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Presentation discusses use of data in radiological emergency response and different methods (applied statistics, spectral clustering, spectral anomaly detection, etc.) that could be used in conjunction with machine learning to optimize response to real-world radiological events.
Measurements over the past few years, taking place within the Multi-Informatics for Nuclear Operations Scenarios (MINOS) project, show that infrasound and low-frequency acoustic monitoring can detect, and often quantify, activities that occur on-site at a large research reactor. Observable activities include: crane translation, lifting, and lowering; differentiation between loaded and unloaded crane operations; access door opening and closing; vehicle operations; and cooling tower fan operation. Advanced data analytic and spectral feature extraction methods can be used to interpret selected signatures to reach a deeper understanding of different reactor activities. These measurements are being conducted using a network of smartphones that continuously operate in conjunction with cloud-based architectures. A recent addition to the system is the development and deployment of a real-time, cloud-based analytic framework that supports near-real-time alarming which can facilitate tip-and-cue protocols. This paper will present recent research advances in this area including studies to explore the transferability of learned parameters from one smartphone to another, the detectability of events from multiple sensors at different locations simultaneously, and the feasibility of porting analytical tools directly to the smartphones to allow edge computing with optimized configurations.
Measurements over the past few years, taking place within the Multi-Informatics for Nuclear Operations Scenarios project, show that infrasound and low-frequency acoustic monitoring can detect, and often quantify, activities that occur on-site at the High Flux Isotope Reactor. Observable activities include: crane translation, lifting, and lowering; differentiation between loaded and unloaded crane operations; access door opening and closing; vehicle operations; and cooling tower fan operations. Advanced data analytic and spectral feature extraction methods can be used to interpret selected signatures to reach a deeper understanding of different reactor activities. These measurements are being conducted using a network of smartphones that continuously operate in conjunction with cloud-based architectures. A recent addition to the system is the development and deployment of a real-time, cloud-based analytic framework that supports near-real-time alarming which can facilitate tip-and-cue protocols. This paper presents recent research in this area including the use of low-frequency acoustics to tip-off transfer operations and cue a more comprehensive analysis by other sensor networks.
Recently, filterbank multi-carrier spread spectrum (FBMC-SS) has been proposed as a candidate waveform for ultrawideband (UWB) communications. It has been noted that FBMCSS is a perfect match to this application, leading to a trivial method of matching to the required spectral mask at different regions of the world. FBMC-SS also allows easy rejection of high-power interfering signals that may appear over different parts of the UWB spectral band. In this paper, we concentrate on the use of staggered multitone spread spectrum (SMT-SS) for UWB communications. SMT makes use of offset QAM modulation to transmit data symbols over narrowband, overlapping subcarrier bands. This form of FBMC-SS is well-suited to UWB communications because it has good spectral efficiency and a flat power spectral density (PSD), resulting in good utilization of the UWB spectral mask. Additionally, we explore new methods for multi-coding that result in higher bit rates than previous FBMCSS systems. Moreover, we study methods for equalizing the UWB multipath channel and cancelling narrowband interference. Excellent performance of the proposed methods are substantiated by presenting simulation results.
Simultaneous acquisition of emission intensity from two channels in dual-color fluorescence measurements can be used to determine the spectral properties of an emitter. We detail a method to extract quantitative information from dual-color measurements by considering two models: a single spectral component model for peak wavelength determination and a dual spectral component model for relative strength determination. The former model is useful for spectral characterization and establishing heterogeneity within an emitter species, while the latter model is useful for analyzing energy transfer efficiency. Both models are formalized to yield probability distributions that reflect uncertainties due to shot noise. We applied these analysis tools to single-particle measurements of CdSe/CdS quantum dots. Isolated emitters had narrow spectral-intensity distributions that reflected their static emission profiles and fluorescence stability. Small assemblies of mixed donor/acceptor particles exhibited dynamic behavior where emission configuration changes reveal different spectral characteristics due to energy transfer.
