Inverse multiple scattering problems. II - Limited information content of partially fitted planetary curves with application to the Venusian visual phase curve
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Fymat, A. L.↗
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Curved-Focal-Plane Arrays Using Deformed-Membrane Photodetectors
A versatile and simple approach to the design and fabrication of curved-focal-plane arrays of silicon-based photodetectors is being developed. This approach is an alternative to the one described in "Curved Focal-Plane Arrays Using Back- Illuminated High-Purity Photodetectors" (NPO-30566), NASA Tech Briefs, Vol. 27, No. 10 (October 2003), page 10a. As in the cited prior article, the basic idea is to improve the performance of an imaging instrument and simplify the optics needed to obtain a given level of performance by making an image sensor (in this case, an array of photodetectors) conform to a curved focal surface, instead of designing the optics to project an image onto a flat focal surface. There is biological precedent for curved-focal-surface designs: retinas - the image sensors in eyes - conform to the naturally curved focal surfaces of eye lenses. The present approach is applicable to both front-side- and back-side-illuminated, membrane photodetector arrays and is being demonstrated on charge-coupled devices (CCDs). The very-large scale integrated (VLSI) circuitry of such a CCD or other array is fabricated on the front side of a silicon substrate, then the CCD substrate is attached temporarily to a second substrate for mechanical support, then material is removed from the back to obtain the CCD membrane, which typically has a thickness between 10 and 20 m. In the case of a CCD designed to operate in back-surface illumination, delta doping can be performed after thinning to enhance the sensitivity. This approach is independent of the design and method of fabrication of the front-side VLSI circuitry and does not involve any processing of a curved silicon substrate. In this approach, a third substrate would be prepared by polishing one of its surfaces to a required focal-surface curvature. A CCD membrane fabricated as described above would be pressed against, deformed into conformity with, and bonded to, the curved surface. The technique used to press and bond the CCD membrane would depend on the nature of the supporting material (see figure). For example, if the third substrate were made of quartz frit, the substrate would be prepared by suffusing it with epoxy. Then one would take advantage of the porosity of the frit by applying a partial vacuum to the opposite surface of the frit, causing atmospheric pressure to push the CCD membrane against the curved surface. The curing of the epoxy would bond the CCD membrane to the curved surface. Alternatively, if the third substrate were made of a nonporous material, the curved substrate surface would be prepared by coating it with a wax or an uncured epoxy. The CCD membrane would be pressed against the coated, curved surface by use of a suitably pressurized balloon. The CCD membrane would then become bonded to the curved surface by curing of the epoxy or freezing of the wax.
Nikzad, Shouleh↗
Future Intensity‐Duration‐Frequency Curves of Extreme Precipitation in the Midwest United States From Convection‐Permitting Modeling
Abstract During the last four decades, global warming has statistically significant intensified extreme precipitation events in the Midwestern United States (defined here as the region covering Illinois, Indiana, Ohio, and Kentucky), leading to increased risks to human life, property, and infrastructure. To enable climate change adaptation and resilience across various economic and social sectors in this region, updated information about future climate changes, specifically at finer spatial scales, is essential. Leveraging a new 150‐year dynamical downscaling data set at convection‐permitting resolution, this study introduces a framework to construct the projected future intensity‐duration‐frequency (IDF) curves of heavy precipitation, which are prominent tools for infrastructure design and water resources management. This framework generates IDF curves at both sub‐daily and multi‐day duration utilizing hourly in situ observations as well as quantile‐based statistical techniques in bias‐correction and return levels selection. The assumption of non‐stationarity in the distribution parameter fitting process is also implemented in this workflow. Compared to historical IDF curves for 1980–2022, future projected IDF curves for 2058–2100 under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios indicate an average intensity increase of approximately 15% and 25%, respectively, across 74 stations, considering both annual and seasonal timescales. Future projections suggest that extreme precipitation events may become more severe across six investigated