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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Statistical Projections for Multi-resolution, Multi-dimensional Visual Data Exploration and Analysis

An ongoing challenge in visual exploration and analysis of large, multi-dimensional datasets is how to present useful, concise information to a user for some specific visualization tasks. Typical approaches to this problem have proposed either reduced-resolution versions of data, or projections of data, or both. These approaches still have some limitations such as consuming high computation or suffering from errors. In this work, we explore the use of a statistical metric as the basis for both projections and reduced-resolution versions of data, with a particular focus on preserving one key trait in data, namely variation. We use two different case studies to explore this idea, one that uses a synthetic dataset, and another that uses a large ensemble collection produced by an atmospheric modeling code to study long-term changes in global precipitation. The primary findings of our work are that in terms of preserving the variation signal inherent in data, that using a statistical measure more faithfully preserves this key characteristic across both multi-dimensional projections and multi-resolution representations than a methodology based upon averaging.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Renewable Energy Data Explorer User Guide

This publication provides a user guide for the Renewable Energy Data Explorer and technical potential tool within the Explorer. The Renewable Energy Data Explorer is a dynamic, web-based geospatial analysis tool that facilitates renewable energy decision-making, investment, and deployment. It brings together renewable energy resource data and other modeled or measured geographic information system (GIS) layers, including land use, weather, environmental, population density, administrative, and grid data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Explore the Techno-Economic Viability of Using Large-Scale Additive Manufacturing (AM) For High-Performance Windows

Use of 3D printing, or additive manufacturing (AM) technology, in window design and construction is an under-explored area of research. Given the rapid advancements in AM technology and the potential for AM to address significant challenges associated with window design and manufacturing, there was a need to explore the feasibility of using AM technology. Alcoa’s Kawneer (Alcoa Building and Construction Systems) partnered with Oak Ridge National Laboratory’s (ORNL) Manufacturing Demonstration Facility (MDF) to explore the technical and commercial viability of large-scale 3D printing technologies with Kawneer’s AM window design. The project combined Alcoa/Kawneer’s design and expertise with ORNL’s equipment capability to create and study the world’s first 3D printed window using Big Area Additive Manufacturing (BAAM) technology. The results show that AM is useful for creating custom window shapes and sizes. With AM, more efficient windows can be made by sealing the glass pane into the printed frame during the printing process. In addition to energy savings, the use of AM reduces the manual labor required to produce custom windows.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exploring Electrocatalysis of Methane on Transition Metal Surfaces

Methane is a potent greenhouse gas because it has a heat-absorbing ability that is 20 times stronger than the better-known carbon dioxide. It is also the dominant component of nature gas (>70%). Its current greenhouse effect is only second to carbon dioxide largely because of its relatively low atmospheric concentration. Alarmingly, however, the latter has seen a 1.5-time increase since 1750, mainly due to anthropogenic livestock farming, coal and natural gas mining, and organic matter decomposing in waste landfills. Mitigating such a trend effectively and efficiently is therefore highly desirable and intensely sought for. This final technical report describes some exciting research progresses made over the last three years supported financially by the aforementioned award. The research conducted focused on exploring electrochemical direct methane to methanol (DMTM) conversion under mild conditions. The goal of research was to explore the possibility of developing potentially game-changing electrocatalysts and electrocatalysis that can convert methane directly to methanol in a most sustainable way for curbing its effect on global warming. Progress has been made in the following three directions. First, by adapting a hydrogen fuel-cell configuration that enables gaseous methane to be transported directly to the electrocatalyst surface, we have overcome the perennial difficulty in measuring activity of methane electro-oxidation at low temperature and ambient pressure that has prevented any meaningful studies of electrocatalysis of methane conversion thus far. Second, we have developed a novel and operational in situ electrochemical infrared (IR) spectral cell for studying electrocatalysis of direct methane to methanol conversion. This novel cell is also expected to alleviate the measuring difficulty caused by low solubility of methane in aqueous electrolyte and low sticking coefficient for methane adsorption onto transition metal surface. It will improve significantly the IR’s signal to noise ratio. Third, we have successfully synthesized nano-sizing copper (Cu) exchanged zeolite mordenite (Cu-ZMOR) and coated it with carbon that enabled electrochemical access to the encaged Cu ions. This opens a door to unravel the chemistry of Cu ions in catalyzing direct methane to methanol conversion under mild experimental conditions. Altogether, these progresses achieved have laid a solid foundation for further exploring electrocatalysis of direct methane to methanol conversion in general where there exists “a plenty of room” for new discoveries and developments.

