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JPL has assembled a team of multidisciplinary experts with corporate knowledge of space mission and instrument development. The advanced Concept Design Team, known as Team X, provides interactive design trades including cost as a design parameter, and advanced visualization for pre-Phase A Studies.
From Introduction: Missions such as the atmospheric probe missions to the outer planets participate in the quest to explain the formation and evolution of the Solar System and the Earth within it. The probe missions described in this Abstract seek: understanding of the origin of the solar nebula and forces that formed Earth and the other planets; to determine the evolutionary processes that led to the diversity of Solar System bodies and the uniqueness of the Planet Earth; and to use the exotic worlds of our Solar System bodies and the uniqueness of the planet Earth; and to use the exotic worlds of our Solar System as natural science laboratories...The key to low cost atmospheric probe missions to the outer planets is low probe mass and the short time to the target planet.
This investigation seeks to establish better criteria that can be used for qualitative visual screening of cylindrical battery cells through exploration of various defects across multiple cylindrical Lithium-Ion battery cells. This investigation focuses on INR18650s and INR21700s cells from Molicel, LG, and Samsung. The principal instrument used is a high-definition 3D optical profilometer with measurement accuracy less than 0.5 microns. Through this effort, a catalog was built discerning minor surface imperfections from significant flaws. The catalog generated covers a comparison of axial and radial scratches, less than 5 microns, greater than 5 microns and greater than 15 microns, as well as the difference between surface discoloration, early-stage pitting, and late stages of corrosion. An exploration is made into cell button defects and crimp shoulder defects. When ultra-high-power optical magnification and three-dimension topological tools are not immediately available, this catalog will help determine if an individual cell is usable, worth further investigation, or failing. The purpose of this catalog is to be a field guide used for quick reference of a potential issue, with the goal of increasing screening throughput while reducing the need for costly examination. Trade names and trademarks are used in this abstract for identification only. Their usage does not constitute an official endorsement, either expressed or implied, by the National Aeronautics and Space Administration.
This investigation seeks to establish better criteria that can be used for qualitative visual screening of cylindrical battery cells through exploration of various defects across multiple cylindrical Lithium-Ion battery cells. This investigation focuses on INR18650s and INR21700s cells from Molicel, LG, and Samsung. The principal instrument used is a high-definition 3D optical profilometer with measurement accuracy less than 0.5 microns. Through this effort, a catalog was built discerning minor surface imperfections from significant flaws. The catalog generated covers a comparison of axial and radial scratches, less than 5 microns, greater than 5 microns and greater than 15 microns, as well as the difference between surface discoloration, early-stage pitting, and late stages of corrosion. An exploration is made into cell button defects and crimp shoulder defects. When ultra-high-power optical magnification and three-dimension topological tools are not immediately available, this catalog will help determine if an individual cell is usable, worth further investigation, or failing. The purpose of this catalog is to be a field guide used for quick reference of a potential issue, with the goal of increasing screening throughput while reducing the need for costly examination. Trade names and trademarks are used in this abstract for identification only. Their usage does not constitute an official endorsement, either expressed or implied, by the National Aeronautics and Space Administration.
Each quarter, the National Renewable Energy Laboratory (NREL) conducts the Quarterly Solar Industry Update, a presentation of technical trends within the solar industry, to the solar office staff. Each presentation focuses on global and U.S. supply and demand, module and system price, investment trends and business models, and updates on U.S. government programs supporting the solar industry.
The power grid is on the cusp of a massive transition driven by three major areas: 1) the growth in demand for electricity, 2) efforts to decarbonize the United States economy, and 3) a desire to mitigate social disparities from the impact of electricity generation on local populations. However, most studies of the electricity sector do not include equity impacts in their models. This study seeks to do so by developing a comprehensive and generalizable model tailored to the Electric Reliability Council of Texas (ERCOT) grid, designed to incorporate the equity impacts of electricity generation in a decarbonized and resilient framework. To integrate equity into our research, we incorporate environmental externalities into our capacity expansion model of ERCOT. Specifically, we factor in intermediate-level local marginal damages of precursor pollutants (NH3, NOx, primary PM2.5, SO2, and VOC) and global pollutant CO2 into the cost of generating electricity. We do this by taking into account county population, county ambient pollution concentration, and generator emission rates. Leveraging open-source modeling tools, such as PowerGenome, pyGRETA, and GenX we construct a county-level model to account for these costs. We integrate these marginal damages into the variable operations and maintenance costs of generators, for both existing and potential future builds. This study’s findings suggest that the value of a dynamic social cost of carbon (SSC) will cover criteria pollutant marginal damages within the ERCOT grid and solar and wind is expected to increase out to 2035. Key metrics evaluated within this research include fuel mix distribution across technologies, transmission and grid infrastructure costs, CO2 emissions, local pollutants marginal damages, and the variation in generation capacity built by the model. These results and framework can be used to support grid decisions that explicitly include distributional and procedural equity within a decarbonized and sustainable grid framework.