return periods, with longer return periods showing a greater increase. The frequency of future extreme precipitation events in the Midwest region is also projected to double. Furthermore, current results reveal spatial heterogeneity of future trends across stations owing to the high‐resolution input data set. Plain Language Summary This study investigates the evolving nature of extreme precipitation events in the Midwestern United States under a changing climate. By leveraging a high‐resolution dynamical downscaling data set, we construct projected intensity‐duration‐frequency (IDF) curves for future extreme rainfall events. These curves serve as vital tools for infrastructure planning and water resource management. Our analysis reveals a significant increase in both the intensity and frequency of extreme precipitation events in the region. Future projected IDF curves for the late century indicate an average intensity increase of approximately 15%–25% compared to historical values. Moreover, the frequency of such events is expected to double. Spatial heterogeneity in future trends is observed across different stations within the Midwest, highlighting the importance of high‐resolution modeling in capturing localized climate variability. These findings underscore the urgent need for climate adaptation strategies to mitigate the increasing risks associated with extreme precipitation events in the region. Key Points This study introduces a workflow to construct future intensity‐duration‐frequency (IDF) curves over the Midwest United States using a new convection‐permitting modeling data set The current IDF construction workflow reproduces well the historical observed IDF 30 curves in summer months with median relative errors of 2.4% among 74 stations and 6 investigated durations The projected IDF curves show diverse future trends of extreme precipitation across stations, with intensity increases of approximately 15% and 25% under RCP4.5 and RCP8.5 climate scenarios, respectively, and a doubling of frequency on average
Nguyen, Trung↗
Numerical investigation of an internal layer in turbulent flow over a curved hill
The development of an internal layer in a turbulent boundary layer flow over a curved hill is investigated numerically. The turbulence field of the boundary layer flow over the curved hill is compared with that of a turbulent flow over a symmetric airfoil (which has the same geometry as the curved hill except that the leading and trailing edge plates were removed) to study the influence of the strongly curved surface on the turbulence field. The turbulent flow equations are solved by a control-volume based finite difference method. The turbulence is described by a multiple-time-scale turbulence model supplemented with a near-wall turbulence model. Computational results for the mean flow field (pressure distributions on the walls, wall shearing stresses and mean velocity profiles), the turbulence structure (Reynolds stress and turbulent kinetic energy profiles), and the integral parameters (displacement and momentum thicknesses) compared favorably with the measured data. Computational results show that the internal layer is a strong turbulence field which is developed beneath the external boundary layer and is located very close to the wall. Development of the internal layer was more obviously observed in the Reynolds stress profiles and in the turbulent kinetic energy profiles than in the mean velocity profiles. In this regard, the internal layers is significantly different from wall-bounded simple shear layers in which the mean velocity profile characterizes the boundary layer most distinguishably. Development of such an internal layer, characterized by an intense turbulence field, is attributed to the enormous mean flow strain rate caused by the streamline curvature and the strong pressure gradient. In the turbulent flow over the curved hill, the internal layer begin to form near the forward corner of the hill, merges with the external boundary layer, and develops into a new fully turbulent boundary layer as the fluid flows in the downstream direction. For the flow over the symmetric airfoil, the boundary layer began to form from almost the same location as that of the curved hill, grew in its strength, and formed a fully turbulent boundary layer from mid-part of the airfoil and in the downstream region. Computational results also show that the detailed turbulence structure in the region very close to the wall of the curved hill is almost the same as that of the airfoil in most of the curved regions except near the leading edge. Thus the internal layer of the curved hill and the boundary layer of the airfoil were also almost the same. Development of the wall shearing stress and separation of the boundary layer at the rear end of the curved hill mostly depends on the internal layer and is only slightly influenced by the external boundary layer flow.
