Flux Estimator for Salient Pole Synchronous Machines Driven by the Cycloconverter Based on Enhanced Current and Voltage Model of the Machine with Fuzzy Logic Transition

Flux estimation is a key feature of the field-oriented control for the electrically excited synchronous machine which enables the high-performance, high-dynamic drive behavior. In this work, an electrically excited synchronous machine flux estimator based on a current and voltage model is proposed. In this case, the transition between the estimators is done with a fuzzy logic set of rules. The flux estimator based on the current model of the machine in this paper considers the saturation and cross-coupling effect in both axis and it is suitable for applications where a limited amount of the machine data is available. The flux estimator based on the voltage model is specially designed for the drives where high voltage and current ripple is present under normal operating conditions, e.g., like in cycloconverter applications. To exploit all the advantages of both models, a fuzzy logic transition is proposed based on multiple choices which manages the transition between the models based on a speed and torque reference. The proposed flux estimator is experimentally verified on a cycloconverter fed salient-pole electrically excited synchronous machine. The experimental results clearly show that the proposed flux estimator enables the accurate and stable operating conditions for different operating points of the cycloconverter-fed salient-pole electrically excited synchronous machine.

Evaluating consistency between total column CO₂ retrievals from OCO-2 and the in situ network over North America: implications for carbon flux estimation

Feedbacks between the climate system and the carbon cycle represent a key source of uncertainty in model projections of Earth's climate, in part due to our inability to directly measure large-scale biosphere–atmosphere carbon fluxes. In situ measurements of the CO2 mole fraction from surface flasks, towers, and aircraft are used in inverse models to infer fluxes, but measurement networks remain sparse, with limited or no coverage over large parts of the planet. Satellite retrievals of total column CO2 (XCO2), such as those from NASA's Orbiting Carbon Observatory-2 (OCO-2), can potentially provide unprecedented global information about CO2 spatiotemporal variability. However, for use in inverse modeling, data need to be extremely stable, highly precise, and unbiased to distinguish abundance changes emanating from surface fluxes from those associated with variability in weather. Systematic errors in XCO2 have been identified and, while bias correction algorithms are applied globally, inconsistencies persist at regional and smaller scales that may complicate or confound flux estimation. To evaluate XCO2 retrievals and assess potential biases, we compare OCO-2 v10 retrievals with in situ data-constrained XCO2 simulations over North America estimated using surface fluxes and boundary conditions optimized with observations that are rigorously calibrated relative to the World Meteorological Organization X2007 CO2 scale. Systematic errors in simulated atmospheric transport are independently evaluated using unassimilated aircraft and AirCore profiles. We find that the global OCO-2 v10 bias correction shifts the distribution of retrievals closer to the simulated XCO2, as intended. Comparisons between bias-corrected and simulated XCO2 reveal differences that vary seasonally. Importantly, the difference between simulations and retrievals is of the same magnitude as the imprint of recent surface flux in the total column. This work demonstrates that systematic errors in OCO-2 v10 retrievals of XCO2 over land can be large enough to confound reliable surface flux estimation and that further improvements in retrieval and bias correction techniques are essential. Finally, we show that independent observations, especially vertical profile data, such as those from the National Oceanic and Atmospheric Administration aircraft and AirCore programs are critical for evaluating errors in both satellite retrievals and carbon cycle models.

Omics-based label-free metabolic flux inference reveals dysregulation of glucose metabolism in liver associated with obesity

Glucose homeostasis is maintained by modulating metabolic flux through various metabolic pathways in metabolic organs such as liver, and a change in metabolic flux is regulated by enzymes and metabolites. Obesity represents dysregulation of the glucose homeostasis, but changes in metabolic fluxes and their regulations associated with obesity have not been fully understood. Here we introduce Omics-based Metabolic flux Estimation without Labeling for Extended Trans-omic analysis (OMELET), an approach that uses metabolomic, proteomic and transcriptomic data to identify changes in metabolic flux, and to quantify contributions of metabolites, enzymes and transcripts to the changes in metabolic flux. We identified obesity-associated increases in metabolic fluxes through gluconeogenesis and pyruvate cycle in fasting ob/ob mice. The increased metabolic flux through gluconeogenesis was contributed by increased transcripts, while that through pyruvate cycle by increased transcripts and substrates. OMELET provided quantitative insights into alteration and dysregulation of metabolic flux in liver associated with obesity.