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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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The life cycle cost analysis of space exploration scenarios is explored via a merger of (1) scenario planning, separating context and (2) modeling and analysis of specific content. Numerous scenarios are presented, leading to cross-cutting recommendations addressing life cycle costs, productivity, and approaches applicable to any scenarios. Approaches address technical and non-technical factors.
Two transient models have been developed to study the catalytic ignition in a monolithic catalytic reactor. The special feature in these models is the inclusion of thermal and species structures in the porous catalytic layer. There are many time scales involved in the catalytic ignition problem, and these two models are developed with different time scales. In the full transient model, the equations are non-dimensionalized by the shortest time scale (mass diffusion across the catalytic layer). It is therefore accurate but is computationally costly. In the energy-integral model, only the slowest process (solid heat-up) is taken as nonsteady. It is approximate but computationally efficient. In the computations performed, the catalyst is platinum and the reactants are rich mixtures of hydrogen and oxygen. One-step global chemical reaction rates are used for both gas-phase homogeneous reaction and catalytic heterogeneous reaction. The computed results reveal the transient ignition processes in detail, including the structure variation with time in the reactive catalytic layer. An ignition map using reactor length and catalyst loading is constructed. The comparison of computed results between the two transient models verifies the applicability of the energy-integral model when the time is greater than the second largest time scale of the system. It also suggests that a proper combined use of the two models can catch all the transient phenomena while minimizing the computational cost.
This paper presents a summary of the results to date of a Jet Propulsion Laboratory internally funded research task to study the costing process and parameters used by internally recognized software cost estimating experts. Protocol Analysis and Markov process modeling were used to capture software engineer's forecasting mental models. While there is significant variation between the mental models that were studied, it was nevertheless possible to identify a core set of cost forecasting activities, and it was also found that the mental models cluster around three forecasting techniques. Further partitioning of the mental models revealed clustering of activities, that is very suggestive of a forecasting lifecycle. The different forecasting methods identified were based on the use of multiple-decomposition steps or multiple forecasting steps. The multiple forecasting steps involved either forecasting software size or an additional effort forecast. Virtually no subject used risk reduction steps in combination. The results of the analysis include: the identification of a core set of well defined costing activities, a proposed software forecasting life cycle, and the identification of several basic software forecasting mental models. The paper concludes with a discussion of the implications of the results for current individual and institutional practices.
High-fidelity modeling is an essential design tool for many engineering applications. However, for complex systems, computational cost can be a limiting factor. Analyzing parameter sensitivity, uncertainty quantification, and design optimization require many model evaluations. Surrogate models are often used to develop the relationship between model parameters and quantities of interest. However, in the case of complex systems, surrogate models require several degrees of freedom and, thus, a large number of data points to determine the correct dependencies. For many applications, this may be prohibitively expensive. The reduction of computational requirements can be achieved by leveraging low-fidelity models. Low-fidelity models represent the system at a coarser resolution with the advantage of computational efficiency. Therefore, a bi-fidelity modeling paradigm, which augments the accuracy of a low-fidelity model in a computationally efficient manner by invoking limited runs of a high-fidelity model, can be leveraged to sufficiently balance the accuracy and computational requirements. In this work, a bi-fidelity weighted transfer learning method using neural networks was applied to a computational fluid dynamics heat transfer modeling problem. The transfer learning advantage was investigated as a function of hyperparameters. Our main finding is that the use of a bi-fidelity modeling paradigm achieves accuracy close to that of a high-fidelity Gaussian process model while significantly reducing computational cost. The bi-fidelity model achieves comparable performance with 90 high-fidelity samples-that is, 60% less than the samples needed to achieve similar accuracy without the use of bi-fidelity modeling,
Low-velocity impact of 2D woven glass fiber reinforced polymer (GFRP) and carbon fiber reinforced polymer (CFRP) composite laminates was studied experimentally and numerically. Hybrid laminates containing blocked layers of GFRP/CFRP/GFRP with all plies oriented at 0° were investigated. Relatively high impact energies were used to obtain full perforation of the laminate in a low-velocity impact setup. Numerical simulations were carried out using the in-house transient dynamics finite element code, Sierra/SM, developed at Sandia National Laboratories. A three-dimensional continuum damage model was used to describe the response of a woven composite ply. Two methods for handling delamination were considered and compared: (1) cohesive zone modeling and (2) continuum damage mechanics. The reduced model size achieved by omission of the cohesive zone elements produced acceptable results at reduced computational cost. Further, the comparison between different modeling techniques can be used to inform modeling decisions relevant to low velocity impact scenarios. The modeling was validated by comparing with the experimental results and showed good agreement in terms of predicted damage mechanisms and impactor velocity and force histories.
The Thermal and Catalytic Process Development Unit at NREL has state-of-the-art equipment for thermochemical process development and testing, ranging from catalyst and feedstock characterizations to bench-scale reactors to pilot plants. Within this facility is the capability to integrate experimental results with detailed process models to estimate biomass conversion costs. This fact sheet summarizes the core capabilities and applications.
