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DOE OSTI2020

Building fault detection data to aid diagnostic algorithm creation and performance testing

It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Improving the Performance of NEML2 with Modern Graph Compilation Backends

NEML2 vectorizes constitutive-model evaluation for large-scale multiphysics simulation, using PyTorch as its tensor backend so that a batch of material-point updates runs on CPU or GPU through a single implementation. In the two prior reports in this series it was a C++-native library, deployed through TorchScript tracing and just-in-time (JIT) compilation; it has since been rewritten from the ground up into a Python-native library deployed through Ahead-of-Time Inductor (AOTInductor), a modern PyTorch graph-compilation backend. The rewrite is driven by a persistent tension, not a language preference: NEML2 composes constitutive models at runtime from a registry of small, independently-authored pieces, and that flexibility is difficult to reconcile with the compile-time knowledge an efficient GPU kernel needs. This report documents the rewrite and the investment that accompanied it: the AOTInductor export pipeline that turns a Python-authored model into a portable, Python-free compiled artifact loadable from pure C++; the eager and compiled runtimes and the new implicit solver layer built on them; a head-to-head benchmark of legacy JIT against AOTInductor; the physics-model catalog and its worked examples; the developer tooling; and the corresponding overhaul of MOOSE’s NEML2 integration that lets MOOSE consume it. A central objective is to examine whether modern PyTorch graph-compilation backends are effective for MOOSE GPU integration. The benchmark answers directly: AOTInductor outperforms legacy JIT on every GPU scenario measured, by 1.0–4.5×. Modern graph-compilation backends are effective for MOOSE GPU integration, and AOTInductor specifically – not compilation in the abstract – is why.

Hu, Gary (Tianchen) [Argonne National Laboratory (↗

NASA NTRS1990

Considerations of persistence and security in CHOICES, an object-oriented operating system

The current design of the CHOICES persistent object implementation is summarized, and research in progress is outlined. CHOICES is implemented as an object-oriented system, and persistent objects appear to simplify and unify many functions of the system. It is demonstrated that persistent data can be accessed through an object-oriented file system model as efficiently as by an existing optimized commercial file system. The object-oriented file system can be specialized to provide an object store for persistent objects. The problems that arise in building an efficient persistent object scheme in a 32-bit virtual address space that only uses paging are described. Despite its limitations, the solution presented allows quite large numbers of objects to be active simultaneously, and permits sharing and efficient method calls.

Campbell, Roy H.↗

NASA NTRS2022

Assimilation of Remotely Sensed Leaf Area Index Enhances the Estimation of Anthropogenic Irrigation Water Use

Representation of irrigation in Earth System Models has advanced over the past decade, yet large uncertainties persist in the effective simulation of irrigation practices, particularly over locations where the on-ground practices and climate impacts are less reliably known. Here we investigate the utility of assimilating remotely sensed vegetation data for improving irrigation water use and associated fluxes within a land surface model. We show that assimilating optical sensor-based leaf area index estimates significantly improves the simulation of irrigation water use when compared to the USGS ground reports. For heavily irrigated areas, assimilation improves the evaporative fluxes and gross primary production (GPP) simulations, with the median correlation increasing by 0.1–1.1 and 0.3–0.6, respectively, as compared to the reference datasets. Further, bias improvements in the range of 14–35 mm mo^(−1) and 10–82 g m^(-2)mo^(−1) are obtained in evaporative fluxes and GPP as a result of incorporating vegetation constraints, respectively. These results demonstrate that the use of remotely sensed vegetation data is an effective, observation-informed, globally applicable approach for simulating irrigation and characterizing its impacts on water and carbon states.

Earth System Models↗

NASA NTRS1989

Urey prize lecture - Water on Mars

Taking the heat-transport physics of ice-covered lakes in the Dry Valleys of Antarctica as a model, it is presently suggested that liquid water lakes could have persisted for significant periods under protective ice covers in the Valles Marineris depressions of Mars. Calculations of ground ice thermodynamic stability in a Martian setting indicate that they may exist close to the surface at high latitudes, but are able to persist near the equator only at substantial depths. Such Martian landforms as terrain-softening are attributable to the creep of the Martian regolith under the influence of ground-ice deformation; FEM modeling of the flow process implies terrain-softening to be a near-surface phenomenon.

