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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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Applications of control theory are considered in the areas of decoupling and wake steering control of submersibles, a method of electrohydraulic conversion with no moving parts, and socio-economic system modelling.
Brouwer and Brouwer-Lyddanes' use of the Von Zeipel-Delaunay method is employed to develop an efficient analytical orbit theory suitable for microcomputers. A succinctly simple pseudo-phenomenologically conceptualized algorithm is introduced which accurately and economically synthesizes modeling of drag effects. The method epitomizes and manifests effortless efficient computer mechanization. Simulated trajectory data is employed to illustrate the theory's ability to accurately accommodate oblateness and drag effects for microcomputer ground based or onboard predicted orbital representation. Real tracking data is used to demonstrate that the theory's orbit determination and orbit prediction capabilities are favorably adaptable to and are comparable with results obtained utilizing complex definitive Cowell method solutions on satellites experiencing significant drag effects.
Shortwave spectral radiance models for use in the spectral correction algorithms for the ERBE Scanner Instrument are provided. The required data base was delivered to the ERBe Data Reduction Group in October 1984. It consisted of two sets of data files: (1) the spectral bidirectional angular models and (2) the spectral flux modes. The bidirectional models employ the angular characteristics of reflection by the Earth-atmosphere system and were derived from detailed radiance calculations using a finite difference model of the radiative transfer process. The spectral flux models were created through the use of a delta-Eddington model to economically simulate the effects of atmospheric variability. By combining these data sets, a wide range of radiances may be approximated for a number of scene types.
The dynamic analysis of complex structural systems using the finite element method and multilevel substructured models is presented. The fixed-interface method is selected for substructure reduction because of its efficiency, accuracy, and adaptability to restart and reanalysis. This method is extended to reduction of substructures which are themselves composed of reduced substructures. The implementation and performance of the method in a general purpose software system is emphasized. Solution algorithms consistent with the chosen data structures are presented. It is demonstrated that successful finite element software requires the use of software executives to supplement the algorithmic language. The complexity of the implementation of restart and reanalysis porcedures illustrates the need for executive systems to support the noncomputational aspects of the software. It is shown that significant computational efficiencies can be achieved through proper use of substructuring and reduction technbiques without sacrificing solution accuracy. The restart and reanalysis capabilities and the flexible procedures for multilevel substructured modeling gives economical yet accurate analyses of complex structural systems.
The purpose here is to suggest that there is at least one fundamental difference between the problems used for testing optimization codes and the problems that engineers often need to solve; in particular, the level of precision that can be practically achieved in the numerical evaluation of the objective function, derivatives, and constraints. This difference affects the performance of optimization codes, as illustrated by two examples. Two classes of optimization problem were defined. Class One functions and constraints can be evaluated to a high precision that depends primarily on the word length of the computer. Class Two functions and/or constraints can only be evaluated to a moderate or a low level of precision for economic or modeling reasons, regardless of the computer word length. Optimization codes have not been adequately tested on Class Two problems. There are very few Class Two test problems in the literature, while there are literally hundreds of Class One test problems. The relative performance of two codes may be markedly different for Class One and Class Two problems. Less sophisticated direct search type codes may be less likely to be confused or to waste many function evaluations on Class Two problems. The analysis accuracy and minimization performance are related in a complex way that probably varies from code to code. On a problem where the analysis precision was varied over a range, the simple Hooke and Jeeves code was more efficient at low precision while the Powell code was more efficient at high precision.
Congestion control is an important feature that directly affects network performance. Network congestion may cause loss of data or long delays. Although this problem has been studied extensively in the Internet, the solutions for Internet congestion control do not apply readily to challenged network environments such as Delay Tolerant Networks (DTN) where end-to-end connectivity may not exist continuously and latency can be high. In DTN, end-to-end rate control is not feasible. This calls for congestion control mechanisms where the decisions can be made autonomously with local information only. We use an economic pricing model and propose a rule-based congestion control mechanism where each router can autonomously decide on whether to accept a bundle (data) based on local information such as available storage and the value and risk of accepting the bundle (derived from historical statistics). Preliminary experimental results show that this congestion control mechanism can protect routers from resource depletion without loss of data.
