Thermodynamics Problem Solving in Physical Chemistry

Murphy, Kathleen E.

doi:10.1201/9780429278402

Cited by 2 publications

(1 citation statement)

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“…In this work, temperature dependence is only considered in the heat capacities of air and water in the heat exchanger part of the model. Temperature dependence in the heat capacities is often expressed as an empirical power series in T or as a polynomial in T (Murphy, 2020). In this work, polynomials were fitted to the experimental data in (Incropera et al, 2013) and compared with the empirical equations in (McBride et al, 2002).…”

Section: Solution Of the Counter-current Heat Exchanger Modelmentioning

confidence: 99%

Hybrid Model for Fast Solution of Thermal Synchronous Generator With Heat Exchanger

Aleikish¹,

Pandey²,

Øyvang³

et al. 2021

Linköping Electronic Conference Proceedings

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Overheating of synchronous generators may lead to a shortened generator lifespan, thus strict constraints are imposed on their operation. A common constraint is to restrict the power factor of the generator to lie below, say, 0.86 overexcited. In some recent work, a dynamic thermal model of the generator with cooling heat exchanger has been developed; the idea is that this allows for better monitoring of generator temperature, while relaxing on the power factor constraint. The current model is only valid for an ideal case of constant heat capacity. In this work, the generator model is extended to allow for temperature dependence in heat capacity of water and air in the heat exchanger model. The consequence of this more realistic model, is that it is no longer possible to find an explicit, analytic solution of the heat exchanger model, and it is now necessary to instead solve numerically a two point boundary value problem for each time step in the differential equation solver. It is shown that the effect of temperature dependence in the heat capacities has a noticeable effect on the solution of the model. The inclusion of on-line numeric solution of the heat exchanger model does, however increase the computation time of the thermal generator model by a factor of several thousand. Here, we instead study the possibility to solve the numeric heat exchanger model multiple times off-line, and then fit a regression model that gives a correction to the analytic solution. Both linear regression and nonlinear regression (neural network) is considered. Both types of regression models allow for a speed-up in the computation time of the thermal generator model of a factor of around 2000. In the computations, computer language Julia was used, with the DifferentialEquations and the Flux packages.

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Section: Solution Of the Counter-current Heat Exchanger Modelmentioning

confidence: 99%