It is increasingly clear that carbon dioxide (CO 2 ) capture and sequestration (CCS) must play a critical role in curbing worldwide CO 2 emissions to the atmosphere. Previously, we have developed ionic liquids (ILs) and phase change ionic liquids (PCILs) for post-combustion CO 2 capture. In this project, we synthesized and tested 19 ILs and PCILs and chose one of each for further development. The IL selected for further development was triethyl(octyl)phosphonium 2-cyanopyrrolide ([P 2228 ][2CNPyr]), which has the same anion but a different cation, than used in our previous work. The new cation imparts a lower viscosity than the prior cation. The PCIL selected for further development was tetraethylphosphonium benzimidazolide ([P 2222 ][BnIm]), which was developed in an Advanced Research Projects Agency-Energy (ARPA-E) project. Although we made numerous other candidates, none were better than this original PCIL. Here we showed that the CO 2 absorption by the IL and the PCIL was completely reversible, even when water was present. We determined and quantified the additional chemistry, involving the formation of bicarbonate, when water is present. The IL and PCIL were encapsulated in silicone shells using a microfluidic device. Compatibility between the IL/PCIL and the polymer, viscosity, and surface tension issues, getting sufficiently high IL/PCIL loading and leakage (likely from insufficient cross-linking), were serious challenges. Small quantities (~ 1 g) of microcapsules with good integrity of both the IL and the PCIL were produced. The CO 2 uptake by these capsules was the same as for the free IL and PCIL and the capacity of the capsules decreased only slightly even after five absorption and desorption cycles. As expected, the IL and PCIL (and subsequently the encapsulated IL and PCIL) reacted readily with SO 2 and NO, so, like aqueous amine absorbents, the CO 2 capture unit would have to be located downstream of the flue gas desulfurization and other gas treatment units. Large samples (~70 g and 100 g) of encapsulated PCIL were produced in a parallel microfluidic device and in an in-air device. These capsules were tested in a Laboratory Scale Unit (LSU) to demonstrate uptake capacity in a fluidized bed. We also used the LSU to determine recyclability and mass transfer coefficients. Unfortunately, equivalent experiments with the IL were not possible due to difficulties with producing large samples of the encapsulated IL. This IL encapsulation “scale-up” problem was not solved during the course of this project. Nonetheless, using the PCIL microcapsules we demonstrated that, as expected, the mass transfer is internally controlled. In concert with a new rate-based model of the fluidized bed, we determined that the productivity is increased by a factor of 4.75 in the microcapsule fluidized bed absorber compared to a conventional liquid-gas packed bed absorber. The initial technoeconomic model shows that the capital cost for the microcapsule IL continuous fluidized bed process is similar to that of an aqueous amine process (specifically, the Econamine FG Plus technology). However, the stripping heat requirements are about 35% less for the model IL microcapsule case compared to the monoethanolamine (MEA) case. Thus, encapsulated ILs/PCILs in a continuous fluidized bed absorber for post-combustion CO 2 capture are a significant improvement over the free IL/PCIL case and represent a major reduction in the parasitic energy requirements compared to aqueous amine process.
JPL is NASA's prime center for deep space missions. In response to the need to reduce the cost and time to complete early concept studies and proposals JPL created the first concurrent engineering team in the aerospace industry: Team X. Started in 1995, Team X has carried out over 800 studies, dramatically reducing the time and cost involved, and has been the model for other concurrent engineering teams both within NASA and throughout the larger aerospace community. Since its inception, the software backbone of this highly successful design team - engaged in examining some of NASA's cutting edge concepts - has been the unassuming spreadsheet. Over the years the Team X spreadsheet-based tools have evolved from simple standalone engineering models into a networked spreadsheet intensive system with real time parameter updating. Recent new capabilities include stochastic cost estimation and a graphical drag and drop block diagram that automatically populates the related spreadsheet parameters of cost, mass and power. This paper describes how the spreadsheet functions within Team X: its history, architecture, current capabilities, enabling strengths and persistent weaknesses. In addition, the verification methods and institutional oversight that have evolved as the Team X products became increasingly critical to Laboratory success are also discussed.
Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO 2 . Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. Furthermore, the robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications.
Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.
