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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Leveraging Prior Concept Learning Improves Generalization From Few Examples in Computational Models of Human Object Recognition

Humans quickly and accurately learn new visual concepts from sparse data, sometimes just a single example. The impressive performance of artificial neural networks which hierarchically pool afferents across scales and positions suggests that the hierarchical organization of the human visual system is critical to its accuracy. These approaches, however, require magnitudes of order more examples than human learners. We used a benchmark deep learning model to show that the hierarchy can also be leveraged to vastly improve the speed of learning. We specifically show how previously learned but broadly tuned conceptual representations can be used to learn visual concepts from as few as two positive examples; reusing visual representations from earlier in the visual hierarchy, as in prior approaches, requires significantly more examples to perform comparably. These results suggest techniques for learning even more efficiently and provide a biologically plausible way to learn new visual concepts from few examples.

Rule, Joshua S.↗
DOE OSTI2021

CATMoS: Collaborative Acute Toxicity Modeling Suite

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. Objectives: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different endpoints: LD50 value, U.S. Environmental Protection Agency hazard categories, Globally Harmonized System for Classification and Labelling hazard categories, very toxic chemicals (LD50 =50 mg/kg), and non-toxic chemicals (LD50 >2000 mg/kg). Methods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared to in vivo results. Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for over 800,000 chemicals have been made available via the NTP’s Integrated Chemical Environment. The models are also implemented in a free, standalone open-source tool, OPERA, which allows predictions of new and untested chemicals to be made.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

A new large-scale suspended sediment model and its application over the United States

Abstract. Suspended sediment plays a vital role in the regional and global cycling of carbon and nutrients by carrying carbon and nutrients from headwaters into lowland rivers and the oceans. Sediment transport through river systems is often fundamentally modified by human activities such as reservoir management. However, a physically based representation of sediment transport is still missing in most existing Earth system models (ESMs), which are essential tools for modeling and predicting Earth system changes. Here, we introduce a multi-process river sediment module for ESMs, which includes (1) hillslope soil erosion and sediment discharge into streams, (2) sediment transport processes through river networks, (3) reservoir operation based on the inflows from upstream areas and water demand from downstream areas, and (4) sediment trapping by reservoirs. All model parameters are estimated a priori without calibration. We apply this new sediment modeling framework to the contiguous United States and validate it against historical observations of monthly streamflow and sediment discharges at 35 river gauges. The model reasonably well captures the long-term balance and seasonal variations of suspended sediment in large river systems. Furthermore, our model results show that suspended sediment discharge in managed rivers is affected more by reservoirs' direct trapping of sediment particles than by their flow regulation. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. Here, the associated notebooks allow to reproduce the reported analyses and apply them to other systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Learning from learning machines: improving the predictive power of energy-water-land nexus models with insights from complex measured and simulated data

Focal Area(s): Insights gleaned from complex data (observed and simulated) using AI; Predictive modeling through the use of AI techniques and AI-derived model components, including physics- and knowledge-informed models; Energy-water-land nexus and integrated energy systems – models of MultiSector dynamics. Science Challenge: Scientific communities are in need of tools for the computational integration of physics-based models, experimental data, and empirical/observational studies across a broad range of temporal and spatial scales to explore and meet EESSD grand challenges. We require AI algorithms for the discovery of process-drivers in the Earth-energy-human system and to link them with complex measured and simulated data. We aim to understand the energy-water-land nexus, particularly under extreme forcing scenarios and rare events, leveraging scale-aware AI process models, including probabilistic uncertainties, and benefiting from the emerging 5G-enabled landscape-scale sensing and edge computing capabilities. These models are needed to identify instabilities and tipping points that manifest extreme system behaviors with consequences for integrated energy systems and the environment. This work requires fundamental advances in uncertainty quantification, in particular, to identify and model unlikely but catastrophic outliers. Specifically, we need models that are interpretable to domain scientists and, essentially, explainable to public and private stake holders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A framework to implement human reliability analysis during early design stages of advanced reactors

