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CuTl2Se2 crystallizes in the tetragonal P4/mmm space group. The structure is zero-dimensional and consists of one CuTl2Se2 cluster. Cu2+ is bonded in a linear geometry to two equivalent Se2- atoms. Both Cu–Se bond lengths are 2.22 Å. Tl1+ is bonded in a single-bond geometry to one Se2- atom. The Tl–Se bond length is 2.64 Å. Se2- is bonded in a linear geometry to one Cu2+ and one Tl1+ atom.
Pt2O is Cuprite structured and crystallizes in the cubic Pn-3m space group. The structure is three-dimensional. Pt1+ is bonded in a linear geometry to two equivalent O2- atoms. Both Pt–O bond lengths are 1.97 Å. O2- is bonded to four equivalent Pt1+ atoms to form corner-sharing OPt4 tetrahedra.
Ti2H is Cuprite structured and crystallizes in the cubic Pn-3m space group. The structure is three-dimensional. Ti is bonded in a linear geometry to two equivalent H atoms. Both Ti–H bond lengths are 1.83 Å. H is bonded to four equivalent Ti atoms to form corner-sharing HTi4 tetrahedra.
Zn2As is Cuprite structured and crystallizes in the cubic Pn-3m space group. The structure is three-dimensional. Zn is bonded in a linear geometry to two equivalent As atoms. Both Zn–As bond lengths are 2.42 Å. As is bonded to four equivalent Zn atoms to form corner-sharing AsZn4 tetrahedra.
XeIrSbF12 crystallizes in the monoclinic Cc space group. The structure is zero-dimensional and consists of four XeIrSbF12 clusters. Xe is bonded in a linear geometry to two F atoms. There are one shorter (2.00 Å) and one longer (2.30 Å) Xe–F bond lengths. Ir is bonded to six F atoms to form IrF6 octahedra that share a cornercorner with one SbF6 octahedra. The corner-sharing octahedral tilt angles are 40°. There are a spread of Ir–F bond distances ranging from 1.87–2.03 Å. Sb is bonded to six F atoms to form SbF6 octahedra that share a cornercorner with one IrF6 octahedra. The corner-sharing octahedral tilt angles are 40°. There are five shorter (1.90 Å) and one longer (2.14 Å) Sb–F bond lengths. There are twelve inequivalent F sites. In the first F site, F is bonded in a bent 120 degrees geometry to one Xe and one Ir atom. In the second F site, F is bonded in a single-bond geometry to one Xe atom. In the third F site, F is bonded in a single-bond geometry to one Sb atom. In the fourth F site, F is bonded in a single-bond geometry to one Sb atom. In the fifth F site, F is bonded in a single-bond geometry to one Sb atom. In the sixth F site, F is bonded in a single-bond geometry to one Sb atom. In the seventh F site, F is bonded in a single-bond geometry to one Sb atom. In the eighth F site, F is bonded in a bent 150 degrees geometry to one Ir and one Sb atom. In the ninth F site, F is bonded in a single-bond geometry to one Ir atom. In the tenth F site, F is bonded in a single-bond geometry to one Ir atom. In the eleventh F site, F is bonded in a single-bond geometry to one Ir atom. In the twelfth F site, F is bonded in a single-bond geometry to one Ir atom.
Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM-LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM-LIN method improved R 2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM-LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.
