Reduced order modeling of non-linear monopile dynamics via an AE-LSTM scheme

Non-linear analysis is of increasing importance in wind energy engineering as a result of their exposure in extreme conditions and the ever-increasing size and slenderness of wind turbines. Whilst modern computing capabilities facilitate execution of complex analyses, certain applications which require multiple or real-time analyses remain a challenge, motivating adoption of accelerated computing schemes, such as reduced order modelling (ROM) methods. Soil structure interaction (SSI) simulations fall in this class of problems, with the non-linear restoring force significantly affecting the dynamic behaviour of the turbine. In this work, we propose a ROM approach to the SSI problem using a recently developed ROM methodology. We exploit a data-driven non-linear ROM methodology coupling an autoencoder with long short-term memory (LSTM) neural networks. The ROM is trained to emulate a steel monopile foundation constrained by non-linear soil and subject to forces and moments at the top of the foundation, which represent the equivalent loading of an operating turbine under wind and wave forcing. The ROM well approximates the time domain and frequency domain response of the Full Order Model (FOM) over a range of different wind and wave loading regimes, whilst reducing the computational toll by a factor of 300. We further propose an error metric for capturing isolated failure instances of the ROM

Teaching reading strategies in science and social sciences in secondary education

Teaching and Teacher Learning (ICLON

Influence of Combination Therapy with Immune Modulators on Anti-TNF Trough Levels and Antibodies in Patients with IBD

Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Novel Perceived Stress and Life Events Precede Flares of Inflammatory Bowel Disease: A Prospective 12-Month Follow-Up Study

Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Telemedicine for management of inflammatory bowel disease (myIBDcoach): a pragmatic, multicentre, randomised controlled trial

Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Lipid-Induced Peroxidation in the Intestine Is Involved in Glucose Homeostasis Imbalance in Mice

BACKGROUND: Daily variations in lipid concentrations in both gut lumen and blood are detected by specific sensors located in the gastrointestinal tract and in specialized central areas. Deregulation of the lipid sensors could be partly involved in the dysfunction of glucose homeostasis. The study aimed at comparing the effect of Medialipid (ML) overload on insulin secretion and sensitivity when administered either through the intestine or the carotid artery in mice. METHODOLOGY/PRINCIPAL FINDINGS: An indwelling intragastric or intracarotid catheter was installed in mice and ML or an isocaloric solution was infused over 24 hours. Glucose and insulin tolerance and vagus nerve activity were assessed. Some mice were treated daily for one week with the anti-lipid peroxidation agent aminoguanidine prior to the infusions and tests. The intestinal but not the intracarotid infusion of ML led to glucose and insulin intolerance when compared with controls. The intestinal ML overload induced lipid accumulation and increased lipid peroxidation as assessed by increased malondialdehyde production within both jejunum and duodenum. These effects were associated with the concomitant deregulation of vagus nerve. Administration of aminoguanidine protected against the effects of lipid overload and normalized glucose homeostasis and vagus nerve activity. CONCLUSIONS/SIGNIFICANCE: Lipid overload within the intestine led to deregulation of gastrointestinal lipid sensing that in turn impaired glucose homeostasis through changes in autonomic nervous system activity

Is There a Place for Dietary Fiber Supplements in Weight Management?

Inadequate dietary fiber intake is common in modern diets, especially in children. Epidemiological and experimental evidence point to a significant association between a lack of fiber intake and ischemic heart disease, stroke atherosclerosis, type 2 diabetes, overweight and obesity, insulin resistance, hypertension, dyslipidemia, as well as gastrointestinal disorders such as diverticulosis, irritable bowel disease, colon cancer, and cholelithiasis. The physiological effects of fiber relate to the physical properties of volume, viscosity, and water-holding capacity that the fiber imparts to food leading to important influences over the energy density of food. Beyond these physical properties, fiber directly impacts a complex array of microbiological, biochemical, and neurohormonal effects directly through modification of the kinetics of digestion and through its metabolism into constituents such as short chain fatty acids, which are both energy substrates and important enteroendocrine ligands. Of particular interest to clinicians is the important role dietary fiber plays in glucoregulation, appetite, and satiety. Supplementation of the diet with highly functional fibers may prove to play an important role in long-term obesity management

Real-time plasma state monitoring and supervisory control on TCV

In ITER and DEMO, various control objectives related to plasma control must be simultaneously achieved by the plasma control system (PCS), in both normal operation as well as off-normal conditions. The PCS must act on off-normal events and deviations from the target scenario, since certain sequences (chains) of events can precede disruptions. It is important that these decisions are made while maintaining a coherent prioritization between the real-time control tasks to ensure high-performance operation. In this paper, a generic architecture for task-based integrated plasma control is proposed. The architecture is characterized by the separation of state estimation, event detection, decisions and task execution among different algorithms, with standardized signal interfaces. Central to the architecture are a plasma state monitor and supervisory controller. In the plasma state monitor, discrete events in the continuous-valued plasma state are modeled using finite state machines. This provides a high-level representation of the plasma state. The supervisory controller coordinates the execution of multiple plasma control tasks by assigning task priorities, based on the finite states of the plasma and the pulse schedule. These algorithms were implemented on the TCV digital control system and integrated with actuator resource management and existing state estimation algorithms and controllers. The plasma state monitor on TCV can track a multitude of plasma events, related to plasma current, rotating and locked neoclassical tearing modes, and position displacements. In TCV experiments on simultaneous control of plasma pressure, safety factor profile and NTMs using electron cyclotron heating (ECH) and current drive (ECCD), the supervisory controller assigns priorities to the relevant control tasks. The tasks are then executed by feedback controllers and actuator allocation management. This work forms a significant step forward in the ongoing integration of control capabilities in experiments on TCV, in support of tokamak reactor operation