Computationally Predicting the Microstructure Formation of Bulk Bismuth Telluride (Bi2Te3) Parts
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Sisik, Bengisu↗
Engineering topics
LeBlanc, Saniya
Publications and source records attributed to LeBlanc, Saniya.
The development of an augmented machine learning approach for the additive manufacturing of thermoelectric materials
Through the integration of machine learning (ML) techniques alongside additive manufacturing (AM) experimentation, we demonstrate an iterative process to rapidly predict laser-material interactions and melt pool geometries throughout the build parameter space for a bismuth telluride thermoelectric (TE) material. In doing so, we determined process parameters that created crack-free, highly dense (>99 %) n-type bismuth telluride (Bi 2 Te 2.7 Se 0.3 ) parts through laser powder bed fusion (LPBF). Furthermore, the ML-assisted understanding of the processing space allowed for the identification of build parameters that successfully yielded geometrically enhanced Bi 2 Te 2.7 Se 0.3 parts with reduced build times and no increase in experimental effort.
36 MATERIALS SCIENCE↗
Urban Combined Heat and Power with Integrated Renewables and Energy Storage
This project demonstrated how incorporating a diverse generation and storage portfolio allows an urban district energy system to improve its efficiency by at least 50% and increase its backup power by at least 40% with a return on investment of at least ten years. The improvement was evaluated against the baseline operations for two urban district energy systems (DESs): a synthetic DES and a George Washington University DES. The DES techno-economic framework we developed yields reliability, resilience, and vulnerability indices for urban DESs (including generation and storage) with designation of which technologies improve the security and resiliency metrics by at least 20% compared to the status quo. The indices are benchmarked against baseline scenarios, and cost projections (capital cost and return on investment) to achieve the 20% improvement are reported. The energy management system we developed to conduct these analyses was incorporated into a user-friendly interface that can be used for decision-making by a user with no technical or programming background.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Accelerating Additive Manufacturing Process Design for Energy Conversion Materials using In-situ Sensing and Machine Learning
One promising candidate for manufacturing of the bismuth Telluride thermoelectric legs is laser powder bed fusion (LPBF) additive Manufacturing (AM). AM processing parameters highly influence the material properties, however current processing parameter development methods in AM are costly and time consuming. In-situ sensors allow for the capture of physically relevant process information on a layer-by-layer basis and will be used to aide process development. To optimize the AM process for the best thermoelectric performance, process variables, in-situ process sensor data and ex-situ material characterization data are collected. Several different interpretable machine learning (ML) approaches are used, and the performance of each method are assessed. Significant input process variables include laser focus, hatch spacing and laser power. The best performing models are used to determine the manufacturing parameters that maximize the power factor. AM of bismuth telluride material provides the ability to create complex geometries enabling more efficient energy conversion.
30 DIRECT ENERGY CONVERSION↗
Cost Scaling of a Real-World Exhaust Waste Heat Recovery Thermoelectric Generator: A Deeper Dive
Cost is equally important to power density or efficiency for the adoption of waste heat recovery thermoelectric generators (TEG) in many transportation and industrial energy recovery applications. In many cases the system design that minimizes cost (e.g., the $/W value) can be very different than the design that maximizes the system's efficiency or power density, and it is important to understand the relationship between those designs to optimize TEG performance-cost compromises. Expanding on recent cost analysis work and using more detailed system modeling, an enhanced cost scaling analysis of a waste heat recovery thermoelectric generator with more detailed, coupled treatment of the heat exchangers has been performed. In this analysis, the effect of the heat lost to the environment and updated relationships between the hot-side and cold-side conductances that maximize power output are considered. This coupled thermal and thermoelectric treatment of the exhaust waste heat recovery thermoelectric generator yields modified cost scaling and design optimization equations, which are now strongly dependent on the heat leakage fraction, exhaust mass flow rate, and heat exchanger effectiveness. This work shows that heat exchanger costs most often dominate the overall TE system costs, that it is extremely difficult to escape this regime, and in order to achieve TE system costs of $1/W it is necessary to achieve heat exchanger costs of $1/(W/K). Minimum TE system costs per watt generally coincide with maximum power points, but Preferred TE Design Regimes are identified where there is little cost penalty for moving into regions of higher efficiency and slightly lower power outputs. These regimes are closely tied to previously-identified low cost design regimes. This work shows that the optimum fill factor Fopt minimizing system costs decreases as heat losses increase, and increases as exhaust mass flow rate and heat exchanger effectiveness increase. These findings have profound implications on the design and operation of various thermoelectric (TE) waste heat 3 recovery systems. This work highlights the importance of heat exchanger costs on the overall TEG system costs, quantifies the possible TEG performance-cost domain space based on heat exchanger effects, and provides a focus for future system research and development efforts.
cost analysis↗