Improvements on Heat Flux and Heat Conductance Estimation with Applications to Metal Castings

Heat flux and heat conductance at the metal mold interface plays a key role in controlling the final metal casting strength. It is difficult to obtain these parameters through direct measurement because of the required placement of sensors, however they can be obtained through inverse heat conduction calculations. Existing inverse heat conduction methods are analyzed and classified into three categories, i.e., direct inverse methods, observer-based methods and optimization methods. The solution of the direct inverse methods is based on the linear relationship between heat flux and temperature (either in the time domain or in the frequency domain) and is calculated in batch mode. The observer-based method consists on the application of observer theory to the inverse heat conduction problem. The prominent characteristic in this category is online estimation, but the methods in this category show weak robustness. Transforming estimation problems into optimization problems forms the methods in the third category. The methods in third category show very good robustness property and can be easily extended to multidimensional and nonlinear problems. The unknown parameters in some inverse heat conduction methods can be obtained by a proposed calibration procedure. A two-index property evaluation (accuracy and robustness) is also proposed to evaluate inverse heat conduction methods and thus determine which method is suitable for a given situation. The thermocouple dynamics effect on inverse calculation is also analyzed. If the thermocouple dynamics is omitted in the inverse calculation, the time constant of thermocouple should be as small as possible. Finally, a simple model is provided simulating the temperature measurement using a thermocouple. FEA (Finite Element Analysis) is employed to simulate temperature measurement

Optimization Design of Electrodes for Anode-Supported Solid Oxide Fuel Cells via Genetic Algorithm

Porous electrode is the critical component of solid-oxide fuel cells (SOFCs) and provides a functional material backbone for multi-physicochemical processes. Model based electrode designs could significantly improve SOFC performance. This task is usually performed via parameter studies for simple case and assumed property distributions for graded electrodes. When nonlinearly coupled multiparameters of electrodes are considered, it could be very difficult for the model based parameter study method to effectively and systematically search the design space. In this research, the optimization approach with a genetic algorithm is demonstrated for this purpose. An anode-supported proton conducting SOFC integrated with a fuel supply system is utilized as a physical base for the model development and the optimization design. The optimization results are presented, which are difficult to obtain for parametric study method

Modeling of Chemical-Mechanical Couplings in Anode-Supported Solid Oxide Fuel Cells and Reliability Analysis

Oxygen ionic transport in conducting ceramics is an important mechanism enabling solid oxide fuel cell (SOFC) technology. The multi-physicochemical processes lead to the fact that the distribution of oxygen vacancy site fraction is not uniform in a positive-electrode electrolyte negative-electrode (PEN) assembly. Different oxygen vacancy concentrations induce different volumetric expansion of ceramics, resulting in complicated chemical–mechanical coupling phenomena and chemical stress in SOFCs. In this research, a mathematical model is developed to study oxygen ionic transport induced chemical stress in an SOFC. The model is validated using experimental polarization curves. Comprehensive simulations are performed to investigate chemical stress distribution in the PEN assembly under different operating conditions and design parameters as well as mechanical constraints. Principal stress analysis is employed to identify the weakest zones in the cell. The Weibull approach is utilized to analyze the failure probability of each component and the elastic energy stored in the cathode layer is employed to evaluate potential delamination failure at the cathode/electrolyte interface. The paper for the first time builds a chemical–mechanical coupling model at a cell level and is an important module complementary to the state-of-the-art electrochemical–thermal–mechanical model of SOFCs

A Platinum Nanowire Network as a Highly Effective Current Collector for Intermediate Temperature Solid Oxide Fuel Cells

We report the fabrication and evaluation of a platinum nanowire network as a highly efficient current collector for solid oxide fuel cells (SOFCs). The ink of carbon-black supported platinum nanoparticles was sprayed onto the cathode. After firing, the carbon black was oxidized and disappeared as carbon dioxide gas while the platinum nanoparticles connect with one another, forming a tree-branch-like nanowire network. The diameters of the nanowires range from 100 nm to 400 nm. Compared to a conventional platinum paste current collector, the polarization resistance of the PrBaCo2O5+δ (PBCO) cathode with a nanowire current collector was reduced by 44% at 650 °C (from 0.18 Ω cm2 to 0.1 Ω cm2). The peak power density of the button cells was improved at different degrees of 31.8–59.6% under temperatures 650–550 °C for typical cathode materials of PBCO, La0.6Sr0.4Co0.2Fe0.8O3−δ (LSCF), and Ba0.5Sr0.5Co0.8Fe0.2O3−δ(BSCF). The nanowire network did not show obvious changes after long term testing (400 h)

