Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “network reduction”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dynamic, Adaptive, Systems and Materials: Complex, Simple and Emergent Behaviors

This program has been funded by DoE/BES for twenty years. It has moved into and out of various subjects as it has developed, but it has retained its focus on complexity and complex systems. The project has evolved in the following way: Self-Assembly and Biomimetic Self-Assembly: All self-assembling systems depend upon a minimum of two types of interaction: a repulsion and an attraction. For the familiar molecular systems, attractive interactions are typically hydrogen bonds and electrostatic interactions. Repulsive interactions include steric effects, hydrophobic effects (in biological systems), and charge-charge repulsion. We have expanded this repertoire to include surface interactions, magnetic interactions, and others. I list these systems in the order in which we have explored them: i) A key emphasis in current work is in understanding how the movement of ions in a magnetic field (the Lorentz effect) interacts with catalytic systems. We have demonstrated that an acceleration in rate of reduction of CO 2 to CO can be accomplished by applying an external magnetic field. This acceleration is largely due to the application of the Lorentz effect on mass transport at the catalyst’s surface. ii) We have also extensively explored the influence of electrostatics, as exhibited in self-assembling systems, by tribocharging. iii) Another key system involves surface tension effects; examples include interactions between heavy particles floating at a liquid-air interface, and interacting by changes in surface area; interactions of bubbles and bubble rafts, behaviors of bubble trains in microfluidic networks, and behaviors of microorganisms in constraining environments. iv) This work has intentionally de-emphasized biological systems; but it does include some work on protein-ligand interactions and interactions among microorganisms. v) We have also explored applications of some of these effects, these explorations include bubble rafts as diffraction gratings, exploration of the structures that can be obtained by tribocharging and uses of these structures in exploring nucleation and melting of crystals. vi) Although not a major focus of this work, several other topics have emerged and offer opportunities for future work. These include the behavior of bubble trains and bubble rafts in microfluidic systems. A particularly interesting example is the formation of bubble trains that repeat in the alteration of large and small bubbles according to rules we do not presently understand, but are uniquely large-period oscillating systems. These systems offer a new route into understanding the instabilities of the type represented by oscillations. vii) We have also begun exploratory projects on magnetic levitation (especially to determine molecular density), and information storage (in molecules). Magnetic Levitation: Self-assembling and biomimetic systems require both attraction and repulsion. We have used electrostatics (tribocharging), interfacial free-energies (surface tension and related forces) and others. Potential uses include reconfigurable diffraction gratings and liquid lenses; exploration of mechanisms in tribocharging; tunneling in EGaIn junctions; and bubble trains (especially in micro-fluidic systems). Examples of systems representing these topics is included in the following papers: Complexity: Disks rotating at a water-air interface; Benard-Marangoni effects; Vortex-Crystals from spinning magnetic disks (Marangoni effects); EGaIn Electrode to study quantum tunneling; Self-Assembly of electrostatically-charged metallic spheres (electrets); Dynamically reconfigurable lens; Using computational designs of ligands for enzymes; Electrostatic self-assembly by tribocharging; Monodisperse bubble trains in microchannel systems; Inverted dripping faucet; Flames; Printing of micro-organisms to regenerate the “ink” of printing device; Using micro-organisms to move loads (“microoxen”); Motion of bacterial swarms near surfaces; Making monodisperse particles in microfluidic systems; Coding/decoding of information stored in droplet trains in microfluidic networks; Magnetic levitation; and Information storage. i) Tribocharging. The change in focus of this work on electrets from the fundamentals of charging to applications of these materials in studying self-assembly using electrostatic interactions. ii) Bubbles in Microchannels. The realization that systems of bubbles in microchannels represented a major opportunity to study complexity in a very tractable system, and the development of a semi-quantitative theory of this subject. iii) Flames. The growth of “flames” remains an exploratory subject for the research, although their currently relatively little active work involving it ongoing. iv) Systems with Microorganisms. The removal of work in biological systems from this project. Based on work supported in this program, we now have a significant project on the development of microfluidic tools for studying C. elegans (a nematode), but this work was not appropriate for a program focused on complexity, and we developed separate support for it. (It is, however, an example of successful seeding of a new area by BES.) The work on electrets has gone through a period in which a part of the program was the subject of a MURI; the focus of this work was to develop materials that did not charge electrostatically on friction or contact. The MURI is now over, and the work on dynamic self-assembly (supported by BES) is the major focus. “Flames” has also enjoyed synergistic support, in terms of a project supported by DARPA on flame suppression (in the absence of extinguishing agents, using acoustic and electrostatic interactions). This work was helpful in understanding some of the basics of flames, but is entirely distinct from the BES focus in complexity. A growing interest is in the Lorentz effect. The Lorentz effect is the force exerted on charged particles (electrons, ions, charged molecules) when they move through a perpendicular magnetic field. The Lorentz effect is almost ubiquitous in modern technology: examples of applications include electric motors, dynamos, cathode ray tubes, many batteries, and most systems that control electrical currents with magnetic forces. We have begun to explore the Lorentz effect in electrochemical systems and heterogeneous catalytic systems involving charged organic species and inorganic ions. This work is still at an early stage, but initial studies that Lorentz effects can be large when ions move through magnetic fields, or magnetic fields move in the presence of ions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Defect Production and Microstructural Feature Impact for Radiation Damage in Additively Manufactured 316 Stainless Steel

