Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

The Industry 4.0 concept of a Digital Twin will bring many advantages for wind energy conversion systems, e.g., in condition monitoring, predictive maintenance and the optimisation of control or design parameters. A virtual replica is at the heart of a digital twin. To construct a virtual replica, appropriate modelling techniques must be selected for the turbine components. These models must be chosen with the intended use case of the digital twin in mind, finding a proper balance between the model fidelity and computational load. This review article presents an overview of the recent literature on modelling techniques for turbine aerodynamics, structure and drivetrain mechanics, the permanent magnet synchronous generator, the power electronic converter and the pitch and yaw systems. For each component, a balanced overview is given of models with varying model fidelity and computational load, ranging from simplified lumped parameter models to advanced numerical Finite Element Method (FEM)-based models. The results of the literature review are presented graphically to aid the reader in the model selection process. Based on this review, a high-level structure of a digital twin is proposed together with a virtual replica with a minimum computational load. The concept of a multi-level hierarchical virtual replica is presented.

Mapping manifestations of parametric uncertainty in projected pelagic oxygen concentrations back to contemporary local model fidelity

Pelagic biogeochemical models (BGCMs) have matured into generic components of Earth System Models. BGCMs mimic the effects of marine biota on oceanic nutrient, carbon and oxygen cycles. They rely on parameters that are adjusted to match observed conditions. Such parameters are key to determining the models’ responses to changing environmental conditions. However, many of these parameters are difficult to constrain and constitute a major source of uncertainty in BGCM projections. Here we use, for the first time, variance-based sensitivity analyses to map BGCM parameter uncertainties onto their respective local manifestation in model entities (such as oceanic oxygen concentrations) for both contemporary climate and climate projections. The mapping effectively relates local uncertainties of projections to the uncertainty of specific parameters. Further, it identifies contemporary benchmarking regions, where the uncertainties of specific parameters manifest themselves, thereby facilitating an effective parameter refinement and a reduction of the associated uncertainty. Our results demonstrate that the parameters that are linked to uncertainties in projections may differ from those parameters that facilitate model conformity with present-day observations. In summary, we present a practical approach to the general question of where present-day model fidelity may be indicative for reliable projections.

Pipeline Model Fidelity for Wave Energy System Models

Ocean wave energy conversion plants that use hydraulic power take-offs (PTOs) have been configured so that the working fluid must travel a significant distance (of several hundred to a few thousand meters) from the wave energy converter (WEC) located offshore to equipment onshore. With the pulsatile flow generated by the WEC having a peak period in the range of 3 to 12 seconds, the wavelengths of the excited pressure waves approach the length of the pipelines themselves. By the standards for modeling pipelines presented in popular fluid power and related textbooks, the system models for these plants should include distributed parameter models of the pipeline dynamics that capture the pressure wave delay effects. This work tests the importance of pipeline model fidelity for wave energy conversion plants. Simulations have been conducted of a simple but representative hydraulic PTO for wave energy conversion and incorporate several common lumped and distributed parameter pipeline models for comparison. These results are used to show the degree to which model fidelity effects several design metrics that are especially useful in the preliminary design phase of system development. The pipeline models used include: 1) a short line model that includes lumped resistive effects only, 2) a medium line model that also includes lumped inertial and capacitive effects for a single pipeline segment, 3) a long line model that uses repeated, lumped parameter line segments to approximate the distributed parameters of a real pipeline, 4) a simple method of characteristics solution to the one-dimensional momentum and continuity equations assuming a fixed wave speed, and 5) a discrete free-gas cavity model augmenting the simple method of characteristic pipeline model. The results suggest a relaxed standard for modeling pipelines in the case of this type of system, in which case, the recommended model is easily implemented in variable time step solvers and CAD software such as Simscape Fluids and can be used within the WEC-Sim modeling framework developed by the National Renewable Energy Lab.

Accuracy vs Realism: Does including reservoirs improve hydrological models?

Brazil has invested considerably in the reservoir construction during the past decades, mainly for irrigation and hydro-power generation. Despite their large impact on catchment hydrology, reservoir dynamics are often not included in hydrological models due to their complexity. In this study, we investigated the effect of including reservoir dynamics (realism) in hydrological models on the model performance (accuracy). Combined, realism and accuracy form the model fidelity. We used the HBV-EC and GR4J models to simulate hydrological processes and daily streamflow of 403 catchments across Brazil in two scenarios, with and without reservoirs. The model performances were assessed with the Kling Gupta Efficiency (KGE) and its components, and were compared between the models and scenarios. We found a significant increase in the HBV-EC model performance when the reservoirs were taken into account, although the overall performance was relatively poor. The average KGE increased from 0.21 without the reservoirs to 0.40 with the reservoirs. The GR4J model, on the other hand, showed better overall performance, but without the improvement when including the reservoirs; the average KGE slightly decreased from 0.57 to 0.56. In the catchments with the largest reservoir capacity, HBV-EC in the scenario with reservoirs outperformed GR4J in both scenarios. We note that better model performance can still be obtained with a smaller spatial scale or other methods of including reservoirs, which require more data and detailed studies. With this paper, we demonstrate that model performance can improve when including reservoir dynamics, but this depends on model structure and does not always increase model fidelity.

A Novel, Low-cost, Low-fidelity Pericardiocentesis Teaching Model

Introduction: Pericardiocentesis is a high-risk/low-frequency procedure important to emergency medicine (EM). However, due to case rarity it is not often performed on a patient during residency training. Because the coronavirus disease 2019 pandemic limited cadaver-based practice, we developed a novel, low-cost, low-fidelity pericardiocentesis model using three dimensional-printing technology to provide advances on prior home-made models. Methods: Residents watched a 20-minute video about performing a pericardiocentesis and practiced both a blind and ultrasound-guided technique. We assessed model fidelity, convenience, and perceived provider competence via post-workshop questionnaire. Results: A total of 24/26 (93%) individuals practicing on the ultrasound-guided model and 22/24 (92%) on the blind approach model agreed or strongly agreed that the models reasonably mimicked a pericardial effusion. Conclusion: Our low-cost, low-fidelity model is durable, mimics the clinical case, and is easy to use. It also addresses known limitations from prior low-fidelity models.

Unsupervised logic-based mechanism inference for network-driven biological processes

Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.