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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 OSTI2024

Marine Algae Industrialization Consortium (MAGIC): Combining biofuel and high-value bioproducts to meet the RFS

The Marine Algae Industrialization Consortium (MAGIC) was formed to address pressing challenges in the commercialization of microalgae as a source of biofuel. The “Marine Algae Industrialization Consortium (MAGIC): Combining biofuel and high-value bioproducts to meet the RFS” project formally addressed two US Department of Energy Bioenergy Technologies Office (BETO) goals: (1) Model the sustainable supply of 1 million metric tonnes ash free dry weight (AFDW) cultivated algal biomass and (2) Demonstrate valuable co-products produced along with biofuel intermediates to increase value of algal biomass by 30%. To achieve these goals, the project demonstrated and validated high-value co-products to drive down the cost of biofuel by increasing the value of algae “co-products” towards increasing the selling price of total algae biomass as one of the key drivers of economics and adoption. This was accomplished through five core, interdependent tasks including: (1) strain selection to identify and deliver strains for mass culture, (2) mass culture using a hybrid cultivation system and following key operating parameters for downstream applications to provide algae feedstock, (3) recovery and conversion to evaluate two alternative methods to separate dry algae biomass into oil and residuals for downstream testing, (4) product assessment to determine biofuel, aquafeed or poultry feed product efficacy using algae biomass fractions as well as to provide critical performance data for valuation and (5) commercialization to use technoeconomic and life cycle assessments (TEA/LCA) as iterative design and assessment tools including consideration of target markets, competitors, and distribution channels to guide product assessment, development and valuation. A total of 46 peer-review publications, many open-access, provide detail of much of the work carried out and the results of the tasks. Additional reports and presentations provide other technical and public engagement material. At a high level, using a variety of approaches, more than 1000 marine microalgae strains were evaluated to ultimately identify the seven winners that were down-selected to be grown in mass culture. Strain selection demonstrated that there were no ‘super strains’ and that each candidate had strengths and limitations for specific products, growth conditions or operational considerations. Mass culture growth of these seven strains at >5000 L / 29 m 2 scale found that four them were suitable for product assessment. More than 250 kg of biomass was produced across hundreds of pond runs along with thousands of cultivation entries on the growth and biomass characteristics as well as environmental parameters. In the process, dozens of standard operating procedures were generated as was custom software to process and analyze cultivation data. Recovery and conversion of algae biomass demonstrated that a hexane solvent based extraction protocol was most effective at recovering oil (biocrude) from algae and four strains were processed to produce oil and lipid extracted algae (residuals) for downstream testing. Membrane-based oil separation was less successful, but may still be applicable to other commercial applications in the future. Product testing demonstrated that algae biocrude is of high quality and hydrotreating generated numerous fractions of high quality composition for fuel and lubricate based applications. Aquafeed studies performed at a variety of scales showed that both whole and defatted (lipid extracted algae) microalgae were suitable as a feed ingredient, but that the specifics of the fed animal and biochemical composition of the algae are critical factors when determining formulation. Similarly, poultry studies on whole and defatted microalgae generally showed positive outcomes on animal growth and health, with some microalgae providing enhanced nutritional composition of the animal product. Economic and life cycle assessments covered a wide range of possible commercialization and sustainability scenarios. Replacement value, improved product value added, consumer values marketing added valuation and improved animal health were considered as alternatives for microalgae valuation. Using the open pond system, algae productivity was identified as the key driver of commercialization economics, but combination of co-products (e.g. animal feed) with biofuel production substantially increased the total selling price of algae. Modeled microalgae selling price exceeded $\$$1500/tonne and could generate competitive biofuel selling prices below $\$$5 gallon gas equivalents using realistic algal productivities. Short (process scale) and longer (decadal trends) sustainability assessments show that marine microalgae can enhance the sustainability of energy production and lead to other realized benefits in water, fertilizer and land use for other sectors (e.g. agriculture). This project successfully demonstrated all of the components of an end-to-end process from mass microalgae cultivation and dewatering, to recovery and conversion of algae biomass components, to final product demonstration and process valuation; the combined results provide a framework for future commercialization of algae based biofuels.