This milestone presents multi-scale modeling research results for additively manufactured 316 stainless steel. A combination of phase field, cluster dynamics, molecular dynamics, and density functional theory with machine learning is used, allowing for predictions of radiation-driven microstructural evolution in additively manufactured 316 stainless steel over a range of temperatures, damage rates, neutron spectra, and microstructures, and supporting the development of combined ion and neutron irradiation for material qualification. Informed by ion irradiation and neutron irradiation results across the Advanced Materials and Manufacturing Technologies program, we investigate the unique aspects of radiation-driven microstructure evolution in additively manufactured 316 stainless steel. In particular, we focus on understanding the impact of carbon concentration (varying, for example, between the 316L and 316H standards) on void formation; radiation-induced segregation at dislocation cells and grain boundaries; and the evolution of dislocation loops and network dislocation populations. We find that the unique characteristics of the additively manufactured microstructures must be accounted for in understanding the evolution of dislocation populations under thermal and irradiation conditions, such as the variation in sink strengths arising due to the variation in dislocation density. We also find that the radiation-induced segregation of Cr and Ni to grain boundaries and cell walls differs due to the differences in their defect sink biases. We also find that increasing the Ni content can slow vacancy diffusion, which may provide a mechanism for the observed reduction in transient swelling rate for austenitic Fe-Cr-Ni alloys with increasing Ni content. In addition, ion irradiations have shown that increasing carbon content in 316 SS results in a larger population of smaller voids, suggesting reduced vacancy diffusion. Our results show that the carbon content of additively manufactured 316 SS has a significant impact on the migration rate of defect clusters. The presence of carbon atoms results in carbon-vacancy trapping, significantly reducing the diffusion rate of vacancies. Carbon atoms may also be trapped near the surface of a void, which may reduce void growth by trapping vacancies that diffuse toward the void.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1998

The Ulysses Supplement to the BATSE 3B Catalog of Cosmic Gamma-Ray Bursts

We present Interplanetary Network Localization information for 219 gamma-ray burst of the 3rd BATSE catalog, obtained by analyzing the arrival times of these bursts at the Ulysses and Compton Gamma Ray Observatory (CGRO) spacecraft. For any given burst observed by these two spacecraft, arrival time analysis (triangulation) results in an annulus of possible arrival directions whose width varies between 7 arcseconds and 32 arcminutes, depending on the intensity and time history of the burst, and the distance of the Ulysses spacecraft from Earth. This annulus generally intersects the BATSE error circle, resulting in an average reduction of the error box area by a factor of 30.

Hurley, K.↗
NASA NTRS2022

Microstructure Segmentation With Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation intersection over union accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures were trained on over 100,000 labeled microscopy images from 54 material classes. These pre-trained encoders were then embedded into multiple segmentation architectures including UNet and DeepLabV3+ to evaluate segmentation performance on created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2% reduction in relative intersection over union error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗
DOE OSTI2013