We present a novel host galaxy subtraction technique in long-slit spectroscopy for extragalactic transients. Unlike classic methods which generally estimate the background using simple interpolation of local galaxy flux in the 2D spectrum, our approach leverages multi-band archival images of the host galaxies to model the background emission from the galaxy in the 2D spectrum. Such imaging encodes the wavelength-dependent galaxy profile along the slit, and is readily accessible through wide-field imaging surveys. We construct a smooth prior for the 2D galaxy profile with a Gaussian process (GP) based on these reference images, and use another GP to model the correlated deviations from the prior in the observed spectrum. This enables accurate inference of the galaxy flux blended with the transient. On synthetic long-slit data of a spiral galaxy extracted from a Multi Unit Spectroscopic Explorer hyper-spectral cube, the GP method remains robust as long as the host galaxy is spatially resolved and consistently outperforms classic methods. We apply the method to archival Keck spectra of two real transients, SN 2019eix and AT 2019qiz, to further demonstrate how the method uniquely recovers weak spectral features amid strong galaxy contamination, enabling refined constraints on the properties of both transients. We have released the software implementation, HostSub_GP, a scalable toolkit that leverages JAX, with an MIT license.
Co-precipitation of contaminants within the crystalline structure of calcite is a promising natural attenuation or remedial technology being considered at contaminated sites. We explore the sensitivity of the spectral induced polarization (SIP) method to induced calcite precipitation in natural sediments as a path forward to non-invasively monitor these sites. We performed time-lapse column experiments using phased (I-IV) injections over 40 days on natural sediments from the Hanford Site (Washington State, USA). In the phased injections, abiotic calcite precipitation was induced and confirmed to have occurred. Previous work in glass beads and homogeneous sand was limited to high frequency detection of calcite, however in this work we observed the development of two polarization mechanisms, one at high frequency (>100 Hz) and one at low frequency (< 100 Hz). Based on the characteristic frequencies from the SIP high and low frequency regimes, characteristic length scales (L) were computed where the adsorption mode of Na+ versus Ca2+ was compared by using diffusion coefficients corresponding to Na+ versus an arithmetically averaged value for Na+ and Ca2+. Using the diffusion coefficient of Na+, the high frequency L was found to correlate well with the size of the calcite crystals. The low frequency L correlated well with the individual natural sediment grain sizes within the columns. During late experimental times (day 36 and 40), the characteristic low frequency in two of the experimental columns shifted to lower frequencies (<0.001 Hz) which may signify SIP sensitivity of the formed calcite with the sediment grains. In field applications, the development of a low frequency polarization length scale to monitor calcite precipitation is promising for field monitoring applications, however further laboratory work needs to be performed to examine the SIP sensitivity of calcite formation in the presence of to natural sediments
Lighting application efficiency (LAE) describes the efficient delivery of light from the light source to the lighted task and is viewed as a new frontier—increasing energy savings with solid-state lighting (SSL) technologies. The framework for LAE that is proposed by the U.S. Department of Energy (DOE) consists of four major elements: light source efficiency, optical delivery efficiency, spectral efficiency, and intensity effectiveness. This report focuses on a sampling of the available SSL products that can be broadly defined as having modified spectral output because the method of spectra modification has a significant impact on light source efficiency and long-term optical delivery and spectral efficiencies. This report focused on the changes in light source, optical delivery, and spectral efficiencies that occur during aging of SSL devices. An accelerated stress test (AST) regiment was developed for the devices under test (DUTs) examined in this report to provide insights into how the performance of SSL devices change with aging. The AST protocols demonstrate that the SSL products discussed in this report often reduce light source efficiency to achieve different spectral characteristics. In addition, aging of the optical components (e.g., lenses, solder masks) in SSL devices can produce increased light absorption, which negatively impacts optical delivery efficiency and light source efficiency. The modified spectral outputs of the selected SSL products discussed in this report come in a variety of form factors and achieve enhanced optical performance by using a variety of methods. All of the products examined in this study use mid-power light-emitting diodes (MP-LEDs), although the number, configuration, phosphor content, and light-emitting diode (LED) pump of the MP-LEDs differ. Product MS-1 is a 60-watt (W) replacement A19 lamp with a hermetically sealed glass globe, which contains an embedded optical filter to absorb green and yellow emissions, thereby creating a “sunlike” modified spectrum. Product MS-2 is a 60-W replacement A19 lamp with 30 MP-LEDs, and it uses a violet LED pump, along with green and red phosphor emissions, to produce a “healthy” spectrum. This spectrum omits blue emissions in an effort to reduce melanopic lux. Product MS-3 is an LED module consisting of 21 MP-LEDs that use a violet LED pump, along with blue, green, and red phosphors, to produce a “sunlike” spectrum. Products MS-4 and MS-5 are both 6-inch (in) downlights that use a manual switching mechanism so that users can select application-specific correlated color temperatures before installation. Products MS-4 and MS-5 both contain two LED primaries (2,700 