Squyres, Steven W.↗

DOE OSTI2024

Abiotic and biotic factors jointly influence the contact and environmental transmission of a generalist pathogen

The joint influence of abiotic and biotic factors is important for understanding the transmission of generalist pathogens. Abiotic factors such as temperature can directly influence pathogen persistence in the environment and will also affect biotic factors, such as host community composition and abundance. At intermediate spatial scales, the effects of temperature, community composition, and host abundance are expected to contribute to generalist pathogen transmission. We use a simple transmission model to explain and predict how host community composition, host abundance, and environmental pathogen persistence times can independently and jointly influence transmission. Our transmission model clarifies how abiotic and biotic factors can synergistically support the transmission of a pathogen. The empirical data show that high community competence, high abundance, and low temperatures correlate with high levels of transmission of ranavirus in larval amphibian communities. Discrete wetlands inhabited by larval amphibians in the presence of ranavirus provide a compelling case study comprising distinct host communities at a spatial scale anticipated to demonstrate abiotic and biotic influence on transmission. We use these host communities to observe phenomena demonstrated in our theoretical model. These findings emphasize the importance of considering both abiotic and biotic factors, and concomitant direct and indirect mechanisms, in the study of pathogen transmission and should extend to other generalist pathogens with the capacity for environmental transmission.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2012

A New Approach to Monitoring Coastal Marshes for Persistent Flooding

Many areas in coastal Louisiana are below sea level and protected from flooding by a system of natural and man-made levees. Flooding is common when the levees are overtopped by storm surge or rising rivers. Many levees in this region are further stressed by erosion and subsidence. The floodwaters can become constricted by levees and trapped, causing prolonged inundation. Vegetative communities in coastal regions, from fresh swamp forest to saline marsh, can be negatively affected by inundation and changes in salinity. As saltwater persists, it can have a toxic effect upon marsh vegetation causing die off and conversion to open water types, destroying valuable species habitats. The length of time the water persists and the average annual salinity are important variables in modeling habitat switching (cover type change). Marsh type habitat switching affects fish, shellfish, and wildlife inhabitants, and can affect the regional ecosystem and economy. There are numerous restoration and revitalization projects underway in the coastal region, and their effects on the entire ecosystem need to be understood. For these reasons, monitoring persistent saltwater intrusion and inundation is important. For this study, persistent flooding in Louisiana coastal marshes was mapped using MODIS (Moderate Resolution Imaging Spectroradiometer) time series of a Normalized Difference Water Index (NDWI). The time series data were derived for 2000 through 2009, including flooding due to Hurricane Rita in 2005 and Hurricane Ike in 2008. Using the NDWI, duration and extent of flooding can be inferred. The Time Series Product Tool (TSPT), developed at NASA SSC, is a suite of software developed in MATLAB(R) that enables improved-quality time series images to be computed using advanced temporal processing techniques. This software has been used to compute time series for monitoring temporal changes in environmental phenomena, (e.g. NDVI times series from MODIS), and was modified and used to compute the NDWI indices and also the Normalized Difference Soil Index (NDSI). Coastwide Reference Monitoring System (CRMS) water levels from various hydrologic monitoring stations and aerial photography were used to optimize thresholds for MODIS-derived time series of NDWI and to validate resulting flood maps. In most of the profiles produced for post-hurricane assessment, the increase in the NDWI index (from storm surge) is accompanied by a decrease in the vegetation index (NDVI) and then a period of declining water. The NDSI index represents non-green or dead vegetation and increases after the hurricane s destruction of the marsh vegetation. Behavior of these indices over time is indicative of which areas remain flooded, which areas recover to their former levels of vegetative vigor, and which areas are stressed or in transition. Tracking these indices over time shows the recovery rate of vegetation and the relative behavior to inundation persistence. The results from this study demonstrated that identification of persistent marsh flooding, utilizing the tools developed in this study, provided an approximate 70-80 percent accuracy rate when compared to the actual days flooded at the CRMS stations.