Groundwater Monitoring Optimization of Post Closure Waste Sites at SRS - 13184

Groundwater monitoring at the Savannah River Site (SRS) is required at dozens of waste sites and includes sampling at over 1,000 monitoring wells. The expected longevity of groundwater contamination and associated groundwater monitoring and reporting constitutes a significant long-term cost that represents an increasing proportion of the environmental management budget as surface waste units are closed. Therefore, a comprehensive evaluation of the monitoring program for eighteen regulated waste units was conducted to identify areas where monitoring could be optimized. The units evaluated varied considerably in the scope of monitoring; ranging from two wells to hundreds of wells. In order to systematically evaluate such disparate monitoring networks, SRS developed a decision-logic analysis using flow sheets to address potential areas of optimization. Five areas were identified for evaluation, including: (1) Comparison of current monitoring to regulatory requirements, (2) Spatial distribution, (3) Temporal sampling, (4) Analyte requirements, and (5) Reporting frequency and content. Optimization recommendations were made for fifteen of the eighteen groundwater units. The spatial evaluation resulted in recommendations to suspend sampling in 79 wells and add sampling at 16 wells. The temporal evaluation resulted in recommendations to reduce the number of well visits per year by 504. Analyte reductions were recommended at three groundwater units, with increases at three other units. Reporting frequency reductions were recommended for five units. Approximately $700,000 (direct dollars) of potential annualized cost savings were identified for these groundwater units, provided all recommendations are approved. The largest area of savings was associated with reducing the reporting frequency. The optimization approach has been presented to the EPA and South Carolina Department of Environmental Control (SCHDEC), with unit-specific recommendations approved for all five units presented. This approach can be expected to be highly successful for sites with rich historical data sets and where the requirements in regulatory monitoring plans can be negotiated. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1981

A multiple access communication network based on adaptive arrays

A single frequency communication system is considered consisting of K possibly moving users distributed in space simultaneously communicating with a central station equipped with a computationally adapted array of n = or K antennas. Such a configuration could result if K spacecraft were to be simultaneously tracked by a single DSN complex consisting of an n antennas array. The array employs K sets of n weights to segregate the signals received from the K users. The weights are determined by direct computation based on known position information of the K users. Currently known techniques require (for n = K) about (4/3)K to the 4th power computer operations (multiply and add) to perform such computations. A technique that accomplishes this same goal in 8 K to the 3rd power operations, yielding a reduction by a factor K/6, was developed.

Zohar, S.↗
DOE OSTI2025

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗
DOE OSTI2022

Polar algae flaunt their zinc assets

Metal ions have been at the centre of pivotal points in the evolution of extant life. Oxygen-evolving photosynthesis, which irrevocably reshaped the geochemistry and biology of our planet, has an absolute requirement for metal ions to harvest light, split water and transfer electrons. Access to abundant oxygen then led to the propagation of organisms capable of oxidative metabolism, a process that is also dependent on metal ions for electron transfer and reduction of molecular oxygen. Because of the essential links between metal ions and the proteins that require them to function, as well as changes in metal bioavailability through time, metals have shaped the trajectories that evolution can take. Iron typically steals this show, but with access to whole-genome sequences and transcriptomes, the imprint that zinc has made on biology is coming into focus. Writing in Nature Ecology & Evolution, Ye and colleagues present new insights into the complex connections between zinc bioavailability, adaptation of algae to the polar oceans and the evolution of regulatory networks. By combining field and laboratory-based analyses, the authors suggest that expanded families of zinc-binding proteins have enabled the green alga Microglena sp. YARC to flourish in the harsh conditions of the polar Southern Ocean (Fig. 1). As the waters where this alga occurs are known for their enrichment of zinc, the authors further propose that availability of this metal ion was directly responsible for successful microalgal colonization of polar oceans. To test this hypothesis, the authors compare meta-transcriptomes collected from pole-to-pole and find positive correlations between higher copy numbers for transcripts encoding putative zinc-binding proteins, higher latitudes, lower surface temperatures and dissolved zinc.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1983

Internationally supported data acquisition for solar system exploration in the 1990's

Procedures that could be followed for cooperative agreements between countries with large ground station antennas to help provide mission telemetry support for increasing solar system exploration are outlined. It is noted that mission cost reductions, and thereby greater chances that missions will be approved, are offered by the opportunity to make planetary probes multinational efforts. The Canberra station is a suitable site for the Japanese Planet A Halley's comet intercept probe. The French have requested U.S. cooperation in developing VLBI stations in the L-band to receive signals from the Venus balloons and landers being sent as part of a joint French-Soviet mission to Venus and Halley's comet. The construction of the stations would extend the capabilities already present with NASA's deep space network, particularly for tracking the Voyager visits to Uranus and Neptune.

Reid, M. S.↗
NASA NTRS1993

Using Transparent Informed Prefetching (TIP) to reduce file read latency

As processor performance gains continue to outstrip Input/Output gains, I/O performance is becoming critical to overall system performance. File read latency is the most significant bottleneck for high performance I/O. Other aspects of I/O performance benefit from recent advances in disk bandwidth and throughput resulting from disk arrays, and in write performance derived from buffered write behind and the Log-structured File System. The access gap problem limiting improvements in read latency is exacerbated by distributed file systems operating over networks with diverse bandwidth. Focus is on extending the power of caching and prefetching to reduce file read latencies by exploiting hints from high-levels of a system. Such Transparent Informed Prefetching, TIP, and its benefits are described. It is argued that hints that disclose high level knowledge are a means for transferring optimization information across, without violating, module boundaries. How TIP can be used to convert the high throughput of new technologies such as disk arrays and log-structured file systems into low latency for applications is discussed. Our preliminary experiments show reductions in wall - clock execution time of 13 percent and 20 percent for a multiple module compilation tool (make) accessing data on a local disk and remote Coda file server, respectively, and a reduction of 30 percent for a text search (grep) remotely accessing many small files.

