A fifty kW, twenty watt per pound solar cell array feasibility study
Feasibility of 20 watt/pound solar cell area - array models, cost estimates, fabrication of assemblies, and large-scale production of cells
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Feasibility of 20 watt/pound solar cell area - array models, cost estimates, fabrication of assemblies, and large-scale production of cells
A rigorous analysis was conducted to estimate relative manufacturing costs for high technology gas turbine blades prepared by three candidate materials process systems. The manufacturing costs for the same turbine blade configuration of directionally solidified eutectic alloy, an oxide dispersion strengthened superalloy, and a fiber reinforced superalloy were compared on a relative basis to the costs of the same blade currently in production utilizing the directional solidification process. An analytical process cost model was developed to quantitatively perform the cost comparisons. The impact of individual process yield factors on costs was also assessed as well as effects of process parameters, raw materials, labor rates and consumable items.
A preliminary evaluation of the feasibility of the use of prestressed concrete as a material for low cost blades for wind turbines was conducted. A baseline blade design was achieved for an experimental wind turbine that met aerodynamic and structural requirements. Significant cost reductions were indicated for volume production. Casting of a model blade section showed no fabrication problems. Coupled dynamic analysis revealed that adverse rotor tower interactions can be significant with heavy rotor blades.
Application of COSMIC NASTRAN to product development in a design department requires low cost, rapid model construction and interactive evaluation of results. The NASTRAN processor is described that meets these requirements by automating many of the modeling activities.
Computational Fluid Dynamics (or CFD) methods are very familiar to the research community. Even the general public has had some exposure to CFD images, primarily through the news media. However, very little attention has been paid to CST--Computational Structures Technology. Yet, no important design can be completed without it. During the first half of this century, researchers only dreamed of designing and building structures on a computer. Today their dreams have become practical realities as computational methods are used in all phases of design, fabrication and testing of engineering systems. Increasingly complex structures can now be built in even shorter periods of time. Over the past four decades, computer technology has been developing, and early finite element methods have grown from small in-house programs to numerous commercial software programs. When coupled with advanced computing systems, they help engineers make dramatic leaps in designing and testing concepts. The goals of CST include: predicting how a structure will behave under actual operating conditions; designing and complementing other experiments conducted on a structure; investigating microstructural damage or chaotic, unpredictable behavior; helping material developers in improving material systems; and being a useful tool in design systems optimization and sensitivity techniques. Applying CST to a structure problem requires five steps: (1) observe the specific problem; (2) develop a computational model for numerical simulation; (3) develop and assemble software and hardware for running the codes; (4) post-process and interpret the results; and (5) use the model to analyze and design the actual structure. Researchers in both industry and academia continue to make significant contributions to advance this technology with improvements in software, collaborative computing environments and supercomputing systems. As these environments and systems evolve, computational structures technology will evolve. By using CST in the design and operation of future structures systems, engineers will have a better understanding of how a system responds and lasts, more cost-effective methods of designing and testing models, and improved productivity. For informational and educational purposes, a videotape is being produced using both static and dynamic images from research institutions, software and hardware companies, private individuals, and historical photographs and drawings. The extensive number of CST resources indicates its widespread use. Applications run the gamut from simpler university-simulated problems to those requiring solutions on supercomputers. In some cases, an image or an animation will be mapped onto the actual structure to show the relevance of the computer model to the structure. Transferring the digital files to videotape presents a number of problems related to maintaining the quality of the original image, while still producing a broadcast quality videotape. Since researchers normally do not create a computer image using traditional composition theories or video production requirements, often the image loses some of its original digital quality and impact when transferred to videotape. Although many CST images are currently available, those that are edited into the final project must meet two important criteria: they must complement the narration, and they must be broadcast quality when recorded on videotape.