Kalcic, M. T.↗

DOE OSTI2021

Eco-evolutionary optimality as a means to improve vegetation and land-surface models

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. Furthermore, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1985

Persistence of shocks to large distances in the solar wind

Present hydrodynamic models of solar wind streams predict that interactions will cause interplanetary shocks to decay and large-scale structures to coalesce and smooth out, with a decay length of 10-15 AU for moderate or small-amplitude shocks. The Pioneer 10 plasma data, extending 1-30 AU, are examined in the light of such predictions. It is found that coalescence of streams into a single stream per solar rotation does occur, in general, but that considerable structure remains by 30 AU. The leading edge of a stream often exhibits a velocity jump of greater than 20 km/s; many of these may be shocks. There is a characteristic velocity-density-temperature signature of these distant streams which differs from the close-in double-shock signature. A unique transient was seen in July 1982, at 28 AU, with a velocity jump of about 235 km/s.

Kayser, S. E.↗

DOE OSTI2019

The quasi-equilibrium framework revisited: analyzing long-term CO 2 enrichment responses in plant–soil models

Abstract. Elevated carbon dioxide ( CO 2 ) can increase plant growth, but themagnitude of this CO 2 fertilization effect is modified by soilnutrient availability. Predicting how nutrient availability affects plantresponses to elevated CO 2 is a key consideration for ecosystemmodels, and many modeling groups have moved to, or are moving towards,incorporating nutrient limitation in their models. The choice of assumptionsto represent nutrient cycling processes has a major impact on modelpredictions, but it can be difficult to attribute outcomes to specificassumptions in complex ecosystem simulation models. Here we revisit thequasi-equilibrium analytical framework introduced by Comins andMcMurtrie (1993) and explore the consequences of specific model assumptionsfor ecosystem net primary productivity (NPP). We review the literature applying this framework to plant–soilmodels and then analyze the effect of several new assumptions on predictedplant responses to elevated CO 2 . Examination of alternativeassumptions for plant nitrogen uptake showed that a linear function of themineral nitrogen pool or a linear function of the mineral nitrogen pool withan additional saturating function of root biomass yield similar CO 2 responses at longer timescales ( >5 years), suggesting that the addedcomplexity may not be needed when these are the timescales of interest. Incontrast, a saturating function of the mineral nitrogen pool with lineardependency on root biomass yields no soil nutrient feedback on thevery-long-term ( >500 years), near-equilibrium timescale, meaning that oneshould expect the model to predict a full CO 2 fertilization effecton production. Secondly, we show that incorporating a priming effect on slowsoil organic matter decomposition attenuates the nutrient feedback effect onproduction, leading to a strong medium-term (5–50 years) CO 2 response. Models incorporating this priming effect should thus predict astrong and persistent CO 2 fertilization effect over time. Thirdly,we demonstrate that using a “potential NPP” approach to represent nutrientlimitation of growth yields a relatively small CO 2 fertilizationeffect across all timescales. Overall, our results highlight thefact that the quasi-equilibriumanalytical framework is effective for evaluating both the consequences andmechanisms through which different model assumptions affect predictions. Tohelp constrain predictions of the future terrestrial carbon sink, werecommend the use of this framework to analyze likely outcomes of new modelassumptions before introducing them to complex model structures.

58 GEOSCIENCES↗

DOE OSTI2014

Estimating the Impact of Vadose Zone Sources on Groundwater to Support Performance Assessment of Soil Vapor Extraction