Kelvin [K] and 5,000 K) for spectral tuning. Product MS-4 contains 12 MP-LEDs for each LED primary, and Product MS-5 contains 10 MP-LEDs for each LED primary. This report summarizes the overall findings from up to 8,000 hours (hrs) of AST on the lamp DUTs (Products MS-1 and MS-2); up to 5,000 hrs of AST on the LED light engine DUTs (Product MS-3); and up to 7,000 hrs of AST on the downlight DUTs (Products MS-4 and MS-5). The AST procedures used in this study included a room temperature operational life (RTOL) test, an operational life test conducted at 45 degrees Celsius (°C; 45OL) test, an operational life test conducted at 75°C (75OL), a wet high-temperature operational life test performed at 65°C and 90% relative humidity (6590), and a wet high-temperature operational life test performed at 75°C and 75% relative humidity (7575). The AST procedures used for Products MS-1 and MS-2 were RTOL, 45OL, and 6590. The AST procedures used for Products MS-3, MS-4, and MS-5 were RTOL, 75OL, and 7575. During the ASTs described herein, separate populations of each product (three DUTs in each population for Products MS-1, MS-2, MS-4, and MS-5; four DUTs in each population for Product MS-3) were subjected to power cycling of 1 hr on and 1 hr off. The key findings from this study include the following. Enhanced optical performance came at the cost of reduced light source efficiency for the lamps and light engines examined in this study. The optical filter used to produce a “sunlike” spectrum for Product MS-1 reduced light source efficiency by 26%, from 113 lumens per watt (lm/W) to 85 lm/W. Products MS-2 and MS-3 that used a violet-pumped LED to achieve “healthy” and “sunlike” enhanced optical performance, respectively, suffered the largest reduction in initial light source efficiency (49 lm/W for Product MS-2 and 68 lm/W for Products MS-3) perhaps because of the use of violet LED as the optical pump. Product MS-2 also had the poorest color fidelity because of undersaturation of blues and oversaturation of greens and yellows. Chromaticity maintenance of the products in this study was generally good, with parametric failure only occurring for one product at one AST condition (i.e., Product MS-3 at 7575). The chromaticity shift for Product MS-3 in 7575 test conditions resulted from reduced emissions of the broad green and red phosphors used to mimic sunlight in the 500–750 nanometer (nm) range. As a result of these phosphor emission losses, chromaticity shifted toward the more stable violet-pumped LED and blue phosphor. Although chromaticity maintenance was acceptable for most products tested, different chromaticity shift mechanisms were observed for temperature and humidity tests compared with temperature alone for Products MS-1, MS-2, and MS-3. For Product MS-1, a relative increase in green emissions was observed with humidity, which may indicate humidity-accelerated degradation of the optical filter at green wavelengths (optical delivery efficiency reduction) or degradation of the emitters in the red region (spectral efficiency reduction). The violet-pumped products (i.e., MS-2 and MS-3) exhibited different chromaticity shift behavior in the temperature-humidity environments, with Product MS-2 shifting generally yellow because of photo-induced oxidation of the plastic globe (i.e., a reduction of optical delivery efficiency) and Product MS-3 shifting toward violet and blue emitters because of faster loss of emission from green and red phosphors (i.e., a reduction in spectral efficiency). Because of an initial drop in power consumption, the light source efficiency of downlights (Products MS-4 and MS-5) increased at RTOL throughout the test duration. The light source efficiency initially increased during 75OL until a decay in luminous flux maintenance (LFM) dominated light source efficiency, whereas the light source efficiency decreased for the entire 7575 test duration. It was found that increasing the ambient environment of an SSL device from 25°C to 75°C decreases the LFM by 2.7 to 5.5, depending on design of the SSL device. Adding humidity to the system (75OL to 7575) was found to decrease LFM by another factor of approximately 3.0. A common location of failure was identified for the downlights (Products MS-4 and MS-5) operated in the 7575 environment. This failure abruptly occurred between 3,000 to 5,000 hrs of testing on the film capacitor of the electromagnetic interference (EMI) filter near the diode bridge for 11 of the 12 downlights. It is likely that this failure was caused by a combination of the high electrical voltage across the capacitor, the location of the capacitor near the heat sources (e.g., transformers, diode bridges, power resistors), and the high stress environment of 7575. Longer test times are needed to fully understand the LFM, luminous efficacy changes, and chromaticity shifts for some of the products. Because of the high reliability, light source efficiency, and spectral tuning capabilities of LEDs, the expectations of LAE for SSL products are greatly increased over traditional lighting products. The data gathered in this report begin to provide an understanding of the tradeoffs that current SSL products undergo when optimizing the different efficiency elements of LAE. As discussed in this report, the findings from the tests conducted show that the optimization of spectral efficiency can come at the cost of initial light source efficiency. Furthermore, the introduction of violet LEDs can promote long-term optical delivery efficiency degradation, and the introduction of optical filters or new phosphors can lead to unwanted spectral efficiency changes because the ratio of phosphor emitters changes with aging. The data presented in this report also identified a common failure location in 6-in downlights. The results regarding long-term behavior of the modified spectra devices studied provide valuable information about changes to the light source, spectral, and optical delivery efficiencies as the devices age. This information can be used to improve future SSL designs.
