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At least 1,081 records · Page 60

Landscape rules predict optimal superhighways for the first peopling of Sahul

Archaeological data and demographic modelling suggest that the peopling of Sahul required substantial populations, occurred rapidly within a few thousand years and encompassed environments ranging from hyper-arid deserts to temperate uplands and tropical rainforests. How this migration occurred and how humans responded to the physical environments they encountered have, however, remained largely speculative. By constructing a high-resolution digital elevation model for Sahul and coupling it with fine-scale viewshed analysis of landscape prominence, least-cost pedestrian travel modelling and high-performance computing, here we create over 125 billion potential migratory pathways, whereby the most parsimonious routes traversed emerge. Our analysis revealed several major pathways—superhighways—transecting the continent, that we evaluated using archaeological data. These results suggest that the earliest Australian ancestors adopted a set of fundamental rules shaped by physiological capacity, attraction to visually prominent landscape features and freshwater distribution to maximize survival, even without previous experience of the landscapes they encountered.

54 ENVIRONMENTAL SCIENCES↗

Low-Pressure Generator Makes Cleanrooms Cleaner

Scientists at NASA's Kennedy Space Center work in cleanrooms: laboratories with high degrees of cleanliness provided by strict control of particles such as dust, lint, or human skin. They are contaminant-free facilities, where the air is repeatedly filtered, and surfaces are smooth to prevent particles from getting lodged. Technicians working in these environments wear specially designed cleanroom "bunny suits" and booties over their street clothes, as well as gloves and face masks to avoid any contamination that may be imparted from the outside world. Even normal paper is not allowed in cleanrooms, only cleanroom low-particulate paper. These are sensitive environments where precision work, like the production of silicon chips or hard disk drives, is performed. Often in cleanrooms, positive air pressure is used to force particles outside of the isolated area. The air pressure in the Kennedy cleanrooms is monitored using high-accuracy, low-differential pressure transducers that require periodic calibration. Calibration of the transducers is a tricky business. In prior years, the analysis was performed by sending the transducers to the Kennedy Standards Laboratory, where a very expensive cross-floated, labor- intensive, dead-weight test was conducted. In the early 1990s, scientists at Kennedy determined to develop a technique and find equipment to perform qualification testing on new low-differential pressure transducers in an accurate, cost-effective manner onsite, without requiring an environmentally controlled room. They decided to use the highly accurate, cost-effective Setra Model C264 as the test transducer. For qualification testing of the Setra, though, a portable, lower-cost calibrator was needed that could control the differential pressure to a high degree of resolution and transfer the accuracy of the Standards Laboratory testing to the qualification testing. The researchers decided that, to generate the low-differential pressure setpoints needed for qualification testing, very small gas volume changes could be made against the test article, and a corresponding pressure change would be detected by a pressure standard. This allowed the researchers to recreate cleanroom air pressure settings without the use of a cleanroom. Thus was born the low-differential pressure generator. In 1993, a prototype was developed using a pair of PVC tanks, a volume controller, and a 1-pound-per-square-inch pressure standard. By 1995, the prototype was perfected into the unit that is still used today.

Source record↗

Iterative Workflow for Quantification and Minimization of Reduced Chemistry-Induced Uncertainties in Reacting Flow Simulation

Reduced chemistry models mitigate computational cost but introduce two sources of uncertainties in reacting flow simulation, including chemical information loss due to model reduction, and approximation errors due to non-optimal projection. We present an iterative workflow for quantification and minimization of reduced chemistry-induced uncertainties in reacting flow simulations.

chemistry↗

Application of parametric weight and cost estimating relationships to future transport aircraft

A model comprised of system level weight and cost estimating relationships for transport aircraft is presented. In order to determine the production cost of future aircraft its weight is first estimated based on performance parameters, and then the cost is estimated as a function of weight. For initial evaluation CERs were applied to actual system weights of six aircraft (3 military and 3 commercial) with mean empty weights ranging from 30,000 to 300,000 lb. The resulting cost estimates were compared with actual costs. The average absolute error was only 4.3%. Then the model was applied to five aircraft still in the design phase (Boeing 757, 767 and 777, and BAC HS146-100 and HS146-200). While the estimates for the 757 and 767 are within 2 to 3 percent of their assumed break-even costs, it is recognized that these are very sensitive to the validity of the estimated weights, inflation factor, the amount assumed for nonrecurring costs, etc., and it is suggested that the model may be used in conjunction with other information such as RDT&E cost estimates and market forecasts. The model will help NASA evaluate new technologies and production costs of future aircraft.

