Surrogate Optimization Model for an Integrated Regenerative Methanol Transcritical Cycle

In order to reduce the cost ($) per megawatts hour (MWh) of electrical energy generated by a nuclear power cycle with a novel small modular reactor (SMR), a new SMR-based nuclear power cycle with Methanol as working fluid was designed. It was built virtually with the Python & Coolprop software based on all components’ physical properties, and it is therefore called the physics-based model. The physics-based model would require seven user-defined values as input for the seven free design parameters, respectively. The physics-based model outcomes include LCOE (the cost per megawatts hour of electrical energy generated by the cycle), the first-law efficiency of the power cycle (the ratio between the net power out of the power cycle and the net thermal energy into the power cycle), and the penalty (severity of system violation with the given design parameters values). In order to compare between different power cycles, the corresponding optimized LCOE are needed. However, in order to find the design parameters that optimize the system LCOE, it can take up to three days with the physics-based model, because the physics-based model is highly complex and it takes thousands of iterations averagely to the optimize the power system. In confronting the time complexity issue in optimization, the study in this paper explores the viability of replacing the physics-based model with a machine learning-based surrogate model. During the optimization procedure, the machine learning-based surrogate model is expected to accelerate process of finding the corresponding outcomes, and thus to save time. Candidate surrogate models are built and analyzed in terms of their prediction accuracy. The last chosen model is further optimized in terms of its structure and hyper-parameters. With the structurally and parametric optimized surrogate model being incorporated, different global optimizers are used and analyzed. As the result, the optimized design parameters from the surrogate-optimizer model are fed into the physics-based model, and their corresponding results are compared with the baseline optimized results of the physics-based model. In conclusion, the study reveals that the Multilayer Perceptron (MLP) networks with two hidden layers gives the best prediction performance, and therefore they are chosen as the surrogate model. In addition, four global optimizers, namely the basinhopping, the differential evolution, the dual annealing and the fmin, are working well along with the chosen surrogate model. They integrated surrogate-optimizer model is capable of finding the optimized LCOE as well as the corresponding design parameters. In comparison with the baseline optimized LCOE, the relative error is less than 3.5%, and this searching procedure completed within 30 minutes

Quantum Computing Simulation of the Hydrogen Molecule System with Rigorous Quantum Circuit Derivations

Quantum computing has been an emerging technology in the past few decades. It utilizes the power of programmable quantum devices to perform computation, which can solve complex problems in a feasible time that is impossible with classical computers. Simulating quantum chemical systems using quantum computers is one of the most active research fields in quantum computing. However, due to the novelty of the technology and concept, most materials in the literature are not accessible for newbies in the field and sometimes can cause ambiguity for practitioners due to missing details. This report provides a rigorous derivation of simulating quantum chemistry systems using quantum computers. The Hydrogen molecule is used as an example throughout the process to make it readable to a broader audience. Specifically, the ground state energies and the first-excited energies of the Hydrogen molecule, as well as the ground state energies of the Lithium Hydride molecule at different bond lengths under the governing of their corresponding Hamiltonians are explored through the Schrodinger’s equation, the Phase Estimation Algorithm (PEA), the second quantization, the Bravyi-Kitaev transformation (BKT), and the Hamiltonian establishment. Then, a quantum circuit is built from scratch based on the second-quantization and BKT Hamiltonian to demonstrate the process of quantum circuit derivation for quantum chemistry system. Lastly, Simulations on both the ground and excited state energies of the Hydrogen molecule and the ground state energies of the Lithium Hydride molecule are carried out based on the design of the circuits with Google’s Cirq quantum simulator. Finally, some quantitative and qualitative comparisons and analysis are conducted with the results

An Empirical Analysis of the Impacts of the Sharing Economy Platforms on the U.S. Labor Market

Each generation of digital innovation has caused a dramatic change in the way people work. Sharing economy is the latest trend of digital innovation, and it has fundamentally changed the traditional business models. In this paper, we empirically examine the impacts of the sharing economy platforms (specifically, Uber) on the labor market in terms of labor force participation, unemployment rate, supply, and wage of low-skilled workers. Combining a data set of Uber entry time and several microdata sets, we utilize a difference-in-differences (DID) method to investigate whether the above measures before and after Uber entry are significantly different across the U.S. metropolitan areas. Our empirical findings show that sharing economy platforms such as Uber significantly decrease the unemployment rate and increase the labor force participation. We also find evidence of a shift in the supply of low skill workers and consequently a higher wage rate for such workers in the traditional industries

A PC-Kriging-HDMR integrated with an adaptive sequential sampling strategy for high-dimensional approximate modeling

High-dimensional complex multi-parameter problems are prevalent in engineering, exceeding the capabilities of traditional surrogate models designed for low/medium-dimensional problems. These models face the curse of dimensionality, resulting in decreased modeling accuracy as the design parameter space expands. Furthermore, the lack of a parameter decoupling mechanism hinders the identification of couplings between design variables, particularly in highly nonlinear cases. To address these challenges and enhance prediction accuracy while reducing sample demand, this paper proposes a PC-Kriging-HDMR approximate modeling method within the framework of Cut-HDMR. The method leverages the precision of PC-Kriging and optimizes test point placement through a multi-stage adaptive sequential sampling strategy. This strategy encompasses a first-stage adaptive proportional sampling criterion and a second-stage central-based maximum entropy criterion. Numerical tests and a practical application involving a cantilever beam demonstrate the advantages of the proposed method. Key findings include: (1) The performance of traditional single-surrogate models, such as Kriging, significantly deteriorates in high-dimensional nonlinear problems compared to combined surrogate models under the Cut-HDMR framework (e.g., Kriging-HDMR, PCE-HDMR, SVR-HDMR, MLS-HDMR, and PC-Kriging-HDMR); (2) The number of samples required for PC-Kriging-HDMR modeling increases polynomially rather than exponentially as the parameter space expands, resulting in substantial computational cost reduction; (3) Among existing Cut-HDMR methods, no single approach outperforms the others in all aspects. However, PC-Kriging-HDMR exhibits improved modeling accuracy and efficiency within the desired improvement range compared to PCE-HDMR and Kriging-HDMR, demonstrating robustness.Comment: 17 pages with 7 figures and 9 table