Abstract Accurate predictions of inclusive scattering cross sections in the linear response regime require efficient and controllable methods to calculate the spectral density in a strongly-correlated many-body system. In this work we reformulate the recently proposed Gaussian Integral Transform technique in terms of Fourier moments of the system Hamiltonian which can be computed efficiently on a quantum computer. One of the main advantages of this framework is that it allows for an important reduction of the computational cost by exploiting previous knowledge about the energy moments of the spectral density. For a simple model of medium mass nucleus like $$^{40}$$ 40 Ca and target energy resolution of 1 MeV we find an expected speed-up of $$\approx 125$$ ≈ 125 times for the calculation of the giant dipole response and of $$\approx 50$$ ≈ 50 times for the simulation of quasi-elastic electron scattering at typical momentum transfers.
This work investigates how the spectrum of irradiance incident on the rear of bifacial modules impacts their performance. Light incident on the rear of bifacial modules can be reflected from the ground, mounting structures, and other nearby objects, causing both the intensity and spectrum to be modified. As a result, PV cells and sensors with different spectral responses report different values for the rear irradiance, leading to increased uncertainty when modeling performance. Here, we calculate the spectral irradiance incident on the rear of modules through raytrace simulations and validate with measured field data collected from a 75kW single-axis-tracked bifacial test site. Spectral responses for different sensors and PV cells are applied and compared with field measurements for four days. Based on these results, a method for improving non-spectral simulations is proposed. In addition, a linear regression is applied to the modeled spectral results as a method to gain insight into the contributions of ground reflected light to total rear irradiance.
Constructing surrogate models for uncertainty quantification (UQ) on complex partial differential equations (PDEs) having inherently high-dimensional O(10 n ), n ≥ 2, stochastic inputs (e.g., forcing terms, boundary conditions, initial conditions) poses tremendous challenges. The “curse of dimensionality” can be addressed with suitable unsupervised learning techniques used as a pre-processing tool to encode inputs onto lower-dimensional subspaces while retaining its structural information and meaningful properties. In this work, we review and investigate thirteen dimension reduction methods including linear and nonlinear, spectral, blind source separation, convex and non-convex methods and utilize the resulting embeddings to construct a mapping to quantities of interest via polynomial chaos expansions (PCE). Here, we refer to the general proposed approach as manifold PCE (m-PCE), where manifold corresponds to the latent space resulting from any of the studied dimension reduction methods. To investigate the capabilities and limitations of these methods we conduct numerical tests for three physics-based systems (treated as black-boxes) having high-dimensional stochastic inputs of varying complexity modeled as both Gaussian and non-Gaussian random fields to investigate the effect of the intrinsic dimensionality of input data. We demonstrate both the advantages and limitations of the unsupervised learning methods and we conclude that a suitable m-PCE model provides a cost-effective approach compared to alternative algorithms proposed in the literature, including recently proposed expensive deep neural network-based surrogates and can be readily applied for high-dimensional UQ in stochastic PDEs.
