Development of Electronic Load Controllers for Free-Piston Stirling Convertors Aided by Stirling Simulation Model

The free-piston Stirling convertor end-to-end modeling effort at the NASA Glenn Research Center has produced a software-based test bed in which free-piston Stirling convertors can be simulated and evaluated. The simulation model includes all the components of the convertor: the Stirling cycle engine, heat source, linear alternator, controller, and load. So far, it has been used in evaluating the performance of electronic controller designs. Three different controller design concepts were simulated using the model: 1) Controllers with parasitic direct current loading. 2) Controllers with parasitic alternating current loading. 3) Controllers that maintain a reference current. The free-piston Stirling convertor is an electromechanical device that operates at resonance. It is the function of the electronic load controller to ensure that the electrical load seen by the machine is always great enough to keep the amplitude of the piston and alternator oscillation at the rated value. This is done by regulating the load on the output bus. The controller monitors the instantaneous voltage, regulating it by switching loads called parasitic loads onto the bus whenever the bus voltage is too high and removing them whenever the voltage is too low. In the first type of controller, the monitor-ing and switching are done on the direct-current (dc) bus. In the second type, the alternating current bus is used. The model allows designers to test a controller concept before investing time in hardware. The simulation code used to develop the model also offers detailed models of digital and analog electronic components so that the resulting designs are realistic enough to translate directly into hardware circuits

Development of bio-inspired antifouling coatings

Biofouling is the accumulation of micro and macro organisms on a solid surface exposed to a marine environment. It cause a reduction of operational effectiveness of marine structures[1]. The process begins with the settlement of microorganisms on the surface demonstrated in figure 1, the microorganisms then produce Extracellular Polymeric Substances (EPS) forming a biofilm. Hydrophobic surfaces have been shown to inhibit biofouling and it has been noted that some strains of macroalgae use surface topography and leaching of antimicrobials to minimise biofouling

Examining the Viability of Video Game Interventions for Heavy Alcohol Drinkers

Alcohol cue reactivity is a process in which cues typically signaling alcohol administration come to elicit conditioned responses such as strong, positive emotions and cravings for alcohol in heavy drinkers. Research evidence suggests that impaired memory and/or attentional processes may, in part, contribute to cue reactivity for alcohol. Virtual video games can offer an improved way to measure cue reactivity and/or deliver cue exposure interventions for alcohol, although evidence for their potential efficacy remains understudied. In a series of studies, we examined the ability of novel video games to 1) measure attention and working memory, 2) measure cue reactivity in terms of in-game scoring and 3) examine subjective emotions and cravings for alcohol post-gameplay. We found that our games were significantly correlated with established measures of attention and memory. Performance on one of the games was also dependent upon participants’ drinking levels. Further, a heavy drinking sample playing game versions embedded with alcohol stimuli reported 1) increased cue reactivity for alcohol imagery after a single gaming session, and 2) decreased cue reactivity for neutral imagery after repeated gaming sessions. Our results suggest that video game interventions for heavy alcohol drinkers can decrease their positive feelings and cravings for alcohol, although this is likely influenced by the type of game played and length of exposure received

Development of a Linear Stirling System Model with Varying Heat Inputs

The linear model of the Stirling system developed by NASA Glenn Research Center (GRC) has been extended to include a user-specified heat input. Previously developed linear models were limited to the Stirling convertor and electrical load. They represented the thermodynamic cycle with pressure factors that remained constant. The numerical values of the pressure factors were generated by linearizing GRC's nonlinear System Dynamic Model (SDM) of the convertor at a chosen operating point. The pressure factors were fixed for that operating point, thus, the model lost accuracy if a transition to a different operating point were simulated. Although the previous linear model was used in developing controllers that manipulated current, voltage, and piston position, it could not be used in the development of control algorithms that regulated hot-end temperature. This basic model was extended to include the thermal dynamics associated with a hot-end temperature that varies over time in response to external changes as well as to changes in the Stirling cycle. The linear model described herein includes not only dynamics of the piston, displacer, gas, and electrical circuit, but also the transient effects of the heater head thermal inertia. The linear version algebraically couples two separate linear dynamic models, one model of the Stirling convertor and one model of the thermal system, through the pressure factors. The thermal system model includes heat flow of heat transfer fluid, insulation loss, and temperature drops from the heat source to the Stirling convertor expansion space. The linear model was compared to a nonlinear model, and performance was very similar. The resulting linear model can be implemented in a variety of computing environments, and is suitable for analysis with classical and state space controls analysis techniques