Nuclear power plants require human actions throughout their lifecycle from design, construction, operation, and decommissioning. However, for advanced reactors (e.g., Generation IV), the reliance on human intervention in safety-related actions is expected to be reduced or completely replaced by automated actions. The Probabilistic Risk Assessment (PRA) Standard for Advanced Non-LWR Nuclear Power Plants requires that the impacts of all operator actions are captured and incorporated in the risk of the modeled plant. Moreover, the Modernization of Technical Requirements for Licensing Advanced Reactors requires human reliability analysis (HRA) to be included throughout all design and PRA development stages. However, due to the lack of details during the early design stages, HRA is often postponed until the design is mature enough. Conducting HRA in later design stages, though it may be adequate in capturing pre-, at-, and post-initiators comes short of informing the design itself in the iterative design lifecycle. Hence, this paper presents a framework to include HRA during the design's early stages, pre-conceptual or conceptual. The proposed framework provides a process for the removal of operator actions that do not contribute to the risk and the identification of all key operator actions that are critical to the safety of the design. The results of this framework are then used to inform the design of those safety-related operator actions to update the design further. Then, using information from the updated design, this framework can be reapplied to investigate the impact of the design update on human reliability. The PRA model of the X-energy's pre-conceptual Xe-100 high-temperature gas-cooled pebble-bed reactor (HTGR-PB) design is used to demonstrate the approach. In the pre-conceptual Xe-100 PRA model, also called Phase 0 PRA model, human actions were considered an integral part of analyzing the plant response to different initiating events. Hence, in this paper, all possible human actions in the Xe-100 PRA model are identified, analyzed, and removed to emulate a design relying only on the available automated control systems. The preliminary results of this assessment show how safe the Xe-100 design is even without crediting any human actions. The results also list necessary sequences in which operator actions are critical to the risk profile of the design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Modeling the Economic and Environmental Impacts of Land Scarcity Under Deep Uncertainty

Abstract Land scarcity is increasing over time, driven by complex multisector dynamics. The impacts of land scarcity on the economy and environment are multi‐faceted and regional, so any action to convert land will contain inherent tradeoffs. These impacts are complicated by the deeply uncertain evolution of the various sectors influencing land scarcity. A need therefore exists to provide multi‐metric and multi‐sector assessments that are robust to myriad uncertainties. Land conservation effectively limits the supply of productive land, while biofuel consumption increases the demand and competition for that land, and how these dynamics individually and jointly propagate to economic and environmental impacts is an important open question. To address this, we adopt the Global Change Analysis Model (GCAM) that has representations of various important systems including the climate, macroeconomic, energy, agriculture and land, and water resources systems. Various scenarios of increased land demand (from biofuels) and decreased land supply (from conservation) under various socioeconomic scenarios drawn from the SSPs were simulated using GCAM. We find that while biofuel consumption and land conservation reduce carbon emissions, this comes at the cost of higher food prices, reduced crop production, and increased water withdrawals. Additionally, some regions experience these tradeoffs more severely than others and are more heavily impacted from the same biofuel mandate or by an additional percent of protected land. These and other findings highlight the importance of multisector modeling frameworks that capture many cross‐sector linkages, and acknowledge the important uncertainties confronting the human‐Earth system when making any analysis of land scarcity impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

An antibody-free platform for multiplexed, sensitive quantification of protein biomarkers in complex biomatrices

Sensitive, multiplexed protein quantification remains challenging despite recent advancements in LC-MS assays for targeted protein biomarker quantification. High-sensitivity protein biomarker measurements usually require immuno-affinity enrichment of target protein; a process which is highly dependent on capture reagent and limited in capability to measure multiple analytes. Herein, we report a novel antibody-free platform, which measures multiple biomarkers from complex matrices employing a strategically optimized solid-phase extraction cleanup and orthogonal multidimensional LC-MS. Eight human protein biomarkers with different specifications were spiked into canine plasma as a model investigation system. The developed strategy achieved the desired sensitivity, robustness, and throughput via the following steps: (1) post digestion mixed-mode cation exchange-reverse phase SPE enrichment cleaned up the sample initially; (2) rapid, high-pH peptide fractionation further eliminated background components efficiently while selectively enriched signature peptides (SP) to provide sufficient sensitivity for multiple targets; and (3) trapping-micro-LC-MS analysis delivered high sensitivity comparable to a nano-LC-MS method but with much better robustness and throughput for the final analysis. Compared with a conventional LC-MS assay with direct protein digestion and limited clean-up, analysis with this antibody-free platform improved the LLOQ by 1–2 orders of magnitude for the eight protein biomarkers, reaching as low as 5 ng/mL in plasma, with feasible robustness and throughput. In conclusion, this platform was applied for the quantification of biomarkers of respiratory conditions in patients with various lung diseases, demonstrating real-world applicability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Industrial emission inventories of iron and phosphorus for past, present and future

