Linköping Electronic Conference Proceedings
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Historical Language Models in Cryptanalysis: Case Studies on English and German
Get PDFIn this paper, we study the impact of language models (LM) on decipherment of historical homophonic substitution ciphers. In particular, we investigate if decipherment by using hill-climbing and simulated annealing can benefit from LMs generated from historical texts in general and century-specific texts in particular. We carry out experiments on homophonic substitution ciphers with English and German as plaintext languages. We take into account ciphertext length as well as n-gram size of the LMs. We compare the results on decipherment based on historical LMs with large LMs generated from modern texts. The results show that using historical LMs in decipherment of homophonic substitution ciphers leads to significantly better performance on ciphertext produced in the 17th century or earlier, and century-specific language models yield better results on longer and older ciphertexts
Fan and Pump Efficiency in Modelica based on the Euler Number
Get PDFSimulation programs often assume constant hydraulic efficiency for fan or pump models when performance curves are unavailable. This is inaccurate because the hydraulic efficiency varies with the operation condition. It therefore consistently underestimates the power draw at off-design conditions at which the hydraulic efficiency drops. Use of a modified Euler number allows computing the hydraulic efficiency and shaft power with limited data. Others showed the validity of the modified Euler number for fan efficiency calculations. We show that it is also applicable for pumps, and present its implementation in Modelica for a fan or pump model. The only input required from the user is one data point at which the hydraulic efficiency is at its maximum. The reported method is applicable regardless of the type, size, or operational region of the fan or pump. Across a sample of eighteen sets of pump data and seven sets of fan data, the errors of the computed power from interpolated data were within 15% for the range of 20% - 70% of maximum flow rate and 40% - 90% of maximum pressure rise, excluding outliers
MARCO: An Experimental High-Performance Compiler for Large-Scale Modelica Models
Get PDFThis paper introduces MARCO, a research compiler aimed at the efficient generation of efficient simulation code from a large-scale Modelica model. MARCO’s design goals, requirements, and specifications are dis- cussed in the paper, as well as the software architec- ture, the current development status, and a future develop- ment roadmap. The results of two test cases demonstrate MARCO’s capability to handle non-trivial Modelica mod- els with over 10 million equations very efficiently
ThermalSystemsControlLibrary: A Modelica Library for Developing Control Strategies of Industrial Energy Systems
Get PDFThe transformation of energy-intensive industries towards greenhouse gas neutrality leads to increasing complexity of industrial energy supply systems. This affects particularly thermal energy systems due to waste heat utilization measures as well as the integration of renewable energy sources and further storage capacities. This complexity is also reflected in the control strategies of such systems, which makes the development of dynamic simulation models for testing them a research field of growing interest. The ThermalSystemsControlLibrary is a novel Modelica library, which aims at standardized modeling of industrial energy supply systems for control strategy development. Based on a generic data model, all components cover physical as well as control modeling and are particularly suitable for testing supervisory control strategies within external frameworks using the FMI standard. The library is validated for an exemplary use case of an industrial energy supply system comparing two different supervisory control strategies
Parameter Estimation of Modelica Building Models Using CasADi
Get PDFPredictive control can substantially improve the energy performance of buildings during operation, but it requires a model of the building to be implemented. Gray-box model identification starts from a physics-based model (white-box element) and complements it with measurements from the operation of the building (black-box element). The level of detail of the original model is limited by the optimization problem that needs to be solved when estimating its parameters. Consequently, it is common to heavily simplify building models hindering the intelligibility of their parameters and limiting their application potential. This paper investigates the accuracy and scalability of different transcription methods for parameter estimation of building models. The methodology starts from a Modelica model as an initial guess which is transferred to CasADi using the Functional Mockup Interface to solve the parameter estimation problem. The study demonstrates the high effectiveness of multiple shooting. Single shooting and direct collocation could be more suitable for setups with faster integration times or with increased granularity in the training data, respectively
Using the DLR Thermofluid Stream Library for Thermal Management of Fuel Cell Systems in Aviation