09 BIOMASS FUELS↗
NASA NTRS2011

Orion Spacecraft MMOD Protection Design and Assessment

A first-principals, semi-empirical ballistic performance model has been developed for porous ceramics. Lightweight thermal protection systems protect the crew and vehicle of orbital and exo-orbital missions from the intense heat of atmospheric reentry. To maintain low launch weights these materials are their own protection from space hazards like orbital debris and meteoroids. A ballistic performance model is described here that models the performance under a variety of impact conditions. Using the model described here relative to an energy scaled model results in a significantly reduced prediction of full penetration of this material at ISS orbital parameters.

Miller, Joshua E.↗
DOE OSTI2014

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of nonKEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1990

Statistical methods and neural network approaches for classification of data from multiple sources

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Benediktsson, Jon Atli↗
DOE OSTI2015

Newberry EGS Demonstration: Phase 2.2 Report

The Newberry Volcano EGS Demonstration is a five year field project designed to demonstrate recent technological advances for engineered geothermal systems (EGS) development. Advances in reservoir stimulation, diverter, and monitoring are being tested in a hot (>300 ºC), dry well (NWG 55-29) drilled in 2008. In the fall of 2014, 9,500m3 (2.5 million gallons) of groundwater were injected at a maximum wellhead pressure of 195 bar (2850 psi) over 4 weeks of hydraulic stimulation. Injectivity changes, thermal profiles and seismicity indicate that fracture permeability in well NWG 55-29 was enhanced. The fifteen-station microseismic array (MSA) located 398 seismic events, ranging in magnitude from M 0 to M 2.26. The next step is to drill a production well into the EGS reservoir. Advanced analysis of the microseismic data including hand picking of first arrivals, moment tensors, relative relocations, and velocity model improvements have resulted new higher-quality microseismic catalogs. These catalogs have been combined by relative weighting and gridding of seismic densities, resulting in probability-based maps and cross-sections, which have been used to plan a production well trajectory. The microseismic locations and times were also used to develop a reservoir diffusivity model, which can be used to evaluate stimulation plans such as dual-well stimulation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Development of a Zealand white rabbit deposition model to study inhalation anthrax

Despite using rabbits in several inhalation exposure experiments to study diseases such as anthrax, there is a lack of understanding regarding deposition characteristics and fate of inhaled particles (bio-aerosols and viruses) in the respiratory tracts of rabbits. Such information allows dosimetric extrapolation to humans to inform human outcomes. The lung geometry of the New Zealand white rabbit (referred to simply as rabbits throughout the article) was constructed using recently acquired scanned images of the conducting airways of rabbits and available information on its acinar region. In addition, functional relationships were developed for the lung and breathing parameters of rabbits as a function of body weight. The lung geometry and breathing parameters were used to extend the existing deposition model for humans and several other species to rabbits. Evaluation of the deposition model for rabbits was made by comparing predictions with available measurements in the literature. Deposition predictions in the lungs of rabbits indicated smaller deposition fractions compared to those found in humans across various particle diameter ranges. The application of the deposition model for rabbits was demonstrated by extrapolating deposition predictions in rabbits to find equivalent human exposure concentrations assuming the same dose-response relationship between the two species. Human equivalent exposure concentration levels were found to be much smaller than those for rabbits.

deposition modeling↗
DOE OSTI2021

Network Based Estimation of Wind Farm Power and Velocity Data Under Changing Wind Direction

This paper describes an estimation algorithm for velocity and power output signals in a wind farm under changing wind direction. A graph-theoretic definition describes the wind farm as a collection of nodes (turbines) and time-varying weighted edges (inter-turbine wake propagation) that change as a function of incoming wind direction. The velocity at each turbine is determined through a discrete input-output model. Changes in wind direction serve as the input and the output is defined in terms of a time-varying weighted adjacency matrix that depends on the time-delay of information propagation between turbines. These delays, which are defined in terms of the advection speed of the wind and the distance between the turbines, capture the delayed effect of wind direction changes on the inter-connectivity of the graph as the wind conditions at the farm inlet propagate through the turbine array. An event-based update framework is employed to capture time-dependent topology changes due to shifts in wind direction. Simulation results for dynamically changing wind inlet directions to a circular wind farm are compared to predictions from both the static and dynamic versions of the FLOw Redirection and Induction in Steady State (FLORIS) model. The approach is shown to enable real-time tracking of dynamic changes to wind farm power output within a framework that can be easily integrated into real-time, horizon-based, control strategies that typically do not account for wind direction changes.

