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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Targeting tissues via dynamic human systems modeling in generative design

Drug discovery is a complex, costly process with high failure rates. A successful drug should bind to a target, be deliverable to an intended site of activity, and promote a desired pharmacological effect without causing toxicity. Typically, these factors are evaluated in series over the course of a pipeline where the number of candidates is sequentially whittled down from a very large initial pool. One promise of AI-driven discovery is the opportunity to evaluate multiple facets of drug performance in parallel. However, despite ML-driven advancements, current models for pharmacological property prediction are exclusively trained to predict molecular properties, ignoring important, dynamic biodistribution and bioactivity effects. Here, we present our progress towards incorporating quantitative systems physiology models into an AI-enabled drug design and molecular generation pipeline. Within a genetic algorithm, we include human-relevant physiologically based pharmacokinetic (PBPK) models. These PBPK models leverage properties that are predicted by a fine-tuned molecular language model. Together, these models will aid in capturing the mapping between molecules and therapeutic outcomes that is necessary to accelerate the drug discovery process.

Fox, Zach↗
NASA NTRS2018

2040 Vision: A Roadmap for Integrated, Multiscale Materials and System Modeling and Simulation

Over the last few decades, advances in high-performance computing, new materials characterization methods, and, more recently, an emphasis on integrated computational materials engineering (ICME) and additive manufacturing have been a catalyst for multiscale modeling and simulation-based design of materials and structures in the aerospace industry. While these advances have driven significant progress in the development of aerospace components and systems, that progress has been limited by persistent technology and infrastructure challenges that must be overcome to realize the full potential of integrated materials and systems design and simulation modeling throughout the supply chain. As a result, NASA's Transformational Tools and Technology (TTT) Project sponsored a study (performed by a team led by Pratt & Whitney) to define the potential 25-year future state required for integrated multiscale modeling of materials and systems (e.g., load-bearing structures) to accelerate the pace and reduce the expense of innovation in future aerospace and aeronautical systems. This talk will describe the findings of this 2040 Vision study (e.g., the 2040 vision state; the required interdependent core technical work areas, Key Element (KE); associated critical gaps and actions to close those gaps; and major recommendations). This roadmap is a community consensus document and is a result of over 450 professionals input obtain via: i) four society workshops (AIAA, NAFEMS, and two TMS), ii) community-wide survey, and iii) the establishment of 9 expert panels (one per key element) consisting on average 10 non-team members from academia, government and industry to review, update content, and prioritize gaps and actions. It is NASA's hope and desire that this roadmap provides the strategic guidance to both public and private research and development decision makers to make the proposed 2040 vision state a reality and thereby provide a significant advancement in the United States global competitiveness.

Multiscale Modeling↗
NASA NTRS2003

Systems Modeling of a Hypothetical SSME Channel-Wall Nozzle

A future upgrade to the Space Shuttle Main Engine (SSME) may be the replacement of the current regenerative cooled tube-wall nozzle with a nozzle using a regeneratively-cooled channel-wall design. The current tube-wall design represents the only major piece of SSME hardware that has not been dramatically updated throughout thc long history of the engine. There are a number of advantages to a channel-wall design including the promise of faster and lower cost fabrication and greater reliability in the field. The technical obstacles in the path of making this happen are many, particularly in the realms of metallurgy and manufacturing techniques. However, one technical area that can and should be addressed in the near term as part of the development of detailed component requirements is a systems type model of the fluid flow and heat transfer processes to which the new design will be exposed. This paper presents the results of an effort to develop a mathematical model of the internal flow for a generic channel-wall nozzle functioning as a direct replacement for the current tube-wall nozzle with a minimum of systems-level changes. Comparisons will be made to mathematical modeling results for the current tube-wall design and the results of various geometrical trade studies will be presented. It is the intent of this work to examine the feasibility of the concept of a direct replacement component with minimum systems-!eve impacts and to highlight potential areas of concern requiring further work in the future.

Greene, William D.↗
DOE OSTI2024

Uncertainty characterization in a coupled human-natural system: Modeling agricultural adaptation in the Great Lakes Region

