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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

CMIP6-based Multi-model Streamflow Projections over the Conterminous US

This dataset presents an ensemble of streamflow projections based on the hydroclimate projections dataset supporting the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). The six-member General Climate Model (GCM) ensemble from the Coupled Models Intercomparison Project phase 6 (CMIP6) downscaled using statistical (i.e., DBCCA) and dynamical (i.e., RegCM) and bias-corrected using two meteorological reference observations (Daymet & Livneh) are driven through two calibrated hydrologic models (VIC & PRMS) to simulate projected future hydrologic responses. This leads to the production of an ensemble of hydroclimate projections, including total runoff (surface runoff and baseflow) for 1980–2019 baseline and 2020–2059 near-term future periods under multiple emission scenarios at 1/24° (~4 km) spatial resolution across the CONUS. The total runoff projections are routed through the Routing Application for Parallel computatIon of Discharge (RAPID) routing model to produce an ensemble of streamflow projections for both periods across 2.7 million NHDPlusV2 stream reaches in the CONUS.

13 HYDRO ENERGY↗
DOE OSTI2023

Isolation of Methane Enriched Bacterial Communities and Application as Wheat Biofertilizer under Drought Conditions: An Environmental Contribution

The search for methanotrophs as plant-growth-promoting rhizobacteria (PGPR) presents an important contribution to mitigating the impact of global warming by restoring the natural soil potential for consuming methane while benefiting plants during droughts. Our in silico simulations suggest that water, produced as a byproduct of methane oxidation, can satisfy the cell growth requirement. In addition to water, methanotrophs can produce metabolites that stimulate plant growth. Considering this, we proposed that applying methanotrophs as PGPR can alleviate the effect of droughts on crops, while stimulating atmospheric methane consumption. In this work, we isolated a series of methanotrophic communities from the rhizospheres of different crops, including Italian sweet pepper and zucchini, using an atmosphere enriched with pure methane gas, to determine their potential for alleviating drought stress in wheat plants. Subsequently, 23 strains of nonmethanotrophic bacteria present in the methanotrophic communities were isolated and characterized. We then analyzed the contribution of the methane-consuming consortia to the improvement of plant growth under drought conditions, showing that some communities contributed to increases in the wheat plants’ lengths and weights, with statistically significant differences according to ANOVA models. Furthermore, we found that the presence of methane gas can further stimulate the plant–microbe interactions, resulting in larger plants and higher drought tolerance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Implementing belowground controls on nutrient uptake in ELMv2-SPRUCE improves representation of a boreal peatland ecosystem

Boreal peatlands store 13 %–32 % of the global soil carbon (C) stock, a service dependent on plant-mycorrhizal fungi associations. In these nutrient poor systems, ectomycorrhizal and ericoid mycorrhizal fungi supply up to >80 % of the nutrient requirements of their plant hosts, partly with mined nitrogen (N) and phosphorus (P) from soil organic matter that are otherwise inaccessible to plants. Despite the ecological significance, mycorrhizal associations are only represented in a few land surface or ecosystem models. We modify the peatland branch of version 2 of the Energy Exascale Earth System Land Model (ELMv2-SPRUCE) to replace the default photosynthesis-driven inorganic N and P (NP) uptake process with a more realistic representation of the process via three pathways: (1) direct inorganic NP uptake by uncolonized fine roots, (2) indirect inorganic NP acquisition and (3) indirect NP acquisition from organic sources by mycorrhizal roots. We systematically evaluated the performance of the default and modified models with field observations from a whole ecosystem warming and carbon dioxide fertilization experimental site: Spruce and Peatland Responses Under Changing Environment (SPRUCE), in northern Minnesota, USA. The modified model reduces the underestimation of the growth response of shrubs in the default model to warming from 40 %–80 % to 17 %–35 % and reduces the overall relative absolute error on C fluxes from 1.61 to 1.54 in calibration. Improvements on modeled shrub growths and shrub-moss community net ecosystem exchanges are also seen in validation. The improved growth response of shrubs to warming is accompanied by several-fold increase in direct inorganic NP uptake and decrease in fungal colonization rate. The modified model simulates a smaller magnitude of transition of the ecosystem from C sink to C source under warming due to alleviation of plant nutrient limitation. Equifinality analysis shows the newly added parameters in the modified model can be constrained by the observed C fluxes. Sensitivity analysis shows the newly added parameters have stronger statistical interactions than the preexisting parameters in the default model. Overall, the modified model is an improvement over the default ELMv2-SPRUCE and will be a useful tool for understanding boreal peatland change.

