Amy V. Mueller

Geometric Design of Scroll Expanders Optimized for Small Organic Rankine Cycles

The application of organic Rankine cycles (ORCs) for small scale power generation is inhibited by a lack of suitable expansion devices. Thermodynamic and mechanistic considerations suggest that scroll machines are advantageous in kilowatt-scale ORC equipment, however, a method of independently selecting a geometric design optimized for high-volume-ratio ORC scroll expanders is needed. The generalized 8-dimensional planar curve framework (Gravesen and Henriksen, 2001, “The Geometry of the Scroll Compressor,” Soc. Ind. Appl. Math., 43, pp. 113–126), previously developed for scroll compressors, is applied to the expansion scroll and its useful domain limits are defined. The set of workable scroll geometries is: (1) established using a generate-and-test algorithm with inclusion based on theoretical viability and engineering criteria, and (2) the corresponding parameter space is related to thermodynamically relevant metrics through an analytic ranking quantity fc (“compactness factor”) equal to the volume ratio divided by the normalized scroll diameter. This method for selecting optimal scroll geometry is described and demonstrated using a 3 kWe ORC specification as an example. Workable scroll geometry identification is achieved at a rate greater than 3 s−1 with standard desktop computing, whereas the originally undefined 8-D parameter space yields an arbitrarily low success rate for determining valid scroll mating pairs. For the test case, a maximum isentropic expansion efficiency of 85% is found by examining a subset of candidates selected the for compactness factor (volume expansion ratio per diameter), which is shown to correlate with the modeled isentropic efficiency (R2 = 0.88). The rapid computationally efficient generation and selection of complex validated scroll geometries ranked by physically meaningful properties is demonstrated. This procedure represents an essential preliminary qualification for intensive modeling and prototyping efforts necessary to generate new high performance scroll expander designs for kilowatt scale ORC systems.

Extended artificial neural networks: Incorporation of a priori chemical knowledge enables use of ion selective electrodes for in-situ measurement of ions at environmentally relevant levels

A novel artificial neural network (ANN) architecture is proposed which explicitly incorporates a priori system knowledge, i.e., relationships between output signals, while preserving the unconstrained non-linear function estimator characteristics of the traditional ANN. A method is provided for architecture layout, disabling training on a subset of neurons, and encoding system knowledge into the neuron structure. The novel architecture is applied to raw readings from a chemical sensor multi-probe (electric tongue), comprised of off-the-shelf ion selective electrodes (ISEs), to estimate individual ion concentrations in solutions at environmentally relevant concentrations and containing environmentally representative ion mixtures. Conductivity measurements and the concept of charge balance are incorporated into the ANN structure, resulting in (1) removal of estimation bias typically seen with use of ISEs in mixtures of unknown composition and (2) improvement of signal estimation by an order of magnitude or more for both major and minor constituents relative to use of ISEs as stand-alone sensors and error reduction by 30-50% relative to use of standard ANN models. This method is suggested as an alternative to parameterization of traditional models (e.g., Nikolsky-Eisenman), for which parameters are strongly dependent on both analyte concentration and temperature, and to standard ANN models which have no mechanism for incorporation of system knowledge. Network architecture and weighting are presented for the base case where the dot product can be used to relate ion concentrations to both conductivity and charge balance as well as for an extension to log-normalized data where the model can no longer be represented in this manner. While parameterization in this case study is analyte-dependent, the architecture is generalizable, allowing application of this method to other environmental problems for which mathematical constraints can be explicitly stated.

Field Testing of Lake Water Chemistry with a Portable and an AUV-based Mass Spectrometer

Two mass spectrometers (MS) are tested for the measurement of volatile substances, such as hydrocarbons and metabolic gases, in natural waters. KOALA is a backpackable MS operated from above the water surface, in which samples are pumped through a flow cell using a syringe. NEREUS is an underwater instrument hosted by an autonomous underwater vehicle (AUV) that is linked to a communications network to provide chemical data in real time. The mass analyzers of the two MS are nearly identical cycloids, and both use flat-plate membrane inlets. Testing took place in an eutrophic, thermally stratified lake exhibiting steep chemical gradients and significant levels of methane. KOALA provided rapid multispecies analysis of dissolved gases, with a detection limit for methane of 0. 1 ppm (readily extendable to 0. 01 ppm) and savings of time of at least a factor of 10 compared to that of conventional analysis. The AUV-mounted NEREUS additionally provided rapid spatial coverage and the capability of performing chemical surveys autonomously. Tests demonstrated the need for temperature control of a membrane inlet when steep thermal gradients are present in a water body, as well as the benefits of co-locating all sensors on the AUV to avoid interference from chemically different waters entering and draining from the free-flooding outer hull. The ability to measure dissolved volatiles provided by MS offers potential for complementarity with ionic sensors in the study of natural waters, such as in the case of the carbonate system.

Dynamic Simulation of Performance and Cost of Hybrid PV-CSP-LPG Generator Micro Grids With Applications to Remote Communities in Developing Countries

Energy infrastructure in rural areas of developing countries is currently deployed on an ad-hoc basis via grid extension, public and private sector solar home system (SHS) service using photovoltaic (PV) panels, and community distributed generation systems, also called mini or micro grids. Universal access to energy is increasingly pursued as a policy objective via e.g. the U.N. Millennium Develop Goals (MDG), Sustainable Energy for All (SE4All), and U.S. Power Africa initiatives. Rational allocation of energy infrastructure for 1.6b people currently lacking access requires a screening process to determine the economic break-even distance and consumer connection density favoring topologically diverse energy technology approaches. Previous efforts have developed approaches to determine grid-connection break-even distances, but work on micro-grid and SHS break-even distance and density is limited.The present work develops an open access modeling platform with the ability to simulate various configurations of PV, Concentrating Solar Power (CSP), and fueled generator backup systems with exhaust waste heat recovery. Battery and thermal storage options are examined, and typical meteorological year (TMY) data is combined with probabilistic and empirical load curve data to represent the appropriate physical dynamics. Power flow control strategy and infrastructure is optimized for a minimum tariff (USD/kWh) for cost recovery. Cost functions derived from manufacturers’ data enable performance and economic assessment for a case study micro grid in Lesotho.Copyright