distributed↗
DOE OSTI2024

Droning on to Delivery: Examining the Energy Impacts of Using Drones for Moving Goods

The demand for fast, localized delivery has grown significantly in recent years. Whether for cheesy snacks, prepared food, medical solutions, or business deliveries, fast and efficient delivery is increasingly a demand and differentiator. Drone delivery in the freight sector offers to revolutionize last-mile logistics and improve services. This research analyzes the impacts of drone energy for delivery operations, aiming to compare various types of drones (large and small, rotary and VTOL) and various types of business methods. The project executes novel open-air and laboratory-based testing to look at the impacts of weights, operations, temperatures and weather conditions. The study combines this real-world experimental data with fleet optimization mathematical models to assess energy consumption of different scenarios as well as examine the minimum fleet size and additional battery requirements. These fleet optimization models compare energy between types of deployments with existing delivery methods. The model was also extended to look at mixed fleets of aerial drones and ground vehicles to accommodate different restrictions on drones or when weather prohibits their use. The analysis provided insights into factors such as drone design, payload weight, flight distance, weather conditions and operational parameters and impacts of each. It showed how to combine different types of vehicles to reduce energy and improve services. And it showed that drone speed, routing restrictions, and unfavorable weather significantly influence energy consumption. Our open experiment data and optimization models can assist stakeholders and industry in understanding drone package delivery and offers keys to improving deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2002

Boosting with Averaged Weight Vectors

AdaBoost is a well-known ensemble learning algorithm that constructs its constituent or base models in sequence. A key step in AdaBoost is constructing a distribution over the training examples to create each base model. This distribution, represented as a vector, is constructed to be orthogonal to the vector of mistakes made by the previous base model in the sequence. The idea is to make the next base model's errors uncorrelated with those of the previous model. Some researchers have pointed out the intuition that it is probably better to construct a distribution that is orthogonal to the mistake vectors of all the previous base models, but that this is not always possible. We present an algorithm that attempts to come as close as possible to this goal in an efficient manner. We present experimental results demonstrating significant improvement over AdaBoost and the Totally Corrective boosting algorithm, which also attempts to satisfy this goal.

Oza, Nikunj C.↗
DOE OSTI2017

An integrated environmental and health performance quantification model for pre-occupancy phase of buildings in China

To comprehensively pre-evaluate the damages to both the environment and human health due to construction activities in China, this paper presents an integrated building environmental and health performance (EHP) assessment model based on the Building Environmental Performance Analysis System (BEPAS) and the Building Health Impact Analysis System (BHIAS) models and offers a new inventory data estimation method. The new model follows the life cycle assessment (LCA) framework and the inventory analysis step involves bill of quantity (BOQ) data collection, consumption data formation, and environmental profile transformation. The consumption data are derived from engineering drawings and quotas to conduct the assessment before construction for pre-evaluation. The new model classifies building impacts into three safeguard areas: ecosystems, natural resources and human health. Thus, this model considers environmental impacts as well as damage to human wellbeing. The monetization approach, distance-to-target method and panel method are considered as optional weighting approaches. Finally, nine residential buildings of different structural types are taken as case studies to test the operability of the integrated model through application. The results indicate that the new model can effectively pre-evaluate building EHP and the structure type significantly affects the performance of residential buildings.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Magnetic suspension and balance system study

A compact design for a superconducting magnetic suspension and balance system is developed for a 8 ft. x 8 ft. transonic wind tunnel. The main features of the design are: a compact superconducting solenoid in the suspended airplane model; permanent magnet wings; one common liquid helium dewar for all superconducting coils; efficient new race track coils for roll torques; use of established 11 kA cryostable AC conductor; acceptable AC losses during 10 Hz control even with all steel structure; and a 560 liter/hour helium liquefier. Considerable design simplicity, reduced magnet weights, and reduced heat leak results from using one common dewar which eliminates most heavy steel structure between coils and the suspended model. Operational availability is thought to approach 100% for such magnet systems. The weight and cost of the magnet system is approximately one-third that of previous less compact designs.

