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This paper presents the second part of a study in which the leading-edge and suction surface of a compressor blade was modified to delay onset of phase change for sCO₂ compressors operating near the critical point. Using a first-of-its-kind apparatus for the measurement of sCO₂ flow fields, Particle Image Velocimetry (PIV) is used for local flow field measurements of two compressor blade geometries: the modified “biased-wedge,” and a conventional constant thickness blade. Utilizing the developed hardware, the feasibility of a simple, laser-based diagnostic for qualitatively measuring liquid phase regions, is also presented. The design of the optical diagnostics rig, a discussion of numerous challenges, and necessary considerations involved in performing optical-based measurements like PIV, in sCO₂, are discussed. Velocity field measurements for the modified compressor profile show a much lower suction peak compared to a conventional blade. Furthermore, these results validate numerical results at the tested conditions, where the suction side profile of the biased wedge works to minimize the local pressure gradient.

The present work includes an in-depth performance analysis in fixed-speed reciprocating compressors. The industry standard for compressor characterization is the AHRI-540, which uses a 10-term and third-degree polynomial to characterize mass flow rate and energy consumption. However, the suitability of such a high-degree polynomial is unclear, and the potential for overfitting and extrapolation errors cannot be ignored. This work analyzes the response surfaces of mass flow rate and energy consumption in reciprocating compressors to determine if more concise models with lower degrees are more suitable. For that purpose, a massive experimental dataset with multiple compressors using different refrigerant and suction conditions was analyzed to obtain overall conclusions in the compressor field. The results of the present work showed that mass flow rate modeling requires lower-degree polynomials. However, the energy consumption characterization is more complex, and the model reported in the standard may be justified. Additionally, it was found that, if the specific energy consumption is selected as the modeling variable, it is possible to use a compact polynomial expression, which can also be extended to scroll compressors and also has the advantage of reducing the experimental data necessary for the model fit. Finally, by selecting the mass flow rate and the specific energy consumption as response variables, this work also explores other critical issues related to the experimental points’ location and minimum sample sizes required in order to minimize the experimental costs and increase the model accuracy.

Lossless compressors have very low compression ratios that do not meet the needs of today's large-scale scientific applications that produce vast volumes of data. Error-bounded lossy compression (EBLC) is considered a critical technique for the success of scientific research. Although EBLC allows users to set an error bound for the compression, users have been unable to specify the requirements on the compression quality, limiting practical use. Our contributions are: (1) We formulate the problem of configuring EBLC to preserve a user-defined metric as an optimization problem. This allows many classes of new metrics to be preserved, which improves over current practices. (2) We present a framework, OptZConfig, that can adapt to improvements in the search algorithm, compressor, and metrics with minimal changes, enabling future advancements in this area. (3) We demonstrate the advantages of our approach against the leading methods to configure compressors to preserve specific metrics. Here, our approach improves compression ratios against a specialized compressor by up to 3 x, has a 56x speedup over FRaZ, 1000x speedup over MGARD-QOI post tuning, and 110x speedup over systematic approaches which had not been bounded by compressors before.

Adequate range is a critical criterion in determining the proper compressor for a given application. In industries centering on turbochargers, range is often the dominant concern. Many times, significant sacrifices in performance are made at the design point in order to obtain enough range of the machine for the duty required. Active controls, the most common being inlet guide vanes (IGVs), can provide sufficient range extension while generating minimal degradation of performance at the design point. Such devices, however, add significant cost and complexity to the machine as they require an active control mechanism. Other devices, such as recirculating casing treatment (RCT), have the advantage of both cost and simplicity by virtue of their passive nature. They’ve seen widespread adoption in the turbocharger industry, though they are generally less effective than IGVs in extending range. A novel approach to extend compressor range through a passive method is proposed here. The method takes some inspiration from classical RCT applications in that it prompts recirculation at low flow rates and corresponding higher back pressures. This recirculation then occupies a portion of the primary cross-sectional passage area and confines the rest of the non-recirculating flow to a smaller area. This restriction forces a higher meridional velocity and a more favorable incidence angle at the impeller blade leading edge. The recirculation is accomplished by laying out an expanded secondary flow path at the rear of the impeller leading back to the inlet hub region. Vanes on the rear of the impeller disk provide just enough pressure rise to counter the induced pressure from the primary flow at the design point. This yields very low mass flow in the secondary path, a condition known as “shutoff” in the pump industry. At lower primary flow levels, and higher back pressures, the secondary flow is induced back to the impeller inlet setting up the recirculation region and pinching the primary flow. Various vane configurations and layouts in the secondary region are explained, and advantages and disadvantages of each are covered. The range extensions made possible by this method and the corresponding impact on performance is discussed.

