Utilizing the Exergy Concept to Address Environmental Challenges of Electric Systems
Bibliographic record
Abstract
Theoretically, the concepts of energy, entropy, exergy and embodied energy are founded in the fields of thermodynamics and physics. Yet, over decades these concepts have been applied in numerous fields of science and engineering, playing a key role in the analysis of processes, systems and devices in which energy transfers and energy transformations occur. The research reported here aims to demonstrate, in terms of sustainability, the usefulness of the embodied energy and exergy concepts for analyzing electric devices which convert energy, particularly the electromagnet. This study relies on a dualist view, incorporating technical and environmental dimensions. The information provided by energy assessments is shown to be less useful than that provided by exergy and prone to be misleading. The electromagnet force and torque (representing the driving force of output exergy), accepted as both environmental and technical quantities, are expressed as a function of the electric current and the magnetic field, supporting the view of the necessity of discerning interrelations between science and the environment. This research suggests that a useful step in assessing the viability of electric devices in concert with ecological systems might be to view the magnetic flux density B and the electric current intensity I as environmental parameters. In line with this idea the study encompasses an overview of potential human health risks and effects of extremely low frequency electromagnetic fields (ELF EMFs) caused by the operation of electric systems. It is concluded that exergy has a significant role to play in evaluating and increasing the efficiencies of electrical technologies and systems. This article also aims to demonstrate the need for joint efforts by researchers in electric and environmental engineering, and in medicine and health fields, for enhancing knowledge of the impacts of environmental ELF EMFs on humans and other life forms.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".