Wang, Yaoping [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2020

Effect of Power Plant Capacity on the CAPEX, OPEX, and LCOC of the CO2 Capture Process in Pre-Combustion Applications - Abstract

Aspen Plus v8.8 was used to simulate the pre-combustion CO2 capture process from a typical fuel gas stream at different power plant capacities ranging from 54.3 to 543 MW. Polyethylene glycol polydimethyl siloxane (PEGPDMS-1) was used as a physical solvent to capture CO2 in a countercurrent packed-bed absorber containing a structured packing (Mellapak 250Y). The process pressure was 51.4 bar and the solvent temperature was varied from 10 to 40 <sup>o</sup>C. The internal diameter of the absorber ranged from 1.9 to 7 m and the packing height from 13.9 to 45.5 m. The physico-chemical properties were obtained and modeled using the Perturbed Chain-Statistical Associating Fluid Theory (PC-SAFT) Equation-of-State (EOS).<p> Four process constraints were imposed in Aspen Plus: (1) no flooding in the absorber, (2) the absorber height to diameter ratio (H/D) is greater than or equal 6, (3) at least 90 mol% of the CO2 capture from the fuel gas stream, and (4) the CO2 stream destined to sequestration sites should contain less than 600 ppm water concentration and less than or equal 0.5 mol% of fuel gases (H2, CO, CH4). The plant lifetime was assumed to be 30 years with an annual discount rate of 10% and an annual maintenance cost of 4% of the total capital expenses.</p><p> Seven power plants with different capacities were used and for each plant, the corresponding CO2 capture process was simulated. The absorber flooding was checked using the generalized pressure drop correlation (GPDC) by Leva [1] and the capital expenditure (CAPEX), operating expenditure (OPEX), and levelized costs per ton of CO2 captured (LCOC) were calculated [2-5]. Normalized by the largest power plant capacity (543 MW) used in the simulation, the results indicated that the capital and operating expenditures increased, however, the LCOC decreased with increasing plant capacity. This behavior was due to the increased annual tonnage of the CO2 captured with plant capacity as shown in Figure 1. The calculated CAPEX, OPEX, LCOC and the tonnage of CO2 captured for the 543 MW power plant were about 52 MM$, 22 MM$/year, 7.46 $/ton and 4 MM ton/year.</p>

Ashkanani, Husain↗
DOE OSTI2022

FutureTense

Protective vaccines and reliable diagnostics are essential tools for controlling viral diseases. However, the efficacy of these tools can be diminished by mutations in viral genomes. The delay between the emergence of new viral strains and the redesign of vaccines and diagnostics allows for continued viral transmission. Is it possible to address this challenge by computationally predicting viral genome sequence evolution? Can we “future-proof” vaccines and diagnostics by targeting both current and anticipated future sequence variants? While predicting viral evolution is still an unsolved, “grand challenge” problem in biology, the large, and rapidly growing, number of SARS-CoV-2 genome sequences provide an opportunity to quantify the ability of machine learning to predict viral genome sequence evolution. Towards this end, we have developed a simple computational model for predicting viral evolution at the level of individual nucleotides. The key metric for quantifying the per-base, prediction accuracy for viral evolution is the Mann-Whitney U statistic (or, equivalently, the area under the receiver operator curve). Since the Mann-Whitney U statistic is not a differentiable function, existing deep leaning packages (like Pytorch and Keras/TensorFlow) are not useful, as they require that the accuracy metric/objective function be analytically differentiable with respect to the model parameters. To overcome this challenge, we have implemented custom software, “FutureTense”, that can train a machine learning model by maximizing the non-differentiable Mann-Whitney U statistic. This software trains a machine learning model by exploring along the direction of the discrete gradient of the Mann-Whitney U statistic in the model parameter space. Parallel computing and genome sequence-specific optimizations are used to accelerate model training. The resulting machine learning model learns the observed high C->U mutation rates in the SARS-CoV-2 genome (which are potentially induced by host defenses) and provides prediction accuracies that are significantly better than one would expect from random chance. While predicting viral evolution is still quite far from a solved problem, the surprising performance of this simple model gives hope that the accuracy of predicting viral genome evolution can be further increased by more sophisticated approaches.