03 NATURAL GAS↗
DOE OSTI2019

Understanding of catalytic transformation of shale gas and development of catalysts with high activity and selectivity through experimental exploration and theoretical simulation

Catalysis on transition metal oxides is equivalently important in contrast to that on metal catalysts in chemical and energy transformations. Understanding of catalytic reactions on early transition metal oxide catalysts is fundamentally intriguing and of great practical interest in efficient production of chemical and fuel feedstocks. Fundamental understanding of catalysis on transition metal oxide-based catalysts has been lagged behind catalysis on metals due to the complexity of chemistry and structure of surface and bulk of oxide catalysts and their structural flexibility in reactive environment at high temperatures. The funded DOE project explored surface chemistry of a few reducible oxides including CeO 2 - based, Co 3 O 4 -based, and CoO-based which are significant for heterogeneous catalysis. Through in-situ studies using ambient pressure X-ray photoelectron spectroscopy and high pressure scanning tunneling microscopy, we revealed phase transformation, the accompanying changes of surface chemistry and structure, and uncovered their correlations with catalytic activity and selectivity in catalytic reactions including reduction of NO with CO, water-gas shift, and chemical transformations of methane to methanol and acetic acid under mild conditions. Driven by the following two facts, we propose to pursue fundamental understanding of oxidative dehydrogenation of ethane and oxidative coupling of methane on oxide catalysts through both in-situ studies of catalyst surfaces during catalysis and computational studies in the renewal proposal. One driving force is the significance of oxidative dehydrogenation of ethane and oxidative coupling of methane for utilization of earth-abundant, relatively inexpensive energy resource and the urgent needs of efficient chemical processes to generate ethylene from shale gas. The other is that exploration of surface chemistry of oxide catalysts during catalysis has remained challenging though it is critical in deeply understanding catalysis on oxide catalysts and developing oxide-based catalysts toward high selectivity and activity. Therefore, the overall objective of the renewal proposal is to mechanistically understand the oxidative dehydrogenation of ethane and oxidative coupling of methane at a molecular level through building correlations between surface chemistry of catalysts during catalysis and the corresponding catalytic performances, and thus design catalysts with high selectivity and activity for production of ethylene with the guidance achieved from these fundamental studies. Experiments were designed to explore the influences of electronic states of cations supported on substrate oxides, cations of surface of the substrate oxides, and surface lattice oxygen atoms of the substrate oxides on their catalytic selectivity and activity. Surface chemistry, including oxidation state of the surface monomers and cations of substrate oxides and density of oxygen vacancies during catalysis, will be tracked with AP-XPS during catalysis. A correlation between surface chemistry of these catalysts at different temperatures and their corresponding catalytic selectivity for production of ethylene and activity will be built. Computational studies will calculate adsorption energies of reactant molecules and their dissociated species and intermediates on the catalyst surface, search transition states, and simulate reaction pathway close to experimental studies. By integrating the computational studies into these correlations to be built from experimental studies, fundamental understanding of the catalytic mechanisms of oxidative dehydrogenation of ethane and oxidative coupling of methane at a molecular level will be achieved. With these insights, new catalysts with high selectivity and activity will be developed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Exploration of a Novel Technique for Waste Heat Recovery Through Molecular Dynamics: Influence of Wettability and Electric Field on Water and Water-Based Nanofluids