Global emission inventories of aerosol iron by species, and of phosphorus, were developed for inclusion in Earth system modeling that includes atmospheric transport. The time period covered was 1850-2010 to evaluate human influence. A variety of approaches to compare measured and modeled values of aerosol iron was synthesized to estimate how well anthropogenic emissions are known. Anthropogenic emissions are constrained within a factor of two and if there is a bias, it is an overestimation. The exception is shipping emissions, which may be underestimated. Direct radiative forcing by iron aerosol is small in the global average (0.02 W m -2 ) but reaches 0.25 W m -2 around source regions. Regions of high direct radiative forcing are poorly constrained by observations. The central value of change in oceanic net primary productivity due to iron deposition is about 0.26 PgC yr -1 , also highly localized. This leads to a decrease in atmospheric CO2 accumulation that corresponds to a forcing of 0.008 W m -2 since 1850 (0.003-0.18 W m -2 ). Emissions of particulate matter to 2050 were estimated in response to different policy scenarios, including the need for renewable energy installations under decarbonization scenarios. Under the business-as-usual scenario, the total metal demand is 195 Mt/yr in 2020, peaking at 270 Mt/yr in years 2040-2045; under the Rapid Decarbonization scenario, total metal demand is nearly doubled at its peak: 480 Mt/yr in year 2040. Primary particulate matter emissions attributable to processing metals for renewable energy are also amplified under rapid decarbonization, but total atmospheric emissions are much lower because of the shift away from combustion.

58 GEOSCIENCES↗
DOE OSTI2024

ChatGPT and Other Large Language Models for Cybersecurity of Smart Grid Applications

Cybersecurity breaches targeting electrical substations constitute a significant threat to the integrity of the power grid, necessitating comprehensive defense and mitigation strategies. Any anomaly in information and communication technology (ICT) should be detected for secure communications between devices in digital substations. This paper proposes large language models (LLMs), e.g., ChatGPT, for the cybersecurity of IEC 61850-based communications. Multi-cast messages such as generic object oriented system events (GOOSE) and sampled values (SV) are used for case studies. The proposed LLM-based cybersecurity framework includes, for the first time, data pre-processing of communication systems and human-in-the-loop (HITL) training (considering the cybersecurity guidelines recommended by humans). The results show a comparative analysis of detected anomaly data carried out based on the performance evaluation metrics for different LLMs. A hardware-in-the-loop (HIL) testbed is used to generate and extract a dataset of IEC 61850 communications.

ChatGPT↗
DOE OSTI2024

ML-based calibration and control of the GlueX Central Drift Chamber

The GlueX Central Drift Chamber (CDC) in Hall D at Jefferson Lab, used for detecting and tracking charged particles, is calibrated and controlled during data taking using a Gaussian process. The system dynamically adjusts the high voltage applied to the anode wires inside the chamber in response to changing environmental and experimental conditions such that the gain is stabilized. Control policies have been established to manage the CDC's behavior. These policies are activated when the model's uncertainty exceeds a configurable threshold or during human-initiated tests during normal production running. Finally, we demonstrate the system reduces the time detector experts dedicate to calibration of the data offline, leading to a marked decrease in computing resource usage without compromising detector performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

A prototype cooling blanket for mitigating occupant overheating risk in a hot indoor environment: Modeling and assessments

Conventional ways of cooling a room or an entire house for occupant thermal comfort during summer consume a significant amount of energy and are vulnerable to overheating risk during power outages that lead to loss of cooling system operations. This study investigates a low-power cooling blanket, as a Personal Cooling System (PCS), that covers the upper human body for direct cooling during a five-day heat wave in a single-family house. A modeling framework is developed for evaluating the thermal and energy performance of the cooling blanket, which builds upon the co-simulation of three models: a house energy model, a personal thermal comfort model, and a cooling blanket model. Simulation results show that under the power outage scenario, the cooling blanket can greatly reduce the occupant heat stress with a reduction of daily hours of exceedance (discomfort hours defined as TSV>2) by up to 17.2 h (a 95.3 % improvement from the baseline power outage without the blanket). The cooling blanket, equipped with an innovative electrocaloric heat pump (COP as high as 10.1) consumes 6.31 W and can be operated by a portable battery for several days. The cooling blanket consumes only 0.28 % of the electricity of a central air-conditioning system running to provide cooling for the whole house during the five-day heatwave period. The findings justify further research of electrocaloric wearable PCS as low-power effective cooling to ensure thermal survivability of occupants during extreme indoor environments.