Historically, microbiological spaceflight requirements have been established in a subjective manner based upon expert opinion of both environmental and clinical monitoring results and the incidence of disease. The limited amount of data, especially from long-duration missions, has created very conservative requirements based primarily on the concentration of microorganisms. Periodic reevaluations of new data from later missions have allowed some relaxation of these stringent requirements. However, the requirements remain very conservative and subjective in nature, and the risk of crew illness due to infectious microorganisms is not well defined. The use of modeling techniques for microbial risk has been applied in the food and potable water industries and has exceptional potential for spaceflight applications. From a productivity standpoint, this type of modeling can (1) decrease unnecessary costs and resource usage and (2) prevent inadequate or inappropriate data for health assessment. In addition, a quantitative model has several advantages for risk management and communication. By identifying the variable components of the model and the knowledge associated with each component, this type of modeling can: (1) Systematically identify and close knowledge gaps, (2) Systematically identify acceptable and unacceptable risks, (3) Improve communication with stakeholders as to the reasons for resource use, and (4) Facilitate external scientific approval of the NASA requirements. The modeling of microbial risk involves the evaluation of several key factors including hazard identification, crew exposure assessment, dose-response assessment, and risk characterization. Many of these factors are similar to conditions found on Earth; however, the spaceflight environment is very specialized as the inhabitants live in a small, semi-closed environment that is often dependent on regenerative life support systems. To further complicate modeling efforts, microbial dose-response characteristics may be affected by a potentially dysfunctional crew immune system during a mission. In addition, microbial virulence has been shown to change under certain conditions during spaceflight, further complicating dose-response characterization. An initial study of the applicability of microbial risk assessment techniques was performed using Crew Health Care System (CHeCS) operational data from the International Space Station potable water systems. The risk of infection from potable water was selected as the flight systems and microbial ecology are well defined. This initial study confirmed the feasibility of using microbial risk assessment modeling for spaceflight systems. While no immediate threat was detected, the study identified several medically significant microorganisms that could pose a health risk if uncontrolled. The study also identified several specific knowledge gaps in making a risk assessment and noted that filling these knowledge gaps is essential as the risk estimates may change by orders of magnitude depending on the answers. The current phase of the microbial risk assessment studies focuses on the dose-response relationship of specific infectious agents, focusing on Salmonella enterica Typhimurium, Pseudomonas spp., and Escherichia coli, as their evaluation will provide a better baseline for determining the overall hazard characterization. The organisms were chosen as they either have been isolated on spacecraft or have an identified route of infection during a mission. The characterization will utilize dose-response models selected either from the peer-reviewed literature and/or by using statistical approaches. Development of these modeling and risk assessment techniques will help to optimize flight requirements and to protect the safety, health, and performance of the crew.
Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.
Electrochemical ocean alkalinity enhancement is a form of marine carbon dioxide removal, a rapidly growing industry that is powered by efficient onshore or offshore energy sources. As more and larger deployments are being planned, it is important to consider how variable energy sources like tidal energy can impact plant performance and costs. An open-source Python-based generalizable model for electrodialysis-based ocean alkalinity enhancement has been developed that can capture key system-level insights of the electrochemistry, ocean chemistry, acid disposal, and co-product creation of these plants under various conditions. The model additionally accounts for hybrid energy system performance profiles and costs via the National Laboratory of the Rockies’ H2Integrate tool. The model was used to analyze an example theoretical plant deployment in North Admiralty Inlet, including how the plant is impacted by the available energy sources in the region and the scale at which plant costs are covered by the co-products it generates, such as recycled concrete aggregates, without requiring carbon credits. The results show that the example plant could be profitable without carbon credits at commercial scales of 100,000 to 1 million tons of carbon dioxide removal per year, so long as it uses low-cost electricity sources and either sells acid or recovers recycled concrete aggregates with about 1 molar acid concentrations, though more research is needed to confirm these results.
Designing discharge chambers for ion thrusters involves many geometric configuration decisions. Various decisions will impact discharge chamber performance with respect to propellant utilization efficiency, ion production costs, and grid lifetime. These hardware design decisions can benefit from the assistance of computational modeling. Computational modeling for discharge chambers has been limited to two-dimensional codes that leveraged symmetry for interpretation into three-dimensional analysis. This paper presents model development activities towards a three-dimensional discharge chamber simulation to aid discharge chamber design decisions. Specifically, of the many geometric configuration decisions toward attainment of a worthy discharge chamber, this paper focuses on addressing magnetic circuit considerations with a three-dimensional discharge chamber simulation as a tool. With this tool, candidate discharge chamber magnetic circuit designs can be analyzed computationally to gain insight into factors that may influence discharge chamber performance such as: primary electron loss width in magnetic cusps, cathode tip position with respect to the low magnetic field volume, definition of a low magnetic field region, and maintenance of a low magnetic field region across the grid span. Corroborating experimental data will be obtained from mockup hardware tests. Initially, simulated candidate magnetic circuit designs will resemble previous successful thruster designs. To provide opportunity to improve beyond previous performance benchmarks, off-design modifications will be simulated and experimentally tested.