Beltramo, M. N.↗

Cooperative Transmission Expansion Planning Experiment Data and Results

GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within GO WEST framework. In this sense, TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. TEP minimizes the total cost of the system which comprises the operational cost of satisfying electricity demand (i.e., generation cost), cost of loss of load (i.e., unserved energy), cost of power flow, and cost of new transmission capacity additions (i.e., investment cost). In order to use TEP model, users need to create scenarios with GO WEST framework. In this analysis, outputs from several models are used to create future inputs to GO WEST and TEP models, including GCAM-USA, TELL, CERF and reV. This dataset includes experiment inputs and outputs from three different transmission expansion scenarios (cooperative, intermediate, and individual) for 2019 and 2059. For 2019, a base scenario to illustrate the default (i.e., historical) power grid operations is also included. This study utilizes rcp45hotter_ssp3 scenario from a previous version of GCAM-USA simulations. Sources of the shapefiles in supplementary data are HIFLD Open and U.S. Energy Atlas. Please see the README file for a detailed description of the main and supplementary data.

Capacity Expansion Model↗

Experiences Using Formal Methods for Requirements Modeling

This paper describes three cases studies in the lightweight application of formal methods to requirements modeling for spacecraft fault protection systems. The case studies differ from previously reported applications of formal methods in that formal methods were applied very early in the requirements engineering process, to validate the evolving requirements. The results were fed back into the projects, to improve the informal specifications. For each case study, we describe what methods were applied, how they were applied, how much effort was involved, and what the findings were. In all three cases, the formal modeling provided a cost effective enhancement of the existing verification and validation processes. We conclude that the benefits gained from early modeling of unstable requirements more than outweigh the effort needed to maintain multiple representations.

Easterbrook, Steve↗

AeroFusion: Data Fusion and Uncertainty Quantification for Entry Vehicles

AeroFusion is a NASA Langley initiative to incorporate advances in data science into the aerodynamic modeling process to improve efficiency. The effort can largely be categorized in three components: reduced-order modeling techniques, surrogate modeling techniques, and uncertainty quantification. By combining various methods from these categories, AeroFusion aims to reduce the development cost of aerodynamic models, both in terms of time and money.

Steven Snyder↗

AeroFusion: Data Fusion and Uncertainty Quantification for Entry Vehicles

AeroFusion is a NASA Langley initiative to incorporate advances in data science into the aerodynamic modeling process to improve efficiency. The effort can largely be categorized in three components: reduced-order modeling techniques, surrogate modeling techniques, and uncertainty quantification. By combining various methods from these categories, AeroFusion aims to reduce the development cost of aerodynamic models, both in terms of time and money.

Steven Snyder↗

Technoeconomic Design Optimization for Fast Reactors. Part II: Impact of Technoeconomic Constraints on Optimal Design

There is a current drive toward optimizing reactors, particularly small/micro reactors to minimize cost and maximize performance. Previous work has investigated the development of technoeconomic workflows for the design optimization of pool-type fast reactors that aim to deploy into district energy grids. Initial scoping studies verified that the workflow was capable of capturing design trends throughout a variety of design configurations and problem formulations while remaining sufficiently flexible. In this paper, this methodology is applied to understand how cost functions and technoeconomic constraints can drive optimal reactor design. Specifically, the UPu10Zr-fueled fast reactor model from Part I is adapted to include changes in the fissile content limits, control rod worth limits, control rod drive cost, and assumed fuel form. In the case of constraint relaxation at fixed power (fissile content and control rod worth limits), cost sensitivities of 5% to 10% were uncovered. Multi-objective optimization at varying reactor power levels with individualized control rod drives for each assembly (as opposed to one operational and one safety drive) increased cost by $\$10$ to $\$25$ million and substantially altered the optimal core geometry, favoring geometries with substantially fewer control rod placements relative to baseline optimization. Finally, a multi-objective optimization was performed at varying power levels with the fuel form overhauled to metallic, high-assay low-enriched uranium–based U10Zr with more refined fuel cost models. In the case of uranium fueling, the costs increased by at least $50 million relative to the baseline case. Furthermore, economic fuel zoning and lower reactivity swing cores were recovered. Each case serves to demonstrate the value of applying technoeconomic workflows to initial reactor design scoping studies to better understand the trade-off for a proposed concept between different design options.