The Great Lakes Region's water quality and ecological health are threatened by the export of nutrients from agricultural lands, which causes eutrophication, hypoxia, and destructive algal blooms. The intensification of hydrologic cycles brought about by climate change is expected to exacerbate nutrient loading in the region, and, at the same time, agricultural adaptation to changing conditions is also expected to affect loading through shifting amounts and timing of fertilization. Quantifying these future effects and their interactions necessitates modeling both the human and natural processes as a coupled system, by pairing land use and agricultural management with hydrologic modeling. At the same time, compounding uncertainties arising from the complex interactions in both systems significantly limit our predictive understanding of the region's impacts. This study utilizes the Soil and Water Assessment Tool (SWAT), developed for simulating the impact of various farmer decisions on watershed functions in Western Lake Erie watersheds, and an under-development agent-based model (ABM) for agricultural management decisions. The aim of this study is to use global sensitivity analysis on the coupled ABM and SWAT models to quantify how uncertainty in both models interactively affects nutrient loading. To do so, we will conduct Sobol sensitivity analysis experiments at different levels of coupling assumptions to quantify how various uncertain factors (e.g., soil moisture and crop choice) and their interactions affect our estimates of nutrient loading. The results of this analysis will allow us to quantify how complex interactions and dependencies between both systems amplify the effect of uncertainties. Insights gained from this study will have broader implications for modeling the adaptive co-evolution of human and natural systems under climate change and can inform effective management of nutrient loading in the Great Lakes Region.

Climate Change↗
DOE OSTI2020

Laser-system model for enhanced operational performance and flexibility on OMEGA EP

The development of laser performance models having real-time prediction capability for the OMEGA EP laser system has been essential in meeting requests from its user community for increasingly complex pulse shapes that span a wide range of energies. The laser operations model PSOPS provides rapid and accurate predictions of OMEGA EP laser-system performance in both forward and backward directions, a user-friendly interface and rapid optimization capability between shots. We describe the model’s features and show how PSOPS has allowed real-time optimization of the laser-system configuration in order to satisfy the demands of rapidly evolving experimental campaign needs. We also discuss several enhancements to laser-system performance accuracy and flexibility enabled by PSOPS.

42 ENGINEERING↗
DOE OSTI2022

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Decision support for United States—Canada energy integration is impaired by fragmentary environmental and electricity system modeling capacity

The renewable energy transition is leading to increased electricity trade between the United States and Canada, with Canadian hydropower providing firm lower-carbon power and buffering variability of wind and solar generation in the U.S. However, long-term power purchase agreements and transborder transmission projects are controversial, with two of four proposed transmission lines between Quebec, Canada and the northeast U.S. cancelled since 2018. Here, we argue that controversies are exacerbated by a lack of open-source data and tools to understand tradeoffs of new hydropower generation and transmission infrastructure in comparison to alternatives. This gap includes impacts that incremental transmission and generation projects have on the economics of the entire system, for example, how new transmission projects affect exports to existing markets or incentivize new generation. We identify priority areas for data synthesis and model development, such as integrating linked hydropower and hydrologic interactions in energy system models and openly releasing (by utilities) or back-calculating (by researchers) hydropower generation and operational parameters. Publicly available environmental (e.g. streamflow, precipitation) and techno-economic (e.g. costs, reservoir size,) data can be used to parameterize freely usable and extensible models. Existing models have been calibrated with operational data from Canadian utilities that are not publicly available, limiting the range of scientific and commercial questions these tools have been used to answer and the range of parties that have been involved. Studies conducted using highly resolved, national-scale public data exist in other countries, notably, the United States, and demonstrate how greater transparency and extensibility can drive industry action. Improved data availability in Canada could facilitate approaches that (1) increase participation in decarbonization planning by a broader range of actors; (2) allow independent characterizations of environmental, health, and economic outcomes of interest to the public; and (3) identify decarbonization pathways consistent with community values.

13 HYDRO ENERGY↗
NASA NTRS1981

Pointing system modeling for performance prediction

The paper considers the modeling and analysis of the JPL Precision Pointing and Tracking System, a gyro stabilized platform pointing system using optical target tracking. Sensor and actuator models are described which realistically represent actual hardware, and a practical controller and state estimator is developed through modern control techniques. The effect of the actuator nonlinearities on the system performance evaluation is discussed along with the modeling of the noise sources in the gyro. Finally, the limitations of implementing the control algorithms on the spacecraft computer, and the resulting tradeoffs are presented.

Boussalis, D.↗
DOE OSTI2021

Low global warming potential (GWP) refrigerant supermarket refrigeration system modeling and its application

As an environmentally friendly low global warming potential (GWP) refrigerant, Carbon dioxide (CO 2 ) has continuously gained popularity and research attention as alternative refrigerant for supermarket refrigeration system. In this paper, to fulfill the increasing need of accurate Low-GWP supermarket refrigeration models for development of supervisory level control and optimization strategies, a high fidelity model is developed for CO 2 transcritical supermarket refrigeration system which includes compressor rack of low temperature (LT) compressors and medium temperature (MT) compressors, air-cooled gas cooler, evaporator, expansion valves and other auxiliary equipment. A resistance-capacity model structure is proposed to simulate the display cases. Semi-thermodynamic models are proposed to estimate reciprocating compressors volumetric efficiency and power consumption. Furthermore the zone modeling approach is used for evaporator simulation, and air-cooled gas cooler is modeled with distributed modeling method. The expansion valve simulation is based on orifice flow model. To calibrate these models, both manufacture performance data and experimental data are used. The experiments are conducted with a full instrumental CO 2 supermarket refrigeration system installed in Oak Ridge National Lab Flexible Research Platform (FRP). The simulation model can predict the system performance, including power consumption, cooling capacity, mass flow rate, temperature, and pressure, with high accuracy (within ±4%) compared to experimental data. In addition, this developed model has been used to create the system optimum high side pressure for high side expansion valve control, and to generate wide operating range simulation data for developing the batter-equivalent commercial refrigeration system model which can be used in grid interactive control development.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

