Energy breakeven in thermonuclear fusion. An advanced concept for efficient energy input with positive implications for energy saving and the geoscience of climate change
Bibliographic record
Abstract
The motivation of the present study is energy generation with thermonuclear fusion. Specifically, it is the attainment of breakeven conditions with a fusionable plasma whereby the output fusion energy is at least equal to the energy expended in creating the plasma and bringing it to fusionable conditions. This objective has eluded the physics community for the past seven decades. It is here suggested that perhaps the reason is that ever-bigger fusion machines are built, which unfortunately have brought results not in line with the expectations, in terms of desired fusion output. The opposite view is taken here, where attention is paid to the energy input, with the objective of minimizing the energy losses. One of the most important losses is a consequence of the limited thermodynamic efficiency of conventional engines that convert heat to work, thus generating the electricity involved in the energy input. This preliminary study shows that the efficiency can be improved if a novel thermodynamic cycle is used with heat recovery and recirculation. No attention is paid in the present study to the applicability of the novel cycle to a working engine but only to its feasibility. After the delineation of the concept, we use a simulation program to confirm that such approach is promising, and the objective of improving the thermodynamic efficiency of conventional heat-engines by at least 10% is realistic. Finally, the economic benefits are quantified of such substantial efficiency improvement on a world-wide scale. Mitigation of the damage to our environment due to the reduced heat rejection is also quantified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".