Most of the energy produced globally comes by way of a heat engine. The Carnot principle places a limit as to how thermodynamically efficient a heat engine can be. There is no heat engine that can be 100% thermodynamically efficient and as such a substantial proportion of all heat supplied to a heat engine is lost as waste heat. Waste heat therefore is a large energy source ready to be properly utilized. Herein, a novel approach for converting waste heat to electricity is discussed. It involves the use of the liquid to vapor phase change of a material dielectric (water) or electrolyte (nanofluid) in the embodiment of a capacitor for direct thermal to electrostatic energy conversion. While this method of waste heat recovery could potentially be added to the ever expanding portfolio of energy conversion techniques, a number of aspects must be addressed before it can be brought into practice. Water was seen as an ideal dielectric phase change material given its high relative permittivity ratio when in the liquid form as compared to its vapor form. However, given its short voltage holdoff time the phase change of water would need to occur rapidly. This brings up concerns of explosive boiling. Herein, molecular dynamics analysis into the explosive boiling behavior of thin water films gave more insight into how the interaction between the surface and liquid affected explosive boiling onset time. A Lennard-Jones potential with one interaction site and a Morse potential with three interaction sites between water and solid substrate were used. It was found generally that a stronger interaction between water film and substrate led to faster explosive boiling onset times but an increase in the number of interaction sites delayed explosive boiling, even at the same wettability (contact angle). Understanding changes in the density and enthalpy of vaporization of a liquid dielectric such as water in the presence of an electric field is of importance due to the electrostatic nature of the waste heat conversion method under consideration. Specifically, if both density and enthalpy of vaporization are increased, the thermodynamic efficiency of the waste heat conversion method under consideration is decreased. Electric field effects are explored herein via molecular dynamics using two water models, the TIP4P-Ew and SWM4-NDP. The SWM4-NDP model is polarizable while the TIP4P-Ew model is not, which allows for a determination of the importance of model polarizability (i.e. variation in water model dipole moment) on these two properties of water when subjected to an electric field. Herein it was found that both water models respond similarly in terms of density and vaporization enthalpy variance upon the introduction of an electric field. Comparison was also made to the pressure induced by the electric field (electrostriction pressure) by way of a density comparison and it was found that the predicted electrostriction pressure overestimates the pressure experienced by water. Water by itself has a high enthalpy of vaporization, which limits the efficiency of the newly proposed conversion method. Research both experimental and through simulation has shown that the vaporization enthalpy of nanofluids can be engineered via nanoparticle size and material selection. An avenue less explored is manipulating the enthalpy of vaporization by altering the interaction strength between the nanoparticles and the base fluid. In practice this could be achieved through the addition of coatings to the nanoparticles to alter their wettability to the base fluid. This was explored by using a Lennard-Jones potential and Morse potential to model the interaction between base fluid (water) and the nanoparticle. For nanoparticles 2nm in diameter and at weight percentages up to 6%, the change in vaporization enthalpy due to alterations of the interaction strength between nanoparticle and base fluid was not significant (less than a 1% difference) when compared to the effect of altering the weight percentage of nanoparticles in the nanofluid or introducing an electric field. However, the effect of wettability may still become important at other nanoparticle concentrations and sizes. In all, the studies presented here further the understanding of phase change and thermodynamic properties of water and water based nanofluids under an electrostatic field which will help inform the development of a novel approach to waste heat conversion. The reduction of waste heat will improve energy sustainability outlooks.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale that has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify highvalue data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies.

