Supercritical CO2 (sCO2) power cycles find potential application with a variety of heat sources including nuclear, concentrated solar (CSP), coal, natural gas, and waste heat sources, and consequently cover a wide range of scales. Most studies to date have focused on the performance of sCO2 power cycles, while economic analyses have been less prevalent, due in large part to the relative scarcity of reliable cost estimates for sCO2 power cycle components. Further, the accuracy of existing sCO2 techno-economic analyses suffer from a small sample set of vendor-based component costs for any given study. Improved accuracy of sCO2 component cost estimation is desired to enable a shift in focus from plant efficiency to economics as a driver for commercialization of sCO2 technology. This study reports on sCO2 component cost scaling relationships that have been developed collaboratively from an aggregate set of vendor quotes, cost estimates, and published literature. As one of the world’s largest supporters of sCO2 research and development, the Department of Energy (DOE) National Laboratories have access to a considerable pool of vendor component costs that span multiple applications specific to each National Laboratory’s mission, including fossil-fueled sCO2 applications at the National Energy Technology Laboratory (NETL), CSP at the National Renewable Energy Laboratory (NREL), and CSP, nuclear, and distributed energy sources at Sandia National Laboratories (SNL). The resulting cost correlations are relevant to sCO2 components in all these applications, and for scales ranging from 5–750 MWe. This work builds upon prior work at SNL, in which sCO2 component cost models were developed for CSP applications ranging from 1–100 MWe in size. Similar to the earlier SNL efforts, vendor confidentiality has been maintained throughout this collaboration and in the published results. Cost models for each component were correlated from 4–24 individual quotes from multiple vendors, although the individual cost data points are proprietary and not shown. Cost models are reported for radial and axial turbines, integrally-geared and barrel-style centrifugal compressors, high temperature and low temperature recuperators, dry sCO2 coolers, and primary heat exchangers for coal and natural gas fuel sources. These models are applicable to sCO2-specific components used in a variety of sCO2 cycle configurations, and include incremental cost factors for advanced, high temperature materials for relevant components. Non-sCO2-specific costs for motors, gearboxes, and generators have been included to allow cycle designers to explore the cost implications of various turbomachinery configurations. Finally, the uncertainty associated with these component cost models is quantified by using AACE International-style class ratings for vendor estimates, combined with component cost correlation statistics.

In light of the Army’s intent to leverage advances in Artificial Intelligence (AI) for augmenting dismounted Soldier Lethality through the development of in-scope and Heads-Up Display (HUD)-based Automatic Target Recognition (ATR) systems, the Combat Capabilities Development Command U. S. Army Research Laboratory’s Human Research and Engineering Directorate (CCDC-ARL/HRED) has identified several critical gaps that must be addressed in order to effectively team the Soldier with ATR for the desired augmented Lethality. One of these areas pertains to the way in which ATR is displayed and requires a thorough understanding and leveraging of relevant cognitive processes that will enable use of this technology. Additionally, insufficient consideration of perceptual, attentional, and cognitive capabilities increases the risk of burdening the Soldier with excessive, unnecessary, or distracting representations of information, which may impede Lethality rather than augment it. HRED’s planned and ongoing research is intended to develop novel mechanisms through which Soldiers teamed with ATR will perform more adaptively and effectively than either the Soldier or the intelligent system could accomplish individually. Based on HRED’s significant expertise in the cognitive sciences, coupled with familiarity with the military-relevant domain spaces, the following initial recommendations for ATR information display requirements are made:1.ATR highlighting should leverage a non-binary display schema to continuously encode threat information (e.g., target class/identity, uncertainty, and prioritization).2.ATR highlighting should be integrated with the target itself instead of functioning as a discrete feature of the display (i.e., highlight the target rather than highlighting a region with the target inside).3.Information about threat certainty or classification confidence (which can also include priority) should be embedded into ATR highlighting.4.Yellow highlights may offer advantages for display5.Changing information (e.g., target certainty) should be accomplished through formation or modification of highlight gradients rather than sudden changes in the display.6.Human performance evaluations of ATR should consider the incorporation of changing threat states and contexts into scenarios for more relevant findings.7.Human performance evaluations of ATR should consider the incorporation of uncued (non-highlighted) targets and miscued targets (false identifications; e.g. ATR identifies non-threat as threat) for more relevant findings.

Flow maldistribution in microchannel heat exchanger (MCHEs) can negatively impact heat exchanger effectiveness. Several rules of thumb exist about designing for uniform flow, but very little data are published to support these claims. In this work, complementary experiments and computational fluid dynamics (CFD) simulations of MCHEs enable a solid understanding of flow uniformity to a higher level of detail than previously seen. Experiments provide a validation data source to assess CFD predictive capability. The traditional semi-circular header geometry is tested. Experiments are carried out in a clear acrylic MCHE and water flow is measured optically with particle image velocimetry. CFD boundary conditions are matched to those in the experiment and the outputs, specifically velocity and turbulent kinetic energy profiles, are compared.

Model validation for computational fluid dynamics (CFD), where experimental data and model outputs are compared, is a key tool for assessing model uncertainty. In this work, mixed convection was studied experimentally for the purpose of providing validation data for CFD models with a high level of completeness. Experiments were performed in a facility built specifically for validation with a vertical, flat, heated wall. Data were acquired for both buoyancy-aided and buoyancy-opposed turbulent flows. Measured boundary conditions (BCs) include as-built geometry, inflow mean and fluctuating velocity profiles, and inflow and wall temperatures. Additionally, room air temperature, pressure, and relative humidity were measured to provide fluid properties. Measured system responses inside the flow domain include mean and fluctuating velocity profiles, temperature profiles, wall heat flux, and wall shear stress. All of these data are described in detail and provided in tabulated format.

Empirically-based correlations are commonly used in modeling and simulation but rarely have rigorous uncertainty quantification that captures the nature of the underlying data. In many applications, a mathematical description for a parameter response to some input stimulus is often either unknown, unable to be measured, or both. Likewise, the data used to observe a parameter response is often noisy, and correlations are derived to approximate the bulk response. Practitioners frequently treat the chosen correlation — sometimes referred to as the “surrogate” or “reduced-order” model of the response — as a constant mathematical description of the relationship between input and output. This assumption, as with any model, is incorrect to some degree, and the uncertainty in the correlation can potentially have significant impacts on system responses. Thus, proper treatment of correlation uncertainty is necessary. In this paper, a method is proposed for high-level abstract sampling of uncertain data correlations. Whereas uncertainty characterization is often assigned to scalar values for direct sampling, functional uncertainty is not always straightforward. A systematic approach for sampling univariable uncertain correlations was developed to perform more rigorous uncertainty analyses and more reliably sample the correlation space. This procedure implements pseudo-random sampling of a correlation with a bounded input range to maintain the correlation form, to respect variable uncertainty across the range, and to ensure function continuity with respect to the input variable.

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