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

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At least 1,207 records · Page 67

All sources NASA NTRS DOE OSTI

NASA NTRS1990

Integrated controls-structures optimization of a large space structure

A technique for the simultaneous optimization of structural and control elements of a large space structure is developed and demonstrated for a test problem, the NASA COFS-I Mast Flight System. General-purpose control and structural-analysis codes are applied directly to a large detailed model, with realistic objective and constraint functions. The steps in the process (structural optimization, control optimization, and system coordination) are described and illustrated with diagrams; the numerical implementation (using different computers for different steps) is discussed; and results showing significant design improvements in three COFS-I configurations are presented in graphs. When the weights of both structure and power-generating equipment are taken into account, a 40-bay truss design is found to be better than designs with 42 or 44 bays.

Padula, Sharon L.↗

DOE OSTI2020

Process scale-up and optimization of the metal-organic framework synthesis

Mosaic Materials is incubated under Cyclotron Road LBNL, focusing on design, synthesis, and characterization of metal-organic frameworks as highly selective and energy efficient adsorbents for carbon dioxide. The Chief Science Officer, Dr. Thomas McDonald, invented a new class of phase-change adsorbents for acid gas removal that the company is working to commercialize as its first product. ABPD worked with Mosaic team to scale up their synthesis process and evaluate the downstream processing.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Mechanistic Interpretations and Insights for the Oxidative Dehydrogenation of Propane via CO 2 over Cr 2 O 3 /Al 2 O 3 Catalysts

Oxidative dehydrogenation (ODH) of alkanes using carbon dioxide as a soft oxidant has recently emerged as a potentially attractive alternative to steam cracking for the production of light olefins. To elucidate reaction pathways and their dependence on the operating conditions, CO 2 -assisted propane dehydrogenation over a redox-active Cr 2 O 3 /Al 2 O 3 catalyst was examined in a packed bed reactor as a function of temperature, Cr 2 O 3 /CO 2 feed ratio, and residence time. Previous ODH studies have largely focused on CO 2 -rich conditions with the aim of preventing coke formation. However, at T = 600 °C the present study finds that the use of propane-rich conditions (1 ≤ C 3 H 8 /CO 2 ≤ 2.5) maximizes propylene production and selectivity while maintaining catalyst stability. In this work, it is postulated that the selective Mars van Krevelen dehydrogenation process is optimized at these ratios. Excess CO 2 apparently promotes nonselective dehydrogenation and dry reforming pathways that generate additional CO, adversely impacting catalyst stability via the Bouduard reaction. This hypothesis is supported by complementary investigations of the reverse water gas shift reaction and thermodynamic analysis. The findings and methodology presented here are likely applicable to related ODH processes with other alkanes and redox-active catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2014

National Space Biomedical Research Institute (NSBRI) JSC Summer Projects

This project optimized the calorie content in a breakfast meal replacement bar for the Advanced Food Technology group. Use of multivariable optimization yielded the highest weight savings possible while simultaneously matching NASA Human Standards nutritional guidelines. The scope of this research included the study of shelf-life indicators such as water activity, moisture content, and texture analysis. Key metrics indicate higher protein content, higher caloric density, and greater mass savings as a result of the reformulation process. The optimization performed for this study demonstrated wide application to other food bars in the Advanced Food Technology portfolio. Recommendations for future work include shelf life studies on bar hardening and overall acceptability data over increased time frames and temperature fluctuation scenarios.

Dowdy, Forrest Ryan↗

DOE OSTI2026

Bench-Scale Development of Promoted High-Capacity Structured Sorbents (Final Technical Report)