Generating top-down tandem mass spectra (MS/MS) for complex mixtures of proteoforms has become possible through improvements in fractionation, on-line separation, dissociation, and mass analysis. The algorithms to match tandem mass spectra to sequences have undergone a parallel evolution, with both spectral alignment and peak matching being paired with diverse methods for scoring proteoform-spectral matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification through three distinct challenges. The first is identifying a large yield of PrSMs while controlling false discovery rate (FDR) in identifying thousands of proteoforms from complex cell lysates via four software workflows: ProSight Proteome Discoverer, TopPIC, Informed Proteomics, and pTop. The second is the deconvolution of data from both Thermo Orbitrap-class and Bruker maXis Q-TOF instruments to produce consistent precursor charge and mass determinations while generating fragment mass lists to optimize identification. The third attempts to detect diverse post-translational modifications (PTMs) in proteoforms from cow milk and human ovarian tissue. The data demonstrate that existing software suites produce admirable sensitivity, in some cases identifying a third of collected tandem mass spectra with FDR controlled below 2%; the overlap in these PrSMs, however, illustrates real value in searching data with multiple search engines. Differences among identification workflows seem to result from each search algorithm incorporating its own deconvolution algorithm. By transmitting deconvolution data from multiple deconvolution routes (Thermo Xtract, Bruker Auto MSn, Mascot Distiller, TopFD, and FLASHDeconv) to the downstream TopPIC search algorithm, we were able to detect common causes of deconvolution disagreement. The detection of PTMs was very inconsistent among search algorithms, with some workflows suggesting as little as 1% of PrSMs from cow’s milk were singly-phosphorylated while other workflows found that 18% of PrSMs were singly-phosphorylated. Taken together, these results make a strong argument for top-down researchers to adopt a standard practice of analyzing each MS/MS experiment with at least two different search engines.
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML solutions in high-stakes decision settings, such as healthcare, manufacturing, and aviation, to name a few. In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems. Towards this goal, we start with a comprehensive classification of uncertainty types, sources, and causes pertaining to UQ of ML models. Next, we provide a tutorial-style description of several state-of-the-art UQ methods: Gaussian process regression, Bayesian neural network, neural network ensemble, and deterministic UQ methods focusing on spectral-normalized neural Gaussian process. Established upon the mathematical formulations, we subsequently examine the soundness of these UQ methods quantitatively and qualitatively (by a toy regression example) to examine their strengths and shortcomings from different dimensions. Then, we review quantitative metrics commonly used to assess the quality of predictive uncertainty in classification and regression problems. Afterward, we discuss the increasingly important role of UQ of ML models in solving challenging problems in engineering design and health prognostics. In conclusion, two case studies with source codes available on GitHub are used to demonstrate these UQ methods and compare their performance in the life prediction of lithium-ion batteries at the early stage (case study 1) and the remaining useful life prediction of turbofan engines (case study 2).
This study proposes a novel method for signal detection and feature extraction based on the spectral correlation function, enabling improved characterization of grid-signal distortions. Our approach differs from existing treatments of signal distortion in its analysis of the varied spectral content of signals observed in real-world scenarios. The method we propose has state-of-the-art discriminative power that provides meaningful and understandable characterizations of various grid events and anomalies. In conclusion, to validate the approach, we use real world data from the Grid Event Signature Library, which is maintained jointly by Oak Ridge National Laboratory and Lawrence Livermore National Laboratory.
Few field methods are available for characterizing source zones contaminated with aqueous film forming foam (AFFF). Non-invasive geophysical characterization of AFFF source zone contamination in situ could assist with the delineation and characterization of these sites, allowing for more informed sampling regimes aimed at quantifying subsurface poly- and perfluoroalkyl substances (PFAS) contamination. In this study, we present initial results from the investigation of the sensitivity of two existing surface and borehole-deployable geophysical technologies, spectral induced polarization (SIP), and low field nuclear magnetic resonance (NMR), to soils impacted with AFFF. To investigate the sensitivity of these methods to AFFF-impacted soil, bench-scale column experiments were conducted on samples consisting of natural and synthetic soils and groundwater. While our findings do not show strong evidence of NMR sensitivity to soil PFAS contamination, we do find evidence that SIP has sufficient sensitivity to detect PFAS sorption to soils. This finding is based on evidence that AFFF constituents associated with the pore surface produce a measurable polarization response in both freshly contaminated synthetic soils and in soils historically impacted with AFFF.
