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The radiation pattern of a feed element is approximately described by a simple function (cos theta) to the q power. For a given element spacing of the feed array, simple formulas for estimating the practical value of q when the element is an open-ended rectangular waveguide, an open-ended circular waveguide, a pyramidal horn, or a cigar antenna are given.
Global climate goals require a transition to a deeply decarbonized energy system. Meeting the objectives of the Paris Agreement through countries' nationally determined contributions and long-term strategies represents a complex problem with consequences across multiple systems shrouded by deep uncertainty. Robust, large-ensemble methods and analyses mapping a wide range of possible future states of the world are needed to help policymakers design effective strategies to meet emissions reduction goals. This study contributes a scenario discovery analysis applied to a large ensemble of 5,760 model realizations generated using the Global Change Analysis Model. Eleven energy-related uncertainties are systematically varied, representing national mitigation pledges, institutional factors, and techno-economic parameters, among others. The resulting ensemble maps how uncertainties impact common energy system metrics used to characterize national and global pathways toward deep decarbonization. Results show globally consistent but regionally variable energy transitions as measured by multiple metrics, including electricity costs and stranded assets. Larger economies and developing regions experience more severe economic outcomes across a broad sampling of uncertainty. The scale of CO 2 removal globally determines how much the energy system can continue to emit, but the relative role of different CO 2 removal options in meeting decarbonization goals varies across regions. Previous studies characterizing uncertainty have typically focused on a few scenarios, and other large-ensemble work has not (to our knowledge) combined this framework with national emissions pledges or institutional factors. Our results underscore the value of large-ensemble scenario discovery for decision support as countries begin to design strategies to meet their goals.
Many scientific communities have adopted community-based models that integrate multiple components to simulate whole system dynamics. The community software projects’ complexity, stems from the integration of multiple individual software components that were developed under different application requirements and various machine architectures, has become a challenge for effective software system understanding and continuous software development. The paper presents an integrated software toolkit called X-ray Software Scanner (in abbreviation, XScan) for a better understanding of large-scale community-based scientific codes. Our software tool provides support to quickly summarize the overall information of scientific codes, including the number of lines of code, programming languages, external library dependencies, as well as architecture-dependent parallel software features. The XScan toolkit also realizes a static software analysis component to collect detailed structural information and provides an interactive visualization and analysis of the functions. We use a large-scale community-based Earth System Model to demonstrate the workflow, functions and visualization of the toolkit. We also discuss the application of advanced graph analytics techniques to assist software modular design and component refactoring.
Here, a discovery of neutrinoless double-β decay would be profound, providing the first direct experimental evidence of ΔL = 2 lepton number violating processes. While a natural explanation is provided by an effective Majorana neutrino mass, other new physics interpretations should be carefully evaluated. At low-energies such new physics could man-ifest itself in the form of color and SU(2) L × U(1)Y invariant higher dimension operators. Here we determine a complete set of electroweak invariant dimension-9 operators, and our analysis supersedes those that only impose U(1) em invariance. Imposing electroweak invariance implies: 1) a significantly reduced set of leading order operators compared to only imposing U(1) em invariance; and 2) other collider signatures. Prior to imposing electroweak invariance we find a minimal basis of 24 dimension-9 operators, which is reduced to 11 electroweak invariant operators at leading order in the expansion in the Higgs vacuum expectation value. We set up a systematic analysis of the hadronic realization of the 4-quark operators using chiral perturbation theory, and apply it to determine which of these operators have long-distance pion enhancements at leading order in the chiral expansion. We also find at dimension-11 and dimension-13 the electroweak invariant operators that after electroweak symmetry breaking produce the remaining ΔL = 2 operators that would appear at dimension-9 if only U(1) em is imposed.
High levels of energy from variable generation sources such as wind and solar photovoltaics (PV) can result in significant curtailment, in which the wind and PV energy cannot be used to serve demand. Adding transmission and energy storage can assist in reducing renewable curtailment, but the relative merits of each enabling technology individually or combined is not well understood. Thus, we compare the role of transmission and storage in reducing curtailment, as well as reducing generation costs from conventional sources. Using a high-fidelity model of the electric power grid, we examine a scenario in which the western portion of the U.S. and Canada reaches 37% energy from wind and 12% energy from solar PV. In the case studied, we find that transmission is generally more effective than energy storage in reducing curtailment, due to the curtailment patterns of wind. However, the interaction between transmission and energy storage shows that the two technologies act symbiotically, meaning that their combined energy value is greater than that of each individually. This analysis demonstrates that fully realizing the benefits of wind resources located far from demand centers will require an effective method to deliver wind power at the right times to the right locations.