Boom, R. W.↗
NASA NTRS1988

Precise orbit determination for NASA's earth observing system using GPS (Global Positioning System)

An application of a precision orbit determination technique for NASA's Earth Observing System (EOS) using the Global Positioning System (GPS) is described. This technique allows the geometric information from measurements of GPS carrier phase and P-code pseudo-range to be exploited while minimizing requirements for precision dynamical modeling. The method combines geometric and dynamic information to determine the spacecraft trajectory; the weight on the dynamic information is controlled by adjusting fictitious spacecraft accelerations in three dimensions which are treated as first order exponentially time correlated stochastic processes. By varying the time correlation and uncertainty of the stochastic accelerations, the technique can range from purely geometric to purely dynamic. Performance estimates for this technique as applied to the orbit geometry planned for the EOS platforms indicate that decimeter accuracies for EOS orbit position may be obtainable. The sensitivity of the predicted orbit uncertainties to model errors for station locations, nongravitational platform accelerations, and Earth gravity is also presented.

Williams, B. G.↗
DOE OSTI2023

Explaining machine-learning models for gamma-ray detection and identification

As more complex predictive models are used for gamma-ray spectral analysis, methods are needed to probe and understand their predictions and behavior. Recent work has begun to bring the latest techniques from the field of Explainable Artificial Intelligence (XAI) into the applications of gamma-ray spectroscopy, including the introduction of gradient-based methods like saliency mapping and Gradient-weighted Class Activation Mapping (Grad-CAM), and black box methods like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). In addition, new sources of synthetic radiological data are becoming available, and these new data sets present opportunities to train models using more data than ever before. In this work, we use a neural network model trained on synthetic NaI(Tl) urban search data to compare some of these explanation methods and identify modifications that need to be applied to adapt the methods to gamma-ray spectral data. We find that the black box methods LIME and SHAP are especially accurate in their results, and recommend SHAP since it requires little hyperparameter tuning. We also propose and demonstrate a technique for generating counterfactual explanations using orthogonal projections of LIME and SHAP explanations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Simulating wind power forecast error distributions for spatially aggregated wind power plants

Dispersion and aggregation of wind power plants lower the uncertainty of wind power by reducing wind power forecasting errors. Using quantitative methods, this paper studies the dispersion's impact on the uncertainty of the aggregated wind power production. A method to simulate day-ahead forecast error distributions at different dispersion and forecasting skill scenarios is presented. The proposed method models the uncertainty of wind power forecasting on an annual basis and at different levels of production. As a result, the uncertainty in the forecasting of spatially dispersed wind power plants is modelled using two continuous distributions: Laplace and beta distribution. The analysis shows that even the production level uncertainty can be modelled in various dispersion and forecasting skill scenarios. The model for aggregated forecast error distributions requires only four variables: capacity-weighted distance of the total wind power plant fleet, mean of the site-specific mean absolute errors (MAEs), number of aggregated wind power plants, and the mean variability of the elevations from the proximities of the aggregated wind power plants. Here, the results are especially promising when the number of aggregated wind power plants exceeds five, the terrain complexity is low or moderate, and the aggregation region is large. This is demonstrated through a case study for Texas, United States.

17 WIND ENERGY↗
DOE OSTI2018

An (R, S)-norm fuzzy information measure with its applications in multiple-attribute decision-making

In this paper, we introduce a quantity measure which is called (R, S)-norm entropy and discuss some of its major properties with Shannon’s and other entropies in the literature. Based on this (R, S)-norm entropy, we have proposed a new (R, S)-norm fuzzy information measure and discussed its validity and properties. Further, we have given its comparison with other fuzzy information measures to prove its effectiveness. Attribute weights play an important role in multiple-attribute decision-making problems. In the present communication, two methods of determining the attribute weights are introduced. First is the case when the information regarding attribute weights is incompletely known or completely unknown and second is when we have partial information about attribute weights. For the first case, the extension of ordinary entropy weight method is used to calculate attribute weights and minimum entropy principle method based on solving a linear programming model is used in the second case. Finally, two methods are explained through numerical examples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