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Individual Puck Possessions Part I: Frequency, Duration, and Distance Travelled
Get PDFIn this paper we use puck and player tracking data from the 2023- 24 NHL season to study individual player possessions (focusing on 5v5 situations). We study metrics such as possession count, average and total possession duration, average and total distance travelled with the puck, and examine relationships between these metrics and traditional measures of success (i.e., goals, assists and points). A key finding is that individual offensive zone possession is strongly correlated with points (r = 0.70) and is moderately correlated with goals (r = 0.64), assists (r = 0.54), and shots on goal (r = 0.69). We also observe differences in individual possessions based on position (forwards versus defence), zone of play, and strength and large and statistically significant differences between top ranked players and league averages (across most possession metrics). Finally, we examine the benefits of our individual possession metrics and find that they are highly stable (so they are useful for predictions), able to differentiate players, and provide information not captured by existing metrics
Merging Shot Data and Measuring Player Importance to Team’s Offensive Flow
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Context-Oriented Modelica for Advanced Variability Management
Get PDFContext-aware systems are crucial in modern cyber-physicalsystems (CPS), enabling dynamic adaptation to changingconditions. Many such systems involve complex variability.Modelica, a leading equation-based language for modelingand simulating physical systems, struggles to manage thiscomplexity. As variability management becomes more complex,traditional Modelica constructs such as conditionalstatements, state machines, and state graphs becomeincreasingly inadequate and difficult to maintain. Thiswork introduces a context-oriented modeling paradigm forModelica by integrating Petri Net-based context controlwith FMI-based Variable Structure Systems (VSS).Specifically, we present Context Petri Nets (CoPN) as aformal mechanism for representing and managing contextualdependencies. By combining CoPN with VSS, our approachenables advanced variability management, supporting themodeling and simulation of complex, context-aware CPS
Constant Time Causalization using Resizable Arrays
Get PDFEquation-based modeling that utilizes reusable componentsto represent real-world systems can result in excessivelylarge models. This, in turn, significantly increasescompilation time and code size, even when employingstate-of-the-art scalarization and causalizationtechniques. This paper presents an algorithm that leveragesrepeating patterns and uniform causalization to enablearray-size-independent constant time processing. Allowingstructural parameters that govern array sizes to remainresizable during and after the causalization processenables the formulation of an integer-valued nonlinearoptimization problem. This approach identifies the minimalmodel configuration that preserves the required structuralintegrity, which can subsequently be resized as needed forsimulation. The proposed method has been implemented inOpenModelica and builds upon preliminary work aimed atpreserving array structures during causalization, whilestill resolving the underlying problem in a scalarizedmanner
Development of a Refrigerant Mixture Package for Dynamic Simulation of Auto-Cascade Refrigeration: A Case Study with R23/R134a
Get PDFThe auto-cascade refrigeration cycle offers higherreliability and lower manufacturing costs compared to thecascade refrigeration cycle, making it particularlyattractive for applications involving significanttemperature differences between the heat source and sink.However, this advantage comes with increased complexity inits operation. This article aims to develop a media packagein Modelica to simulate auto-cascade refrigeration cycles,with the goal of enhancing understanding of their operationand control. The method is demonstrated using an R23/R134amixture, a commonly used refrigerant combination in suchapplications. The media package is compatible with theModelica Standard Library and is constructed using curvefits based on the REFPROP dataset. Unlike typicalrefrigeration systems, the media package for auto-cascadecomponents includes the independent mixture composition(Xi) as an additional variable in function calls. Acombination of polynomial and Chebyshev polynomialcurve-fits for the refrigerant properties has been shown toprovide an optimal balance between accuracy andcomputational efficiency. The article presents examplesimulations performed to demonstrate use of the mediapackage with component models from the Modelica.Fluidpackage. A lumped model for the phase separator isdeveloped and simulated with the R23/R134a media todemonstrate ideal phase separation
Modelica Meets ASHRAE: Towards A Digital Standard for Building Control
