Combined cycle power plants haven becoming one of the mainstream power plants in the twenty-one century. The emergence of high 600¢J exhaust temperature of the gas turbine, due to the recent rapid enhancement of aerospace material and blade cooling methods, upgrades the gas turbine from low efficiency dual pressure non-reheat unit to high efficiency triple pressure reheat combined cycle power plants. In addition, the increase of turbine inlet temperature by 10~15¢J every year leads to the renewal of the advanced models gas turbine less than ten years. There are three-turbine inlet temperature (TIT) definitions in the gas turbine: (1) TA defines firing temperature as the mass flow mean total temperature before the first-stage stationary diagram edge plane.( Westinghouse or MHI product) (2) TB defines fire temperature as the mass flow mean total temperature at the first-stage nozzle trailing edge plane, ( GE product). (3) TC defines ISO firing temperature; it is a stoichiometric combustion temperature. It is not a physical temperature. ( Siemens ¡® Alstom ABB product). This study shows how to calculate compressor inlet mass flow balance, turbine power balance and heat balance on the combustion chamber system. In order to prove correctness of the balance equation, the data are taken from the heat balance diagram and acceptance test of Nan-pu power station combined cycle. The result shows that the study is sultable for application of the optimum analysis for CCPP operation performance. This type of combined cycle power plant suits not only for the base-load but also for the cycling-load operation.

Every day, machine learning (ML) and artificial intelligence (AI) embeds itself further into domestic and industrial technologies. Interaction de- signers have historically struggled to engage directly with the subject, facing a shortage of appropriate methods and abstractions. There is a need to find ways though which interaction design practitioners might integrate ML into their work, in order to democratize and diversify the field. This thesis proposes a mode of inquiry that considers the inter- active qualities of what machine learning does, as opposed the tech- nical specifications of what machine learning is. A shift in focus from the technicality of ML to the artifacts it creates allows the interaction designer to situate its existing skill set, affording it to engage with ma- chine learning as a design material. A Research-through-Design pro- cess explores different methodological adaptions, evaluated through user feedback and an-in depth case analysis. An elaborated design experiment, Multiverse, examines the novel, non-anthropomorphic aesthetic qualities of generative literature. It prototypes interactions with bidirectional literature and studies how these transform the reader into a cybertextual “user-reader”. The thesis ends with a discussion on the implications of machine written literature and proposes a number of future investigations into the research space unfolded through the prototype.

Large Language Models (LLMs) have become ever larger in recent years, typically demonstrating improved performance as the number of parameters increases. This thesis investigates how the probability distributions output by language models differ depending on the size of the model. For this purpose, three features for capturing the differences between the distributions are defined, namely the difference in entropy, the difference in probability mass in different slices of the distribution, and the difference in the number of tokens covering the top-p probability mass. The distributions are then put into different distribution classes based on how they differ from the distributions of the differently-sized model. Finally, the distributions are transformed to be more similar to the distributions of the other model. The results suggest that classifying distributions before transforming them, and adapting the transformations based on which class a distribution is in, improves the transformation results. It is also shown that letting a classifier choose the class label for each distribution yields better results than using random labels. Furthermore, the findings indicate that transforming the distributions using entropy and the number of tokens in the top-p probability mass makes the distributions more similar to the targets, while transforming them based on the probability mass of individual slices of the distributions makes the distributions more dissimilar.

In this research project, we aim to explore the use of Large Language Models (LLMs) as an interface to interact with API tools in natural language. Bubeck et al. [1] shed some light on how LLMs could be used to interact with API tools. Since then, new versions of LLMs have been launched and the question of how reliable a LLM can be in this task remains unanswered. The main goal of our thesis is to investigate the designs of the available system prompts for LLMs, identify the best-performing prompts, and evaluate the reliability of different LLMs when using the best-identified prompts. We will employ a multiple-stage controlled experiment: A literature review where we reveal the available system prompts used in the scientific community and open-source projects; then, using F1-score as a metric we will analyse the precision and recall of the system prompts aiming to select the best-performing system prompts in interacting with API tools; and in a latter stage, we compare a selection of LLMs with the best-performing prompts identified earlier. From these experiences, we realize that AI-generated system prompts perform better than the current prompts used in open-source and literature with GPT-4, zero-shot prompts have better performance in this specific task with GPT-4 and that a good system prompt in one model does not generalize well into other models.

This thesis investigates the applications and implications of Generative Pretrained Transformer (GPT) technology in international entrepreneurship. The research questions focus on how GPT can serve as a strategic tool, communication tool, and knowledge leverage tool, and how these applications influence decision-making, enhance performance, The findings from the analysis chapters highlights that GPT significantly contributes to strategic planning, market analysis, and operational management, thereby enhancing decision-making and performance. GPT technology, acting as a potent communication tool, nurtures more robust client relationships and eases cross-cultural interactions, courtesy of its superior language processing capabilities. The thesis discusses how these capabilities of GPT can lead to reduced miscommunications and enhanced client satisfaction. This, in turn, contributes to cost savings by retaining existing customers and attracting new ones, thereby enhancing profits. Moreover, The main theoretical implications that this  thesis has resulted in will showcase the time efficiency brought by automated, high-quality communication that reduces man-hours spent on routine interactions, freeing resources for strategic tasks for international entrepreneurship.  Furthermore, this study enriches the literature on generative AI exemplified by models like GPT in the world of international business, particularly within the context of international entrepreneurship. It offers essential insights that can guide international entrepreneurs in understanding the potential advantages of integrating GPT technology into their business operations.

In this thesis, I delve into how Rhetorical Genre Study and Systemic-Functional Grammar can be used to assess the extent to which GPT-4 adheres to generic features and can be deemed adapted for its function. The objective is to establish a systematic model for objectively evaluating the degree to which AI-generated text is suitable for its intended purpose. To achieve this, I perform a rhetorical genre analysis on a crisis communication genre, which I subsequently quantify. I utilize the concept of topoi to pinpoint the arguments that serve to fulfill the functions of crisis communication. Subsequently, I prompt GPT-4 to produce texts within the genre and contrast them with my discoveries. Alongside this, I investigate the disparate results produced when using text and meta-text as input. The findings reveal a functional model to appraise the degree of text adaptation for its purpose within the analyzed genre. Although the model necessitates further fine-tuning, it effectively distinguishes nongeneric texts. The scope of the material selected was too limited to indicate any disparities in outcomes between different types of input. However, all input models generated texts that substantially conformed to the generic features.