Optimal Economic Gas Turbine Dispatch with Deep Reinforcement Learning

Dispatching strategies for gas turbines (GTs) are changing in modern electricity grids. A growing incorporation of intermittent renewable energy requires GTs to operate more but shorter cycles and more frequently on partial loads. Deep reinforcement learning (DRL) has recently emerged as a tool that can cope with this development and dispatch GTs economically. The key advantages of DRL are a model-free optimization and the ability to handle uncertainties, such as those introduced by varying loads or renewable energy production. In this study, three popular DRL algorithms are implemented for an economic GT dispatch problem on a case study in Alberta, Canada. We highlight the benefits of DRL by incorporating an existing thermodynamic software provided by Siemens Energy into the environment model and by simulating uncertainty via varying electricity prices, loads, and ambient conditions. Among the tested algorithms and baseline methods, Deep Q-Networks (DQN) obtained the highest rewards while Proximal Policy Optimization (PPO) was the most sample efficient. We further propose and implement a method to assign GT operation and maintenance cost dynamically based on operating hours and cycles. Compared to existing methods, our approach better approximates the true cost of modern GT dispatch and hence leads to more realistic policies.Comment: This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-N

Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams

Recent applications of machine learning in metal additive manufacturing (MAM) have demonstrated significant potential in addressing critical barriers to the widespread adoption of MAM technology. Recent research in this field emphasizes the importance of utilizing melt pool signatures for real-time defect prediction. While high-quality melt pool image data holds the promise of enabling precise predictions, there has been limited exploration into the utilization of cutting-edge spatiotemporal models that can harness the inherent transient and sequential characteristics of the additive manufacturing process. This research introduces and puts into practice some of the leading deep spatiotemporal learning models that can be adapted for the classification of melt pool image streams originating from various materials, systems, and applications. Specifically, it investigates two-stream networks comprising spatial and temporal streams, a recurrent spatial network, and a factorized 3D convolutional neural network. The capacity of these models to generalize when exposed to perturbations in melt pool image data is examined using data perturbation techniques grounded in real-world process scenarios. The implemented architectures demonstrate the ability to capture the spatiotemporal features of melt pool image sequences. However, among these models, only the Kinetics400 pre-trained SlowFast network, categorized as a two-stream network, exhibits robust generalization capabilities in the presence of data perturbations.Comment: This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-N

Information structure for manufacturing sustainability assessment : step for LCA

http://conferences.chalmers.se/index.php/LCM/LCM2013/paper/view/695/293Environmental performances need product, processes and life cycle modeling and evaluations. There is direct need of assessment tools to monitor and estimate environmental impact generated by different types of manufacturing processes. Indeed, eco design and products optimizations can be done with the manufacturing process choices. But the manufacturing systems are very complex and they are driven by physicals laws that are heavy to model. This research proposes a manufacturing informatics framework for the assessment of manufacturing sustainability based on an EXPRESS information model developed to represent sustainability information. It is the first step of association of sustainability information with product design specification. In the next phase of research, investigation will be conducted to integrate sustainability information model and existing standardized product design model ISO 10303 AP 242.EcoSD networ

A Manufacturing Informatics Framework for Manufacturing Sustainability Assessment. In : Re-engineering Manufacturing for Sustainability, Springer

Manufacturing firms that wish to improve their environmental performance of their product, process, and systems are faced with a complex task because manufacturing systems are very complex and they come in many forms and life expectancies. To achieve desired product functionalities, different design and material can be selected; thus the corresponding manufacturing processes are also changed accordingly. There is direct need of assessment tools to monitor and estimate environmental impact generated by different types of manufacturing processes. This research proposes a manufacturing informatics framework for the assessment of manufacturing sustainability. An EXPRESS information model is developed to represent sustainability information such as sustainability indicators and their associated weighting and uncertainty factors, material declaration information, and hazardous condition information, etc. This information model is tested with industrial products to validate its completeness and correctness. This information model serves as the first step of establishing close association of sustainability information with product design specification. In the next phase of research, investigation will be conducted to integrate sustainability information model and existing standardized product design model ISO 10303 AP 242.EcoSD networ

Numerical Simulation of Temperature Fields in Powder Bed Fusion Process by Using Hybrid Heat Source Model