Micro Modeling Study of Cathode/Electrolyte Interfacial Stresses for Solid Oxide Fuel Cells

Delamination of the cathode/electrolyte interface is an important degradation phenomenon in solid oxide fuel cells (SOFCs). While the thermal stress has been widely recognized as one of the major reasons for such delamination failures, the role of chemical stress does not receive too much attention. In this paper, a micro-model is developed to study the cathode/electrolyte interfacial stresses, coupling oxygen ion transport process with structural mechanics. Results indicate that the distributions of chemical stress are very complicated at the cathode/electrolyte interface and show different patterns from those of thermal stress. The maximum principal stresses take place at the cathode/electrolyte interface and are affected by the distribution of oxygen vacancy concentration on the cathode particle surface. The model is able to readily study complicated interfacial stresses in SOFCs, which otherwise would be difficult for experimental techniques

A Ceramic-Anode Supported Low Temperature Solid Oxide Fuel Cell

We report the fabrication and evaluation of a ceramic-anode supported button cell LSCM-SDC/SDC/PBSC (thickness 400 μm/20 μm/20 μm). The anode/electrolyte assembly LSCM-SDC/SDC was co-fired at low temperature of 1250°C, where a slight amount of CuO was mixed with LSCM. The CuO (20.3 wt%) were impregnated into the porous substrate to enhance current collecting effect. The cell exhibited power density of 596 mWcm−2 and 381 mWcm−2 at 700°C with wet hydrogen and methane as the fuel respectively, where the silver paste was used as current collectors, the highest performance up to date for the cells with metal oxide anodes at this temperature

Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers

Prompt tuning attempts to update few task-specific parameters in pre-trained models. It has achieved comparable performance to fine-tuning of the full parameter set on both language understanding and generation tasks. In this work, we study the problem of prompt tuning for neural text retrievers. We introduce parameter-efficient prompt tuning for text retrieval across in-domain, cross-domain, and cross-topic settings. Through an extensive analysis, we show that the strategy can mitigate the two issues -- parameter-inefficiency and weak generalizability -- faced by fine-tuning based retrieval methods. Notably, it can significantly improve the out-of-domain zero-shot generalization of the retrieval models. By updating only 0.1% of the model parameters, the prompt tuning strategy can help retrieval models achieve better generalization performance than traditional methods in which all parameters are updated. Finally, to facilitate research on retrievers' cross-topic generalizability, we curate and release an academic retrieval dataset with 18K query-results pairs in 87 topics, making it the largest topic-specific one to date

CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE

A complete and pure sample of quasars with accurate redshifts is crucial for quasar studies and cosmology. In this paper, we present CatNorth, an improved Gaia DR3 quasar candidate catalog with more than 1.5 million sources in the 3$\pi$ sky built with data from Gaia, Pan-STARRS1, and CatWISE2020. The XGBoost algorithm is used to reclassify the original Gaia DR3 quasar candidates as stars, galaxies, and quasars. To construct training/validation datasets for the classification, we carefully built two different master stellar samples in addition to the spectroscopic galaxy and quasar samples. An ensemble classification model is obtained by averaging two XGBoost classifiers trained with different master stellar samples. Using a probability threshold of $p_{\mathrm{QSO\_mean}}>0.95$ in our ensemble classification model and an additional cut on the logarithmic probability density of zero proper motion, we retrieved 1,545,514 reliable quasar candidates from the parent Gaia DR3 quasar candidate catalog. We provide photometric redshifts for all candidates with an ensemble regression model. For a subset of 89,100 candidates, accurate spectroscopic redshifts are estimated with the Convolutional Neural Network from the Gaia BP/RP spectra. The CatNorth catalog has a high purity of > 90% while maintaining high completeness, which is an ideal sample to understand the quasar population and its statistical properties. The CatNorth catalog is used as the main source of input catalog for the LAMOST phase III quasar survey, which is expected to build a highly complete sample of bright quasars with $i < 19.5$.Comment: 24 pages, 13 figures, submitted to AAS journals. Table 4 (The CatNorth quasar candidate catalog) is available at https://nadc.china-vo.org/res/r101313