Electrocaloric heat pump↗
DOE OSTI2024

Subject-specific multi-scale modeling of the fate of inhaled aerosols

Determining the fate of inhaled aerosols in the respiratory system is essential in assessing the potential toxicity of inhaled airborne materials, responses to airborne pathogens, or in improving inhaled drug delivery. The availability of high-resolution clinical lung imaging and advances in the reconstruction of lung airways from CT images have led to the development of subject-specific in-silico 3D models of aerosol dosimetry, often referred to as computational fluid-particle-dynamics (CFPD) models. As CFPD models require extensive computing resources, they are typically confined to the upper and large airways. These models can be combined with lower-dimensional models to form multiscale models that predict the transport and deposition of inhaled aerosols in the entire respiratory tract. Understanding where aerosols deposit is only the first of potentially several key events necessary to predict an outcome, being a detrimental health effect or a therapeutic response. To that end, multiscale approaches that combine CFPD with physiologically-based pharmacokinetics (PBPK) models have been developed to evaluate the absorption, distribution, metabolism, and excretion (ADME) of toxic or medicinal chemicals in one or more compartments of the human body. CFPD models can also be combined with host cell dynamics (HCD) models to assess regional immune system responses. Here, this paper reviews the state of the art of these different multiscale approaches and discusses the potential role of personalized or subject-specific modeling in respiratory health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

The Implications of Global Change for the Co-Evolution of Argentina's Integrated Energy-Water-Land Systems

This study seeks to understand how Argentina’s energy, water, and land (EWL) systems will co-evolve under a representative array of human and earth system influences, including socioeconomic change, climate change, and climate policy. To capture Argentina’s sub-national EWL dynamics in the context of global change, we couple the Global Change Analysis Model with a suite of consistent, gridded sectoral downscaling models to explore multiple stakeholder-engaged scenarios. Across scenarios, Argentina has the economic opportunity to use its vast land resources to satisfy growing domestic and international demand for crops, such as oil (e.g., soy) and biomass. The human (rather than earth) system produces the most dominant changes in mid-century EWL resource use. A Reference scenario characterized by modest socioeconomic growth projects a 40% increase in Argentina’s agricultural production by 2050 (relative to 2020) by using 50,000 km 2 of additional cropland and 40% more water. A Climate Policy scenario designed to achieve net-zero carbon emissions globally shortly after mid-century projects that Argentina could use 100,000 km 2 of additional land (and 65% more water) to grow biomass and other crops. The burden of navigating these national opportunities and challenges could fall disproportionately on a subset of Argentina’s river basins. The Colorado and Negro basins could experience moderate-to-severe water scarcity as they simultaneously navigate substantial irrigated crop demand growth and climate-induced declines in natural water availability. Argentina serves as a generalizable testbed to demonstrate that multi-scale EWL planning challenges can be identified and managed more effectively via integrated analysis of coupled human-earth systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Formal verification and validation of run-to-completion style state charts using Event-B

State chart notations with ‘run to completion’ semantics are popular with engineers for designing controllers that react to environment events with a sequence of state transitions but lack formal refinement and rigorous verification methods. State chart models are typically used to design complex control systems that respond to environmental triggers with a sequential process. The model is usually constructed at a concrete level and verified and validated using animation techniques relying on human judgement. Event-B, on the other hand, is based on refinement from an initial abstraction and is designed to make formal verification by automatic theorem provers feasible. Abstraction and formal verification provide greater assurance that critical (e.g. safety or security) properties are not violated by the control system. In this paper, we introduce a notion of refinement into a ‘run to completion’ state chart modelling notation and leverage Event-B’s tool support for theorem proving. We describe the difficulties in translating ‘run to completion’ semantics into Event-B refinements and suggest a solution. We illustrate our approach and show how models can be validated at different refinement levels using our scenario checker animation tools. We show how critical invariant properties can be verified by proof despite the reactive nature of the system and how behavioural aspects of the system can be verified by testing the expected reactions using a temporal logic, model checking approach. To verify liveness, we outline a proof that the run to completion is deadlock-free and converges to complete the run.

97 MATHEMATICS AND COMPUTING↗