In the design of projectile penetration experiments a matter of considerable interest is scaling: that is, the potential relevance of small-scale experiments to their full-scale counterparts, in a manner analogous to that most often encountered in the context of fluid mechanics. From the theoretical standpoint, phenomena associated with scaling and scalability can be assessed using the well-established tools of dimensional analysis and the Buckingham-Pi Theorem. However, the familiar precepts of dimensional analysis are themselves a specific manifestation of the broader group invariance properties or symmetries of a mathematical model. Here, this work explores these notions – that is, dimensional analysis, the conditions for realizing complete similarity, and any additional symmetry structures – in the context of a Riccati differential equation appearing in the context of penetration mechanics. The aim of the investigation is twofold: 1) to complement existing empirical considerations with a concrete theoretical basis, and 2) to provide a deeper theoretical understanding of the projectile penetration model and its many implications.
We propose a Bayesian approach for seismic inversion to estimate acoustic impedance, porosity and lithofacies within the reservoir conditioned to post-stack seismic and well data. The link between elastic and petrophysical properties is given by a joint prior distribution for the logarithm of impedance and porosity, based on a rock-physics model. The well conditioning is performed through a background model obtained by well log interpolation. Two different approaches are presented: in the first approach, the prior is defined by a single Gaussian distribution, whereas in the second approach it is defined by a Gaussian mixture to represent the well data multimodal distribution and link the Gaussian components to different geological lithofacies. The forward model is based on a linearized convolutional model. For the single Gaussian case, we obtain an analytical expression for the posterior distribution, resulting in a fast algorithm to compute the solution of the inverse problem, i.e. the posterior distribution of acoustic impedance and porosity as well as the facies probability given the observed data. For the Gaussian mixture prior, it is not possible to obtain the distributions analytically, hence we propose a Gibbs algorithm to perform the posterior sampling and obtain several reservoir model realizations, allowing an uncertainty analysis of the estimated properties and lithofacies. Both methodologies are applied to a real seismic dataset with three wells to obtain 3D models of acoustic impedance, porosity and lithofacies. The methodologies are validated through a blind well test and compared to a standard Bayesian inversion approach. Using the probability of the reservoir lithofacies, we also compute a 3D isosurface probability model of the main oil reservoir in the studied field.
Here, we propose an anisotropic stochastic growth model to rationalize the anisotropic self-assembly of supramolecules to form elongated two-dimensional ribbon structures in a recent experiment. The model exhibits distinct growth scenarios that are critically controlled by the ratio of the transverse and the longitudinal growth rate. In the regime of suppressed transverse growth, the model generates the experimentally observed elongated structures through layer-by-layer growing. We further observe the convergence of rough clusters toward smooth regular elliptic patterns by averaging over a number of independent growth processes. Remarkably, these resulting elliptic clusters are self-similar in each instantaneous moment in the growth process. Statistical analysis suggests that the realization of such ordered patterns does not rely on the delicate coordination of different parts in the cluster growth. The self-similarity phenomenon derived from this idealized model may have wider implications, notably in the designed clustering of various elementary building blocks with anisotropic interactions.
Corrosion is a ubiquitous failure mode of materials. Often, the progression of localized corrosion is accompanied by the evolution of porosity in materials previously reported to be either three-dimensional or two-dimensional. However, using new tools and analysis techniques, we have realized that a more localized form of corrosion, which we call 1D wormhole corrosion, has previously been miscategorized in some situations. Using electron tomography, we show multiple examples of this 1D and percolating morphology. To understand the origin of this mechanism in a Ni-Cr alloy corroded by molten salt, we combined energy-filtered four-dimensional scanning transmission electron microscopy and ab initio density functional theory calculations to develop a vacancy mapping method with nanometer-resolution, identifying a remarkably high vacancy concentration in the diffusion-induced grain boundary migration zone, up to 100 times the equilibrium value at the melting point. Deciphering the origins of 1D corrosion is an important step towards designing structural materials with enhanced corrosion resistance.