The United State's big space projects of the next decades, such as Space Station and the Human Exploration Initiative, will need the development of many millions of lines of mission critical software. NASA-Johnson (JSC) is identifying and developing some of the Computer Aided Software Engineering (CASE) technology that NASA will need to build these future software systems. The goal is to improve the quality and the productivity of large software development projects. New trends are outlined in CASE technology and how the Software Technology Branch (STB) at JSC is endeavoring to provide some of these CASE solutions for NASA is described. Key software technology components include knowledge-based systems, software reusability, user interface technology, reengineering environments, management systems for the software development process, software cost models, repository technology, and open, integrated CASE environment frameworks. The paper presents the status and long-term expectations for CASE products. The STB's Reengineering Application Project (REAP), Advanced Software Development Workstation (ASDW) project, and software development cost model (COSTMODL) project are then discussed. Some of the general difficulties of technology transfer are introduced, and a process developed by STB for CASE technology insertion is described.
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Strategic biorefinery siting is critical for a diversified bioeconomy, yet industry, policy, and research often focus on either large-scale biofuel plants or smaller-scale specialty bioproduct facilities, with limited coordination across scales. We address this gap by modeling biorefinery deployment spanning a 28-fold difference in capacity. We developed an open-source, spatially explicit framework integrating techno-economic analysis with logistics and refinery cost surrogate models to evaluate multiscale miscanthus-derived biorefineries across the rainfed U.S. for the production of ethanol, succinic acid, lactic acid, potassium sorbate, and acrylic acid. Overall costs change little as feedstock density increases, while transport distances decrease by ∼30 to 67% (∼100 km) and siting flexibility improves. Specifically, a 5-fold feedstock density increase (2% to 10% of suitable land) reduces minimum selling prices by <10% (e.g., 0.27 USD·gal –1 for ethanol). This limited economic sensitivity suggests dense planting is not required for competitive deployment, particularly for smaller-scale facilities. Representing collection areas as irregular rather than circular expands the feasible space under low-density scenarios. While large-scale refineries anchor regional supply chains, smaller facilities retain spatial flexibility even when large refineries are established. These findings highlight the importance of spatial representation and multiscale coordination for robust, regionally tailored biomanufacturing networks to advance renewable carbon integration without extensive land conversion.
For businesses and organizations to remain competitive today they must have processes and systems in place that will allow them to first identify customer needs and then develop products/processes that will meet or exceed the customers needs and expectations. Customer needs, once identified, are normally stated as requirements. Designers can then develop products/processes that will meet these requirements. Several functions, such as quality management and systems engineering management are used to assist product development teams in the development process. Both functions exist in all organizations and both have a similar objective, which is to ensure that developed processes will meet customer requirements. Are efforts in these organizations being duplicated? Are both functions needed by organizations? What are the similarities and differences between the functions listed above? ISO 9000 is an international standard of goods and services. It sets broad requirements for the assurance of quality and for management's involvement. It requires organizations to document the processes and to follow these documented processes. ISO 9000 gives customers assurance that the suppliers have control of the process for product development. Systems engineering can broadly be defined as a discipline that seeks to ensure that all requirements for a system are satisfied throughout the life of the system by preserving their interrelationship. The key activities of systems engineering include requirements analysis, functional analysis/allocation, design synthesis and verification, and system analysis and control. The systems engineering process, when followed properly, will lead to higher quality products, lower cost products, and shorter development cycles. The System Engineering Capability Maturity Model (SE-CMM) will allow companies to measure their system engineering capability and continuously improve those capabilities. ISO 9000 and SE-CMM seem to have a similar objective, which is to document the organization's processes and certify to potential customers the capability of a supplier to control the processes that determine the quality of the product or services being produced. The remaining sections of this report examine the differences and similarities between ISO 9000 and SE-CMM and make recommendations for implementation.
A simulation modeling approach is proposed for the prediction of software process productivity indices, such as cost and time-to-market, and the sensitivity analysis of such indices to changes in the organization parameters and user requirements. The approach uses a timed Petri Net and Object Oriented top-down model specification. Results demonstrate the model representativeness, and its usefulness in verifying process conformance to expectations, and in performing continuous process improvement and optimization.