The current state-of-art for estimating long-term PV production uses long-term estimates of solar radiation variables, such as global horizontal irradiance (GHI), from previous years. This data is used in models such as the System Advisor Model (SAM) or PYSyst to predict annual production for a PV plant. This information is then used to estimate the production over the next 20 years (a typical plant lifetime) under the assumption that the variability over the current period is representative of the future. As the PV industry moves to extend plant lifetimes to 50 years the current assumptions of representativeness of weather may not be appropriate. This is especially true as our climate changes rapidly. To assess long-term PV production, future projections for solar radiation based on projected carbon emissions are readily available in regional and global climate models. However, climate model projections contain inherent biases that may need to be corrected for accurate analysis of future projections of climate variables. Several studies have analyzed projections of solar radiation for future years, however the accuracy of the model output compared to current and historic data has not been widely studied. Chen (2021) showed that available climate models do not accurately represent solar radiation in some cases, over-projecting GHI at the surface while under-projecting its obstructions, such as clouds and aerosols. This works aims to (1) increase understanding of the accuracy of solar radiation currently available in global and regional climate models and (2) implement bias correction through linear models based on reanalysis data compared to observed solar radiation. The latter aim will be conducted using available observed solar radiation data and modeled data from several regional climate models (RCMs). The bias correction method will be applied to projections of solar radiation resulting in a more accurate representation of the future of solar production.
The radiation environment in low-Earth orbital flights is complex. It is strongly influenced by altitude, orbital inclination, time within a given solar cycle, flight duration, and shielding configuration. At any specified shielded location, both primary and secondary particles generated by nuclear interactions of primary particles with spacecraft structure are present. In addition, there are atmospheric secondary albedo protons and neutrons. No single detector can adequately measure this complex radiation field, and measurements of very high linear energy transfer target fragmentation products are particularly difficult. Crew radiation exposure have exclusively been measured using passive thermoluminescent detectors (TLDs). The cosmonaut exposures on the Mir station, uncorrected for the TLD inefficiency and neutron contribution, have varied from a low of 2.43 cGy to a high of 8.70 cGy. These correspond to dose rates of 144 microGy d(-1) to 468 microGy d(-1). These are consistent with rates observed by the D2 ion-chamber. Using the rates measured by the D1 chamber, dose rates under 4 cm of water vary from about 60 microGy d-1 to about 350 microGy d(-1). There is variation of about a factor of two between the dose rates at various locations in the same module. There is also a variation of dose rates of about a factor two between various modules. The highest astronaut dose for a Shuttle flight (STS-82) was 3.205 cGy with a dose rate of 3,221 microGy d(-1). Neutron contribution could be 36 +/- 15% of the astronaut charged particle dose equivalent. East-West asymmetry of dose rate is significant for spacecrafts that fly in an fixed altitude, such as the International Space Station.
Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, the component reduced order modeling method has been developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has only been demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier–Stokes flow, with application to general nonlinear physics in view. The large‐scale, global domain is decomposed into a combination of small‐scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected from each small‐scale unit component. Velocity bases are augmented with a pressure supremizer to satisfy the inf–sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: A third‐order tensor projection operator and the empirical quadrature procedure. The proposed method is demonstrated on the flow over arrays of five different unit objects, achieving a 23‐fold speedup with less than 4% relative error in domains up to 256 times larger than the unit components. Furthermore, a numerical experiment with the pressure supremizer strongly indicates the need for a supremizer for stable pressure prediction. A comparison between the tensorial approach and the empirical quadrature procedure revealed a slight advantage of the empirical quadrature procedure. The framework is compared with an alternating Schwarz‐based reduced‐order approach, demonstrating improved efficiency and robustness for the DG‐based global solver while retaining flexibility for sub‐scale iterative solvers. The method is further extended to a coupled advection–diffusion and Navier–Stokes system, illustrating its applicability to multi‐physics problems and its potential for more general, inter‐coupled nonlinear systems.
The particle fallout limitations and periodic allocations for the James Webb Space Telescope are very stringent. Standard prediction methods are complicated by non-linearity and monitoring methods that are insufficiently responsive. A method for dynamically predicting the particle fallout in a cleanroom using air particle counter data was determined by numerical correlation. This method provides a simple linear correlation to both time and air quality, which can be monitored in real time. The summation of effects provides the program better understanding of the cleanliness and assists in the planning of future activities. Definition of fallout rates within a cleanroom during assembly and integration of contamination-sensitive hardware, such as the James Webb Space Telescope, is essential for budgeting purposes. Balancing the activity levels for assembly and test with the particle accumulation rate is paramount. The current approach to predicting particle fallout in a cleanroom assumes a constant air quality based on the rated class of a cleanroom, with adjustments for projected work or exposure times. Actual cleanroom class can also depend on the number of personnel present and the type of activities. A linear correlation of air quality and normalized particle fallout was determined numerically. An air particle counter (standard cleanroom equipment) can be used to monitor the air quality on a real-time basis and determine the "class" of the cleanroom (per FED-STD-209 or ISO-14644). The correlation function provides an area coverage coefficient per class-hour of exposure. The prediction of particle accumulations provides scheduling inputs for activity levels and cleanroom class requirements.
