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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions

The development of effective vaccines is crucial for combating current and emerging pathogens. Despite significant advances in the field of vaccine development there remain numerous challenges including the lack of standardized data reporting and curation practices, making it difficult to determine correlates of protection from experimental and clinical studies. Significant gaps in data and knowledge integration can hinder vaccine development which relies on a comprehensive understanding of the interplay between pathogens and the host immune system. In this review, we explore the current landscape of vaccine development, highlighting the computational challenges, limitations, and opportunities associated with integrating diverse data types for leveraging artificial intelligence (AI) and machine learning (ML) techniques in vaccine design. We discuss the role of natural language processing, semantic integration, and causal inference in extracting valuable insights from published literature and unstructured data sources, as well as the computational modeling of immune responses. Furthermore, we highlight specific challenges associated with uncertainty quantification in vaccine development and emphasize the importance of establishing standardized data formats and ontologies to facilitate the integration and analysis of heterogeneous data. Through data harmonization and integration, the development of safe and effective vaccines can be accelerated to improve public health outcomes. Looking to the future, we highlight the need for collaborative efforts among researchers, data scientists, and public health experts to realize the full potential of AI-assisted vaccine design and streamline the vaccine development process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

C-FOG: Life of Coastal Fog

C-FOG is a comprehensive bi-national project dealing with the formation, persistence, and dissipation (life cycle) of fog in coastal areas (coastal fog) controlled by land, marine, and atmospheric processes. Given its inherent complexity, coastal-fog literature has mainly focused on case studies, and there is a continuing need for research that integrates across processes (e.g., air–sea–land interactions, environmental flow, aerosol transport, and chemistry), dynamics (two-phase flow and turbulence), microphysics (nucleation, droplet characterization), and thermodynamics (heat transfer and phase changes) through field observations and modeling. Central to C-FOG was a field campaign in eastern Canada from 1 September to 8 October 2018, covering four land sites in Newfoundland and Nova Scotia and an adjacent coastal strip transected by the Research Vessel Hugh R. Sharp . An array of in situ, path-integrating, and remote sensing instruments gathered data across a swath of space–time scales relevant to fog life cycle. Satellite and reanalysis products, routine meteorological observations, numerical weather prediction model (WRF and COAMPS) outputs, large-eddy simulations, and phenomenological modeling underpin the interpretation of field observations in a multiscale and multiplatform framework that helps identify and remedy numerical model deficiencies. An overview of the C-FOG field campaign and some preliminary analysis/findings are presented in this paper.

58 GEOSCIENCES↗
NASA NTRS1989

Integrated multidisciplinary optimization of rotorcraft: A plan for development

This paper describes a joint NASA/Army initiative at the Langley Research Center to develop optimization procedures aimed at improving the rotor blade design process by integrating appropriate disciplines and accounting for important interactions among the disciplines. The paper describes the optimization formulation in terms of the objective function, design variables, and constraints. Additionally, some of the analysis aspects are discussed, validation strategies are described, and an initial attempt at defining the interdisciplinary couplings is summarized. At this writing, significant progress has been made, principally in the areas of single discipline optimization. Accomplishments are described in areas of rotor aerodynamic performance optimization for minimum hover horsepower, rotor dynamic optimization for vibration reduction, and rotor structural optimization for minimum weight.

Adelman, Howard M.↗
DOE OSTI2014

Evaluation of Carbon Dioxide Capture From Existing Coal Fired Plants by Hybrid Sorption Using Solid Sorbents