A major outage in the electricity distribution system may affect the operation of water and natural gas supply systems, leading to an interruption of multiple services to critical customers. Therefore, enhancing resilience of critical infrastructures requires joint efforts of multiple sectors. In this paper, a distribution system service restoration method considering the electricity-water-gas interdependency is proposed. The objective is maximizing the supply of electricity, water, and gas to critical customers after an extreme event. The operational constraints of electricity, water, and natural gas networks are considered. Additionally, the characteristics of electricity-driven coupling components, including water pumps and gas compressors, are also modeled. Relaxation techniques are applied to non convex constraints posed by physical laws of those networks. Consequently, the restoration problem is formulated as a mixed-integer second-order cone program, which can readily be solved by the off-the-shelf solvers. The proposed method is validated by numerical simulations on an electricity-water-gas integrated system, developed based on benchmark models of the subsystems. The results indicate that considering the interdependency refines the allocation of limited generation resources and demonstrate the exactness of the proposed convex relaxation

Purge flow is bled from the upstream compressor and supplied to the under-platform region to prevent hot main gas path ingress that damages vulnerable under-platform hardware components. A majority of turbine rim seal research has sought to identify methods of improving sealing technologies and understanding the physical mechanisms that drive ingress. While these studies directly support the design and analysis of advanced rim seal geometries and purge flow systems, the studies are limited in their applicability to real-time monitoring required for condition-based operation and maintenance. As operational hours increase for in-service engines, this lack of rim seal performance feedback results in progressive degradation of sealing effectiveness, thereby leading to reduced hardware life. To address this need for rim seal performance monitoring, the present study utilizes measurements from a one-stage turbine research facility operating with true-scale engine hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are regressed with sealing effectiveness through the use of common machine learning techniques to provide real-time feedback of sealing effectiveness. Two modelling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. Results show that an initial purely data-driven model can be further improved using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within three percent of measured values.

Compressors in supercritical CO2 (sCO2) power cycles operate close to the critical point (304.12K, 7.37 MPa) to take advantage of high liquid-like densities at these conditions. This results in compact turbomachinery rotating at very high speeds. These power dense compressors present many design challenges due to the large variations in fluid properties at these conditions that have a strong effect on the performance and dynamic stability of these compressors. The goal of this SBIR effort was to develop a well-validated, high-fidelity CFD tool, CRUNCH CFD®, that could be used by the energy industry to improve and optimize designs for sCO2 compressors and mitigate risk by identifying and eliminating dynamic instabilities. There were three main areas of focus in this effort: 1) Detailed validation of the CRUNCH CFD® software tool for mean performance and unsteady dynamics in a 10 MWe scale sCO2 centrifugal compressor system. This system was designed by Hanwha Power Systems Americas and tested at SwRI as part of the DoE Apollo Sunshot program, 2) Development of equations of state and thermodynamic models for CO2 mixtures contaminated with water, and 3) Maturation of non-equilibrium condensation and droplet growth in CRUNCH CFD® at near critical conditions. The SBIR effort successfully achieved all three goals in this effort. The ability to provide accurate predictions of full scale sCO2 compressors was demonstrated with pre-test predictions of the 10 MWe Hanwha compressor. Subsequent test data, obtained after the simulations were completed, confirmed that the pre-test CRUNCH CFD® predictions were accurate and identified the following important qualitative trends: a) sensitivity of efficiency to inlet temperature with efficiency decreasing as inlet temperature comes closer to the critical point, and b) steep drop-off in efficiency at higher flow rates from multi-phase effects in the inlet throat region of the compressor. Furthermore, unsteady dynamic effects in compressor at off-design conditions were identified and quantified. In particular, a system wide surge condition was identified at high flow rates which couples with the phase change in the inlet throat region and results in large amplitude pulsations at low frequencies. This system wide surge is a particularly dangerous condition in direct fired cycles where the pulsations can couple to the combustor and potentially lead to catastrophic combustion instability. As part of our second objective, equations of state and thermodynamic properties for CO2 contaminated with water were characterized analytically. Phase separation was found to occur at all pressures of relevance to sCO2 compressors with water rich droplets being created from the contamination. Analytical correlations for phase boundaries of CO2/water mixtures were developed and trained/evaluated against experimental data. Accurate procedures for computing mixture density and enthalpy were developed. These analytical models will be crucial in the analysis of direct fired systems where some level of water contamination can be expected. The presence of droplet even at high pressures would potentially have implications for life-time predictions of compressors and other components in the cycle. Lastly, advanced non-equilibrium condensation models that account for droplet growth from condensation nuclei were developed. These advanced models allow for the characterization of condensation at near critical conditions where surface tension values are low and can result in longer time scales for droplet formation. Validation was performed for CO2 condensation in a nozzle for which detailed data is available in the literature at conditions ranging from supercritical inlet pressures to subcritical pressures. The non-equilibrium condensation models implemented in the CRUNCH CFD® tool were shown to accurately predict the Wilson line for the condensation front as well as the pressure profiles resulting from the condensation shock at all conditions tested. Subsequently, the model was applied to the Sandia compressor at near critical conditions and it was shown that small regions of condensation do occur at the leading-edge suction surface of the blade despite the proximity of the conditions to the critical point. This is an important result since it has implications for erosion and the lifetime of full-scale compressor blading in commercial operations.