The project objective was to develop high-capacity structured sorbent capable of achieving low CO 2 removal from air. The sorbent framework consists of an amine-functionalized onto hydrophobic polymer backbone with an added promoter. The functionalized amine provides high CO 2 capacity and adsorption rates and the polymer backbone to reduce water uptake. For the sorbent development, multiple functionalized amines and promoters were assessed to select a candidate that achieved high CO 2 capacity, high adsorption rate, and high stability. A sorbent-coated filter design was selected as the structured sorbent, which provides high sorbent loading capacity and contains an electrically conductive nonwoven filter substrate that can be Joule-heated to provide efficient utilization of available electricity for sorbent regeneration. A commercial partner operated a pilot filter manufacturing line to produce the filter panels coated with the developed sorbent. A 1 kg CO 2 /day bench unit was designed and fabricated to test the structured filter sorbent. The key achievements from the structured sorbent development activity were demonstrating that existing filter industrial-scale processes can be used for manufacturing Susteon’s structured sorbent and completing a proof-of-concept demonstration of the commercially manufactured structured sorbent filters for CO 2 capture from air with direct, Joule-heated regeneration. A techno-economic assessment with sensitivity analysis was conducted on a 100,000 TPY facility with 85% operating capacity. Through sorbent optimization and process design improvements, it is estimated that the cost of capture was reduced from $\$$349/tCO 2 to $\$$241/tCO 2 . The TEA projects further reductions to $165/tCO 2 through enhancements in CO 2 adsorption rate and sorbent capacity and reducing manufacturing and scale up risks. A life cycle analysis was conducted on the same 100,000 TPY facility and confirmed that the facility’s electricity demand drives its greenhouse gas impact. It was determined that electricity supplied through the current grid mix would result in net-positive CO 2 emissions and that achieving net-negative emissions is only possible by powering the system with renewable electricity or fossil fuel sources equipped with carbon capture and sequestration.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Abstract The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

M2CT-22IN1202096 Design for Carbon Conversion Product Pathways with Nuclear Power Plant Integration

Coal is a globally abundant resource that historically has been used for power generation via combustion. As the power industry replaces coal with cleaner methods of generation, energy-rich coal could be used in other chemical and fuel applications. This study presents a coal utilization option in which coal combustion is replaced with a carbon-free nuclear power plant and the coal is upgraded to valuable products for a variety of markets. Coal is prepared for conversion first by the pyrolysis process, which will optimize solid, liquid, and gaseous products based on the market size and potential product value, maximizing the monetary value of coal. This process is designed using bituminous coal from the Appalachian region as a basis to provide a pathway to preserve or transition coal-related jobs and create new jobs associated with the clean energy transition. Process modeling in the AspenOne Suite will be used to determine each component’s sensitivities, costs, inputs, and outputs. Dispatch modeling in the FORCE toolset will optimize the entire system and calculate the NPV for the refinery lifetime. Advanced and light-water reactors are considerations to supply the heat, steam, and electricity to the process. This paper focuses on the technical and market analysis used to determine the optimal processes and product pathways for the carbon refinery. Product pathways are on activated carbon, formic acid synthesis, and methanol synthesis for further upgrading to marketable chemical and polymer products.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

M2CT-22IN1202096 Design for Carbon Conversion Product Pathways with Nuclear Power Plant Integration

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS1997

A Model-Based Approach for the Measurement of Eye Movements Using Image Processing

This paper describes a video eye-tracking algorithm which searches for the best fit of the pupil modeled as a circular disk. The algorithm is robust to common image artifacts such as the droopy eyelids and light reflections while maintaining the measurement resolution available by the centroid algorithm. The presented algorithm is used to derive the pupil size and center coordinates, and can be combined with iris-tracking techniques to measure ocular torsion. A comparison search method of pupil candidates using pixel coordinate reference lookup tables optimizes the processing requirements for a least square fit of the circular disk model. This paper includes quantitative analyses and simulation results for the resolution and the robustness of the algorithm. The algorithm presented in this paper provides a platform for a noninvasive, multidimensional eye measurement system which can be used for clinical and research applications requiring the precise recording of eye movements in three-dimensional space.

Sung, Kwangjae↗

DOE OSTI2022

Binding kinetics of harmonically confined random walkers

Diffusion-mediated binding of molecules under the influence of discrete spatially confining potentials is a commonly encountered scenario in systems subjected to explicit fields or implicit fields arising from tethering restraints. Here, in this work, we derive analytical expressions for the mean binding time of two random walkers geometrically confined by means of two harmonic potentials in one- and two-dimensional systems, which show excellent agreement with Brownian dynamics simulations. As a demonstration of its utility, we use this theory to maximize the communication speed in existing DNA walkers, obtaining quantitative agreement with previously reported experimental findings. The analytical expressions derived in this paper are broadly applicable to diverse systems, providing ways to characterize communication processes and optimize the rate of signal propagation for sensing and computing applications at the nanoscale.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Scalable, safer and greener syntheses of vinylimidazoles via reactive distillation of hydroxyethylimidazole intermediates

Not provided.