58 GEOSCIENCES↗
DOE OSTI2024

Exploration with Scalable Gaussian Process Reinforcement Learning

Exploration is a challenging problem in reinforcement learning (RL), especially in environments with sparse rewards. Quantifying and utilizing the parametric uncertainty has been shown to be paramount for successful exploration [Osband et al., 2018]. Bayesian, or approximately Bayesian, methods present a principled means of estimating the parametric uncertainty in RL problems. Gaussian processes, nonparametric Bayesian models, are often impractical due to poor scalability and computational bottlenecks. We introduce a scalable Gaussian process RL (GPRL) method which directly induces sparsity in the covariance matrix to facilitate faster computation. This is a departure from previous GPRL methods which instead rely on data reduction and subsampling. We compare various covariance-based exploration techniques (Thompson sampling, upper confidence bound, and probabilistic maximum variance) which leverage our scalable GP framework in sparse reward environments. Finally, we show favorable comparison against the bootstrapped deep Q-Network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Using Homogenized Models to Explore the Effect of Fracture Densities on Weathering

Despite its importance, only a few researchers have incorporated the effects of fracturing into models of reactive transport for rock weathering. Here we explore 2D simulations that describe weathering under conditions of diffusive and advective transport within heterogeneous media consisting of rocky blocks and fractures. In our simulations, the Darcy velocities vary in space and time and depend on weathering processes within the rock matrix. We explore simulations with saturated and unsaturated flow for weathering bedrock that consists of blocks separated by inert or weathered material. The simulations show that a simplified homogenized model can approximate exact solutions for some of the simulated columns and hills and can allow exploration of coupling between flow and reaction in fractured rock. These hillslope simulations document that, even in the presence of 2D water flow, i) an increase in fracture density results in faster weathering advance rates; and ii) the water table locates deeper for a rock system that is weathered and fractured rather than weathered and unfractured. Some of these patterns have also been observed for natural systems. But these simulations also highlight how simplified models that do not use appropriate averaging of heterogeneities can be inaccurate in predicting weathering rate for natural systems. For example, if water flows both vertically and laterally through the vadose zone of a hill, then a prediction of the depth of regolith that is based on modeling strictly unidirectional downward infiltration will be unrealistically large. Likewise, if the fracture density observed near the land surface is used in a model to predict depth of weathering for a system where the fracture density decreases downward, the model will overestimate regolith depth. Learning how to develop accurately homogenized models could thus enable better conceptual models and predictions of weathering advance in natural systems.

58 GEOSCIENCES↗
DOE OSTI2022

Design-Space Exploration for Inverse-Design of Wind Turbine Blades Using Data-Driven Methods

The state-of-the-practice aerodynamic design methods for wind turbine blades is typically based on Blade-Element Momentum (BEM) theory using a pre-designed frozen family of airfoils. The airfoils are themselves typically designed using panel methods. The design of the next-generation of large flexible rotors will need to capture the non-linear aerodynamics and three-dimensional flow to reduce the levelized cost of wind energy. Data-driven methods for aerodynamic design using data generated by computational fluid dynamics offer an attractive alternative to BEM-based methods that captures the non-linear aerodynamics of the component airfoils as well as the root and tip sections. In this work, we develop and demonstrate a framework to "smartly" explore the relevant design space in combination with an appropriate automated CFD pipeline to evaluate the aerodynamics of each design. The design-space exploration framework uses appropriate perturbations to the airfoil shape and induction profile from a baseline shape in combination with the inverse-design using BEM. The resulting designs are evaluated using an automated CFD pipeline using the in-house CFD solver framework "Mercury". We perform verification and validation to establish the capability of the Mercury framework to predict the aerodynamic performance of wind turbines. The CFD simulation of the perturbed blade shapes are optimized to restart from the converged baseline simulation to reduce the computational time. Finally, we demonstrate the design-space exploration technique for the design of the outboard section and the full rotor using perturbations to the shape and operating conditions of the NREL 5-MW turbine.

aerodynamic design methods↗
DOE OSTI2023

Nuclear data for space exploration

Understanding the harmful effects of galactic cosmic rays (GCRs) on space exploration requires a substantial amount of nuclear data. Specifically, the interaction of energetic GCR charged particles with spacecraft materials generates secondary radiations that, through energy deposition, can harm astronauts and electronic systems. By identifying the gaps in our knowledge of the relevant nuclear data—such as interaction cross sections—and identifying ways to fill those gaps—with measurements, compilations, evaluations, dissemination, reaction modeling, sensitivity studies, and uncertainty quantification—the safety and viability of space exploration can be improved. This work surveys the state of the art in this interdisciplinary field and identifies promising collaborative research topics that have significant potential to advance our understanding of the effects of the space radiation environment on space exploration.

