A method for cementing a borehole includes pumping a collection of fluids into the borehole through a tubular string in the borehole and flowing the collection of fluids up an annulus positioned between the tubular string and a sidewall of the borehole. The method additionally includes monitoring a volume of the fluids pumped into the borehole, and performing a first estimation of a position of the fluids based on the volume of the collection of fluids pumped into the borehole, and an initial estimate of an average diameter of the sidewall of at least a portion of the borehole. The method further includes calculating a corrected estimate of the average diameter based on the first estimation and a pressure of the fluids measured at an inlet of the tubular string, and performing a second estimation of the position of the fluids based on the corrected estimate of the average diameter.

A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components/nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and/or simulations thereof. Virtual components may include virtual run-time nodes and/or simulated nodes. The MPDSC includes an I/O Switch which delivers I/O data between virtual and/or physical nodes, e.g., by using publish/subscribe mechanisms, thereby virtualizing physical I/O process data delivery. Nodes serviced by the I/O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and/or simulations may be manipulated as desired (speed, values, administration, etc.). The platform simultaneously supports simulation and run-time operations and interactions/intersections therebetween.

A measuring system is provided that includes a turbine engine thrust estimator that computes “virtual measurements” of dynamic engine thrust and other parameters of interest from test cell data in a very short amount of time. The measuring system ‘tunes’ a user's engine model, in a numerical propulsion system simulation, by optimizing system biases and health parameters to match the sensor outputs of a set of steady state data points across the operating range. The tuned model is then utilized by the measuring system to create a constant gain extended Kalman filter that is added directly within a code of the numerical propulsion system simulation. Results, including thrust, from the numerical propulsion system simulation with Kalman filter are then presented as ‘actual’ corrected data.

A method and a device for controlling an energy-generating system are operated with a renewable energy source. In the method, a prediction about an energy yield of the energy-generating system is made for a predefined prediction time period, and a predefined area, using a learning system with an input vector and an output vector. The output vector includes operating variables for a multiplicity of successive future times of the time period. The input vector includes variables, influencing the operating variables, for a point in time from a multiplicity of points in time of a predefined observation time period. The input variables include at least three items of information for the observation time period and the predefined area. The energy-generating system is controlled on the basis of the generated prediction such that weather-conditioned fluctuations in the energy yield of the energy-generating system are reduced.

A method and a device for controlling an energy-generating system are operated with a renewable energy source. In the method, a prediction about an energy yield of the energy-generating system is made for a predefined prediction time period, and a predefined area, using a learning system with an input vector and an output vector. The output vector includes operating variables for a multiplicity of successive future times of the time period. The input vector includes variables, influencing the operating variables, for a point in time from a multiplicity of points in time of a predefined observation time period. The input variables include at least three items of information for the observation time period and the predefined area. The energy-generating system is controlled on the basis of the generated prediction such that weather-conditioned fluctuations in the energy yield of the energy-generating system are reduced.

A network (100) for replacement of a living tissue, said network is a scaffold-free artificial hollow biological tissue network comprising a plurality of longitudinal multicellular aggregates (11) arranged in a plurality of bioprinted layers (22) which are located on top of one another, further comprising an inner surface (20) and an outer surface (21), wherein at least one of said bioprinted layers (22) is in shape of a planar closed loop such that a conduit for conveying fluids is defined, and said longitudinal multicellular aggregate (11) is a mixture of at least two cell types. Also a method for obtaining said longitudinal multicellular aggregate, and a further method for biomodeling and planning said network are proposed.

A network (100) for replacement of a living tissue, said network is a scaffold-free artificial hollow biological tissue network comprising a plurality of longitudinal multicellular aggregates (11) arranged in a plurality of bioprinted layers (22) which are located on top of one another, further comprising an inner surface (20) and an outer surface (21), wherein at least one of said bioprinted layers (22) is in shape of a planar closed loop such that a conduit for conveying fluids is defined, and said longitudinal multicellular aggregate (11) is a mixture of at least two cell types. Also a method for obtaining said longitudinal multicellular aggregate, and a further method for biomodeling and planning said network are proposed.

A system and approach for catalyst model parameter identification with modeling accomplished by an identification procedure that may incorporate a catalyst parameter identification procedure which may include determination of parameters for a catalyst device, specification of values for parameters and component level identification. Component level identification may be of a thermal model, adsorption and desorption, and chemistry. There may then be system level identification to get a final estimate of catalyst parameters.

A system and approach for catalyst model parameter identification with modeling accomplished by an identification procedure that may incorporate a catalyst parameter identification procedure which may include determination of parameters for a catalyst device, specification of values for parameters and component level identification. Component level identification may be of a thermal model, adsorption and desorption, and chemistry. There may then be system level identification to get a final estimate of catalyst parameters.

This disclosure relates generally to a method and system that provides personalized neuro motor rehabilitation therapy using a musculoskeletal arm model. The arm model is personalized using anthropometric measures and further adapted to operate using an optimized set of muscle actuators considering associated redundancy. The method generates trajectories associated with reach motion profiles for each motion task utilizing joint kinematics and inverse dynamics by integrating forward dynamics and muscle synergy concepts to select the optimized set of muscle actuators. The generated trajectories are further ranked based on muscle synergy, minimum energy consumption and optimized trajectory to select rehabilitation therapy best suited for effective recovery. Conventional methods that work with neural dynamics in deriving muscle synergy are dependent on single tasks, leaving synergy variation with task variability unexplored. The present disclosure provides understanding of work space, task variability and synergy paradigm to derive conclusive control actions for aiding rehabilitation effectively.