Kim, S-W.↗
Variation of the nebular dust attenuation curve with the properties of local star-forming galaxies
ABSTRACT We use a sample of 78 340 star-forming galaxies at z ≃ 0.04–0.1 from the Sloan Digital Sky Survey (SDSS) data release (DR8) survey to calculate the average nebular dust attenuation curve and its variation with the physical properties of galaxies. Using the first four low-order Balmer emission lines (H α, H β, H γ, and H δ) detected in the composite spectrum of all galaxies in the sample, we derive a nebular attenuation curve in the range of 0.41 to $0.66\, \mu$m that has a similar shape and normalization to that of the Galactic extinction curve (Milky Way curve), the SMC curve and the nebular attenuation curve derived recently for typical star-forming galaxies at z ∼ 2. We divide the galaxies into bins of stellar mass, gas-phase metallicity, and specific star formation rate, and derive the nebular attenuation curve in each of these bins. This analysis indicates that there is very little variation in the shape of the nebular dust attenuation curve with the properties used to bin the galaxies, and suggests a near universal shape of the nebular dust attenuation curve at least among the galaxies and the range of properties considered in our sample.
Rezaee, Saeed (ORCID:0000000262899918)↗
Four Years of Type Ia Supernovae Observed by TESS: Early-time Light-curve Shapes and Constraints on Companion Interaction Models
Abstract We present 307 type Ia supernova (SN) light curves from the first 4 yr of the Transiting Exoplanet Survey Satellite mission. We use this sample to characterize the shapes of the early-time light curves, measure the rise times from first light to peak, and search for companion star interactions. Using simulations, we show that light curves must have noise <10% of the peak flux to avoid biases in the early-time light-curve shape, restricting our quantitative analysis to 74 light curves. We find that the mean power-law index t β 1 of the early-time light curves isβ 1 = 1.93 ± 0.57, and the mean rise time to peak is 15.7 ± 3.5 days. The underlying population distribution forβ 1 may instead consist of a Gaussian component with mean 2.29, width 0.34, and a long tail extending to values less than 1.0. We find that the data can rarely distinguish between models with and without companion interaction models. Nevertheless, we find three high-quality light curves that tentatively prefer the addition of a companion interaction model, but the statistical evidence for the companion interactions is not robust. We also find two SNe that disfavor the addition of a companion interaction model to a curved power-law model. Taking the 74 SNe together, we calculate 3σupper limits on the presence of companion signatures to control for orientation effects that can hide companions in individual light curves. Our results rule out common progenitor systems with companions having Roche lobe radii >31R ⊙ (separations >5.7 × 10 12 cm, 99.9% confidence level) and disfavor companions having Roche lobe radii >10R ⊙ (separations >1.9 × 10 12 cm, 95% confidence level). Lastly, we discuss the implications of our results for the intrinsic fraction of single degenerate progenitor systems.
Astronomy & Astrophysics↗
In Vitro Experimental Model to Investigate the Biological Effects across the Bragg Curve of High-LET Radiation
The space environment consists of a varying field of radiation particles including high energy ions, with a spacecrafts shielding material providing the only major protection to astronauts from harmful exposure. Unlike lowLET gamma or Xrays, the presence of shielding does not always reduce the radiation risks for energetic charged particle exposure since the dose delivered by the charged particle increases sharply as the particle approaches the end of its range, a position known as the Bragg peak and the correlating spatial dose distribution identified as the Bragg curve. The Bragg curve does not necessarily represent the biological damage along the particle traversal since biological effects are influenced by the track structure of both primary and secondary particles. Therefore, the biological Bragg curve is dependent of the energy and the type of the primary particle, and may vary for different biological endpoints. Here we describe a unique irradiation geometry and experimental system to measure the biological response across the Bragg curve in one consistent biological sample. Polyethylene shielding was used to achieve a Bragg curve distribution with the beam geometry parallel to a monolayer of fibroblast cells. We present data that highlights the differential formation of DNA double strand breaks (DSBs) and chromosomal deletions across the Bragg curve in human fibroblasts irradiated with 600 MeV/nucleon iron ion beams. Qualitative analyses of gammaH2AX fluorescence, a known marker of DSBs, indicated potentially increased clustering of DNA damage before the Bragg peak, enhanced homogenous distribution at the peak, and provided visual evidence of high linear energy transfer (LET) particle traversal of cells beyond the Bragg peak in agreement with one-dimensional transport approximations. A biological response curve generated for micronuclei induction across the Bragg curve for 600 MeV/n Fe ions did not reveal an increase in the yield of micronuclei at the Bragg peak location. Assessment of such biological parameters employing the described in vitro experimental system may provide improved platforms to measure a number of biological consequences of shielding materials across the Bragg curve for high charge and energy (HZE) ions.