The nuclear industry is developing small reactor designs that can target a variety of deployment locations and energy products. Smaller nuclear designs have traditionally struggled to handle the steep trade-offs between size and cost that have historically incentivized large reactors. This motivates computational optimization of small reactors to minimize costs and quantify the trade-off between size and cost. In this paper, the cost/size trade-off for a small fast reactor is derived using a multi-objective genetic algorithm optimization, with steady-state, transient, and cost analysis of the fast reactor being performed. Specifically, the method is demonstrated on a small 10- to 120-MW(thermal) U-Pu-Zr–fueled lead-cooled fast reactor with a 10-year core life for district energy applications, which can have a thermal load compatible with this range. The results reinforced that fast reactor cores at the lower end of this power range suffer cost penalties due to critical mass considerations. It was found that high power density cores with strong reactivity swings and many control rods were favored over designing to minimize reactivity swing. Furthermore, this contrasts with some traditional configurations designed using engineering judgment and demonstrates that optimizers can find nontraditional but realistic solutions, along with demonstrating the value of incorporating cost functions into whole-reactor design optimization.
Network topology greatly influences both the economic and environmental performance of fifth generation district heating and cooling (5GDHC) systems. In this study the optimal trade-offs between the environmental and economic performance of 5GDHC network topologies for a five-building district with waste heat recovery were explored. A life cycle assessment method was used to calculate the total life cycle CO 2 emissions (LCCO2) associated with the installation and operation of various network topologies. Twelve months of empirical data from a data center cooling system were analyzed to assess its suitability for integration into a 5GDHC system. The most suitable method for utilizing this waste heat was selected based on the ambient loop warm pipe setpoint, waste heat temperature, and district energy system configuration. A multi-objective optimization algorithm was used to select the 5GDHC network topology that provided the optimal trade-off between LCCO2 and life cycle cost (LCC). A trade-off parameter was employed to weigh the importance of each objective in the selection process. The results showed waste heat from the data center was suitable for integration into the 5GDHC system due to its availability and consistent temperature profile. When return temperatures of 25 °C or higher were available from the liquid-cooled system, direct pre-heating of the ambient loop warm pipe was found to be the most effective waste heat integration method. The selection of the network topology that provided the optimal trade-off between LCCO2 and LCC (optimal trade-off topology) was highly dependent on factors such as fuel prices, CO 2 prices, electricity CO 2 emissions factors, availability of waste heat, embodied CO 2 emissions associated with network installation and network infrastructure costs. Optimal trade-off topologies produced substantial LCCO2 reductions relative to corresponding LCC increases. LCCO2 reduction to LCC increase ratios from 5.78 to 117.79 were identified with CO 2 offset costs ranging from 4.77 to 60.08 ($/tCO 2 e).
Engineering trade study analyses demand consideration of performance, cost and schedule impacts across the spectrum of alternative concepts and in direct reference to product requirements. Prior to detailed design, requirements are too often ill-defined (only goals ) and prone to creep, extending well beyond the Systems Requirements Review. Though lack of engineering design and definitive requirements inhibit the ability to perform detailed cost analyses, affordability trades still comprise the foundation of these future product decisions and must evolve in concert. This presentation excerpts results of the recent NASA subsonic Engine Concept Study for an Advanced Single Aisle Transport to demonstrate an affordability evaluation of performance characteristics and the subsequent impacts on engine architecture decisions. Applying the Process Based Economic Analysis Tool (PBEAT), development cost, production cost, as well as operation and support costs were considered in a traditional weighted ranking of the following system-level figures of merit: mission fuel burn, take-off noise, NOx emissions, and cruise speed. Weighting factors were varied to ascertain the architecture ranking sensitivities to these performance figures of merit with companion cost considerations. A more detailed examination of supersonic variable cycle engine cost is also briefly presented, with observations and recommendations for further refinements.