WinnowML: Stable feature selection for maximizing prediction accuracy of time-based system modeling

Online deep learning (ODL) has become an important methodology for modeling time-based performance of computer systems. An open problem is the intelligent selection of features from raw workload traces of computer systems. The best methods are overly sensitive to noisy data, causing frequent feature changes and re-training. Using all available features inflates training time and introduces model artifacts if some features should have been dropped. We present WinnowML, a method for automatically determining the most relevant feature subset for a predictive time-series model. WinnowML combines existing feature ranking algorithms and a history of each feature's ranking to iteratively rank a feature set to lower prediction error and maximize long term relevance. From this ranked feature set, the most relevant and stable subset is selected to train a model. Experimentally, we show how WinnowML can lower a model's mean absolute relative error up to 42% on average compared to the closest performing approach. Additionally, we lower the fluctuation in feature ranking and selection up to 65%. We also demonstrate how to combine WinnowML and a model search tool to provide improvements in performance of up to 14.5% when compared to using all the feature available.

Bel, Oceane MS↗
NASA NTRS2014

Full System Modeling and Validation of the Carbon Dioxide Removal Assembly

The Atmosphere Revitalization Recovery and Environmental Monitoring (ARREM) project was initiated in September of 2011 as part of the Advanced Exploration Systems (AES) program. Under the ARREM project, testing of sub-scale and full-scale systems has been combined with multiphysics computer simulations for evaluation and optimization of subsystem approaches. In particular, this paper describes the testing and modeling of various subsystems of the carbon dioxide removal assembly (CDRA). The goal is a full system predictive model of CDRA to guide system optimization and development. The development of the CO2 removal and associated air-drying subsystem hardware under the ARREM project is discussed in a companion paper.

Coker, Robert↗
DOE OSTI2021

The response of the Amazon ecosystem to the photosynthetically active radiation fields: integrating impacts of biomass burning aerosol and clouds in the NASA GEOS Earth system model

The Amazon experiences fires every year, and the resulting biomass burning aerosols, together with cloud particles, influence the penetration of sunlight through the atmosphere, increasing the ratio of diffuse to direct photosynthetically active radiation (PAR) reaching the vegetation canopy and thereby potentially increasing ecosystem productivity. In this study, we use the NASA Goddard Earth Observing System (GEOS) model with coupled aerosol, cloud, radiation, and ecosystem modules to investigate the impact of Amazon biomass burning aerosols on ecosystem productivity, as well as the role of the Amazon's clouds in tempering this impact. The study focuses on a 7-year period (2010–2016) during which the Amazon experienced a variety of dynamic environments (e.g., La Niña, normal years, and El Niño). The direct radiative impact of biomass burning aerosols on ecosystem productivity – called here the aerosol diffuse radiation fertilization effect – is found to increase Amazonian gross primary production (GPP) by 2.6 % via a 3.8 % increase in diffuse PAR (DFPAR) despite a 5.4 % decrease in direct PAR (DRPAR) on multiyear average during burning seasons. On a monthly basis, this increase in GPP can be as large as 9.9 % (occurring in August 2010). Consequently, the net primary production (NPP) in the Amazon is increased by 1.5 %, or ~92 Tg C yr -1 – equivalent to ~37 % of the average carbon lost due to Amazon fires over the 7 years considered. Clouds, however, strongly regulate the effectiveness of the aerosol diffuse radiation fertilization effect. The efficiency of this fertilization effect is the highest in cloud-free conditions and linearly decreases with increasing cloud amount until the cloud fraction reaches ~0.8, at which point the aerosol-influenced light changes from being a stimulator to an inhibitor of plant growth. Nevertheless, interannual changes in the overall strength of the aerosol diffuse radiation fertilization effect are primarily controlled by the large interannual changes in biomass burning aerosols rather than by changes in cloudiness during the studied period.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

The Response of the Amazon Ecosystem to the Photosynthetically Active Radiation Fields: Integrating Impacts of Biomass Burning Aerosol and Clouds in the NASA GEOS Earth System Model