Get PDFToday's process for designing, specifying, installing andtesting building HVAC control is not digitalized, leadingto expensive manual workflowsand missed operational performance.To digitalize the design-build-operate process for buildingHVAC control, the authors developed a process, associatedtools and initiated the voluntary ASHRAE Standard 231P.This paper describes this process and tools,which are both based on Modelica.Standardization through a proposed voluntary ASHRAE Standardand the existing Modelica Language Specification provides arobust technology foundation for industry investment.It is based on declarative specification of the controllogic,and allows reuse of existing technologies foropen-loop and closed-loop control testing through couplingwith an HVAC system or a whole building energy model. Itsupports control testing using MIL, SIL and HIL,and export of digital twins for operational support.The process and ASHRAE Standard 231P have been designed toaccommodateexisting Building Automation Systems product lines, whilealsoenabling direct code generation such as by using FMI oreFMI.Control deployment can be digital or manual andconformance to the digital specificationcan be tested formally and programmatically at each step ofthe control delivery
Master controller for offshore wind power and hybrid grids
Get PDFOffshore power generation and transmission re-quires longsubsea cables. When using AC, the in-ductance of the cablesresults in reactive power that contributes to the load ofthe cables and increases the voltage at busbars. At thesame time, offshore power systems are becoming increasinglycomplex. They are evolving from dedicated collector gridsand transmission cables for wind farms through shared useof transmission cables by multiple wind farms to hybridoffshore grids that connect wind farms to multiplecountries.This paper discusses operational challenges of off-shoregrids and how they are solved with a model-based mastercontroller that solves optimal power flow problems inreal-time.The Kriegers Flak Combined Grid Solution serves as example.It transports wind power from the four offshore wind farms(Baltic 1 and 2, Kriegers Flak A and B) with a totalcapacity of 950 MW to shore. Additionally, it promotesenergy trade between Germany and Denmark. At the heart ofthe connec-tion of these two energy grids through theBaltic Sea there is a Master Controller for InterconnectedOperations (MIO). Based on a Modelica model and using thePowerSystems library, it solves several state estimationand optimal power flow problems in real-time. MIO isimplemented as geo-redundant system with ABB AbilityTMOPTIMAX® and System 800xA
A Tool for the Implementation of Open Neural Network Exchange Models in Functional Mockup Units
Get PDFThe Functional Mock-up Interface (FMI) standard is aflagship in the co-simulation and model exchange domain.However, the integration of graph-based computationalmodels—particularly neural networks—into Functional Mock-upUnits (FMUs) has remained a technical challenge due tointeroperability and platform-specific limitations.To address this, we propose ONNX2FMU, a command-line Pythontool that facilitates the deployment of Open Neural NetworkExchange (ONNX) models into FMUs. According to FMI's goodpractices, ONNX2FMU generates C source code to wrap ONNXmodels in Functional Mockup Units, supports FMI versions2.0 and 3.0, and provides multi-platform compilationcapabilities. The tool simplifies the mapping processbetween model description and ONNX model inputs and outputsvia JSON files, ensuring accessibility and flexibility.This paper presents the tool architecture and methodologyand showcases its applicability through illustrativeexamples, including a reduced-order model powered by arecurrent neural network
Integration of Geometric Tolerance Analysis in System Simulations via Functional Mock-up Units
Get PDFThis paper presents a concept for integrating geometrictolerance analysis into system simulations using FunctionalMock-up Units (FMUs). While various methods and tools fortolerance analysis exist in the mechanical domain, there iscurrently no standardized or widely established approachfor their integration into multi-domain system simulation.This work proposes a structured FMU interface based on theFunctional Mock-up Interface (FMI) standard, enabling amodular and reusable representation of tolerance behaviour.The concept is demonstrated in a case study, in which anFMU for static tolerance calculation was implemented andsuccessfully verified against a commercial analysis tool.Furthermore, the use of the FMU with FMUGym illustrates thepotential of the FMI ecosystem to flexibly combinetolerance models with other simulation environments andanalysis methods.The results demonstrate that FMUs can provide a suitableand tool-independent interface for integrating geometricaltolerance effects into system-level simulations andmodel-based engineering processes