This study analyzes the energy consumption and saving performance in the industries in the U.S.A. All energy assessments implemented were for facilities whose annual energy consumptions were less than 9,000,000 kWh (small- and medium-sized industries) that belong to the manufacturing industries with Standard Industrial Classification (SIC) codes ranging from 2000 to 3999 in addition to SIC codes starting with 49. In this study, assessments are classified based on the SIC codes with recommendations analysis for each classification to get a better idea of what recommendations were suggested in each major industrial sector, knowing that 68 assessments were made, and their SIC ranged from 14 to 49. In addition, this study could be considered as a guide for energy engineers and other personnel involved in the energy assessment process. The information investigated can give a better prediction for composing better energy-demanding industries and minimizing energy consumption. More than 61 energy assessments were conducted for manufacturing facilities and analyzing the data gathered and processed. Through the research, the Fabricated Metal industry achieved the highest average kWh savings and cost savings within the industries studied in this study. According to the average gigajoule (GJ) savings, the fabricated metal industry ranked second within the studied industries. Conversely, Food and Kindred Products achieved the highest GJ energy savings within the studied industries. Overall, lighting, motors, compressors, and heating, ventilation, and air conditioning (HVAC) were the most contributing industries in a total of 547 recommendations.

Clean energy has become an increasingly important consideration in today’s power systems. As the push for clean energy continues, many coal-fired power plants are being decommissioned in favor of renewable power sources such as wind and solar. However, the intermittent nature of renewables means that dynamic load following traditional power systems is crucial to grid stability. With high flexibility and fast response at a wide range of operating conditions, gas turbine systems are poised to become the main load following component in the power grid. Yet, rapid changes in load can lead to fluid flow instabilities in gas turbine power systems. These instabilities often lead to compressor surge and stall, which are some of the most critical problems facing the safe and efficient operation of compressors in turbomachinery today. Although the topic of compressor surge and stall has been extensively researched, no methods for early prediction have been proven effective. This study explores the utilization of machine learning tools to predict compressor stall. The long short-term memory (LSTM) model, a form of recurrent neural network (RNN), was trained using real compressor stall datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. Two variations of the LSTM model, classification and regression, were tested to determine optimal performance. The regression scheme was determined to be the most accurate approach, and a tool for predicting compressor stall was developed using this configuration. Overall, results show that the tool is capable of predicting stalls 5–20 ms before they occur. With a high-speed controller capable of 5 ms time-steps, mitigating action could be taken to prevent compressor stall before it occurs.

H2@Scale is focused on the wide scale adoption of hydrogen as a flexible energy storage medium. Due to the low volumetric energy density of gaseous hydrogen, compressors are an essential component of hydrogen storage. Hydrogen compressors that support a flexible grid and on-demand vehicle fueling undergo challenging operating conditions, such as a high number of start and stop cycles and wide input and discharge pressure ranges. Equally robust components are required for these conditions while still meeting the financial requirements of sustainable hydrogen compression.