A simulation platform for systems analysis theory and applications
Bibliographic record
Abstract
The analysis of systems can often benefit from the use of simulation. Simulation can be used to study the behavior of individual components in the system, study the interaction of various components, or fine-tune the set points of control devices. Prosim, a general-purpose simulation platform developed at the University of Waterloo, allows the interactive definition and simulation of individual components. Using drag and drop techniques, individual components can be assembled into larger systems which can be simulated. The outputs of the simulation are available either in numeric or graphical form. Prosim is characterized by its totally interactive approach to simulation, its use of graph-theoretic concepts such as through and nodal variables, and its innovative use of symbolic processing to solve for the state of the system. In this paper we show how Prosim can be used to study the interactions between various components of a system. The system chosen is a directly-coupled solar photovoltaic (PV) water-pumping system. The PV array is used to power an electric motor, which rotates a centrifugal pump circulating water in a closed loop. Inefficiencies resulting from mismatches between the characteristics of the array and the motor are shown with the help of simulations.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".