Desai, N.↗
Curvilinear Displacement Transfer Functions for Deformed Shape Predictions of Curved Structures Using Distributed Surface Strains
Curvilinear Displacement Transfer Functions were formulated for deformed shape predictions of different curved structures using surface strains. The embedded curved beam (depth-wise cross section of a curved structure along a surface strain-sensing line) was discretized into multiple small domains, with domain junctures matching the strain-sensing stations. Thus, the surface strain distribution can be described with a piecewise linear or a piecewise nonlinear function. The discrete approach enabled piecewise integrations of a curvature-strain differential equation for the embedded curved beam to yield closed-form Curvilinear Displacement Transfer Functions, which are written in terms of embedded curved-beam geometrical parameters and surface strains. By inputting the surface strain data, the Curvilinear Displacement Transfer Functions can transform surface strains into deflections along each embedded curved beam for mapping out the overall structural deformed shapes. The finite-element method was used to analytically generate the surface strains of the curved beams. The deformed shape prediction accuracies were then determined by comparing the theoretical deflections with the finite-element-generated deflections, which were used as yardsticks. By introducing the correction factors in simple mathematical forms, the Curvilinear Displacement Transfer Functions can be quite accurate for shape predictions of different curved-beam structures ranging from limit case of straight beam up to semicircular curved beam.
Ko, William L.↗
Results of an interlaboratory study on the working curve in vat photopolymerization II: Towards a standardized method
The working curve measurement in photopolymer additive manufacturing is a ubiquitous measure of the cure depth of a printing resin as a function of radiant exposure of light. The fit parameters from this measurement (the depth of light penetration D p and the critical exposure E c ) are used to evaluate and report a resin’s printability, optimize processing parameters, and inform print and resin quality control. Despite its widespread use, the working curve lacks a standard measurement method. Here, following up on our paper “Results of an Interlaboratory Study on the Working Curve in Vat Photopolymerization” from last year, an interlaboratory study on the working curve was performed using calibrated, reproducible, bandpass filtered light sources. With these light sources, the variability between labs in measured working curves was dramatically reduced from the initial interlaboratory study. Aggregate data from this experiment produced reliable D p and E c measurements at 385 nm of 39.2 ± 3.7 µm and 12.3 ± 3.0 mJ cm −2 , respectively. At 405 nm the values of D p and E c are 69.3 ± 3.8 µm and 17.9 ± 2.3 mJ cm −2 , respectively. The results are agnostic to the thickness measurement tool utilized by participants, ensuring broad applicability across laboratories. We also tested the generalizability of the proposed method of using a filtered light source by filtering a commercial 405 nm light source and obtaining a working curve in agreement with the aggregate data from the interlaboratory study. This interlaboratory study provides a basis for a documentary standard for the working curve, so that the entire photopolymer additive manufacturing industry can share reproducible and interoperable working curve data.
36 MATERIALS SCIENCE↗
U.S. Wind and Solar PV Supply Curves with Future Land-use Change
This dataset provides future supply curves representing the total resource potential for land-based wind and solar photovoltaic (PV) deployment in the conterminous United States after accounting for the impact of land-use and land-cover change (LULC). We use LULC projections from 2010 to 2050 developed based on the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios. The LULC projections are subsequently fed into the renewable energy potential model (reV), which estimates total available wind and solar capacity after excluding non-developable land. Supply curves are provided for four IPCC scenarios in 2050: A1B, A2, B1, and B2. As a baseline, we also provide the supply curve from the B2 scenario in 2010. In addition to the supply curves, we also provide representative wind and solar generation profiles for each supply curve point. These generation profiles are provided as capacity factors and are based on a 2012 weather year using NSRDB and WindToolkit resource data. For more details on the supply curve and profile datasets included here please refer to README. Additional information on the supply curves and the LULC projections used to generate them, as well as an analysis of their impact on wind and solar deployment under decarbonization can be found in the publication linked below: "U.S. Wind and Solar PV Supply Curves with Future Land-use Change Publication".