42 ENGINEERING↗
DOE OSTI2024

Methodology for Exploring Water and Hydropower Operating Criteria That Simultaneously Improve Economic and Environmental Considerations

Despite the reliability and flexibility of hydropower, the operation of hydroelectric power plants may have significant impacts on the downstream river system, including its water stage, sediment transport, and water temperature, ultimately affecting the ecology. To address these challenges, there is a need to identify water scheduling patterns that improve both hydropower economics and the environment relative to current operations. This paper presents a new methodology to explore promising operational criteria/rules that can achieve such improvements. Typical environmental impact statements and relicensing processes generally perform detailed site-specific analyses of a few alternatives that focus on reservoir water release operating rules and their associated environmental impacts. In contrast, the methodology presented in this article uses a widely applicable approach that explores a much larger solution space. This large set of potential alternatives can be represented in a multidimensional space for which one axis represents the economic value and the other axis quantify individual environmental impacts (e.g., sediment transport and fish growth), and they are explored via two approaches: a Monte Carlo simulation that identifies “win–win” alternatives and a multi-objective optimization problem that identifies Pareto-optimal alternatives.

13 HYDRO ENERGY↗
DOE OSTI2021

The Instrument of the Imaging X-Ray Polarimetry Explorer

While X-ray spectroscopy, timing, and imaging have improved much since 1962 when the first astronomical nonsolar source was discovered, especially wi the launch of the Newton/X-ray Multi-Mirror Mission, Rossi/X-ray Timing Explorer, and Chandra/Advanced X-ray Astrophysics Facility, the progress of X-ray polarimetry has been meager. This is in part due to the lack of sensitive polarization detectors, which in turn is a result of the fate of approved missions and because celestial X-ray sources appear less polarized than expected. Only one positive measurement has been available until now: the Orbiting Solar Observatory measured the polarization of the Crab Nebula in the 1970s. The advent of microelectronics techniques has allowed for designing a detector based on the photoelectric effect of gas in an energy range where the optics are efficient at focusing in X-rays. Here we describe the instrument, which is the major contribution of the Italian collaboration to the Small Explorer mission called IXPE, the Imaging X-ray Polarimetry Explorer, which will launch in late 2021. The instrument is composed of three detector units based on this technique and a detector service unit. Three mirror modules provided by Marshall Space Flight Center focus X-rays onto the detectors. We show the technological choices, their scientific motivation, and results from the calibration of the instrument. IXPE will perform imaging, timing, and energy-resolved polarimetry in the 2–8 keV energy band opening this window of X-ray astronomy to tens of celestial sources of almost all classes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Prospects for a Precise Equation of State Measurement from Advanced LIGO and Cosmic Explorer

Gravitational-wave observations of neutron star mergers can probe the nuclear equation of state by measuring the imprint of the neutron star's tidal deformability on the signal. We investigate the ability of future gravitational-wave observations to produce a precise measurement of the equation of state from binary neutron star inspirals. Because measurability of the tidal effect depends on the equation of state, we explore several equations of state that span current observational constraints. We generate a population of binary neutron stars as seen by a simulated Advanced LIGO–Virgo network, as well as by a planned Cosmic Explorer observatory. We perform Bayesian inference to measure the parameters of each signal, and we combine measurements across each population to determine R 1.4 , the radius of a 1.4 M ⊙ neutron star. We find that, with 321 signals, the LIGO–Virgo network is able to measure R 1.4 to better than 2% precision for all equations of state we consider; however, we also find that achieving this precision could take decades of observation, depending on the equation of state and the merger rate. On the other hand, we find that with one year of observation, Cosmic Explorer will measure R 1.4 to better than 0.6% precision. In both cases, we find that systematic biases, such as from an incorrect mass prior, can significantly impact measurement accuracy, and efforts will be required to mitigate these effects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Exploring Bioeconomy Growth through the Public Release of the Biomass Scenario Model

