The electro-dip coating (ED-coating) process plays a pivotal role in achieving superior finishes on automotive bodies and parts. Optimizing this process involves reducing liquid carryover between baths and enhancing accessibility within the baths. A novel geometric simulation method based on connectivity, designed to predict accessibility and drainage of liquid, is introduced. It addresses the intrinsic challenges of varying scales, where small gaps and holes must be resolved on large objects over long dipping motions, making physics-based simulations infeasible. Validation with a car door shows close agreement between simulations and experiments. The different grid refinement levels provide valuable insight into the resolution necessary for achieving accurate results. Furthermore, application of the method to a car body demonstrates its ability to handle the complex geometries and motions associated with the ED-coating process. The analysis predicts a total of 0.61 liters of trapped air and 0.41 liters of trapped liquid within the car body. The presented work offers a valuable tool for assessing and optimizing accessibility and drainage in the ED-coating process, thereby improving corrosion protection and product quality. It also contributes to sustainable manufacturing practices by enhancing process efficiency and minimizing material waste.
The present study proposes a two-warehouse inventory model with a fuzzy demand rate and partial backlogging shortages to determine the optimal total cost. This research involves an optimal green technology investment to reduce carbon emissions from goods transportation and proposes an environmentally sustainable inventory model. The primary objective of this study is to allow a permissible delay in payment to enhance the financial benefits to investors and end-users. The model suggests a prepayment option for a portion of the purchasing cost intended for the retailer, and the preservation technology cost is incorporated to maintain the items’ freshness. Algorithms are developed to highlight the solution approach for the model problems, and the optimality criterion is verified using an appropriate numerical example. Again, the optimal results for the two warehouse inventory problems are presented in graphs, and managerial insights are derived from sensitivity analysis. Lastly, the paper concludes with a discussion of the scope for future research.
Differential-algebraic equations (DAEs) present a ubiquitous model class in the automated modeling, simulation, control and optimization of complex finite or infinite dimensional dynamical systems, in particular in the construction of digital twins. When combined with adequate space and time discretization methods they present the work-horse of developments in many engineering domains. However, this approach comes with certain challenges for the numerical methods and in particular in the coupling of systems from different physical domains with different scales or regularity requirements. We survey the modeling with DAEs and address how to overcome some of the challenges via the framework of port-Hamiltonian differential-algebraic systems (pHDAEs) and their simulation and optimization with space-time-model adaptive methods.
Abstract In this study, five distinct COVID-19 models developed in different countries, each designed to reflect the prevailing epidemiological condition at the time of formulation, are examined. The models are reformulated while still maintaining their original compartmental structure, using their common transmissions from one compartment to the other. Modified Patankar–Runge–Kutta (MPRK) methods are then applied to approximate the solutions of the resulting system of nonlinear ordinary differential equations (ODEs) representing each model to produce unconditionally positive approximations and to preserve the conservative part of the ODEs. In particular, we incorporate the numerical solution into a cost function to improve the estimates for the non-autonomous model hyperparameters. In a first step we obtain piecewise constant parameters that fit real data. Later we perform a WENO reconstruction in a post-process to approximate the true time-dependent coefficients inside the ODEs. As a proof-of-concept, we apply our approach to improve the parameters of a paper concerned with modeling COVID-19 in Ghana, where we can make 5-day predictions within a 10% error range.
Abstract Uncertainty is fundamental in modern power systems, where renewable generation and fluctuating demand make stochastic optimization indispensable. In stochastic optimization, chance constraints enable the representation of uncertainty in renewable energy sources that affect the dispatch and commitment of bulk generation. This is commonly known as the chance-constrained unit commitment problem (UCP), which rapidly becomes computationally challenging as the number of scenarios grows. Quantum computing has been proposed as a potential route to overcome such scaling barriers. In this work, we evaluate the applicability of quantum annealing platforms to the chance-constrained UCP. Focusing on a scenario approximation, we reformulate the problem as a mixed-integer linear program (MILP) and solve it using D-Wave’s hybrid quantum–classical solver alongside the classical solver Gurobi. The hybrid solver proves competitive under strict runtime limits for large scenario sets (15,000 in our experiments), while Gurobi remains superior on smaller cases. QUBO reformulations have also been tested, but current annealers cannot accommodate stochastic UCPs due to hardware limits, and deterministic cases suffer from embedding overhead. Our study delineates where chance-constrained UCPs can already be addressed with hybrid quantum–classical methods, and where current quantum annealers remain fundamentally limited.