Array↗
Advanced Photovoltaic Module Characterization: Using Image Transformers for Current–Voltage Curve Prediction From Electroluminescence Images
Individual photovoltaic (PV) module health monitoring can be a daunting task for operation and maintenance of solar farms. Modules can be inspected through luminescence, thermal imaging, and current–voltage (I–V) curve analyzes for identification of damage and power loss. I–V curves provide easily interpretable data to determine module health as they directly provide electrical performance metrics. However, in order to obtain these curves, modules must be disconnected from the array and either removed to a solar simulator or characterized in situ with corrections for module temperature, the incident solar spectrum, and intensity. Luminescence or thermal images of a module are relatively easy to acquire in situ. Electroluminescence (EL) images highlight physical defects in the modules but do not provide easily interpretable features to correlate with electrical performance. This work presents a SWin transformer network to predict I–V curves for PV modules from their corresponding EL images. The predicted I–V curves allow the accurate prediction of the maximum power point (MPP), short-circuit current I sc , and open-circuit voltage V oc with a mean error less of than 1%. Comparing single diode model (SDM) parameters extracted from the predicted curves to those extracted from the true curves, the series resistance R s demonstrates a mean error of 5.19%, and the photocurrent I a mean error of 0.197%. The shunt resistance R sh and dark current Io parameters are predicted with larger errors because of their sensitivity to small changes in the I–V curve.
Byford, Brandon K. [New Mexico State Univ., Las Cr↗
On the equivalence between semiempirical fracture analyses and R-curves
The relationships between several semiempirical fracture analyses (Brockrath and Glassco, 1974; Newman, 1973; Kuhn, 1968; Orange, 1971; Feddersen, 1971) and the R-curve concept of fracture mechanics are examined. Some characteristics of the R-curve concept when applied to finite-width specimens are reviewed, and conditions for equivalence between a semiempirical analysis (SEFA) and an R-curve are derived. The relationship between R-curves and SEFAs is studied for a hypothetical material. It is shown that for each SEFA there is an equivalent R-curve, the magnitude and shape of which are determined by the SEFA formulation and parameters, and which predicts precisely the same relationship between fracture stress and original crack length. A given SEFA correlates residual strength data closely if its equivalent R-curve closely matches the actual R-curve of the material studied. The SEFA given by Newman is found to yield best results for the hypothetical case considered. Equivalent R-curves for real materials are developed using data from the literature.
Orange, T. W.↗
A Numerical Method for Calculating Stellar Occultation Light Curves from an Arbitrary Atmospheric Model
We present a method for speeding up numerical calculations of a light curve for a stellar occultation by a planetary atmosphere with an arbitrary atmospheric model that has spherical symmetry. This improved speed makes least-squares fitting for model parameters practical. Our method takes as input several sets of values for the first two radial derivatives of the refractivity at different values of model parameters, and interpolates to obtain the light curve at intermediate values of one or more model parameters. It was developed for small occulting bodies such as Pluto and Triton, but is applicable to planets of all sizes. We also present the results of a series of tests showing that our method calculates light curves that are correct to an accuracy of 10(exp -4) of the unocculted stellar flux. The test benchmarks are (i) an atmosphere with a l/r dependence of temperature, which yields an analytic solution for the light curve, (ii) an atmosphere that produces an exponential refraction angle, and (iii) a small-planet isothermal model. With our method, least-squares fits to noiseless data also converge to values of parameters with fractional errors of no more than 10(exp -4), with the largest errors occurring in small planets. These errors are well below the precision of the best stellar occultation data available. Fits to noisy data had formal errors consistent with the level of synthetic noise added to the light curve. We conclude: (i) one should interpolate refractivity derivatives and then form light curves from the interpolated values, rather than interpolating the light curves themselves; (ii) for the most accuracy, one must specify the atmospheric model for radii many scale heights above half light; and (iii) for atmospheres with smoothly varying refractivity with altitude, light curves can be sampled as coarsely as two points per scale height.