System level modeling and component level control of fuel cells

This dissertation investigates the fuel cell systems and the related technologies in three aspects: (1) system-level dynamic modeling of both PEM fuel cell (PEMFC) and solid oxide fuel cell (SOFC); (2) condition monitoring scheme development of PEM fuel cell system using model-based statistical method; and (3) strategy and algorithm development of precision control with potential application in energy systems. ^ The dissertation first presents a system level dynamic modeling strategy for PEM fuel cells. It is well known that water plays a critical role in PEM fuel cell operations. It makes the membrane function appropriately and improves the durability. The low temperature operating conditions, however, impose modeling difficulties in characterizing the liquid-vapor two phase change phenomenon, which becomes even more complex under dynamic operating conditions. This dissertation proposes an innovative method to characterize this phenomenon, and builds a comprehensive model for PEM fuel cell at the system level. The model features the complete characterization of multi-physics dynamic coupling effects with the inclusion of dynamic phase change. The model is validated using Ballard stack experimental result from open literature. The system behavior and the internal coupling effects are also investigated using this model under various operating conditions. ^ Anode-supported tubular SOFC is also investigated in the dissertation. While the Nernst potential plays a central role in characterizing the electrochemical performance, the traditional Nernst equation may lead to incorrect analysis results under dynamic operating conditions due to the current reverse flow phenomenon. This dissertation presents a systematic study in this regard to incorporate a modified Nernst potential expression and the heat/mass transfer into the analysis. The model is used to investigate the limitations and optimal results of various operating conditions; it can also be utilized to perform the optimal design of tubular SOFC. ^ With the system-level dynamic model as a basis, a framework for the robust, online monitoring of PEM fuel cell is developed in the dissertation. The monitoring scheme employs the Hotelling T2 based statistical scheme to handle the measurement noise and system uncertainties and identifies the fault conditions through a series of self-checking and conformal testing. A statistical sampling strategy is also utilized to improve the computation efficiency. ^ Fuel/gas flow control is the fundamental operation for fuel cell energy systems. In the final part of the dissertation, a high-precision and robust tracking control scheme using piezoelectric actuator circuit with direct hysteresis compensation is developed. The key characteristic of the developed control algorithm includes the nonlinear continuous control action with the adaptive boundary layer strategy.

System level modeling and component level control of fuel cells

This dissertation investigates the fuel cell systems and the related technologies in three aspects: (1) system-level dynamic modeling of both PEM fuel cell (PEMFC) and solid oxide fuel cell (SOFC); (2) condition monitoring scheme development of PEM fuel cell system using model-based statistical method; and (3) strategy and algorithm development of precision control with potential application in energy systems. ^ The dissertation first presents a system level dynamic modeling strategy for PEM fuel cells. It is well known that water plays a critical role in PEM fuel cell operations. It makes the membrane function appropriately and improves the durability. The low temperature operating conditions, however, impose modeling difficulties in characterizing the liquid-vapor two phase change phenomenon, which becomes even more complex under dynamic operating conditions. This dissertation proposes an innovative method to characterize this phenomenon, and builds a comprehensive model for PEM fuel cell at the system level. The model features the complete characterization of multi-physics dynamic coupling effects with the inclusion of dynamic phase change. The model is validated using Ballard stack experimental result from open literature. The system behavior and the internal coupling effects are also investigated using this model under various operating conditions. ^ Anode-supported tubular SOFC is also investigated in the dissertation. While the Nernst potential plays a central role in characterizing the electrochemical performance, the traditional Nernst equation may lead to incorrect analysis results under dynamic operating conditions due to the current reverse flow phenomenon. This dissertation presents a systematic study in this regard to incorporate a modified Nernst potential expression and the heat/mass transfer into the analysis. The model is used to investigate the limitations and optimal results of various operating conditions; it can also be utilized to perform the optimal design of tubular SOFC. ^ With the system-level dynamic model as a basis, a framework for the robust, online monitoring of PEM fuel cell is developed in the dissertation. The monitoring scheme employs the Hotelling T2 based statistical scheme to handle the measurement noise and system uncertainties and identifies the fault conditions through a series of self-checking and conformal testing. A statistical sampling strategy is also utilized to improve the computation efficiency. ^ Fuel/gas flow control is the fundamental operation for fuel cell energy systems. In the final part of the dissertation, a high-precision and robust tracking control scheme using piezoelectric actuator circuit with direct hysteresis compensation is developed. The key characteristic of the developed control algorithm includes the nonlinear continuous control action with the adaptive boundary layer strategy.