Gans, Jason↗
DOE OSTI2021

Estimating uncertainty: A Bayesian approach to modelling photosynthesis in C3 leaves

The Farquhar-von Caemmerer-Berry (FvCB) model is extensively used to model pho-tosynthesis from gas exchange measurements. Since its publication, many methods have been developed to measure, or more accurately estimate, parameters of this model. Here, we have created a tool that uses Bayesian statistics to fit photosyn-thetic parameters using concurrent gas exchange and chlorophyll fluorescence mea-surements whilst evaluating the reliability of the parameter estimation. We have tested this tool on synthetic data and experimental data from rice leaves. Our results indicate that reliable parameter estimation can be achieved whilst only keeping one parameter, Km, that is, Michaelis constant for CO2 by Rubisco, prefixed. Additionally, we show that including detailed low CO2 measurements at low light levels increases reliability and suggests this as a new standard measurement protocol. By providing an estimated distribution of parameter values, the tool can be used to evaluate the quality of data from gas exchange and chlorophyll fluorescence measurement proto-cols. Compared to earlier model fitting methods, the use of a Bayesian statistics-based tool minimizes human interaction during fitting, reducing the subjectivity which is essential to most existing tools. A user friendly, interactive Bayesian tool script is provided.

Bayesian statistics, leaf photosynthesis, mesophyl↗
DOE OSTI2021

Corrosion of rebar in concrete. Part II: Literature survey and statistical analysis of existing data on chloride threshold

The literature is reviewed with respect to defining, measuring, and interpreting the chloride threshold (CT). CT was defined with a strong fundamental basis in the Point Defect Model. which also dictates the nature of the statistical analysis of surveyed CT values. A database of CT for various primary and secondary independent variables has been established. Statistical analyses reveal that CT is lognormally distributed, in accordance with the PDM, with most probable values being 0.85, 0.69, and 1.19 for %total Cl/cem, %free Cl/cem, and [Cl - ]/[OH - ], respectively. The breakdown and corrosion potentials follow normal distributions with means of 0.056 and -0.322 V SCE , respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Recommended conventions for reporting results from direct dark matter searches

Abstract The field of dark matter detection is a highly visible and highly competitive one. In this paper, we propose recommendations for presenting dark matter direct detection results particularly suited for weak-scale dark matter searches, although we believe the spirit of the recommendations can apply more broadly to searches for other dark matter candidates, such as very light dark matter or axions. To translate experimental data into a final published result, direct detection collaborations must make a series of choices in their analysis, ranging from how to model astrophysical parameters to how to make statistical inferences based on observed data. While many collaborations follow a standard set of recommendations in some areas, for example the expected flux of dark matter particles (to a large degree based on a paper from Lewin and Smith in 1995), in other areas, particularly in statistical inference, they have taken different approaches, often from result to result by the same collaboration. We set out a number of recommendations on how to apply the now commonly used Profile Likelihood Ratio method to direct detection data. In addition, updated recommendations for the Standard Halo Model astrophysical parameters and relevant neutrino fluxes are provided. The authors of this note include members of the DAMIC, DarkSide, DARWIN, DEAP, LZ, NEWS-G, PandaX, PICO, SBC, SENSEI, SuperCDMS, and XENON collaborations, and these collaborations provided input to the recommendations laid out here. Wide-spread adoption of these recommendations will make it easier to compare and combine future dark matter results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A Statistical Evaluation of WRF-LES Trace Gas Dispersion Using Project Prairie Grass Measurements

In recent years, new measurement systems have been deployed to monitor and quantify methane emissions from the natural gas sector. Large-eddy simulation (LES) has complemented measurement campaigns by serving as a controlled environment in which to study plume dynamics and sampling strategies. However, with few comparisons with controlled-release experiments, the accuracy of LES for modeling natural gas emissions is poorly characterized. In this paper, we evaluate LES from the Weather Research and Forecasting (WRF) Model against Project Prairie Grass campaign measurements and surface layer similarity theory. Using WRF-LES, we simulate continuous emissions from 30 near-surface trace gas sources in two stability regimes: strong convection and weak convection. We examine the impact of grid resolutions ranging from 6.25 to 52 m in the horizontal dimension on model results. We evaluate performance in a statistical framework, calculating fractional bias and conducting Welch’s t tests. WRF-LES accurately simulates observed surface concentrations at 100 m and beyond under strong convection; simulated concentrations pass t tests in this region irrespective of grid resolution. However, in weakly convective conditions with strong winds, WRF-LES substantially overpredicts concentrations—the magnitude of fractional bias often exceeds 30%, and all but one t test fails. The good performance of WRF-LES under strong convection correlates with agreement with local free convection theory and a minimal amount of parameterized turbulent kinetic energy. The poor performance under weak convection corresponds to misalignment with Monin–Obukhov similarity theory and a significant amount of parameterized turbulent kinetic energy.