Although a large literature now exists on the drivers of tropical deforestation, less is known about its spatial manifestation. This is a critical shortcoming in our knowledge base since the spatial pattern of land-cover change and forest fragmentation, in particular, strongly affect biodiversity. The purpose of this article is to consider emergent patterns of road networks, the initial proximate cause of fragmentation in tropical forest frontiers. Specifically, we address the road-building processes of loggers who are very active in the Amazon landscape. To this end, we develop an explanation of road expansions, using a positive approach combining a theoretical model of economic behavior with geographic information systems (GIs) software in order to mimic the spatial decisions of road builders. We simulate two types of road extensions commonly found in the Amazon basin in a region: showing the fishbone pattern of fragmentation. Although our simulation results are only partially successful, they call attention to the role of multiple agents in the landscape, the importance of legal and institutional constraints on economic behavior, and the power of GIs as a research tool.
Congestion control is an important feature that directly affects network performance. Network congestion may cause loss of data or long delays. Although this problem has been studied extensively in the Internet, the solutions for Internet congestion control do not apply readily to challenged network environments such as Delay Tolerant Networks (DTN) where end-to-end connectivity may not exist continuously and latency can be high. In DTN, end-to-end rate control is not feasible. This calls for congestion control mechanisms where the decisions can be made autonomously with local information only. We use an economic pricing model and propose a rule-based congestion control mechanism where each router can autonomously decide on whether to accept a bundle (data) based on local information such as available storage and the value and risk of accepting the bundle (derived from historical statistics).
Robust heterogeneous catalysts are essential for enabling biomass conversion; however, harsh reaction environments introduce durability challenges for many conventional catalyst materials [1]. The hydrogenation of biobased muconic acid to adipic acid is one such emerging chemistry that faces PGM catalyst stability challenges [2]. Muconic acid is a heavily-investigated biobased platform chemical that can be converted into an array of large-market commodity chemicals [2]. PGM catalysts are exceptionally effective for muconic acid hydrogenation to adipic acid, with Pd the most active to date. [2] However, Pd leaches in an acidic environment and this chemistry has a high propensity for fouling. Atomic layer deposition (ALD) is one such material design strategy that has emerged to stabilize supported metal catalysts [3]. ALD coatings are theorized to stabilize supported metal active sites by i) covering high-energy facets most susceptible to degradation, ii) disrupting the physical mobility of active sites, and iii) reinforcing the structure of the underlying catalyst support [3]. However, ALD coatings for catalyst durability with carboxylic acids remains an underdeveloped area of research and literature reports have yet to consider the techno-economic tradeoffs between the ALD manufacturing cost and catalyst lifetime productivity. This study examines low-cycle Al2O3 ALD coatings to stabilize Pd/TiO2 against deactivation during muconic acid hydrogenation. The unique harshness of muconic acid for Pd leaching was evaluated by both experiment and computation. Based on batch reactor screening results, uncoated and ALD coated catalysts were evaluated in a continuous flow reactor for their productivity, stability, and post-reaction regenerability at 700 degrees C. Characterization was performed to assess the impact of ALD coatings on catalyst morphology, as well as following regeneration. Finally, techno-economic analysis models evaluated the value proposition for ALD-coated catalysts within an nth-generation adipic acid biorefinery.
Electronic waste recycling industry needs a decision support tool for optimizing their processes to become cost competitive. We developed a software called CMAT, Comprehensive Manufacturing Assessment Tool. The aim of CMAT is to provide the e-waste recycling companies with a fully customizable decision support framework that analyzes the optimal supply chain configurations. The software optimizes the logistics operations, helps to identify the best recycling process configuration, and generates valuable insights regarding the economic performance of different categories of e-waste. The ultimate purpose of the tool is to assist the users developing a digital twin of their processes and to provide insights on questions pertinent to the e-waste recycling industry including how to increase efficiency and reduce costs, energy consumption, and greenhouse gas emissions.
Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry developed and regulatory programs. However, these programs have proven to be labor intensive and expensive. There is an opportunity to significantly enhance the collection, analysis, and use of this information to provide more cost-effective plant operation. Additionally, there is an acute industry need to leverage advanced technology to reduce costs and improve operational effectiveness. The goal of this paper is to provide effective and efficient analytical methods and tools to support risk-informed decisions for the equipment reliability and asset management programs at nuclear power plants. This is accomplished by creating a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). Here we are supporting typical system engineer decisions regarding maintenance activity scheduling and component ageing management. This is performed in a risk-informed context where herein the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow. A challenge is that the structure of this workflow strongly depends on the decision that needs to be made, the type of data available, and the constraints that need to be considered. Current methods are designed to provide specific answers to specific problems; however, these methods might prove to be inadequate even when problem settings slightly change (e.g., different types of requirements, additional dependencies between system reliability and economics). We tackled this challenge by designing framework in a flexible and modular fashion such that the user can assemble and customize his/her own workflow that integrates SSC economic lifecycle models (e.g., maintenance and replacement costs), system reliability models, and optimization methods.
The economic viability of light-water reactors (LWRs) in the United States is declining in heavily subsidized markets, and as a result, the nuclear industry is looking for opportunities to enhance the economic competitiveness of nuclear power. This is not a foreign concept to the nuclear industry: in the mid-2000s, the nuclear industry set out to achieve zero fuel failures by 2010. The goal in this effort was to drive down the cost of reactor shut down by replacing a pin or bundle in response to fuel rod failure. 2010 brought about the initiative to deliver the nuclear promise to reduce operating cost by 30% to improve nuclear energy’s economic competitiveness before 2020. The emergence of accident-tolerant fuel also offers the nuclear industry an opportunity to build on these past successes and deliver affordable, clean energy. Accident-tolerant fuel has been shown to provide superior performance compared to traditional Zircaloy/UO2 fuel concepts, offering the unique ability to remove operational limitations that inhibit the economic viability of nuclear power. This has led the industry to begin building a technical case to extend the peak rod average burnup beyond 62 GWd/tU to extend pressurized water reactor cycle lengths to 24 months and to develop more efficient boiling water reactor core designs. The Nuclear Energy Advanced Modeling and Simulation (NEAMS) program mission is to develop advanced modeling and simulation tools and capabilities to accelerate the deployment of advanced nuclear energy technologies. The primary safety concern inhibiting the nuclear industry from extending burnup is related to high-burnup fuel fragmentation, relocation, and dispersal. Therefore, the NEAMS program developed a targeted 5-year plan to support the industry’s efforts to extend burnup. This milestone report summarizes the 5-year plan that was enacted in FY20, followed by a discussion of the ongoing activates required to fulfill the 5-year plan, as well as the approach to address the current modeling gaps. Additionally, an LWR stakeholder meeting was held to communicate work performed in the NEAMS program over the past three years, to assess the LWR community’s perspective on the impact of the program, and to identify remaining significant gaps in the NEAMS suite of capabilities. This engagement will be documented by the Electric Power Research Institute and used by NEAMS to redirect current LWR scope as needed and to develop the next phase for LWR research and development.
This study examines the impact of natural gas prices on the power systems of Mexico and the United States. For this, we develop an integrated modeling framework by soft linking three different techno-economic bottom-up models of the power and energy systems, one partial equilibrium model of the natural gas sector, and a partial equilibrium model of the Mexican energy sector. Our results show several interesting results: high natural gas prices raise the use of carbon-intensive technologies in the short-term and boost renewable investments at longer time intervals, increasing emissions in earlier periods and reducing them thereafter. Regarding system costs, because of more capital-intensive green power and lower expenditures in raw energy carriers, capital costs rise and operating costs decrease in the long haul. Furthermore, we see an increase in natural gas demand when its price is low, reducing long-term capital and operating costs through cheaper energy inputs in natural gas facilities and a lower share of capital-intensive renewable facilities in the power system. Concerning emissions, low natural-gas prices decrease coal use in the United States, reducing anthropogenic emissions until the last stages of the optimization period. For Mexico, they show heterogeneous results across models. Policymakers can use this study's results to understand the influence of natural gas prices in the Mexican and United States energy sectors.