Stirling Convertor System Dynamic Model Developed

Free-piston Stirling convertors are being developed for potential use on NASA exploration missions. In support of this effort, the NASA Glenn Research Center has developed the Stirling convertor System Dynamic Model (SDM). The SDM models the Stirling cycle thermodynamics; heat flow; gas, mechanical, and mounting dynamics; the linear alternator; and the controller. The SDM s scope extends from the thermal energy input to thermal, mechanical, and electrical energy output, allowing one to study complex system interactions among subsystems. Thermal, mechanical, fluid, magnetic, and electrical subsystems can be studied in one model. The SDM is a nonlinear time-domain model containing sub-cycle dynamics, which simulates transient and dynamic phenomena that other models cannot. The entire range of convertor operation is modeled, from startup to full-power conditions

Development of a Stirling System Dynamic Model With Enhanced Thermodynamics

The Stirling Convertor System Dynamic Model developed at NASA Glenn Research Center is a software model developed from first principles that includes the mechanical and mounting dynamics, the thermodynamics, the linear alternator, and the controller of a free-piston Stirling power convertor, along with the end user load. As such it represents the first detailed modeling tool for fully integrated Stirling convertor-based power systems. The thermodynamics of the model were originally a form of the isothermal Stirling cycle. In some situations it may be desirable to improve the accuracy of the Stirling cycle portion of the model. An option under consideration is to enhance the SDM thermodynamics by coupling the model with Gedeon Associates Sage simulation code. The result will be a model that gives a more accurate prediction of the performance and dynamics of the free-piston Stirling convertor. A method of integrating the Sage simulation code with the System Dynamic Model is described. Results of SDM and Sage simulation are compared to test data. Model parameter estimation and model validation are discussed

Using monodromy to recover symmetries of polynomial systems

Galois/monodromy groups attached to parametric systems of polynomial equations provide a method for detecting the existence of symmetries in solution sets. Beyond the question of existence, one would like to compute formulas for these symmetries, towards the eventual goal of solving the systems more efficiently. We describe and implement one possible approach to this task using numerical homotopy continuation and multivariate rational function interpolation. We describe additional methods that detect and exploit a priori unknown quasi-homogeneous structure in symmetries. These methods extend the range of interpolation to larger examples, including applications with nonlinear symmetries drawn from vision and robotics.Comment: Extended journal version of conference paper published at ISSAC 202

Control of Dual-Opposed Stirling Convertors with Active Power Factor Correction Controllers

When using recently-developed active power factor correction (APFC) controllers in power systems comprised of dual-opposed free-piston Stirling convertors, a variety of configurations of the convertors and controller(s) can be considered, with configuration ultimately selected based on benefits of efficiency, reliability, and robust operation. The configuration must not only achieve stable control of the two convertors, but also synchronize and regulate motion of the pistons to minimize net dynamic forces. The NASA Glenn Research Center (GRC) System Dynamic Model (SDM) was used to study ten configurations of dual-opposed convertor systems. These configurations considered one controller with the alternators connected in series or in parallel, and two controllers with the alternators not connected (isolated). For the configurations where the alternators were not connected, several different approaches were evaluated to synchronize the two convertors. In addition, two thermodynamic configurations were considered: two convertors with isolated working spaces and convertors with a shared expansion space. Of the ten configurations studied, stable operating modes were found for four. Three of those four had a common expansion space. One stable configuration was found for the dual-opposed convertors with separate working spaces. That configuration required isochronous control of both convertors, and two APFC controllers were used to accomplish this. A frequency/phase control loop was necessary to allow each APFC controller to synchronize its associated convertor with a common frequency

Coastal fog detection using visual sensing

Use of visual sensing techniques to detect low visibility conditions may have a number of advantages when combined with other methods, such as satellite based remote sensing, as data can be collected and processed in real or near real time. Camera-enabled visual sensing can provide direct confirmation of modelling and forecasting results. Fog detection, modelling and prediction are a priority for maritime communities and coastal cities due to economic impacts of fog on aviation, marine, and land transportation. Canadian and Irish coasts are particularly vulnerable to dense fog under certain environmental conditions. Offshore oil and gas production on Grand Bank (off the Canadian East Coast) can be adversely affected by weather and sea state conditions. In particular, fog can disrupt the transfer of equipment and people to/from the production platforms by helicopter. Such disruptions create delays and the delays cost money. According to offshore oil and gas industry representatives at a recent workshop on metocean monitoring and forecasting for the NL offshore, there is a real need for improved forecasting of visibility (fog) out to 3 days. The ability to accurately forecast future fog conditions would improve the industry’s ability to adjust its schedule of operations accordingly. In addition, it was recognized by workshop participants that the physics of Grand Banks fog formation is not well understood, and that more and better data are needed