Modeling the Physical and Biochemical Influence of Ocean Thermal Energy Conversion Plant Discharges into their Adjacent Waters

This paper describes the modeling work by Makai Ocean Engineering, Inc. to simulate the biochemical effects of of the nutrient-enhanced seawater plumes that are discharged by one or several 100 megawatt OTEC plants. The modeling is needed to properly design OTEC plants that can operate sustainably with acceptably low biological impact. In order to quantify the effect of discharge configuration and phytoplankton response, Makai Ocean Engineering implemented a biological and physical model for the waters surrounding O`ahu, Hawai`i, using the EPA-approved Environmental Fluid Dynamics Code (EFDC). Each EFDC grid cell was approximately 1 square kilometer by 20 meters deep, and used a time step of three hours. The biological model was set up to simulate the biochemical response for three classes of organisms: Picoplankton ( 20 um) e.g., diatoms. The dynamic biological phytoplankton model was calibrated using chemical and biological data collected for the Hawaii Ocean Time Series (HOTS) project. Peer review of the biological modeling was performed. The physical oceanography model uses boundary conditions from a surrounding Hawai'i Regional Ocean Model, (ROM) operated by the University of Hawai`i and the National Atmospheric and Oceanic Administration. The ROM provided tides, basin scale circulation, mesoscale variability, and atmospheric forcing into the edges of the EFDC computational domain. This model is the most accurate and sophisticated Hawai'ian Regional Ocean Model presently available, assimilating real-time oceanographic observations, as well as model calibration based upon temperature, current and salinity data collected during 2010 near the simulated OTEC site. The ROM program manager peer-reviewed Makai's implementation of the ROM output into our EFDC model. The supporting oceanographic data was collected for a Naval Facilities Engineering Command / Makai project. Results: The model was run for a 100 MW OTEC Plant consisting of four separate ducts, discharging a total combined flow rate of 420 m3/s of warm water and 320 m3/s of cold water in a mixed discharge at 70 meters deep. Each duct was assumed to have a discharge port diameter of 10.5m producing a downward discharge velocity of about 2.18 m/s. The natural system, as measured in the HOTS program, has an average concentration of 10-15 mgC/m3. To calibrate the biological model, we first ran the model with no OTEC plant and varied biological parameters until the simulated data was a good match to the HOTS observations. This modeling showed that phytoplankton concentration were patchy and highly dynamic. The patchiness was a good match with the data variability observed within the HOTS data sets. We then ran the model with simulated OTEC intake and discharge flows and associated nutrients. Directly under the OTEC plant, the near-field plume has an average terminal depth of 172 meters, with a volumetric dilution of 13:1. The average terminal plume temperature was 19.8oC. Nitrate concentrations are 1 to 2 umol/kg above ambient. The advecting plume then further dilutes to less than 1 umol/kg above ambient within a few kilometers downstream, while remaining at depth. Because this terminal near-field plume is well below the 1% light limited depths (~120m), no immediate biological utilization of the nutrients occurs. As the nitrate is advected and dispersed downstream, a fraction of the deep ocean nutrients (< 0.5 umol/kg perturbation) mix upward where they are utilized by the ambient phytoplankton population. This occurs approximately twenty-five kilometers downstream from the plant at 110 - 70 meters depth. For pico-phytoplankton, modeling results indicate that this nutrient perturbation causes a phytoplankton perturbation of approximately 1 mgC/m3 (~10% of average ambient concentrations) that covers an area 10x5 km in size at the 70 to 90m depth. Thus, the perturbations are well within the natural variability of the system, generally corresponding to a 10 to 15% increase above the average pico-phytoplankton biomass. This perturbation exhibits a meandering horizontal plume trajectory and spatial extent, but remains similar in magnitude (generally 1-2 mgC/m3). The diatom perturbations become more noticeable after three weeks of the simulation period, when the nearshore diatom population trends towards a greater concentration of 1 to 3 mgC/m3 . Relative to the background concentrations, this increased response is a fraction of the ambient, with perturbations remaining within fluctuations of the existing system. The perturbations were quantified by post-processing each time-step of model simulations without OTEC plants, with identical simulations that included OTEC plumes. Without this post processing, the 10-25% perturbations were obscured by the larger dynamic variations naturally caused by ocean circulation

Measurement of the Charged Multiplicities in b, c and Light Quark Events from Z0 Decays

Average charged multiplicities have been measured separately in $b$, $c$ and light quark ($u,d,s$) events from $Z^0$ decays measured in the SLD experiment. Impact parameters of charged tracks were used to select enriched samples of $b$ and light quark events, and reconstructed charmed mesons were used to select $c$ quark events. We measured the charged multiplicities: $\bar{n}_{uds} = 20.21 \pm 0.10 (\rm{stat.})\pm 0.22(\rm{syst.})$, $\bar{n}_{c} = 21.28 \pm 0.46(\rm{stat.}) ^{+0.41}_{-0.36}(\rm{syst.})$ $\bar{n}_{b} = 23.14 \pm 0.10(\rm{stat.}) ^{+0.38}_{-0.37}(\rm{syst.})$, from which we derived the differences between the total average charged multiplicities of $c$ or $b$ quark events and light quark events: $\Delta \bar{n}_c = 1.07 \pm 0.47(\rm{stat.})^{+0.36}_{-0.30}(\rm{syst.})$ and $\Delta \bar{n}_b = 2.93 \pm 0.14(\rm{stat.})^{+0.30}_{-0.29}(\rm{syst.})$. We compared these measurements with those at lower center-of-mass energies and with perturbative QCD predictions. These combined results are in agreement with the QCD expectations and disfavor the hypothesis of flavor-independent fragmentation.Comment: 19 pages LaTex, 4 EPS figures, to appear in Physics Letters