Parametric cost models can be used by designers and project managers to perform relative cost comparisons between major architectural cost drivers and allow high-level design trades; enable cost-benefit analysis for technology development investment; and, provide a basis for estimating total project cost between related concepts. This paper reports on recent revisions and improvements to our ground telescope cost model and refinements of our understanding of space telescope cost models. One interesting observation is that while space telescopes are 50X to 100X more expensive than ground telescopes, their respective scaling relationships are similar. Another interesting speculation is that the role of technology development may be different between ground and space telescopes. For ground telescopes, the data indicates that technology development tends to reduce cost by approximately 50% every 20 years. But for space telescopes, there appears to be no such cost reduction because we do not tend to re-fly similar systems. Thus, instead of reducing cost, 20 years of technology development may be required to enable a doubling of space telescope capability. Other findings include: mass should not be used to estimate cost; spacecraft and science instrument costs account for approximately 50% of total mission cost; and, integration and testing accounts for only about 10% of total mission cost.
Parametric cost models can be used by designers and project managers to perform relative cost comparisons between major architectural cost drivers and allow high-level design trades; enable cost-benefit analysis for technology development investment; and, provide a basis for estimating total project cost between related concepts. This paper hypothesizes a single model, based on published models and engineering intuition, for both ground and space telescopes: OTA Cost approximately (X) D(exp (1.75 +/- 0.05)) lambda(exp(-0.5 +/- 0.25) T(exp -0.25) e (exp (-0.04)Y). Specific findings include: space telescopes cost 50X to 100X more ground telescopes; diameter is the most important CER; cost is reduced by approximately 50% every 20 years (presumably because of technology advance and process improvements); and, for space telescopes, cost associated with wavelength performance is balanced by cost associated with operating temperature. Finally, duplication only reduces cost for the manufacture of identical systems (i.e. multiple aperture sparse arrays or interferometers). And, while duplication does reduce the cost of manufacturing the mirrors of segmented primary mirror, this cost savings does not appear to manifest itself in the final primary mirror assembly (presumably because the structure for a segmented mirror is more complicated than for a monolithic mirror).
Parametric cost models can be used by designers and project managers to compare cost between major architectural cost drivers and allow high-level design trades; enable cost-benefit analysis for technology development investment; and, provide a basis for estimating total project cost between related concepts. The NASA Marshall Space Flight Center has developed a 5- parameter cost model that explains 93% (Adjusted R2) of the cost variation in a database of 46 total ground and space telescope assemblies. This model can be used to estimate the most probably cost for the Habitable Exoplanet Telescope Assembly.
Parametric cost models can be used by designers and project managers to compare cost between major architectural cost drivers and allow high-level design trades; enable cost-benefit analysis for technology development investment; and, provide a basis for estimating total project cost between related concepts. The NASA Marshall Space Flight Center has developed a 5-parameter first-article optical telescope assembly cost model OTA$ (FY17) = $20M x 30(S/G)x D(1.7)x λ(-0.5)x T(-0.25)x e(-0.028) (Y-1960)Where S/G = 1 for space and 0 for ground telescopes, D = diameter, λ= diffraction limited wavelength, T = operating temperature and Y = year of development. The model explains 92% (Adjusted R2) of the cost variation in a database of 47 total ground and space telescope assemblies (OTA). The MSFC model estimates the most likely cost for only the OTA. Where an OTA is defined as the subsystem which collects electromagnetic radiation and focuses it (focal) or concentrates it (afocal) into the science instruments. An OTA consists of the primary mirror, secondary mirror, auxiliary optics and support structure (such as optical bench or truss structure, primary support structure, secondary support structure or spiders, straylight baffles, mechanisms for adjusting the optical components, electronics or power systems for operating these mechanisms, etc.). Finally, duplication only reduces cost for the manufacture of identical systems(i.e. multiple aperture sparse arrays or interferometers). And, while duplication does reduce the cost of manufacturing the mirrors of segmented primary mirror, this cost savings does not appear to manifest itself in the final primary mirror assembly(presumably because the structure for a segmented mirror is more complicated than for a monolithic mirror).
Processes for the manufacture of advanced metal matrix composites are rapidly approaching maturity in the research laboratory and there is growing interest in their transition to industrial production. However, research conducted to date has almost exclusively focused on overcoming the technical barriers to producing high-quality material and little attention has been given to the economical feasibility of these laboratory approaches and process cost issues. A quantitative cost modeling (QCM) approach was developed to address these issues. QCM are cost analysis tools based on predictive process models relating process conditions to the attributes of the final product. An important attribute, of the QCM approach is the ability to predict the sensitivity of material production costs to product quality and to quantitatively explore trade-offs between cost and quality. Applications of the cost models allow more efficient direction of future MMC process technology development and a more accurate assessment of MMC market potential. Cost models were developed for two state-of-the art metal matrix composite (MMC) manufacturing processes: tape casting and plasma spray deposition. Quality and Cost models are presented for both processes and the resulting predicted quality-cost curves are presented and discussed.