The CORDEX-CORE initiative was developed with the aim of producing homogeneous regional climate model (RCM) projections over domains world wide. In its first phase, two RCMs were run at 0.22° resolution downscaling 3 global climate models (GCMs) from the CMIP5 program for 9 CORDEX domains and two climate scenarios, the RCP2.6 and RCP8.5. The CORDEX-CORE simulations along with the CMIP5 GCM ensemble and the most recently produced CMIP6 GCM ensemble are analyzed, with focus on several temperature, heat, wet and dry hazard indicators for present day and mid-century and far future time slices. The CORDEX-CORE ensemble shows a better performance than the driving GCMs for several hazard indices due to its higher spatial resolution. For the far future time slice the 3 ensembles project an increase in all temperature and heat indices analyzed under the RCP8.5 scenario. The largest increases are always shown by the CMIP6 ensemble, except for Tx > 35 °C, for which the CORDEX-CORE projects higher warming. Extreme wet and flood prone maxima are projected to increase by the RCM ensemble over the la Plata basin in South America, the Congo basin in Africa, east North America, north east Europe, India and Indochina, regions where a better performance is obtained, whereas the GCM ensembles show small or negligible signals. Furthermore, compound hazard hotspots based on heat, drought and wet indicators are detected in each continent worldwide in region like Central America, the Amazon, the Mediterranean, South Africa and Australia, where a linear relation is shown between the heatwave and drought change signal, and region like Arabian peninsula, the central and south east Africa region (SEAF), the north west America (NWN), south east Asia, India, China and central and northern European regions (WCE, NEU) where the same linear relation is found for extreme precipitation and HW increases. Although still limited, the CORDEX-CORE initiative was able to produce high resolution climate projections with almost global coverage and can provide an important resource for impact assessment and climate service activities.
Ce3AlC is (Cubic) Perovskite structured and crystallizes in the cubic Pm-3m space group. The structure is three-dimensional. Ce is bonded in a linear geometry to four equivalent Al and two equivalent C atoms. All Ce–Al bond lengths are 3.49 Å. Both Ce–C bond lengths are 2.47 Å. Al is bonded to twelve equivalent Ce atoms to form AlCe12 cuboctahedra that share corners with twelve equivalent AlCe12 cuboctahedra, faces with six equivalent AlCe12 cuboctahedra, and faces with eight equivalent CCe6 octahedra. C is bonded to six equivalent Ce atoms to form CCe6 octahedra that share corners with six equivalent CCe6 octahedra and faces with eight equivalent AlCe12 cuboctahedra. The corner-sharing octahedral tilt angles are 0°.
Co3AlC is (Cubic) Perovskite structured and crystallizes in the cubic Pm-3m space group. The structure is three-dimensional. Co is bonded in a linear geometry to four equivalent Al and two equivalent C atoms. All Co–Al bond lengths are 2.63 Å. Both Co–C bond lengths are 1.86 Å. Al is bonded to twelve equivalent Co atoms to form AlCo12 cuboctahedra that share corners with twelve equivalent AlCo12 cuboctahedra, faces with six equivalent AlCo12 cuboctahedra, and faces with eight equivalent CCo6 octahedra. C is bonded to six equivalent Co atoms to form CCo6 octahedra that share corners with six equivalent CCo6 octahedra and faces with eight equivalent AlCo12 cuboctahedra. The corner-sharing octahedral tilt angles are 0°.