Powder bed fusion (PBF) process is capable of producing a complex geometrical part with less material and energy consumption compared with conventional manufacturing methods. The performance of PBF processed part is mainly controlled by many process parameters such as scanning speed, scanning pattern, scanning strategy, and layer thickness. Usually, these parameters are optimized through detailed experiments which are time-consuming and costly. Therefore, numerical methods have been widely adopted to investigate the effects of these process parameters on temperature fields and thermal stress fields. As the laser/electron beam introduces huge temperature gradients within the irradiated region, which will result in the distortion even delamination of solidified layers, the study of the history of temperature distribution is the basic and crucial step in the modeling of PBF process. Most of the current research utilizes moving Gaussian point heat source as heat input to model the temperature distribution of a part. However, due to the small diameter of laser/electron beam, a small enough time step size is required to accurately model the real heat input, which will lead to significant computational burden. In this research, a hybrid of moving Gaussian point and line heat source model is developed, which makes the modeling of PBF process efficient without losing too much accuracy. In addition, an adaptive mesh scheme, which is capable of dynamically refining the mesh near the beam spot and coarsening the mesh far away from the beam spot, is adopted to accelerate the simulation process. Specifically, moving Gaussian point heat source is applied to the region of interest where accuracy is more concerned such as the temperature field within overhang feature. While the line heat source is applied to the region of interest where efficiency is more concerned such as temperature field within the inner region of a square. The simulation result shows that the temperature fields by using hybrid source model are comparable to the temperature fields by using the moving Gaussian point heat source model, and much less central processing unit time is required when the hybrid heat source is applied.Mechanical Engineerin

Lattice Structure Design and Optimization With Additive Manufacturing Constraints

As additive manufacturing (AM) process evolves from rapid prototyping to the end-of-use product manufacturing process, manufacturing constraints have largely been alleviated and design freedom has been significantly broadened, including shape complexity, material complexity, hierarchical complexity, and functional complexity. Inevitably, conventional Design Theory and Methodology (DTM) especially life-cycle objectives oriented ones are challenged. In this paper, firstly, the impact of AM on conventional DTM is analyzed in terms of design for manufacturing (DFM), design for assembly (DFA), and design for performance (DFP). [...] A new design method partially overcoming these drawbacks by integrating function integration and structure optimization to realize less part count and better performance is discussed. Design tools as a necessary part for supporting design are also studied. In the meantime, the review also identified the possible areas for future research

Supporting Multifunctional Bio-Inspired Design Concept Generation through Case-Based Expandable Domain Integrated Design (xDID) Model

Combining different features inspired by biological systems is necessary to obtain uncommon and unique multifunctional biologically inspired conceptual designs. The Expandable Domain Integrated Design (xDID) model is proposed to facilitate the multifunctional concept generation process. The xDID model extends the previously defined Domain Integrated Design (DID) method. The xDID model classifies biological features by their feature characteristics taken from various case-based bio-inspired design examples into their respective geometric designations called domains. The classified biological features are mapped to the respective plant and animal tissues from which they originate. Furthermore, the paper proposes a representation of the functions exhibited by the biological features at the embodiment level as a combination of the integrated structure (multiscale) and the structural strategy associated with the integrated structure. The xDID model is validated using three multifunctional bio-inspired design case studies at the end of the paper

A Comparative Analysis of the State-of-the-Art Methods for Multifunctional Bio-Inspired Design and an Introduction to Domain Integrated Design (DID)

Nature is a continuous source of inspiration for scientists and engineers for creating innovative products. In the past decade, many methods, frameworks, and tools have been developed to support the design and development of biologically inspired products. This research provides an overview of the current state-of-the-art bio-inspired design methods and identifies that there is a need for the development of methods to support multifunctionality in design. Although there are several methods that assist in the development of multifunctional designs inspired by biology, there is still a gap identified in the emulation and integration of biological features to achieve multifunctional bio-inspired designs. This paper presents a comparative analysis of the current methods for multifunctional bio-inspired design based on nine specific criteria and, in the end, introduces a new design method called Domain Integrated Design (DID) that will further aid in the generation of multifunctional design concepts inspired from biology

A new part consolidation method to embrace the design freedom of additive manufacturing

As additive manufacturing (AM) evolves from Rapid Prototyping (RP) to the end-of-use product manufacturing process, manufacturing constraints have been largely alleviated and design freedom for part consolidation is extremely broadened. AM enabled part consolidation method promises a more effective way to achieve part count reduction and the ease of assembly compared with traditional Design for Manufacture and Assembly (DFMA) method. [...] The other module is to realize better performance through the introduction and optimization of heterogeneous lattice structures according to performance requirements. The proposed part consolidation method highlights itself from the perspective of functionality achievement and performance improvement. An example of a triple clamp is studied to verify the effectiveness of the proposed model. The optimized results show that the part count has been reduced from 19 to 7 with a less weight by 20% and demonstrates better performance