Polymer Science↗

DOE OSTI2026

Optimizing Energy Use in Pulp & Paper with DOE’s Energy Intensive Industries Resources

The U.S. pulp and paper industry is the third-largest energy consumer in manufacturing, accounting for roughly 10% of sector energy use. Improving energy efficiency reduces operating costs and strengthens competitiveness. To support this effort, the U.S. Department of Energy (DOE), through Oak Ridge National Laboratory (ORNL), launched the Energy Intensive Industries (EII) Initiative. A two-year pilot across 45 industrial sites identified more than 4 trillion Btu/year in potential energy savings. This presentation outlines plans for a follow-up technical assistance program tailored to pulp and paper mills. Available resources include a cost-savings scoping tool, implementation planning guidance, and technical support for applying advanced methods such as Pinch Analysis for integrated process-utilities optimization. The session introduces key Pinch Analysis principles and highlights case studies demonstrating measurable improvements. ORNL also seeks industry feedback on barriers to efficiency improvements, including technology gaps and resource needs. DOE’s broader objective is to accelerate productivity and economic competitiveness across U.S. energy-intensive industries.

Kamath, Dipti [ORNL] (ORCID:0000000278739994)↗

DOE OSTI2020

Process Intensification for Nanocomposite Aluminum Extrusions: CRADA 411 (Final Report)

The project evaluated the feasibility of using Shear Assisted Processing and Extrusion (ShAPE) for the fabrication of extrusions with a diameter of 0.5” and higher from an Al-alloy powder feedstock that contains nanoscale second phases for achieving enhanced elevated temperature strength. PNNL has previously extruded 0.2” diameter rods with high strength aluminum alloy powder Al-12.4TM. This project aimed to scale the process up to a diameter of 1.0” at a length of >24” while retaining the excellent mechanical properties achieved for the 0.2” diameter rods. As part of the project, extrusion dies were fabricated with various design features. As an example, scroll features on the tool die face was introduced to enhance powder mixing, and reduce extrusion load. Varying extrusion ratios were used to obtain better microstructural homogeneity. Use of tool design features together with the optimization of process parameters helped in the fabrication of extrusions that were up to 48” long with a diameter of 0.5” using Al-12.4 TM powder feedstock in a two-step process involving cold compaction followed by extrusion. Extrusions with a diameter of 1.0” could also be fabricated, but limitations on the extent of consolidation were experienced as a result of the torque and ram force capacity of the current ShAPE machine.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations

Abstract Long-term environmental monitoring is critical for managing the soil and groundwater at contaminated sites. Recent improvements in state-of-the-art sensor technology, communication networks, and artificial intelligence have created opportunities to modernize this monitoring activity for automated, fast, robust, and predictive monitoring. In such modernization, it is required that sensor locations be optimized to capture the spatiotemporal dynamics of all monitoring variables as well as to make it cost-effective. The legacy monitoring datasets of the target area are important to perform this optimization. In this study, we have developed a machine-learning approach to optimize sensor locations for soil and groundwater monitoring based on ensemble supervised learning and majority voting. For spatial optimization, Gaussian process regression (GPR) is used for spatial interpolation, while the majority voting is applied to accommodate the multivariate temporal dimension. Results show that the algorithms significantly outperform the random selection of the sensor locations for predictive spatiotemporal interpolation. While the method has been applied to a four-dimensional dataset (with two-dimensional space, time, and multiple contaminants), we anticipate that it can be generalizable to higher-dimensional datasets for environmental monitoring sensor location optimization.

Siddiquee, Masudur R.↗

NASA NTRS1999

Friction Stir Welding of Curved Plates

Friction stir welding (FSW) is a remarkable technology for making butt and lap joints in aluminum alloys. The process operates by passing a rotating tool between two closely butted plates. This process generates heat and the heated material is stirred from both sides of the plates to generate a high quality weld. Application of this technique has a very broad field for NASA. In particular, NASA is interested in using this welding process to manufacture tanks and curved elements. Therefore, this research has been oriented to the study the FSW of curved plates. The study has covered a number of topics that are important in the model development and to uncover the physical process involve in the welding itself. The materials used for the experimental welds were as close to each other as we could possibly find, aluminum 5454-0 and 5456-0 with properties listed at http://matweb.com. The application of FSW to curved plates needs to consider the behavior that we observed in this study. There is going to be larger force in the normal direction (Fz) as the curvature of the plate increases. A particular model needs to be derived for each material and thickness. A more complete study should also include parameters such as spin rate, tool velocity, and power used. The force in the direction of motion (Fx) needs to be reconsidered to make sure of its variability with respect to other parameters such as velocity, thickness, etc. It seems like the curvature does not play a role in this case. Variations in temperature were found with respect to the curvature. However, these changes seem to be smaller than the effect on Fz. The temperatures were all below the melting point. We understand now that the process of FSW produces a three dimensional flow of material that takes place during the weld. This flow needs to be study in a more detailed way to see in which directions the flow of material is stronger. It could be possible to model the flow using a 2-dimensional model in the particular directions where the flow moves faster. More experimental information is required to enrich the knowledge about FSW, and from this point, derive useful mathematical formulas to optimize the process and the design of the machines that will perform it. More experiments and experimental equipment are required to uncover the mathematics of the process.