Patterson, R. H.↗
NASA NTRS2006

A Robust Scalable Transportation System Concept

This report documents the 2005 Revolutionary System Concept for Aeronautics (RSCA) study entitled "A Robust, Scalable Transportation System Concept". The objective of the study was to generate, at a high-level of abstraction, characteristics of a new concept for the National Airspace System, or the new NAS, under which transportation goals such as increased throughput, delay reduction, and improved robustness could be realized. Since such an objective can be overwhelmingly complex if pursued at the lowest levels of detail, instead a System-of-Systems (SoS) approach was adopted to model alternative air transportation architectures at a high level. The SoS approach allows the consideration of not only the technical aspects of the NAS", but also incorporates policy, socio-economic, and alternative transportation system considerations into one architecture. While the representations of the individual systems are basic, the higher level approach allows for ways to optimize the SoS at the network level, determining the best topology (i.e. configuration of nodes and links). The final product (concept) is a set of rules of behavior and network structure that not only satisfies national transportation goals, but represents the high impact rules that accomplish those goals by getting the agents to "do the right thing" naturally. The novel combination of Agent Based Modeling and Network Theory provides the core analysis methodology in the System-of-Systems approach. Our method of approach is non-deterministic which means, fundamentally, it asks and answers different questions than deterministic models. The nondeterministic method is necessary primarily due to our marriage of human systems with technological ones in a partially unknown set of future worlds. Our goal is to understand and simulate how the SoS, human and technological components combined, evolve.

Hahn, Andrew↗
DOE OSTI2019

Tailorable Adhesives for Multi-material Joining, Facile Repair and Re-assembly

Joining of materials and components is inevitable as it allows versatility in assembly and repair along with reduction in time and cost of manufacturing. This work uses ‘active adhesives’ that inherit all the benefits of bonded joints, such as lightweight, eliminate holes and associated stress-concentrations, and overcomes the shortcomings of disassembly and repair. Thermoplastic adhesives modified by the incorporation of electrically conductive graphene nanoplatelets at a concentration above the percolation point provide a unique synergy of mechanical, thermal and electrical properties. While the choice of the thermoplastic is governed by the desired application the addition of the graphene nanoplatelets allows energy to be deposited primarily in the adhesive. The percolated network of graphene particles in the adhesive at less than 2% can quickly couple to high frequency radiation (microwave, or MW) via non-contact methods and increase the adhesive temperature to above the required processing temperatures. The adhesive melts and flows over the adherends and upon cooling forms a structural adhesive bond. Furthermore, the process can be used to disassemble the adhesive joint if repair or reworking is required. In this work, the concept of aforementioned active adhesives was studied using varying graphene contents in nylon-6 adhesive. Activation and re-assembly was studied on in-plane (lap-shear) joints. Structural behavior of re-assembled joints was found to be similar to thermally bonded joints. Overall, active adhesives such as the ones attempted in this work have a great potential in a wide range of applications wherein in-situ repair, re-assembly and recyclability are essential.

36 MATERIALS SCIENCE↗
NASA NTRS2014

Cryogenic Boil-Off Reduction System Testing

Cryogenic propellants such as liquid hydrogen (LH2) and liquid oxygen (LO2) are a part of NASA's future space exploration due to the high specific impulse that can be achieved using engines suitable for moving 10's to 100's of metric tons of payload mass to destinations outside of low earth orbit. However, the low storage temperatures of LH2 and LO2 cause substantial boil-off losses for missions with durations greater than several days. The losses can be greatly reduced by incorporating high performance cryocooler technology to intercept heat load to the propellant tanks and by the integration of self-supporting multi-layer insulation. The active thermal control technology under development is the integration of the reverse turbo- Brayton cycle cryocooler to the propellant tank through a distributed cooling network of tubes coupled to a shield in the tank insulation and to the tank wall itself. Also, the self-supporting insulation technology was utilized under the shield to obtain needed tank applied LH2 performance. These elements were recently tested at NASA Glenn Research Center in a series of three tests, two that reduced LH2 boil-off and one to eliminate LO2 boil-off. This test series was conducted in a vacuum chamber that replicated the vacuum of space and the temperatures of low Earth orbit. The test results show that LH2 boil-off was reduced 60% by the cryocooler system operating at 90K and that robust LO2 zero boil-off storage, including full tank pressure control was achieved.