In January 2004, President Bush announced a new vision for space exploration. This included retirement of the current Space Shuttle fleet by 2010 and the development of new set of launch vehicles. The President's vision did not include significant increases in the NASA budget, so these development programs need to be cost conscious. Current trade study procedures address factors such as performance, reliability, safety, manufacturing, maintainability, operations, and costs. It would be desirable, however, to have increased insight into the cost factors behind each of the proposed system architectures. This paper reports on a set of component trade studies completed on the upper stage engine for the new launch vehicles. Increased insight into architecture costs was developed by including a Net Present Value (NPV) method and applying a set of associated risks to the base parametric cost data. The use of the NPV method along with the risks was found to add fidelity to the trade study and provide additional information to support the selection of a more robust design architecture.
Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to ab initio molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g., How much and what kind of atomic configurations should be included in the training set? Which level of ab initio convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we use a configurationally diverse beryllium dataset and quadratic spectral neighbor analysis potential. We demonstrate that joint optimization of energy versus force weights, training set selection strategies, and convergence settings of the ab initio reference simulations, as well as model complexity can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs.
"Right now, solar electric propulsion is being looked at very seriously," says Michael Piszczor, chief of the photovoltaic and power technologies branch at Glen Research Center. The reason, he explains, originates with a unique NASA mission from the late 1990s. In 1998, the Deep Space 1 spacecraft launched from Kennedy Space Center to test a dozen different space technologies, including SCARLET, or the Solar Concentrator Array with Refractive Linear Element Technology. As a solar array that focused sunlight on a smaller solar cell to generate electric power, SCARLET not only powered Deep Space 1 s instruments but also powered its ion engine, which propelled the spacecraft throughout its journey. Deep Space 1 was the first spacecraft powered by a refractive concentrator design like SCARLET, and also utilized multi-junction solar cells, or cells made of multiple layers of different materials. For the duration of its 38-month mission, SCARLET performed flawlessly, even as Deep Space 1 flew by Comet Borrelly and Asteroid Braille. "Everyone remembers the ion engine on Deep Space 1, but they tend to forget that the SCARLET array powered it," says Piszczor. "Not only did both technologies work as designed, but the synergy between the two, solar power and propulsion together, is really the important aspect of this technology demonstration mission. It was the first successful use of solar electric propulsion for primary propulsion." More than a decade later, NASA is keenly interested in using solar electric propulsion (SEP) for future space missions. A key issue is cost, and SEP has the potential to substantially reduce cost compared to conventional chemical propulsion technology. "SEP allows you to use spacecraft that are smaller, lighter, and less costly," says Piszczor. "Even though it might take longer to get somewhere using SEP, if you are willing to trade time for cost and smaller vehicles, it s a good trade." Potentially, SEP could be used on future science missions in orbit around the Earth or Moon, to planets or asteroids, on deep space science missions, and even on exploration missions. In fact, electric propulsion is already being used on Earth-orbiting satellites for positioning.
Many scientific applications are inherently multiscale in nature. Such complex physical phenomena often require simultaneous execution and coordination of simulations spanning multiple time and length scales. This is possible by combining expensive small-scale simulations (such as molecular dynamics simulations) with larger scale simulations (such continuum limit/hydro solvers) to allow for considerably larger systems using task and data parallelism. However, the granularity of the tasks can be very large and often leads to load imbalance. Traditionally, we use approximations to streamline the computation of the more costly interactions and this introduces trade-offs between simulation cost and accuracy. In recent years, the available computational power and the advances in machine learning have made computing these scale-bridging interactions and multiscale simulations more feasible. One driving application has been plasma modeling in inertial confinement fusion (ICF), which is fundamentally multiscale in nature. This requires deep understanding of how to extrapolate microscopic information into macroscopically relevant scales. For example, in ICF one needs an accurate understanding of the connection between experimental observables and the underlying microphysics. The properties of the larger scales are often affected by the microscale behavior incorporated usually into the equations of state and ionic and electronic transport coefficients (Liboff, 1959; Rinderknecht et al., 2014; Rosenberg et al., 2015; Ross et al., 2017). Instead of incorporating this information using reliable molecular dynamics (MD) simulations, one often needs to use theoretical models, due to the inability of MD to reach engineering scales (Glosli et al., 2007; Marinak et al., 1998). One approach to resolve this issue is by coupling two MD simulations of different scales via force interpolation, e.g., the AdResS method (Krekeler et al., 2018; Nagarajan et al., 2013). Another approach, which we will pursue in the scope of this work, is by enabling scale bridging between MD simulations and meso/macro-scale models through the development and support of application programming interfaces that these different applications can interact through.