The Amazon experiences fires every year, and the resulting biomass burning aerosols, together with cloud particles, influence the penetration of sunlight through the atmosphere, increasing the ratio of diffuse to direct photosynthetically active radiation (PAR) reaching the vegetation canopy and thereby potentially increasing ecosystem productivity. In this study, we use the NASA Goddard Earth Observing System (GEOS) model with coupled aerosol, cloud, radiation, and ecosystem modules to investigate the impact of Amazon biomass burning aerosols on ecosystem productivity, as well as the role of the Amazon’s clouds in tempering this impact. The study focuses on a seven-year period (2010-2016) during which the Amazon experienced a variety of dynamic environments (e.g., La Niña, normal years, and El Niño). The direct radiative impact of biomass burning aerosols on ecosystem productivity—called here the aerosol diffuse radiation fertilization effect —is found to increase Amazonian Gross Primary Production (GPP) by 2.6% via a 3.8% increase in diffuse PAR (DFPAR) despite a 5.4% decrease in direct PAR (DRPAR) on multiyear average during burning seasons. On a monthly basis, this increase in GPP can be as large as 9.9% (occurring in August 2010). Consequently, the net primary production (NPP) in Amazon is increased by 1.5%, or ~92 Tg C a-1– equivalent to ~37% of the average carbon lost due to Amazon fires over the seven years considered. Clouds, however, strongly regulate the effectiveness of the aerosol diffuse radiation fertilization effect. The efficiency of this fertilization effect is the highest in cloud-free conditions and linearly decreases with increasing cloud amount until the cloud fraction reaches ~0.8, at which point the aerosol-influenced light changes from being a stimulator to an inhibitor of plant growth. Nevertheless, interannual changes in the overall strength of the aerosol diffuse radiation fertilization effect are primarily controlled by the large interannual changes in biomass burning aerosols rather than by changes in cloudiness during the studied period.

biomass burning aerosols on ecosystem productivity↗
NASA NTRS2003

System Modeling and Diagnostics for Liquefying-Fuel Hybrid Rockets

A Hybrid Combustion Facility (HCF) was recently built at NASA Ames Research Center to study the combustion properties of a new fuel formulation that burns approximately three times faster than conventional hybrid fuels. Researchers at Ames working in the area of Integrated Vehicle Health Management recognized a good opportunity to apply IVHM techniques to a candidate technology for next generation launch systems. Five tools were selected to examine various IVHM techniques for the HCF. Three of the tools, TEAMS (Testability Engineering and Maintenance System), L2 (Livingstone2), and RODON, are model-based reasoning (or diagnostic) systems. Two other tools in this study, ICS (Interval Constraint Simulator) and IMS (Inductive Monitoring System) do not attempt to isolate the cause of the failure but may be used for fault detection. Models of varying scope and completeness were created, both qualitative and quantitative. In each of the models, the structure and behavior of the physical system are captured. In the qualitative models, the temporal aspects of the system behavior and the abstraction of sensor data are handled outside of the model and require the development of additional code. In the quantitative model, less extensive processing code is also necessary. Examples of fault diagnoses are given.

Poll, Scott↗
DOE OSTI2023

Criticality Accident Alarm System Modeling for the Uranium Processing Facility

The Criticality Accident Alarm System (CAAS) for the Uranium Processing Facility (UPF) provides coverage for two primary processing facilities. For UPF, detailed documentation and drawings of the design are readily available. While this design information allows for some CAAS modeling features to match the actual design with a high level of accuracy, assumptions and simplifications are necessary as it is unrealistic and unnecessary to replicate many aspects of the design. A set of modeling guidelines and techniques were used to develop consistency between the MCNP6 models. While ensuring conservatism, overly conservative choices must be avoided to demonstrate the minimum accident of concern can be detected. To ensure proper modeling choices were made for the final models, a group of parametric studies were performed to evaluate the impact on the dose rates detected at the individual detectors. Some of these modeling choices and/or associated parametric studies include: - Modeling the grid steel area above the detectors as homogeneous stainless steel and determining the impact on detectability with an increasing density. - Modeling the presence of equipment in close proximity to the CAAS detectors. - Determining an appropriate material density to represent miscellaneous items not specifically modeled. - Evaluating the impact of sprinkler activation. The results of UPF CAAS analyses show that the modeling assumptions made based on the detailed design and parametric studies are appropriate and demonstrate accurate yet conservative models have been developed for detecting minimum accident of concern within the UPF.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Final Report Collaborative Project: Improving the Representation of Coastal and Estuarine Processes in Earth System Models

This project aimed to improve long term global climate simulations by resolving and enhancing the representation of the processes involved in the cycling of freshwater through estuaries and coastal regions. This was a collaborative multi-institution project consisting of physical oceanographers, climate model developers, and computational scientists. It specifically targeted the DOE objectives of advancing simulation and predictive capability of climate models through improvements in resolution and physical process representation.

54 ENVIRONMENTAL SCIENCES↗