The Wind Asset Value Estimation System (WAVES) model is a coupling framework for core NREL techno economic analysis software models to estimate capital expenditures (ORBIT), operational expenditures (WOMBAT), and energy production (FLORIS) for offshore wind power plants. Existing workflows to couple the three models for lifecycle performance and cost estimation require a large amount of manual and error-prone setup to combine both shared inputs and dependent outputs, as such WAVES's primary functionality is to wrap the core logic for running standard modeling workflows to ensure shared settings and entangled results are correctly and efficiently combined every time. SEE ALSO: https://pypi.org/project/WAVES/
Tidal energy is still in the early research and development phases, therefore the ability to quickly manufacture and evaluate prototype blades with new blade designs and materials is critical. This paper investigates using a Stratasys three-dimensional printer to rapidly prototype scale-model turbine blades by printing the blade tooling and using vacuum-assisted resin transfer molding (VARTM) to manufacture blades. This work outlines a methodology for cost effectively designing and manufacturing model-scale tidal turbine blades to be used in testing campaigns, and for evaluating new thermoplastic composite materials for tidal energy applications. Demonstration blades were designed in ANSYS and manufactured using an infusible thermoplastic resin system called Elium(R). The development of scale-model thermoplastic blades enables research into their fatigue performance in realistic conditions at a cost-effective scale, and manufacturing using 3-D printed tooling enables fast and low-cost blade design iterations.
Demonstrations of quantum advantage for certain sampling problems have generated considerable excitement for quantum computing and have further spurred the development of circuit-model quantum computers, which represent quantum programs as a sequence of quantum gates acting on a finite number of qubits. Amongst this excitement, analog quantum computation has become less prominent, with the expectation that circuit-model quantum computers will eventually be sufficient for emulating analog quantum computation and thus rendering analog quantum computation obsolete. In this work we explore the basic requirements for emulating a specific analog quantum computation in the circuit model: the preparation of a biased superposition of degenerate ground states of an Ising Hamiltonian using an adiabatic evolution. We show that the overhead of emulation is substantial even for this simple problem. This supports using analog quantum computation for solving time-dependent Hamiltonian dynamics in the short term and midterm, assuming analog errors can be made low enough and coherence times long enough to solve problems of practical interest.
No abstract provided
Tokamaks are often considered to be a leading candidate for near-term, cost-effective fusion energy, but these devices are susceptible to sudden loss of confinement events called disruptions. The threat of disruptions has garnered serious attention in research for the next generation of burning plasma experiments, such as ITER, but has received little treatment in economic studies of magnetic fusion energy. In this paper, we present a model for quantifying the effect of disruptions on the cost of electricity produced by a tokamak power plant (TPP). We outline the various ways disruptions increase costs and decrease revenues, introduce metrics to quantify these effects, and add them to a levelized cost of electricity (LCOE) model. Additionally, we identify several rate-limiting repair steps and introduce a classification system of disruption types based on the time to return to operations. We demonstrate how the LCOE model can be used to find the cost of electricity and the requirements for disruption handling of a TPP, and we further highlight where future research can have a strong impact in neutralizing the “showstopping” potential of disruptions.
Modeling and simulation is a cost- and time-saving method to study conceptual designs of radiation detection systems. MCNP radiation transport code has been an indispensable tool in development of radiation detection systems, and the PINS systems have also utilized MCNP modeling and simulation in the system development. MCNP models of the PINS systems have been used to understand detector responses and to fill the gap in experimental data. Recently, the MIND backpack system was announced to be developed, and its prototype design was required to conduct laboratory tests and demonstrations. A shadow shield is the component to be designed by Idaho National Laboratory in order to maximize the performance of a backpack system while meeting the sponsor’s requirements. MCNP modeling and simulation was utilized to find the best specification of a shadow shield. This study was performed to down-select a shadow shield design for the MIND system. A total of 24 designs for a shadow shield were identified to build MCNP models, and their simulation results were used to prioritize each design. As a quantitative performance metric, an equation of Figure-of-Merit was introduced in the decision making analysis. In addition, the Analytic Hierarchy Process was adopted to prioritize and recommend best performing shadow shield designs.
Scaling up variational quantum eigensolver (VQE) algorithms to practical applications utilizing quantum advantage with noisy intermediate-scale quantum (NISQ) devices is challenging. The expansive cost-function evaluation and demanding optimization quickly exhaust the precious quantum resource available on NISQ devices. In this work, we discuss the appropriate VQE strategy suitable for multi-site interacting boson systems, for example, the Holstein model and the Rabi lattice model. We investigate the cost-function setup and the optimization tactics to utilize the limited quantum resource efficiently. Our strategy illustrates that the scalable VQE algorithms of interacting bosons have a promising future.
This paper proposes an optimization model for the optimal configuration of an grid-connected electric vehicle (EV) extreme fast charging station considering integration of photovoltaic (PV) and energy storage. The proposed model minimizes the annualized net cost (i.e., maximizes the annualized net profit) of the extreme fast charging station, including investment and maintenance cost of charging ports, PV and energy storage, net cost of purchasing energy from utility and selling energy to EV customers, degradation cost of energy storage and demand charge. The decision variables are number of charging ports, capacity of invested PV and the power and energy ratings of invested energy storage. The Erlang-loss system is adopted to model the EV mobility. Results of numerical simulations indicate that investment of PV and energy storage could increase the annualized profit of the extreme fast charging station. In addition, the impacts of various parameters on the optimal solution are investigated by sensitivity analysis.