Chamberlain, D. M.↗
Extension of Ko Straight-Beam Displacement Theory to Deformed Shape Predictions of Slender Curved Structures
The Ko displacement theory originally developed for shape predictions of straight beams is extended to shape predictions of curved beams. The surface strains needed for shape predictions were analytically generated from finite-element nodal stress outputs. With the aid of finite-element displacement outputs, mathematical functional forms for curvature-effect correction terms are established and incorporated into straight-beam deflection equations for shape predictions of both cantilever and two-point supported curved beams. The newly established deflection equations for cantilever curved beams could provide quite accurate shape predictions for different cantilever curved beams, including the quarter-circle cantilever beam. Furthermore, the newly formulated deflection equations for two-point supported curved beams could provide accurate shape predictions for a range of two-point supported curved beams, including the full-circular ring. Accuracy of the newly developed curved-beam deflection equations is validated through shape prediction analysis of curved beams embedded in the windward shallow spherical shell of a generic crew exploration vehicle. A single-point collocation method for optimization of shape predictions is discussed in detail
Ko, William L.↗
Application of Geodetic VLBI Data to Obtaining Long-Term Light Curves for Astrophysics
The long-term light curve is important to research on binary black holes and disk instability in AGNs. The light curves have been drawn mainly using single dish data provided by the University of Michigan Radio Observatory and the Metsahovi Radio Observatory. Hence, thus far, we have to research on limited sources. I attempt to draw light curves using VLBI data for those sources that have not been monitored by any observatories with single dish. I developed software, analyzed all geodetic VLBI data available at the IVS Data Centers, and drew the light curves at 8 GHz. In this report, I show the tentative results for two AGNs. I compared two light curves of 4C39.25, which were drawn based on single dish data and on VLBI data. I confirmed that the two light curves were consistent. Furthermore, I succeeded in drawing the light curve of 0454-234 with VLBI data, which has not been monitored by any observatory with single dish. In this report, I suggest that the geodetic VLBI archive data is useful to obtain the long-term light curves at radio bands for astrophysics.
Kijima, Masachika↗
Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning
Real-time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery-powered devices. In this study, a methodology to predict the entire constant-current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO 2 -based batteries at a constant C-rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO 2 -based) using open-access datasets. The prediction error of the charge curves for the LiCoO 2 -based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.
25 ENERGY STORAGE↗
Learning curves for drug response prediction in cancer cell lines
Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.
60 APPLIED LIFE SCIENCES↗
Dysfunctionality Hazard Curve: Risk-Based Tool to Support the Resilient Design of Systems Subjected to Multihazards
While resilience metrics have been proposed and studied given a functionality recovery curve, they have not emphasized enough on accounting for the uncertainties in the multihazard occurrences and intensities. Moreover, these resilience metrics are not risk-based (i.e., they do not express the system's resilience loss as a frequency of exceedance), leading to inconsistencies in system performance description when compared to performance-based engineering frameworks. A risk-based tool termed dysfunctionality hazard curve is proposed to assess the resilience of systems subjected to single hazards or multihazards with inter-event dependencies. Dysfunctionality hazard curve expresses system resilience performance as frequency of exceedance of time to full functionality. In doing so, it characterizes system recovery as a sequence of repair activities and also considers the uncertainties in the multihazard occurrences and intensities. Dysfunctionality hazard curve is demonstrated for a residential building susceptible to earthquake and hurricane hazards. Results indicate that Dysfunctionality hazard curve for earthquakes is greater than that for hurricane winds under single hazards. Under multihazards, considering inter-event dependencies during system recovery leads to larger dysfunctionality hazard curve than ignoring them. Finally, the concept of dysfunctionality hazard curve is also extended to a system-of-systems consisting of residential and commercial buildings.
42 ENGINEERING↗