Modeling the Physical and Biochemical Influence of Ocean Thermal Energy Conversion Plant Discharges into their Adjacent Waters

This paper describes the modeling work by Makai Ocean Engineering, Inc. to simulate the biochemical effects of of the nutrient-enhanced seawater plumes that are discharged by one or several 100 megawatt OTEC plants. The modeling is needed to properly design OTEC plants that can operate sustainably with acceptably low biological impact. In order to quantify the effect of discharge configuration and phytoplankton response, Makai Ocean Engineering implemented a biological and physical model for the waters surrounding O`ahu, Hawai`i, using the EPA-approved Environmental Fluid Dynamics Code (EFDC). Each EFDC grid cell was approximately 1 square kilometer by 20 meters deep, and used a time step of three hours. The biological model was set up to simulate the biochemical response for three classes of organisms: Picoplankton ( 20 um) e.g., diatoms. The dynamic biological phytoplankton model was calibrated using chemical and biological data collected for the Hawaii Ocean Time Series (HOTS) project. Peer review of the biological modeling was performed. The physical oceanography model uses boundary conditions from a surrounding Hawai'i Regional Ocean Model, (ROM) operated by the University of Hawai`i and the National Atmospheric and Oceanic Administration. The ROM provided tides, basin scale circulation, mesoscale variability, and atmospheric forcing into the edges of the EFDC computational domain. This model is the most accurate and sophisticated Hawai'ian Regional Ocean Model presently available, assimilating real-time oceanographic observations, as well as model calibration based upon temperature, current and salinity data collected during 2010 near the simulated OTEC site. The ROM program manager peer-reviewed Makai's implementation of the ROM output into our EFDC model. The supporting oceanographic data was collected for a Naval Facilities Engineering Command / Makai project. Results: The model was run for a 100 MW OTEC Plant consisting of four separate ducts, discharging a total combined flow rate of 420 m3/s of warm water and 320 m3/s of cold water in a mixed discharge at 70 meters deep. Each duct was assumed to have a discharge port diameter of 10.5m producing a downward discharge velocity of about 2.18 m/s. The natural system, as measured in the HOTS program, has an average concentration of 10-15 mgC/m3. To calibrate the biological model, we first ran the model with no OTEC plant and varied biological parameters until the simulated data was a good match to the HOTS observations. This modeling showed that phytoplankton concentration were patchy and highly dynamic. The patchiness was a good match with the data variability observed within the HOTS data sets. We then ran the model with simulated OTEC intake and discharge flows and associated nutrients. Directly under the OTEC plant, the near-field plume has an average terminal depth of 172 meters, with a volumetric dilution of 13:1. The average terminal plume temperature was 19.8oC. Nitrate concentrations are 1 to 2 umol/kg above ambient. The advecting plume then further dilutes to less than 1 umol/kg above ambient within a few kilometers downstream, while remaining at depth. Because this terminal near-field plume is well below the 1% light limited depths (~120m), no immediate biological utilization of the nutrients occurs. As the nitrate is advected and dispersed downstream, a fraction of the deep ocean nutrients (< 0.5 umol/kg perturbation) mix upward where they are utilized by the ambient phytoplankton population. This occurs approximately twenty-five kilometers downstream from the plant at 110 - 70 meters depth. For pico-phytoplankton, modeling results indicate that this nutrient perturbation causes a phytoplankton perturbation of approximately 1 mgC/m3 (~10% of average ambient concentrations) that covers an area 10x5 km in size at the 70 to 90m depth. Thus, the perturbations are well within the natural variability of the system, generally corresponding to a 10 to 15% increase above the average pico-phytoplankton biomass. This perturbation exhibits a meandering horizontal plume trajectory and spatial extent, but remains similar in magnitude (generally 1-2 mgC/m3). The diatom perturbations become more noticeable after three weeks of the simulation period, when the nearshore diatom population trends towards a greater concentration of 1 to 3 mgC/m3 . Relative to the background concentrations, this increased response is a fraction of the ambient, with perturbations remaining within fluctuations of the existing system. The perturbations were quantified by post-processing each time-step of model simulations without OTEC plants, with identical simulations that included OTEC plumes. Without this post processing, the 10-25% perturbations were obscured by the larger dynamic variations naturally caused by ocean circulation

Eastern versus Western Control Beliefs at Work: An Investigation of Secondary Control, Socioinstrumental Control, and Work Locus of Control in China and the US

Research and theory concerning beliefs (locus of control) and perceptions of control suggest that Asians tend to be lower and more passive than Americans, but this work has been conducted mainly with US‐developed constructs and scales that assess primary control (i.e. changing the environment to adapt to the self). An international research team expanded the notion of control beliefs by developing scales to assess secondary control beliefs (i.e. adapting the self to the environment) and the new construct of socioinstrumental control beliefs (i.e. control via interpersonal relationships), both of which were thought to better fit the control beliefs of collectivist cultures than Western‐developed control scales. We expected that, when culturally appropriate scales were employed, Americans would not show higher control beliefs than Asians. Hypotheses were partially confirmed that Americans would be lower than Chinese (Hong Kong and PR China) on these new scales. It is suggested that views of Asians as passive avoiders of control at work may be incorrect and due to the overlooking of socioinstrumental control