Climate change can significantly impact agriculture, leading to food security challenges. Most previous studies have investigated the direct climate impact on crops while neglecting the impact of heat stress on agricultural labor. Here, we assess the economic consequences of climate impacts on four major crops—maize, soybean, wheat, and rice—for scenarios involving low and high greenhouse gas emissions. Our analysis is based on the output from a new generation of global climate and crop models to drive a multiregional economic model. We find that, even under a high-emission scenario, the effect of CO 2 fertilization could lead to higher yields, resulting in lower prices for major crops, except for maize. However, heat-induced losses in agricultural labor could offset the potential economic benefits of CO 2 fertilization in crop production in Asia and Africa. Our findings emphasize the importance of addressing heat-stress impacts on agricultural labor through proactive adaptation measures.
Energy Intensity Indicators provides a framework to quantify how energy efficiency may or may not be affecting energy use relative to other economic trends. The model enables the decomposition of energy use for economic sectors through the use of a Log Mean Divisia Index (LMDI) model. The LMDI methodology decomposes energy use into three main categories: activity, structure and intensity (i.e. energy efficiency). A user can thus explain changes to overall energy use in a sector through changes to sector output (activity), structural shifts within sectors (e.g. across transportation modes), and efficiency of energy use in that sector.
Chemistry predictions are critical for an accurate estimation of performance and costs in desalination process models, which allows for the estimation of the value of new technologies and the viability of treating new water sources. Herein, we present how an implicit function formulation can be used to integrate the chemical modeling package, Reaktoro, into the techno-economic assessment and modeling platform, WaterTAP. This approach resolves the critical issues of integrating large-scale thermodynamic models and databases into equation-oriented process models while allowing more flexibility relative to previously presented surrogate-based methods. We describe how this integration into Pyomo and WaterTAP models is implemented and used through the open-source package Reaktoro-PSE . We first validate this integration approach by performing optimization on a previously presented desalination treatment train with softening and acid addition as the pretreatment steps. Then, to demonstrate the value of this approach, we extend the cost-optimization problem to include the simultaneous addition of lime and soda ash for softening, and HCl and H 2 SO 4 in the acidification steps. Finally, we were able to confirm the previously established results that were obtained by using surrogate models and demonstrate that the implicit function approach enables exploration of different feedwater compositions and a larger number of chemicals and their combinations.
Feedstock attributes of lignocellulosic biomass, such as particle size, compositional makeup, and moisture content, can vary substantially even within pre-processed materials and have a significant effect on conversion in fast pyrolysis-based processes. However, the economic impacts of these attributes are not well understood. To address this, biomass deconstruction phenomena captured with a versatile particle-scale simulation were linked to techno-economic impacts via reduced-order models. Parametric analysis of the particle-scale model, which was validated using literature data, was used in combination with multiple linear regression models to develop correlations between feedstock attributes and yields of pyrolysis oil, gas, and char. Yields were then correlated with the minimum fuel selling price (MFSP) using a techno-economic model, bridging the gap between physics-based biomass conversion simulations and predictions of MFSP for a catalytic fast-pyrolysis process. Empirical correlations derived from the literature regarding the impact of mineral matter (ash) on oil yield were also considered. The model correlations deployed in the integrated framework capture the impacts of variation in feedstock attributes on the MFSP. Variations in ash were shown to have the biggest impact, varying MFSP by -13%/+22% due to catalytic effects and lower relative amounts of convertible lignocellulosic material. It was also found that, if ash can be controlled to low levels, the increased extractives in forest residues can help compensate for some yield losses associated with increased ash. As a result, other inputs considered (particle size, moisture content, and reactor temperature) had relatively negligible effects on process economics within the ranges analyzed considering particle-scale effects alone.
Reproduce and ascertain additional experimental catalyst performance data for 1-step conversion of oxygenated feedstocks to butadiene. Experimental data will also be obtained for a 2-step processing configuration where we will tailor the PNNL catalyst originally developed for 1-step processing by tailoring the Lewis acidity and metal properties, with a limited number of experiments. We will measure preliminary catalyst performance results for producing Butadiene from the two oxygenated feedstocks. Additionally, we will produce 30 g of butadiene that will be sent to the client for use in producing polybutadiene. Finally, experimental results will inform techno-economic analysis (TEA) modeling. TEA will focus on identifying the most economically favorable processing route to BD from either oxygenated feedstock. We will also project GHG emissions associated with each of the process models being considered for BD production, and compare such results to GHG emissions when produced from conventional methods (e.g., cracking of naphtha) using values from the literature.