Techniques for measuring and characterizing the ultrahigh vacuum in the wake of an orbiting spacecraft are studied. A high sensitivity mass spectrometer that contains a double mass analyzer consisting of an open source miniature magnetic sector field neutral gas analyzer and an identical ion analyzer is proposed. These are configured to detect and identify gas and ion species of hydrogen, helium, nitrogen, oxygen, nitric oxide, and carbon dioxide and any other gas or ion species in the 1 to 46 amu mass range. This range covers the normal atmospheric constituents. The sensitivity of the instrument is sufficient to measure ambient gases and ion with a particle density of the order of one per cc. A chemical pump, or getter, is mounted near the entrance aperture of the neutral gas analyzer which integrates the absorption of ambient gases for a selectable period of time for subsequent release and analysis. The sensitivity is realizable for all but rare gases using this technique.
An improved operational CAT detection and forecasting technique is developed and detailed. This technique is the specific clear air turbulence risk (SCATR) index. This index shows some promising results. The improvements seen using hand analyzed data, as a result of the more realistic representation of the vertical shear of the horizontal wind, are also realized in the data analysis used in the PROFS/CWP application. The SCATR index should improve as database enhancements such as profiler and VAS satellite data, which increase the resolution in space and time, are brought into even more sophisticated objective analysis schemes.
As the workstation and personal computer become more popular than a centralized mainframe to perform thermal analysis, the methods for space vehicle thermal analysis will change. Already, many thermal analysis codes are now available for workstations, which were not in existence just five years ago. As these changes occur, some organizations will adopt the new codes and analysis techniques, while others will not. This might lead to misunderstandings between thermal shops in different organizations. If thermal analysts make an effort to understand the major differences between the new and old methods, a smoother transition to a more efficient and more versatile thermal analysis environment will be realized.
NASA Engineering requested a Probabilistic Risk Assessment (PRA) to compare the difference in the risk of Loss of Crew (LOC) and Loss of Mission (LOM) between different designs of a fluid assembly. They were concerned that the configuration favored by the design team was more susceptible to leakage than a second proposed design, but realized that a quantitative analysis to compare the risks between the two designs might strengthen their argument. The analysis showed that while the second design did help improve the probability of LOC, it did not help from a probability of LOM perspective. This drove the analysis team to propose a minor design change that would drive the probability of LOM down considerably. The analysis also demonstrated that there was another major risk driver that was not immediately obvious from a typical engineering study of the design and was therefore unexpected. None of the proposed alternatives were addressing this risk. This type of trade study demonstrates the importance of performing a PRA in order to completely understand a system's design. It allows managers to use risk as another one of the commodities (e.g., mass, cost, schedule, fault tolerance) that can be traded early in the design of a new system.
The Galleries-to-Calories Geobattery concept explores the use of abandoned coal mine workings for large-scale thermal energy transport and storage. The system involves injecting waste heat from a supercomputing facility into flooded mine galleries, where groundwater flow can store and transport thermal energy for potential recovery in downgradient district heating and cooling applications. To evaluate the feasibility and performance of the Geobattery under geological and operational uncertainty, we developed a suite of stochastic thermo-hydrological (TH) simulations using Monte Carlo sampling of key uncertain parameters (e.g., permeability, porosity, thermal conductivity, specific heat capacity) and operating conditions (e.g., injection rate, injection temperature). Results identified injection rate and temperature as the most influential parameters governing thermal front propagation, while the geometry of the room-and-pillar structure played a critical role in directing the extent and orientation of thermal advancement. Optimal combinations of material properties for maximizing heat recovery were also determined. To address the high computational cost of coupled-process stochastic modeling, we trained a neural network surrogate model on 24,000 physics-based realizations, achieving an R² > 0.99 and MAE < 0.1 for temperature predictions at monitoring locations. This surrogate enabled an additional 100,000 realizations for global sensitivity analysis and probabilistic thermal resource assessment. The integrated stochastic physics–surrogate modeling framework offers a computationally efficient tool for quantifying uncertainty, identifying key drivers, and informing early-stage design decisions for Geobattery systems.