17 WIND ENERGY↗
DOE OSTI2025

Constraining primordial non-Gaussianity from the large scale structure two-point and three-point correlation functions

Surveys of cosmological large-scale structure (LSS) are sensitive to the presence of local primordial non-Gaussianity (PNG), and may be used to constrain models of inflation. Local PNG, characterized by f NL ⁠, the amplitude of the quadratic correction to the potential of a Gaussian random field, is traditionally measured from LSS two-point and three-point clustering via the power spectrum and bi-spectrum. We propose a framework to measure f NL using the configuration space two-point correlation function (2pcf) monopole and three-point correlation function (3pcf) monopole of survey tracers. Our model estimates the effect of the scale-dependent bias induced by the presence of PNG on the 2pcf and 3pcf from the clustering of simulated dark matter haloes. We describe how this effect may be scaled to an arbitrary tracer of the cosmological matter density. The 2pcf and 3pcf of this tracer are measured to constrain the value of f NL ⁠. In LSS surveys, the effect of imaging systematics on two-point statistics is often degenerate with the PNG signal. Our proposed model employs three-point statistics primarily to break this degeneracy. Using simulations of luminous red galaxies observed by the Dark Energy Spectroscopic Instrument (DESI), we demonstrate the accuracy and constraining power of our method. Our forecast indicates the ability to constrain f NL to a precision of σf NL ≈ 22 with one year of DESI survey data, as well as the ability to constrain the imaging systematic weights in situ.

early Universe↗
DOE OSTI2021

The first Hubble diagram and cosmological constraints using superluminous supernovae

In this work, we present the first Hubble diagram of superluminous supernovae (SLSNe) out to a redshift of two, together with constraints on the matter density, Ω M , and the dark energy equation-of-state parameter, w(≡p/ρ). We build a sample of 20 cosmologically useful SLSNe I based on light curve and spectroscopy quality cuts. We confirm the robustness of the peak–decline SLSN I standardization relation with a larger data set and improved fitting techniques than previous works. We then solve the SLSN model based on the above standardization via minimization of the χ2 computed from a covariance matrix that includes statistical and systematic uncertainties. For a spatially flat Λ cold dark matter (ΛCDM) cosmological model, we find $\Omega _{\rm M}=0.38^{+0.24}_{-0.19}$, with an rms of 0.27 mag for the residuals of the distance moduli. For a w 0 w a CDM cosmological model, the addition of SLSNe I to a ‘baseline’ measurement consisting of Planck temperature together with Type Ia supernovae, results in a small improvement in the constraints of w 0 and wa of 4 per cent. We present simulations of future surveys with 868 and 492 SLSNe I (depending on the configuration used) and show that such a sample can deliver cosmological constraints in a flat ΛCDM model with the same precision (considering only statistical uncertainties) as current surveys that use Type Ia supernovae, while providing a factor of 2–3 improvement in the precision of the constraints on the time variation of dark energy, w 0 and w a . This paper represents the proof of concept for superluminous supernova cosmology, and demonstrates they can provide an independent test of cosmology in the high-redshift (z > 1) universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Exploiting Machine Learning in Multiscale Modelling of Materials

Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and Engineering pose a formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed.

42 ENGINEERING↗
DOE OSTI2024

Nonparametric Multiparticle Set Methods for Interpreting Environmental Samples

Collection and analysis of environmental samples is commonly used by a range of stakeholders in nuclear safeguards and security contexts. While the ubiquity of samples and their transport in the environment allow regular collection, developing and demonstrating methods for analyzing these samples is difficult. In this work, an environmental sample consists of a set of one or more individual particles. Recent advances in reactor simulation have allowed us to generate data that are more representative of real-world environmental samples, enabling statistically defensible method development and testing. The most notable of these advances is a drastic increase in the number of material depletion regions, which allows our simulations to capture the variation in isotopic composition seen at length scales consistent with environmental samples. Traditional approaches for handling multiparticle samples treat each particle in the sample individually, estimating the quantity of interest (e.g., core-average burnup) resulting from measurement and analysis of signatures (e.g., nuclide assays) from each individual particle. Individual estimates are then averaged to generate a single estimate of the quantity of interest over the entire sample. In this presentation, we introduce two novel approaches for interpreting environmental samples that comprise of multiple particles: (1) the Quantile-Quantile Comparator, which uses a multivariate generalization of quantile-quantile plots for comparing unknown statistical distributions, and (2) the Set Transformer, an attention-based neural network module designed to model interactions among elements (particles) in the input set (sample). Statistically representative sampling cannot be guaranteed as samples are passively collected and are beholden to what particles are available in the environment. These new analysis methods for set-input problems are expected to be more robust than traditional approaches to issues of sampling bias where particles are not uniformly distributed throughout regions of interest, as well as generally outperform traditional approaches by jointly considering all elements in the set. We will present results comparing the performance of traditional single particle approaches and the novel Quantile-Quantile Comparator and Set Transformer for interpretation of simulated environmental samples.