Pr3AlC is (Cubic) Perovskite structured and crystallizes in the cubic Pm-3m space group. The structure is three-dimensional. Pr is bonded in a linear geometry to four equivalent Al and two equivalent C atoms. All Pr–Al bond lengths are 3.63 Å. Both Pr–C bond lengths are 2.57 Å. Al is bonded to twelve equivalent Pr atoms to form AlPr12 cuboctahedra that share corners with twelve equivalent AlPr12 cuboctahedra, faces with six equivalent AlPr12 cuboctahedra, and faces with eight equivalent CPr6 octahedra. C is bonded to six equivalent Pr atoms to form CPr6 octahedra that share corners with six equivalent CPr6 octahedra and faces with eight equivalent AlPr12 cuboctahedra. The corner-sharing octahedral tilt angles are 0°.
GaAs is High Pressure Cadmuum Telluride structured and crystallizes in the orthorhombic Pmm2 space group. The structure is three-dimensional. Ga3+ is bonded in a linear geometry to two equivalent As3- atoms. Both Ga–As bond lengths are 2.73 Å. As3- is bonded to two equivalent Ga3+ and four equivalent As3- atoms to form a mixture of edge and corner-sharing AsGa2As4 octahedra. The corner-sharing octahedra tilt angles range from 0–1°. There are two shorter (2.71 Å) and two longer (2.72 Å) As–As bond lengths.
Sc3TlB is (Cubic) Perovskite structured and crystallizes in the cubic Pm-3m space group. The structure is three-dimensional. Sc is bonded in a linear geometry to four equivalent Tl and two equivalent B atoms. All Sc–Tl bond lengths are 3.32 Å. Both Sc–B bond lengths are 2.35 Å. Tl is bonded to twelve equivalent Sc atoms to form TlSc12 cuboctahedra that share corners with twelve equivalent TlSc12 cuboctahedra, faces with six equivalent TlSc12 cuboctahedra, and faces with eight equivalent BSc6 octahedra. B is bonded to six equivalent Sc atoms to form BSc6 octahedra that share corners with six equivalent BSc6 octahedra and faces with eight equivalent TlSc12 cuboctahedra. The corner-sharing octahedral tilt angles are 0°.
Mg3ZnC is (Cubic) Perovskite structured and crystallizes in the cubic Pm-3m space group. The structure is three-dimensional. Mg is bonded in a linear geometry to four equivalent Zn and two equivalent C atoms. All Mg–Zn bond lengths are 3.12 Å. Both Mg–C bond lengths are 2.20 Å. Zn is bonded to twelve equivalent Mg atoms to form ZnMg12 cuboctahedra that share corners with twelve equivalent ZnMg12 cuboctahedra, faces with six equivalent ZnMg12 cuboctahedra, and faces with eight equivalent CMg6 octahedra. C is bonded to six equivalent Mg atoms to form CMg6 octahedra that share corners with six equivalent CMg6 octahedra and faces with eight equivalent ZnMg12 cuboctahedra. The corner-sharing octahedral tilt angles are 0°.
Mn3PdN is (Cubic) Perovskite structured and crystallizes in the cubic Pm-3m space group. The structure is three-dimensional. Mn is bonded in a linear geometry to four equivalent Pd and two equivalent N atoms. All Mn–Pd bond lengths are 2.79 Å. Both Mn–N bond lengths are 1.97 Å. Pd is bonded to twelve equivalent Mn atoms to form PdMn12 cuboctahedra that share corners with twelve equivalent PdMn12 cuboctahedra, faces with six equivalent PdMn12 cuboctahedra, and faces with eight equivalent NMn6 octahedra. N is bonded to six equivalent Mn atoms to form NMn6 octahedra that share corners with six equivalent NMn6 octahedra and faces with eight equivalent PdMn12 cuboctahedra. The corner-sharing octahedral tilt angles are 0°.