A novel hybrid solid sorbent technology for CO₂ capture and separation from coal combustion-derived flue gas was evaluated. The technology – Capture of CO₂ by Hybrid Sorption (CACHYS™) – is a solid sorbent technology based on the following ideas: 1) reduction of energy for sorbent regeneration, 2) utilization of novel process chemistry, 3) contactor conditions that minimize sorbent-CO₂ heat of reaction and promote fast CO₂ capture, and 4) low-cost method of heat management. This report provides key information developed during the course of the project that includes sorbent performance, energy for sorbent regeneration, physical properties of the sorbent, the integration of process components, sizing of equipment, and overall capital and operational cost of the integrated CACHYS™ system. Seven sorbent formulations were prepared and evaluated at the lab-scale for energy requirements and CO₂ capture performance. Sorbent heat of regeneration ranged from 30-80 kJ/mol CO₂ and was found to be dependent on process conditions. Two sorbent formulations (designated HCK-4 & HCK-7) were down-selected for additional fixed-bed testing. Additional testing involved subjecting the sorbents to 100 continuous cycles in the fixed-bed reactor to determine performance as a function of time. The working capacity achieved for HCK-4 sorbent ranged from 5.5-8.0 g CO₂/100 g sorbent, while the HCK-7 typically ranged from 8.0-10.0 g CO₂/100 g sorbent. Overall, there was no deterioration in capacity with continuous cycling for either sorbent. The CACHYS™ bench-scale testing system designed and fabricated under this award consists of a dual circulating fluidized-bed adsorber and a moving-bed regenerator. The system takes a flue gas slipstream from the University of North Dakota’s coal-fired steam plant. Prior to being sent to the adsorber, the flue gas is scrubbed to remove SO₂ and particulate. During parametric testing of the adsorber, CO₂ capture achieved using the 2-bed configuration with recirculation in both beds was 65-70% with a high flue gas CO₂ loading (~7%) and up to 85% with a low flue gas CO₂ loading (~4%). A sorbent regenerator system consisting of a pre-heater, desorber, and cooler is used to heat the CO₂-rich sorbent with direct and indirect steam producing a nearly 100% pure stream of CO₂. Parametric testing of the regenerator system demonstrated the impact of process conditions on both desorption rate and the heat of regeneration. Clear evidence of the use of specific process conditions that lower the overall energy of desorption was identified. This observation validates measurements made at the laboratory-scale. Several longer-term continuous tests were conducted to evaluate the performance of the sorbent/process as a function of time. Using a 2-bed configuration, sustained capture efficiency of 40-60% with a high flue gas CO₂ loading (~8%) and 70-80% with a low flue gas CO₂ loading (~4%) were achieved. However, sorbent working capacity was found to be considerably lower than laboratory-scale measurements. The low working capacity is attributed to insufficient sorbent/gas contact time in the adsorber. Sorbent properties that had a significant impact on CO₂ capture performance were identified. The results show that controlling these sorbent properties substantially improves CO₂ capture performance, with preliminary estimates indicating that relative improvement of ~30% is possible. Testing culminated with an operationally trouble-free test of 15 hours with sustainable performance. Overall, several practical strategies to increase performance of the sorbent and process were identified. The initial technical and economic assessment of the CACHYS™ process estimated the cost of CO2 capture was $36.19/ton with a 48.6% increase in levelized cost of electricity (LCOE) for the 550 MWe net plant. Using additional data gathered over the course of the project, and with revised technical and economic assumptions, the estimated cost of CO₂ capture with the CACHYS™ process is $39/ton (only includes the cost of the CO2 capture system) with an increase in LCOE of 55.9%. Overall, CACHYS™ represents a significant improvement over the benchmark MEA system, and has demonstrated progress towards achieving DOE’s goals for CO₂ capture technologies.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Chemistry↗
NASA NTRS1990

Eigensensitivity in integrated design

An application of eigensensitivity analysis to the control-structure integrated design process is presented with an emphasis placed on computational efficiency improvement of the overall design optimization process. The computational efficiency of eigenvalue/vector sensitivity analysis is demonstrated using the Earth Pointing Satellite in the context of a control-structure integrated design program. Results for a 2 percent design variable perturbation with and without the effects of the actuator mass show a 42 and 52 percent reduction in CPU time, respectively.

Kenny, Sean P.↗
NASA NTRS2000

An architecture for a brain-image database

The widespread availability of methods for noninvasive assessment of brain structure has enabled researchers to investigate neuroimaging correlates of normal aging, cerebrovascular disease, and other processes; we designate such studies as image-based clinical trials (IBCTs). We propose an architecture for a brain-image database, which integrates image processing and statistical operators, and thus supports the implementation and analysis of IBCTs. The implementation of this architecture is described and results from the analysis of image and clinical data from two IBCTs are presented. We expect that systems such as this will play a central role in the management and analysis of complex research data sets.

NASA Discipline Neuroscience↗
DOE OSTI2021

A hybrid data–model approach to map soil thickness in mountain hillslopes

Abstract. Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.

58 GEOSCIENCES↗
NASA NTRS1990

Status of integrated multidisciplinary rotorcraft optimization research at the Langley Research Center

This paper describes a joint NASA/Army research activity at the Langley Research Center to develop optimization procedures aimed at improving the rotor blade design process by integrating appropriate disciplines and accounting for important interactions among the disciplines. The activity is being guided by a Steering Committee made up of key NASA and Army researchers and managers. The paper describes the optimization formulation in terms of the objective function, design variables, and constraints. The analysis aspects are discussed, and the interdisciplinary interactions are defined in terms of the information that must be transferred among disciplinary analyses as well as the trade-offs between disciplines in determining the details of the design. At this writing, some significant progress has been made. Results given in the paper represent accomplishments in rotor aerodynamic performance optimization for minimum horsepower, rotor dynamic optimization for vibration reduction, approximate analysis of frequencies and mode shapes, rotor structural optimization for minimum weight, and integrated aerodynamic load/dynamics optimization for minimum vibration and weight.

Mantay, Wayne R.↗
DOE OSTI2025

Integrating Cyber-Informed Engineering into Process Automation

As organizations increasingly automate their core missions and essential functions to address business risks and enhance efficiency, process automation becomes pivotal. This shift, involving minimal or no manual intervention, significantly impacts an organization's cyber-risk landscape. While automation drives efficiencies, it also introduces new cyber risks if not properly managed. Cyber-Informed Engineering (CIE) provides a proactive framework for managing these digital risks, enhancing cyber-resilience in process automation. This document supports organizations in applying CIE principles to mitigate the cyber risks associated with automation. The outlined approach can be independently implemented to improve any organization’s cyber-resilience, ensuring that the advantages of automation do not result in unaddressed or unmanaged digital risks. It serves as a starting point, offering considerations for integrating CIE principles and practices into organizational processes. CIE is presented as an iterative process, fostering continuous improvement and reinforcing the engineering and operational cultures to manage digital risks effectively. The document is structured as follows: Section 1 provides background on CIE and process automation, and their integration. Section 2 explores the twelve CIE principles in the context of process automation, highlighting key questions, engineering considerations, and implications for digital risk management. Section 3 synthesizes the findings and offers recommendations to advance resilience by design.