A generalized conceptual model approach was developed that can be used to estimate the impact of volatile contaminant sources in the vadose zone on groundwater for sites where soil vapor extraction (SVE) operations have diminished source strength. The model has the potential to be broadly applicable for sites where vapor-phase transport dominates. The primary target for this conceptual model and related numerical modeling estimate of groundwater impact is for sites where contaminants have been removed from readily accessible portions of the subsurface, but where contaminants may persist in localized portions of the vadose zone. This paper describes the conceptual model, uses numerical simulations to evaluate the parameters controlling impact to groundwater, and presents estimated results for a range of input conditions. Over the implied ranges, the compliance well concentrations showed (inverse) proportionality with source concentration, Henry’s Law constant, and well screen length. An increase in site recharge caused a linear response in well concentration, with slopes dependent on the groundwater velocity. All other tested parameters resulted in nonlinear responses. The functional relationship between dimensional and transport parameters and resulting groundwater concentrations provide a basis for establishing a structured approach to evaluating the potential risk to groundwater posed by a vadose zone source. This type of evaluation is particularly important to sites where SVE has been applied and reduced contaminant concentrations, but has reached a condition of diminishing returns such that a site must consider whether continuation of SVE, remedy modifications, or closure is warranted.

remediation↗

DOE OSTI2021

Global Temperature Responses to Large Tropical Volcanic Eruptions in Paleo Data Assimilation Products and Climate Model Simulations Over the Last Millennium

Abstract Large volcanic eruptions are one of the dominant perturbations to global and regional atmospheric temperatures on timescales of years to decades. Discrepancies remain, however, in the estimated magnitude and persistence of the surface temperature cooling caused by volcanic eruptions, as characterized by paleoclimatic proxies and climate models. We investigate these discrepancies in the context of large tropical eruptions over the Last Millennium using two state‐of‐the‐art data assimilation products, the Paleo Hydrodynamics Data Assimilation product (PHYDA) and the Last Millennium Reanalysis (LMR), and simulations from the National Center for Atmospheric Research Community Earth System Model‐Last Millennium Ensemble (NCAR CESM‐LME). We find that PHYDA and LMR estimate mean global and hemispheric cooling that is similar in magnitude and persistence once effects from eruptions occurring in short succession are removed. The estimates also compare well to Northern‐Hemisphere reconstructions based solely or partially on tree‐ring density, which have been proposed as the most accurate proxy estimates of surface cooling due to volcanism. All proxy‐based estimates also agree well with the magnitude of the mean cooling simulated by the CESM‐LME. Differences remain, however, in the spatial patterns of the temperature responses in the PHYDA, LMR, and the CESM‐LME. The duration of cooling anomalies also persists for several years longer in the PHYDA and LMR relative to the CESM‐LME. Our results demonstrate progress in resolving discrepancies between proxy‐ and model‐based estimates of temperature responses to volcanism, but also indicate these estimates must be further reconciled to better characterize the risks of future volcanic eruptions.

Tejedor, E.↗

DOE OSTI2024

Controls and relationships of soil organic carbon abundance and persistence vary across pedo–climatic regions

One of the largest uncertainties in the terrestrial carbon cycle is the timing and magnitude of soil organic carbon (SOC) response to climate and vegetation change. This uncertainty prevents models from adequately capturing SOC dynamics and challenges the assessment of management and climate change effects on soils. Reducing these uncertainties requires simultaneous investigation of factors controlling the amount (SOC abundance) and duration (SOC persistence) of stored C. We present a global synthesis of SOC and radiocarbon profiles (n Profile = 597) to assess the timescales of SOC storage. We use a combination of statistical and depth–resolved compartment models to explore key factors controlling the relationships between SOC abundance and persistence across pedo–climatic regions and with soil depth. This allows us to better understand (i) how SOC abundance and persistence covary across pedo–climatic regions and (ii) how the depth dependence of SOC dynamics relates to climatic and mineralogical controls on SOC abundance and persistence. We show that SOC abundance and persistence are differently related; the controls on these relationships differ substantially between major pedo–climatic regions and soil depth. For example, large amounts of persistent SOC can reflect climatic constraints on soils (e.g., in tundra/polar regions) or mineral absorption, reflected in slower decomposition and vertical transport rates. In contrast, lower SOC abundance can be found with lower SOC persistence (e.g., in highly weathered tropical soils) or higher SOC persistence (e.g., in drier and less productive regions). We relate variable patterns of SOC abundance and persistence to differences in the processes constraining plant C input, microbial decomposition, vertical C transport and mineral SOC stabilization potential. This process–oriented grouping of SOC abundance and persistence provides a valuable benchmark for global C models, highlighting that pedo–climatic boundary conditions are crucial for predicting the effects of climate change and soil management on future C abundance and persistence.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Robustness of Meteorological Droughts in Dynamically Downscaled Climate Simulations