The Biomass Scenario Model (BSM) is an important tool for exploring vibrant future bioeconomy scenarios that leverage domestic resources. Developed by NREL and BETO, this model of the domestic biofuels supply chain has been used to explore success strategies for BETO's activities towards bioeconomy growth. The BSM offers a robust test bed for detailed exploration of effects of BETO activities within the complex context of resource availability; physical, technological, and economic constraints; behavior; and policy. The public release of the model in 2017 will allow broad engagement with the theme of the conference as model users can analyze bioeconomy growth, domestic biomass resource use, and associated effects. The BSM is a carefully validated, state-of-the-art, dynamic model of the biomass to biofuels supply chain. Using a system dynamics simulation modeling approach, the model tracks long-term deployment of biofuels given technology development and investment, considering land availability, the competing oil market, consumer demand, and government policies over time. Sample outputs include biofuels production, feedstock use, capital investment, incentives, and costs of feedstocks and fuels. BSM scenarios reveal technological, economic, and policy challenges, as well as opportunities for dynamic growth of the bioeconomy with strategic public and private investment at key points in the system. The model logic and results have been reviewed extensively, through collaborative analysis, expert reviews and external publications (https://www.zotero.org/groups/bsm_publications/).

09 BIOMASS FUELS↗
DOE OSTI2019

Poster and Slide: Energy Loss: Exploring Dissipation Pathways from Ions to In-reactor

Young Researcher Association is hosting an early career poster and networking session on July 25th, 2019. The poster session is designed to provide an overview of the researcher’s experience prior to employment at INL, and their current area of research and interests. The following is a brief description of the major sections of the poster. Before starting at INL in May of this year, my research focused on exploring the effects of electronic and nuclear energy loss on the damage evolution of Ti3SiC2 MAX phase during energetic irradiations. A series of ion irradiations revealed a threshold in electronic energy loss (Se), above which the formation of a FCC-MAX structure is enhanced, accommodating point defects and alleviating lattice strain. However, systematically separating the effects reveals that increasing Se/Sn suppresses this phase formation, thus allowing the strain from point defects to increase and saturate. Presently my research supports Post Irradiation Examination (PIE) of U-Mo fuel for the USHPRR and KJRR programs. The PIE evaluation currently consists of non-destructive examination (NDE) of fuel plates irradiated in the Advanced Test Reactor (ATR), focusing on plate profilometry and fuel swelling analysis. NDE will culminate in finalizing the destructive examination (DE) test plans and execution of said DE. Additionally, I am interested in exploring the roles that different energy dissipation pathways play in the damage evolution of the U-Mo fuel, exploring the mechanisms by which the fuel remains in its meta-stable phase under irradiation at high temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Design Space Exploration of Emerging Memory Technologies for Machine Learning Applications

Memory design space exploration methods study memory systems’ performances and limitations before implementation. The computer memory design space has grown exponentially because of the enormous growth of memory types, memory controllers, and application software. Computer simulators are commonly used for memory design space exploration. However, complex memory simulations take an enormous amount of time. Hence, in this paper, we proposed a machine learning-based design space exploration method for dynamic random-access memory and non-volatile memory systems. We applied our method to the CosmoGAN and LeNet applications to predict the following six memory response parameters: (i) bandwidth, (ii) power, (iii) average latency, (iv) average total latency, (v) memory reads, and (vi) memory writes. Our experimental results show that machine learning models can predict memory response parameter values faster than simulations. We used support vector machine, random forest, and gradient boosting machine learning models. We observed that the support vector machine provides better performance for bandwidth, average latency, and average total latency. The random forest model works better for memory reads and writes. The gradient boosting model provides superior prediction performance for power. We provide a detailed discussion on learning curve characteristics, error analysis, and memory type recommendation.

Hasan, S M Shamimul↗