Get PDFFor more environmental friendly aircraft, different propulsion systems are considered. Either fuel cell or fully electrically driven aircraft come along with challenging heat dissipation tasks. An intelligent thermal management system is essential to prevent failures and to ensure a reliable operation of the propulsion system. The exploration space for appropriate cooling systems seems endless, hence it is vital to rely on robust modeling libraries that enable a quick design and simulation of different architectures. The open source DLR Thermofluid Stream Library (TFS) forms such a basis and proved to be expedient in that sense. This paper gives an overview of a complete fuel cell system for future aircraft that covers the most essential subsystems and is modeled solely of components contained in the TFS. The focus is on different cooling systems and methods that can be quickly investigated in the context of the overall fuel cell system throughout an entire flight mission
A preCICE-FMI Runner to Couple FMUs to PDE-Based Simulations
Get PDFPartitioned simulation or co-simulation allows simulating complex systems by breaking them up into smaller subsystems. The Functional Mock-Up Interface (FMI) enables co-simulation for models based on ODEs and DAEs, but typically not PDEs. However, only PDE-based models are able to accurately simulate physical aspects requiring spatial resolution, such as heat transfer or fluid-structure interaction. We present a preCICE-FMI runner software to integrate FMUs with the open-source coupling library preCICE. preCICE couples PDE-based simulation programs, such as OpenFOAM or FEniCS, in a black-box fashion to achieve partitioned multi-physics simulations. The runner serves as an importer to execute any FMU and to steer the simulation. Additionally, it calls preCICE to communicate and coordinate with other programs. The software is written in Python and relies on the Python package FMPy. We showcase two example cases for the coupling of FMUs to ODE- and PDE-based models
Integration of Heat Flow through Borders between Adjacent Zones in AixLib's Reduced-Order Model
Get PDFFor dynamically simulating the thermal behavior of a building, the reduced-order model (ROM) implemented in the Modelica IBPSA and AixLib libraries provides a time-efficient calculation method based on the standard VDI 6007-1. Additionally, the Python package TEASER features a possilibity to fill the model parameters with automatically generated typical and/or enriched building data. So far, both have not been capable of modelling heat flow through borders between thermal zones. In this contribution, we present the integration of this feature into the open-source software combination. Additional new features include non-constant soil temperatures and a new approach to estimate interior building elements in cases without proper knowledge. Calculation results are presented for an exemplary application and show satisfactory agreement with measured values. The respective code (including the example presented here) is in the process of being published as part of the AixLib and TEASER open-source repositiories
Modeling Specialized Electric Power Generators, Excitation Systems and Prime Movers used by North American Utilities
Get PDFThe North American Electric Reliability Corporation (NERC) is expected to mandate model validation of power plant equipment in the near future. This will create a need to validate models for a large fleet of existing and future power plants. Historically, model validation of synchronous generators, excitation system, turbine governor, and other power system equipment has been conducted in diverse platforms. As a contribution to the power system model implementation using Modelica language and validation against commercial tools this work continues to develop power system component models and enriching the Open-Instance Power System Library (OpenIPSL). As a part of the development of OpenIPSL this paper describes the development of models used by North American utilities that follow NERC modeling requirements, including models of a synchronous generator, an excitation system, a turbine and governor using Modelica language in Dymola. The component implementation process is described and the validation of the models implemented in Modelica against PSS/E using both a single machine infinite bus (SMIB) and multi-machine system models is illustrated
EliCoDe at MultiGED2023: fine-tuning XLM-RoBERTa for multilingual grammatical error detection
Get PDFIn this paper we describe the participation of our team, ELICODE, to the first shared, task on Multilingual Grammatical Error Detection, MultiGED, organised within the workshop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL). The multilingual shared task includes five languages: Czech, English, German, Italian and Swedish. The shared task is tackled as a binary classification task at token level aiming at identifying correct or incorrect tokens in the provided sentences. The submitted system is a token classifier based on XLMRoBERTa language model. We fine-tuned five different models—one per each language in the shared task. We devised two different experimental settings: first, we trained the models only on the provided training set, using the development set to select the model achieving the best performance across the training epochs; second, we trained each model jointly on training and development sets for 10 epochs, retaining the 10-epoch fine-tuned model. Our submitted systems, evaluated using F0.5 score, achieved the best performance in all evaluated test sets, except for the English REALEC data set (second classified). Code and models are publicly available at https://github.com/davidecolla/EliCoDe