Abstract We examine the robustness of a suite of regional climate models (RCMs) in simulating meteorological droughts and associated metrics in present‐day climate (1971‐2003) over the conterminous United States (U.S.). The RCMs that are part of North American Regional Climate Change Assessment Program (NARCCAP) simulations are compared with multiple observations over the climatologically homogeneous regions of the U.S. The seasonal precipitation, climatology, drought attributes, and trends have been assessed. The reanalysis‐based multi‐model median RCM reasonably simulates observed statistical attributes of drought and the regional detail due to topographic forcing. However, models fail to simulate significant drying trend over the Southwest and West. Further, reanalysis‐based NARCCAP runs underestimate the observed drought frequency overall, with the exception of the Southwest; whereas they underestimate persistence in the drought‐affected areas over the Southwest and West‐North Central regions. However, global climate model‐driven NARCCAP ensembles tend to overestimate regional drought frequencies. Models exhibit considerable uncertainties while reproducing meteorological drought statistics, as evidenced by a general lack of agreement in the Hurst exponent, which in turn controls drought persistence. Water resources managers need to be aware of the limitations of current climate models, while regional climate modelers may want to fine‐tune their parameters to address impact‐relevant metrics.

Ganguli, Poulomi↗

NASA NTRSDate not supplied

Utilizing Advanced Air Mobility Rotorcraft Tools for Wildfire Applications

Over the past decade, due in large part to heavy investment in the field of Advanced Air Mobility (AAM), significant progress in rotorcraft-focused modeling tools has been made. Such progress has notably increased AAM rotorcraft modeling capabilities in the topics of conceptual design, preliminary design, and more recently flight dynamics. Yet, due to recent and persistent increases in extreme weather events, an emerging interest has been raised in utilizing such modeling capabilities for aiding in emergency relief efforts and other public good missions. This paper uses wildfire fighting as a representative public good mission and demonstrates the relevance of the NASA Revolutionary Vertical Lift Technology (RVLT) rotorcraft toolchain to such missions. An emphasis is placed on flight dynamics modeling and control because of the hazards and challenges associated with the atmospheric environment of wildfires. In this work, the NASA FlightCODE tool was used to analyze both a UH-60 and the NASA six-passenger quadcopter reference model hovering in an experimentally informed wildfire turbulent environment. Preliminary results of this study estimate actuator usage exceedances and disturbance rejection capabilities of the vehicles’ translational rate command systems. Leveraging the RVLT toolchain, refinement and expansion of this work could lead to handling qualities envelope estimation and design optimization for wildfire turbulent environments. This would provide pilots with additional information to make real-time decisions in high-risk scenarios and begins preparations for simulating these dangerous environments for pilot training and experimentation.

Rotorcraft↗

NASA NTRSDate not supplied

Utilizing Advanced Air Mobility Rotorcraft Tools for Wildfire Applications

Rotorcraft↗

DOE OSTI2023

Active Colloids as Models, Materials, and Machines

Active colloids use energy input at the particle level to propel persistent motion and direct dynamic assemblies. We consider three types of colloids animated by chemical reactions, time-varying magnetic fields, and electric currents. For each type, we review the basic propulsion mechanisms at the particle level and discuss their consequences for collective behaviors in particle ensembles. These microscopic systems provide useful experimental models of nonequilibrium many-body physics in which dissipative currents break time-reversal symmetry. Freed from the constraints of thermodynamic equilibrium, active colloids assemble to form materials that move, reconfigure, heal, and adapt. Colloidal machines based on engineered particles and their assemblies provide a basis for mobile robots with increasing levels of autonomy. This review provides a conceptual framework for understanding and applying active colloids to create material systems that mimic the functions of living matter. We highlight opportunities for chemical engineers to contribute to this growing field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