Propellant Storage↗
NASA NTRS1988

Autonomous image data reduction by analysis and interpretation

Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.

Eberlein, Susan↗
NASA NTRS1988

Autonomous image data reduction by analysis and interpretation

Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision-making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.

Eberlein, Susan↗
DOE OSTI2019

Approximate Latent Factor Algorithm for Scenario Selection and Weighting in Transmission Expansion Planning

One major difficulty in transmission expansion planning is selecting the representative scenarios to use to evaluate candidate transmission networks. The variability in demand and renewable generation makes the inclusion of several scenarios critical when calculating reliability and cost, but including too many scenarios in an optimization is computationally intractable. To reduce the number of representative operating conditions needed to obtain an accurate approximation of the system, we propose a method rooted in multivariate statistics that exploits the latent correlative structure between different scenarios and network configurations. The proposed algorithm includes an objective and rigorous way to select a subset of scenarios that provide as much information about the system as possible, and a method to accurately approximate the system cost from that scenario subset. The result is a set of scenarios and weights that are easily incorporated into traditional transmission expansion planning formulations. We apply this to a 312-bus WECC model with 8,736 distinct operating conditions. The transmission plans found with the proposed method are more reliable and have a lower total cost than those from other scenario reduction techniques, as well as a smaller error between the expected system performance from the optimization objective and the actual system performance.

42 ENGINEERING↗
NASA NTRS2006

Relationship between Weather, Traffic and Delay Based on Empirical Methods

The steady rise in demand for air transportation over the years has put much emphasis on the need for sophisticated air traffic flow management (TFM) within the National Airspace System (NAS). The NAS refers to hardware, software and people, including runways, radars, networks, FAA, airlines, etc., involved in air traffic management (ATM) in the US. One of the metrics that has been used to assess the performance of NAS is the actual delays provided through FAA's Air Traffic Operations Network (OPSNET). The OPSNET delay data includes those reportable delays, i.e. delays of 15 minutes or more experienced by Instrument Flight Rule (IFR) flights, submitted by the FAA facilities. These OPSNET delays are caused by the application of TFM initiatives in response to, for instance, weather conditions, increased traffic volume, equipment outages, airline operations, and runway conditions. TFM initiatives such as, ground stops, ground delay programs, rerouting, airborne holding, and miles-in-trail restrictions, are actions which are needed to control the air traffic demand to mitigate the demand-capacity imbalance due to the reduction in capacity. Consequently, TFM initiatives result in NAS delays. Of all the causes, weather has been identified as the most important causal factor for NAS delays. Therefore, in order to accurately assess the NAS performance, it has become necessary to create a baseline for NAS performance and establish a model which characterizes the relation between weather and NAS delays.

Sridhar, Banavar↗
DOE OSTI2023

Accurate Prediction of Algal Biomass Lipid, Protein, and Carbohydrate Composition with Machine Learning Regression Modelling of Near-IR Spectra

During large scale algal biomass cultivation, it is difficult to reliably control relative composition to target levels. Rapid determination of chemical composition is feasible by using near infrared (NIR) spectral data. We sought to build and improve on reliable high-throughput screening prediction method based on partial least squares regression (PLSR) by the application of artificial neural networks (ANN) and associated optimization strategies. The algal biomass sample set was designed and created in an iterative process of culturing in physiologically diverse conditions at the GAI field site, followed by compositional analyses at NREL. The workflow allowed us to identify gaps in compositional space for informing the subsequent cultivation and sampling efforts and generated a high quality set of 210 unique samples with chemical analysis results, spectral scanning data, and cultivation metadata. We observed a significant improvement in the performance of carbohydrate content predictions using an optimized ANN model compared to PLSR, with > 16% reduction in mean absolute percent error (MAPE) when tested on the same set of reserved data. The optimized ANN models for FAME and protein prediction performed exceptionally well with 5.99% and 5.09% MAPE, respectively. Application of these methods to detection and quantification of minor biomass constituents that are relevant to certain product streams has shown positive preliminary results, opening the possibility for extensions to the outputs of this powerful data type. All models are accompanied by prediction uncertainties and unsupervised spectral outlier detection to alert an operator to unreliable spectral data. These tools can be deployed for rapid determination of algal culture status, and cultivation and biomass quality improvement.

algal biofuels↗