Numerical modeling of radiative transfer in nongray reacting media is a challenging problem in computational science and engineering. The choice of radiation models is important for accurate and efficient high-fidelity combustion simulations. Different applications usually involve different degrees of complexity, so there is yet no consensus in the community. In this paper, the performance of different radiative transfer equation (RTE) solvers and spectral models for a turbulent piloted methane/air jet flame are studied. The flame is scaled from the Sandia Flame D with a Reynolds number of 22,400. Three classes of RTE solvers, namely the discrete ordinates method, spherical harmonics method, and Monte Carlo method, are examined. The spectral models include the Planck-mean model, the full-spectrum k-distribution (FSK) method, and the line-by-line (LBL) calculation. The performances of different radiation models in terms of accuracy and computational cost are benchmarked. The results have shown that both RTE solvers and spectral models are critical in the prediction of radiative heat source terms for this jet flame. The trade-offs between the accuracy, the computational cost, and the implementation difficulty are discussed in detail. The results can be used as a reference for radiation model selection in combustor simulations.
Galaxy evolution has been studied by interpreting the spectral energy distribution of galaxies using spectral synthesis codes. This method has been crucial in discovering different pillars of modern galaxy evolution theories. However, this analysis was mostly carried out using spectral synthesis codes that are purely stellar, that is, they assume that the nebular contribution to the total continuum is negligible. The code FADO is the first publicly available population spectral synthesis tool that treats the contribution from ionised gas to the observed emission self-consistently. This is expected to have a particularly strong effect in star-forming (SF) galaxies. We study the impact of the nebular contribution on the determination of the star formation rate (SFR), stellar mass, and consequent effect on the star-forming main sequence (SFMS) at low redshift. We applied FADO to the spectral database of the SDSS to derive the physical properties of galaxies. As a comparison, we used the data in the MPA-JHU catalogue, which contains the properties of SDSS galaxies derived without the nebular contribution. We selected a sample of SF galaxies with Hα and Hβ flux measurements, and we corrected the fluxes for the nebular extinction through the Balmer decrement. We then calculated the Hα luminosity to estimate the SFR. Then, by combining the stellar mass and SFR estimates from FADO and MPA-JHU, the SFMS was obtained. The Hα flux estimates are similar between FADO and MPA-JHU. Because the Hα flux was used as tracer of the SFR, FADO and MPA-JHU agree in their SFR. The stellar mass estimates are slightly higher for FADO than for MPA-JHU on average. However, considering the uncertainties, the differences are negligible. With similar SFR and stellar mass estimates, the derived SFMS is also similar between FADO and MPA-JHU. Our results show that for SDSS normal SF galaxies, the additional modelling of the nebular contribution does not affect the retrieved fluxes and consequentially also does not influence SFR estimators based on the extinction-corrected Hα luminosity. For the stellar masses, the results point to the same conclusion. These results are a consequence of the fact that the vast majority of normal SF galaxies in the SDSS have a low nebular contribution. However, the obtained agreement might only hold for local SF galaxies, but higher-redshift galaxies might show different physical properties when FADO is used. This would then be an effect of the expected increased nebular contribution.
Type Ia supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleration of expansion and determine its physical nature. This document develops a new SNe Ia spectral energy distribution (SED) model, called the SUpernova Generator And Reconstructor (SUGAR), which improves the spectral description of SNe Ia, and consequently could improve the distance measurements. This model was constructed from SNe Ia spectral properties and spectrophotometric data from the Nearby Supernova Factory collaboration. In a first step, a principal component analysis-like method was used on spectral features measured at maximum light, which allowed us to extract the intrinsic properties of SNe Ia. Next, the intrinsic properties were used to extract the average extinction curve. Third, an interpolation using Gaussian processes facilitated using data taken at different epochs during the lifetime of an SN Ia and then projecting the data on a fixed time grid. Finally, the three steps were combined to build the SED model as a function of time and wavelength. This is the SUGAR model. Results. The main advancement in SUGAR is the addition of two additional parameters to characterize SNe Ia variability. The first is tied to the properties of SNe Ia ejecta velocity and the second correlates with their calcium lines. The addition of these parameters, as well as the high quality of the Nearby Supernova Factory data, makes SUGAR an accurate and efficient model for describing the spectra of normal SNe Ia as they brighten and fade. The performance of this model makes it an excellent SED model for experiments like the Zwicky Transient Facility, the Large Synoptic Survey Telescope, or the Wide Field Infrared Survey Telescope.