysis enables us to propose a set of necessary physics-based constraints, leading to a realizable EPF. We apply this constrained procedure to the illustrative test case of a converging-diverging channel, and we demonstrate that these constraints limit physically implausible dynamics of the Reynolds stress tensor, while enhancing the accuracy and stability of the uncertainty estimation procedure.Comment: The following article has been submitted to Physics of Fluid

Improved self-consistency of the Reynolds stress tensor eigenspace perturbation for Uncertainty Quantification

The limitations of turbulence closure models in the context of Reynolds-averaged NavierStokes (RANS) simulations play a significant part in contributing to the uncertainty of Computational Fluid Dynamics (CFD). Perturbing the spectral representation of the Reynolds stress tensor within physical limits is common practice in several commercial and open-source CFD solvers, in order to obtain estimates for the epistemic uncertainties of RANS turbulence models. Recent research revealed, that there is a need for moderating the amount of perturbed Reynolds stress tensor tensor to be considered due to upcoming stability issues of the solver. In this paper we point out that the consequent common implementation can lead to unintended states of the resulting perturbed Reynolds stress tensor. The combination of eigenvector perturbation and moderation factor may actually result in moderated eigenvalues, which are not linearly dependent on the originally unperturbed and fully perturbed eigenvalues anymore. Hence, the computational implementation is no longer in accordance with the conceptual idea of the Eigenspace Perturbation Framework. We verify the implementation of the conceptual description with respect to its self-consistency. Adequately representing the basic concept results in formulating a computational implementation to improve self-consistency of the Reynolds stress tensor perturbationComment: The following article has been submitted to AIP/Physics of Fluid

Efficient preliminary floating offshore wind turbine design and testing methodologies and application to a concrete spar design

The current key challenge in the floating offshore wind turbine industry and research is on designing economic floating systems that can compete with fixed-bottom offshore turbines in terms of levelized cost of energy. The preliminary platform design, as well as early experimental design assessments, are critical elements in the overall design process. In this contribution, a brief review of current floating offshore wind turbine platform pre-design and scaled testing methodologies is provided, with a focus on their ability to accommodate the coupled dynamic behaviour of floating offshore wind systems. The exemplary design and testing methodology for a monolithic concrete spar platform as performed within the European KIC AFOSP project is presented. Results from the experimental tests compared to numerical simulations are presented and analysed and show very good agreement for relevant basic dynamic platform properties. Extreme and fatigue loads and cost analysis of the AFOSP system confirm the viability of the presented design process. In summary, the exemplary application of the reduced design and testing methodology for AFOSP confirms that it represents a viable procedure during pre-design of floating offshore wind turbine platforms.Peer ReviewedPostprint (author’s final draft

Evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification

In order to achieve a virtual certification process and robust designs for turbomachinery, the uncertainty bounds for Computational Fluid Dynamics have to be known. The formulation of turbulence closure models implies a major source of the overall uncertainty of Reynolds-averaged Navier-Stokes simulations. We discuss the common practice of applying a physics constrained eigenspace perturbation of the Reynolds stress tensor in order to account for the model form uncertainty of turbulence models. Since the basic methodology often leads to overly generous uncertainty estimates, we extend a recent approach of adding a machine learning strategy. The application of a data-driven method is motivated by striving for the detection of flow regions, which are prone to suffer from a lack of turbulence model prediction accuracy. In this way any user input related to choosing the degree of uncertainty is supposed to become obsolete. This work especially investigates an approach, which tries to determine an a priori estimation of prediction confidence, when there is no accurate data available to judge the prediction. The flow around the NACA 4412 airfoil at near-stall conditions demonstrates the successful application of the data-driven eigenspace perturbation framework. Furthermore, we especially highlight the objectives and limitations of the underlying methodology