Phathanapirom, Birdy↗
DOE OSTI2020

An Analysis of the Statistics and Systematics of Limb Anomaly Detections in HST/STIS Transit Images of Europa

Several recent studies derived the existence of plumes on Jupiter’s moon Europa. The only technique that provided multiple detections is the far-ultraviolet imaging observations of Europa in transit of Jupiter taken by the Space Telescope Imaging Spectrograph (STIS) on the Hubble Space Telescope (HST). In this study, we reanalyze the three HST/STIS transit images in which Sparks et al. identified limb anomalies as evidence for Europa’s plume activity. After reproducing the results of Sparks et al., we find that positive outliers are similarly present in the images as the negative outliers that were attributed to plume absorption. A physical explanation for the positive outliers is missing. We then investigate the systematic uncertainties and statistics in the images and identify two factors that are crucial when searching for anomalies around the limb. One factor is the alignment between the actual and assumed locations of Europa on the detector. A misalignment introduces distorted statistics, most strongly affecting the limb above the darker trailing hemisphere where the plumes were detected. The second factor is a discrepancy between the observation and the model used for comparison, adding uncertainty in the statistics. When accounting for these two factors, the limb minima (and maxima) are consistent with random statistical occurrence in a sample size given by the number of pixels in the analyzed limb region. The plume candidate features in the three analyzed images can be explained by purely statistical fluctuations and do not provide evidence for absorption by plumes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Quantitative and interpretable order parameters for phase transitions from persistent homology

Here, we apply modern methods in computational topology to the task of discovering and characterizing phase transitions. As illustrations, we apply our method to four two-dimensional lattice spin models: the Ising, square ice, XY, and fully frustrated XY models. In particular, we use persistent homology, which computes the births and deaths of individual topological features as a coarse-graining scale or sublevel threshold is increased, to summarize multiscale and high-point correlations in a spin configuration. We employ vector representations of this information called persistence images to formulate and perform the statistical task of distinguishing phases. For the models we consider, a simple logistic regression on these images is sufficient to identify the phase transition. Interpretable order parameters are then read from the weights of the regression. This method suffices to identify magnetization, frustration, and vortex-antivortex structure as relevant features for phase transitions in our models. We also define “persistence” critical exponents and study how they are related to those critical exponents usually considered.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Code for the manuscript "Lagrangian Large Eddy Simulations vis Physics-informed Machine Learning"

We would like to disclose a code based on Python/Pytorch, in which we implement the data-driven physics-informed machine learning of Lagrangian large eddy simulation that is described in the manuscript "Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning ". High-Reynolds number Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as Large Eddy Simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about the effects of sub-grid scales on the resolved scales. Here, we take an alternative approach and design novel LES heuristics stated in terms of Lagrangian particles moving with the flow. Our \emph{Lagrangian LES}, thus L-LES, is described by equations generalizing the weakly compressible Smoothed Particle Hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from Direct Numerical Simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The sub-grid scale contributions are modeled separately with physical constraints to account for the effects from un-resolved scales. We build the resulting model under the Differentiable Programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our Lagrangian LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

Tian, Yifeng↗
DOE OSTI2021

A Distinct Atmospheric Mode for California Precipitation

The hydrologic cycle in California is strongly influenced by wet-season (November–April) precipitation. Here, we demonstrate the existence of an influential mode of North Pacific atmospheric pressure variability that regulates wet-season precipitation variability over both northern and southern California. This mode, named as the “California precipitation mode” (CPM), is statistically distinct from other well-known modes of pressure variability such as the Pacific-North American pattern. In addition to controlling wet-season mean precipitation, positive days of the CPM coincide with up to 90% of the extreme (>99th percentile) precipitation days and 76% of detected atmospheric rivers (ARs) days, while the negative days correspond with 60% of the dry days. CMIP6 models capture the CPM remarkably well, including its statistical separation from the other well-known modes of pressure variability. The models also reproduce the CPM's strong association with California wet-season precipitation, giving confidence in the models’ dynamics relating to regional hydrologic extremes. However, the models also exhibit biases in regional hydrologic extremes. The CPM is a useful way to understand the origins of those biases and select the more credible models for further analysis: Models with unrealistically strong gradients in the CPM pressure pattern generally oversimulate larger wet extremes and produce excessively long dry intervals in the historical period. Thus the hydrologic biases can be traced to the particular aspects of North Pacific atmospheric dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