Closed-loop geothermal systems provide an alternative to resource-constrained hydrothermal systems and stimulation-intensive enhanced geothermal systems. In this work, we apply the slender-body theory (SBT) model, to simulate the well flow and heat transfer performance of U-loop well designs drilled in the Wattenberg area of the Denver-Julesburg Basin. Three U-loop well patterns are investigated including a single, double, and multi-lateral design. The subsurface within area is characterized by deep, hot (> 200degreesC) igneous/metamorphic basement rock underlying multiple sedimentary formations. The lateral section(s) of the U-loop lies within a target depth of 6 km where temperatures are estimated to approach 300degreesC. As a base case, conduction-only heat transfer is investigated through simulations with the SBT model within U-loops with open-hole laterals that exchange heat directly with the hot dry rock using water as a working fluid. The utilization of supercritical CO2 as a heat transfer fluid is also considered. For each scenario, the system performance in terms of annual heat production and temperature profile over a 20-year project lifetime are assessed. Also, the levelized costs of heat and electricity (LCOH and LCOE) are determined using a top-down technoeconomic analysis model. The results show that the performance and cost optimized U-loop design is one having an injection-production well spacing of 1,000 meters with ten 50-meter-spaced laterals that traverse a subsurface system with a temperature gradient of 60degreesC/km. By injecting 20 degreesC-water at a rate of 60 kg/s through this loop, an average heat production of 19 MWth can be achieved, resulting in an LCOE and LCOH of $136/MWh and $1.53/GJ, respectively, over a 20-year project life.
Vegetation processes are fundamentally limited by nutrient and water availability, the uptake of which is mediated by plant roots in terrestrial ecosystems. While tropical forests play a central role in global water, carbon, and nutrient cycling, we know very little about tradeoffs and synergies in root traits that respond to resource scarcity. Tropical trees face a unique set of resource limitations, with rock-derived nutrients and moisture seasonality governing many ecosystem functions, and nutrient versus water availability often separated spatially and temporally. Root traits that characterize biomass, depth distributions, production and phenology, morphology, physiology, chemistry, and symbiotic relationships can be predictive of plants’ capacities to access and acquire nutrients and water, with links to aboveground processes like transpiration, wood productivity, and leaf phenology. In this review, we identify an emerging trend in the literature that tropical fine root biomass and production in surface soils are greatest in infertile or sufficiently moist soils. We also identify interesting paradoxes in tropical forest root responses to changing resources that merit further exploration. For example, specific root length, which typically increases under resource scarcity to expand the volume of soil explored, instead can increase with greater base cation availability, both across natural tropical forest gradients and in fertilization experiments. Also, nutrient additions, rather than reducing mycorrhizal colonization of fine roots as might be expected, increased colonization rates under scenarios of water scarcity in some forests. Efforts to include fine root traits and functions in vegetation models have grown more sophisticated over time, yet there is a disconnect between the emphasis in models characterizing nutrient and water uptake rates and carbon costs versus the emphasis in field experiments on measuring root biomass, production, and morphology in response to changes in resource availability. Closer integration of field and modeling efforts could connect mechanistic investigation of fine-root dynamics to ecosystem-scale understanding of nutrient and water cycling, allowing us to better predict tropical forest-climate feedbacks.
Rigorous stakeholder-vetted techno-economic analysis (TEA) was conducted to estimate the cost of hydrogen (H 2 ) production using Proton-Conducting Solid Oxide (PSO) electrolysis. The analysis evaluates Current (2025) and Future (2035) technology cases at centralized plant scales of 50 and 500 metric tonnes per day (MTD), assuming electricity, water, and air as the only system inputs. Untaxed, unsubsidized levelized cost of hydrogen (LCOH) is projected to range from 2020 $\$$1.81 to $\$$2.47/kg H 2 at an electricity price of $\$$0.03/kWh and 97% capacity factor under Nth-of-a-kind (NOAK) deployment assumptions. Total installed capital cost was developed using bottom-up Design for Manufacture and Assembly (DFMA) stack cost modeling and detailed balance-of-plant estimates, including mechanical and electrical subsystems, installation, site preparation, engineering, and contingency. Stack performance assumptions include thermoneutral operation, degradation over time, and periodic replacement. LCOH was calculated using the Hydrogen Analysis (H2A) discounted cash flow model in constant 2020 dollars. Results indicate PSO electrolysis has potential for competitive hydrogen production costs under low-cost electricity and mature manufacturing conditions.
HelioScope is a unique solar design and energy performance modeling tool that bridges the worlds of research and industry, bringing the most rigorous methods from the performance modeling community to thousands of solar developers of all backgrounds. However, many financial institutions and municipalities are hesitant to accept the energy production modeling results of HelioScope (including shading losses) in place of costly, time-intensive, and/or redundant methods due to lack of vetting from a respected institution like NREL. We expect that the proposed validation exercises will give municipalities and financial institutions the comfort needed to incorporate HelioScope into their operations, thereby significantly reducing the needs of existing and potential HelioScope users' to otherwise obtain information that HelioScope produces automatically. Folsom Labs was selected for a Small Business Voucher from the U.S. Department of Energy for the National Renewable Energy Laboratory to validate the performance of HelioScope’s simulation engine against measured PV system performance.