The effects of a secondary heat source on the performance ofthermophotovoltaic systems
Bibliographic record
Abstract
Thermophotovoltaics (TPV) is a versatile thermal-to-power conversion technology with many applications including uninterruptable power supplies, combined heat and power systems, self-powered heating devices, and industrial waste heat recovery. Recently, the efficiency of TPV systems has surpassed 30% and is expected to increase towards 50%. As their efficiency increases, the applications of TPV systems continue to expand due to their inherent advantages: they can be made to be lightweight, with no moving components, and with exceptionally high-power densities. To further increase their utility and reliability, TPV systems can be powered using multiple sources of heat. For example, solar-powered TPV systems are a promising source of remote or transportable power in sunny regions. However, solar irradiance is variable and intermittent. By using a secondary heat source, such as a fuel, solar-powered TPV systems can operate without interruption. Furthermore, the efficiency of TPV systems increases rapidly as their operating temperature increases, and when TPV systems are powered using heat from the sun and fuels simultaneously, their power output can exceed the sum of the power that would be generated when using these sources individually. In principle, hydrocarbon fuels, concentrated solar irradiance, industrial waste heat, and biomass/gas can be used to power TPV systems at the same time. Despite the impressive performance improvements that can be achieved by using multiple sources to power TPV systems simultaneously, very little research has been done on "multisource" TPV systems. Herein, numerical calculations are performed to determine the efficiency and power output from a TPV system that is powered by a primary source (that provides 20, 40, 60, 80, or 100 W of heat per cm 2 of emitter area) and a secondary source that provides thermal energy ranging from 0 to 100 W/cm 2 . Results show that the addition of a second source can have an immense effect on the output power and efficiency of TPV systems. For example, when a small amount of energy is added as a secondary source, 5 W/cm 2 for instance, to a TPV system powered using a primary heat source of 5 W/cm 2 , the output power increases by a factor of about four. Also, for TPV systems operating with a greater heat input of 100 W/cm 2 , the output power can be doubled by adding a secondary power source that provides heat at a rate of 80 W/cm 2 . The results presented in this work bode well for the advancement of multisource TPV systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".