42 - ENGINEERING↗
DOE OSTI2018

Development of a Pre-combustion CO 2 Capture Process Using High-Temperature PBI Hollow-Fiber Membranes

The overall goal of this research project was to develop a high-temperature, membrane-based CO 2 capture technology for coal gasification facilities and make progress toward meeting overall fossil energy performance goals of CO 2 capture with 95% CO 2 purity at a cost of electricity (COE) 30% less than baseline capture approaches. The primary objectives of the project were to evaluate, at a bench-scale size, a technically and economically viable CO 2 capture system based on a high-temperature polybenzimidazole (PBI) polymer hollow-fiber membrane (HFM) separation system, and to optimize the process for integration of that system into an integrated gasification combined cycle (IGCC) plant. The specific objectives of the proposed project are to: (1) fabricate high-performance PBI-HFMs for H 2 /CO 2 separation; (2) collect laboratory data for separating hydrogen from simulated synthesis gas using PBI-based membrane modules; (3) transfer the membrane fabrication technology to an industrial firm that specializes in the manufacture of HFMs; (4) fabricate membrane modules to process a syngas stream (50 kWth) equivalent to that of shifted gas from an oxygen-blown gasifier; (5) obtain design and steadystate performance data for membrane modules using syngas from an operating coal gasifier; (6) estimate the cost of CO 2 capture from pre-combustion gas streams integrated into a 550-MW (net) power plant; and (7) conduct a process environmental health & safety (EH&S) assessment.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Testing to Evaluate Processes Expected to Occur during MSR Salt Spill Accidents

Obtaining a license for a new nuclear reactor requires the identification and assessment of the potential consequences of specified accident scenarios, which are achieved using accident progression models. Those models need to be parameterized and validated using experimental data, but existing experimental data addressing processes relevant to molten salt reactor accidents are sparse. Specifically, experimental data that quantify the sensitives of important processes to the initial conditions of the spill, the ambient environment, and the containment features are needed to parameterize individual process models. Integrated experiments that simulate accident scenarios are also needed to provide data for model validation, but these experiments will require the use of proven methods to quantify the processes under evaluation. The overarching objectives of this work are to develop the methods for simulating the targeted processes, to determine the effectiveness of the methods in producing the data required for model development, to generate data that can be used to parameterize individual process models, and to provide key insights into the behavior of spilled molten salt that should be considered in models. Experimental methods were designed to quantify aspects of individual processes expected to occur during or after a molten salt spill accident that will affect the fate of spilled molten salt and the radionuclides within. These processes include 1) molten salt spreading and heat transfer, 2) molten salt flowing and freezing in tubing, 3) stainless steel corrosion kinetics in molten salt, and 4) molten salt splashing and aerosol generation. The initial tests described in this report were conducted using eutectic FLiNaK to demonstrate the test methods, the data that are generated, and the analyses of the data to derive values needed for modeling. The primary variables that were tested include initial salt temperature and the presence of volatile surrogate fission products (e.g., cesium and iodine). The developed methods are shown to be effective in quantifying the desired processes and can be applied to study more complex salt compositions of interest to molten salt reactor developers, a wide range of environmental conditions of interest to modelers, and additional variables relevant to salt spill accidents. The developed methods and insights gained from laboratory tests can also be incorporated in future large-scale integrated tests used to simulate molten salt spill accidents.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1993

Engine management during NTRE start up

The topics are presented in viewgraph form and include the following: total engine system management critical to successful nuclear thermal rocket engine (NTRE) start up; NERVA type engine start windows; reactor power control; heterogeneous reactor cooling; propellant feed system dynamics; integrated NTRE start sequence; moderator cooling loop and efficient NTRE starting; analytical simulation and low risk engine development; accurate simulation through dynamic coupling of physical processes; and integrated NTRE and mission performance.

Bulman, Mel↗
NASA NTRS2004

Security Risks: Management and Mitigation in the Software Life Cycle

A formal approach to managing and mitigating security risks in the software life cycle is requisite to developing software that has a higher degree of assurance that it is free of security defects which pose risk to the computing environment and the organization. Due to its criticality, security should be integrated as a formal approach in the software life cycle. Both a software security checklist and assessment tools should be incorporated into this life cycle process and integrated with a security risk assessment and mitigation tool. The current research at JPL addresses these areas through the development of a Sotfware Security Assessment Instrument (SSAI) and integrating it with a Defect Detection and Prevention (DDP) risk management tool.

securiy↗