An n-dodecane spray flame was simulated using a dynamic structure large eddy simulation (LES) model coupled with a detailed chemistry combustion model to understand the ignition processes and the quasi-steady state flame structures. This study focuses on the effect of different ambient oxygen concentrations, 13%, 15% and 21% at an ambient temperature of 900 K and an ambient density of 22.8 kg/m3, which are typical diesel-engine relevant conditions with different levels of exhaust gas recirculation (EGR). The liquid spray was treated with a traditional Lagrangian method. A 103-species skeletal mechanism was used for the n-dodecane chemical kinetic model. It is observed that the main ignitions occur in rich mixture and the flames are thickened around 35 to 40 mm off the spray axis due to the enhanced turbulence induced by the strong recirculation upstream, just behind the head of the flames at different oxygen concentrations. At 1 ms after the start of injection, the soot production is dominated by the broader region of high temperature in rich mixture instead of the stronger oxidation of the high peak temperature. Multiple realizations were performed for the 15% O2 condition to understand the realization to realization variation and to establish best practices for ensembleaveraging diesel spray flames. Two indexes are defined. The structure-similarity index analysis suggests at least 5 realizations are needed to obtain 99% similarity for mixture fraction if the average of 16 realizations are used as the target at 0.8 ms. However, this scenario may be different for different scalars of interest. It is found that 6 realizations would be enough to reach 99% of similarity for temperature, while 8 and 14 realizations are required to achieve 99% similarity for soot and OH mass fraction, respectively. Similar findings are noticed at 1 ms. More realizations are needed for the magnitude-similarity index for the similar level of similarity as the structure-similarity index
In this paper, we introduce PYSIMFRAC, an open-source python library for generating 3-D synthetic fracture realizations, integrating with fluid simulators, and performing analysis. PYSIMFRAC allows the user to specify one of three fracture generation techniques (Box, Gaussian, or Spectral) and perform statistical analysis including the autocorrelation, moments, and probability density functions of the fracture surfaces and aperture. This analysis and accessibility of a python library allows the user to create realistic fracture realizations and vary properties of interest. In addition, PYSIMFRAC includes integration examples to two different pore-scale simulators and the discrete fracture network simulator, dfnWorks. The capabilities developed in this work provides opportunity for quick and smooth adoption and implementation by the wider scientific community for accurate characterization of fluid transport in geologic media. We present PYSIMFRAC along with integration examples and discuss the ability to extend PYSIMFRAC from a single complex fracture to complex fracture networks.
How does experimental mechanics contribute to aircraft aeroelastic airworthiness? The aviation community is always improving analysis and testing techniques to realize quicker, cheaper, reliable solutions. A ground vibration test (GVT) is conducted to identify structural mode shapes, frequencies, and damping values to validate analytical models used for flutter analysis, which shows whether a structure has acceptable aeroelastic flutter margins for airworthiness. The FLL recently conducted a GVT of the Passive Aeroelastic Tailored (PAT) Wing using an experimental modal technique called Fixed Base Correction (FBC). The GVT objective was to obtain the PAT wing modal characteristics to compare test results with finite element model (FEM) results for the tow-steered wingbox.
Radiation dose reconstruction systems for large-scale epidemiological studies are sophisticated both in providing estimates of dose and in representing dosimetry uncertainty. For example, a computer program was used by the Hanford Thyroid Disease Study to provide 100 realizations of possible dose to study participants. The variation in realizations reflected the range of possible dose for each cohort member consistent with the data on dose determinates in the cohort. Another example is the Mayak Worker Dosimetry System 2013 which estimates both external and internal exposures and provides multiple realizations of "possible" dose history to workers given dose determinants. This paper takes up the problem of dealing with complex dosimetry systems that provide multiple realizations of dose in an epidemiologic analysis. In this paper we derive expected scores and the information matrix for a model used widely in radiation epidemiology, namely the linear excess relative risk (ERR) model that allows for a linear dose response (risk in relation to radiation) and distinguishes between modifiers of background rates and of the excess risk due to exposure. We show that treating the mean dose for each individual (calculated by averaging over the realizations) as if it was true dose (ignoring both shared and unshared dosimetry errors) gives asymptotically unbiased estimates (i.e. the score has expectation zero) and valid tests of the null hypothesis that the ERR slope β is zero. Although the score is unbiased the information matrix (and hence the standard errors of the estimate of β) is biased for β≠0 when ignoring errors in dose estimates, and we show how to adjust the information matrix to remove this bias, using the multiple realizations of dose. Use of these methods for several studies, including the Mayak Worker Cohort and the U.S. Atomic Veterans Study, is discussed.