Sanchez, Nestor↗

DOE OSTI2022

Perspectives on Polyolefin Catalysis in Microfluidics for High-Throughput Screening: A Minireview

Polyolefins are the largest produced plastics in the world which traditionally employ continuous stirred tank reactors and fluidized bed reactors for commercial production. The operating condition, reaction kinetics, and molecular interactions inside the reactor strongly affect the polyolefin properties, which require stringent process control in conventional procedures. Understanding the catalytic pathway, behavior of polymer particles and effect of reactor conditions are essential for designing specific polymer properties, namely the molecular weight, chain length, polydispersity, etc. Microfluidics can play a significant role in designing polymers tailored to the user needs. Smaller channel dimensions help obtain uniform reaction conditions over the length of the microfluidic reactor in a controlled environment. With real-time monitoring techniques in microfluidics, even single particle growth of polymer can be studied to understand the parameters affecting the polymer properties. High throughput microfluidics can help catalyst screening in a short duration with less consumption of reagents generating less waste. When supplemented with efficient machine learning algorithms, automated high throughput microfluidics has the potential to rapidly optimize the process and develop new knowledge even with a limited data set. When trained on data sets generated using microfluidic experiments that are designed efficiently with working knowledge of the process, machine learning algorithms can provide the relationship between the multivariable parameters space and polymer properties, which is not possible with the traditional statistical methods and interpolation techniques. Here, the rise in the utilization of microfluidics, with the advancement of machine learning algorithms, for polyolefin catalysis, highlights the importance of microfluidics for catalyst discovery, parameter optimization, and understanding reaction pathway for producing polymers with specific properties for specialized applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Tailoring Molecular Space to Navigate Phase Complexity in Cs-Based Quasi-2D Perovskites via Gated-Gaussian-Driven High-Throughput Discovery

Cesium-based quasi-2D halide perovskites (HPs) offer promising functionalities and low-temperature manufacturability, suited to stable tandem photovoltaics. However, the chemical interplays between the molecular spacers and the inorganic building blocks during crystallization cause substantial phase complexities in the resulting matrices. To successfully optimize and implement the quasi-2D HP functionalities, a systematic understanding of spacer chemistry, along with the seamless navigation of the inherently discrete molecular space, is necessary. Herein, by utilizing high-throughput automated experimentation, the phase complexities in the molecular space of quasi-2D HPs are explored, thus identifying the chemical roles of the spacer cations on the synthesis and functionalities of the complex materials. Furthermore, a novel active machine learning algorithm leveraging a two-stage decision-making process, called gated Gaussian process Bayesian optimization is introduced, to navigate the discrete ternary chemical space defined with two distinctive spacer molecules. Through simultaneous optimization of photoluminescence intensity and stability that “tailors” the chemistry in the molecular space, a ternary-compositional quasi-2D HP film realizing excellent optoelectronic functionalities is demonstrated. Finally, this work not only provides a pathway for the rational and bespoke design of complex HP materials but also sets the stage for accelerated materials discovery in other multifunctional systems.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Editors’ Choice—Methods—Pressure Control Apparatus for Lithium Metal Batteries

Lithium (Li) metal anodes are essential for developing next-generation high-energy-density batteries. However, Li dendrite/whisker formation caused short-circuiting issue and short cycle life have prevented lithium metal from being viably used in rechargeable batteries. Numerous works have been done to study how to regulate the Li growth in electrochemical cycling by using external stacking forces. While it is widely agreed that stack pressure positively affects the lithium plating/stripping process, the optimized pressure range provided by different works varies greatly because of the difference in the pressure control setup. In this work, a pressure control apparatus is designed for Li metal batteries with liquid and solid-state electrolytes (SSE). With considerations of minimizing cell to cell variation, a reusable split cell and pressure load cell are made for testing electrochemical cells with high precision pressure control. The capability of the designed setup is demonstrated by studying the pressure effect on the Li plating/stripping process.

25 ENERGY STORAGE↗