Argonne Reactor Computation (ARC) codes↗

A Computational Study of Plume Modeling for Space Launch System Abort Scenarios

A series of viscous CFD simulations depicting Space Launch System (SLS) post Mode-1abort scenarios are conducted and analyzed. The primary purpose of these simulations is to study the effect of launch abort vehicle attitude control motor (ACM) and abort motor(AM) plume modeling fidelity on the drag of the aborted core stage. The simulations are first conducted with a fully-coupled, multi-species and chemically reacting model for the freestream gases and the solid rocket abort motor combustion products. Following this, equivalent species are created for the ACM and AM exhaust gases. These equivalent species aim to provide near the same results for core stage drag as the chemically reacting model at a significantly lower computational cost. Finally, the exhaust gases are modeled as calorically perfect air. It was concluded that under most scenarios, both the equivalent species and the perfect air models predict the drag of the aborted core stage sufficiently close to that predicted by the chemically reacting simulations. In these cases, the flow and geometry conditions did not induce significant changes in the composition of the ‘true’ exhaust gas via afterburning of the combustion products. For cases where this was not the case, the properties of the essentially frozen equivalent species and perfect air models deviated from the true mixture, yielding unsatisfactory results. Finally, the accuracy improvements by using the chemically reacting model are weighed against the significant increase in associated computational costs. It was found that the simplified exhaust gas models are at least 4 times less expensive than the chemically reacting model. This may justify the judicious use of these simple models in future work.

CFD↗

A Computational Study of Plume Modeling for Space Launch System Abort Scenarios

A series of viscous CFD simulations depicting Space Launch System (SLS) post Mode-1abort scenarios are conducted and analyzed. The primary purpose of these simulations is to study the effect of launch abort vehicle attitude control motor (ACM) and abort motor(AM) plume modeling fidelity on the drag of the aborted core stage. The simulations are first conducted with a fully-coupled, multi-species and chemically reacting model for the freestream gases and the solid rocket abort motor combustion products. Following this, equivalent species are created for the ACM and AM exhaust gases. These equivalent species aim to provide near the same results for core stage drag as the chemically reacting model at a significantly lower computational cost. Finally, the exhaust gases are modeled as calorically perfect air. It was concluded that under most scenarios, both the equivalent species and the perfect air models predict the drag of the aborted core stage sufficiently close to that predicted by the chemically reacting simulations. In these cases, the flow and geometry conditions did not induce significant changes in the composition of the ‘true’ exhaust gas via afterburning of the combustion products. For cases where this was not the case, the properties of the essentially frozen equivalent species and perfect air models deviated from the true mixture, yielding unsatisfactory results. Finally, the accuracy improvements by using the chemically reacting model are weighed against the significant increase in associated computational costs. It was found that the simplified exhaust gas models are at least 4 times less expensive than the chemically reacting model. This may justify the judicious use of these simple models in future work.

CFD↗

Evaluating technology upgrades as a complement to traditional bill assistance programs

State programs to improve energy affordability and reduce energy burden (the share of income spent on energy) could mitigate concerns about rising electricity prices. Many states offer bill assistance and rebates for weatherization and rooftop solar to income-qualifying households. Here, we analyse how these approaches may complement one another to improve energy affordability. Results illustrate trade offs between program costs and energy burden reduction, as well as between a program’s upfront and ongoing costs. Generally, the three strategies complement one another to improve energy affordability at lower net present cost than bill assistance alone, with varying effects across regions. Weatherization can reduce the solar installation capacity needed, and both together can reduce or eliminate ongoing reliance on bill assistance. Under full uptake among cost-effective households, fully rebated weatherization and solar rebated at $0.48/Watt, combined with bill assistance, yield the same modelled net present cost as bill assistance alone while reducing the share of low-income owner-occupied households with high energy burdens from 66% to 19%, compared with 34% under bill assistance alone. Absent tax credits, weatherization still reduces energy burden and bill assistance program costs, whereas solar may not be cost-competitive.

Forrester, Sydney P↗

Techno-Economic Optimization of the SurgeWEC Device

This is the post-access report for a Teamer-funded effort to optimize the SurgeWEC device, a near-shore pivoting flap wave energy conversion device used to desalinate water. Parametrically driven cost and performance models enabled an integrated optimization approach at the farm scale. The metric used for this study was the levelized cost of water (LCOW). The data-set includes: 1. A public-domain post access report 2. An excel file with the data and plots generated under this study

16 TIDAL AND WAVE POWER↗

Machine learning for surrogate process models of bioproduction pathways

Technoeconomic analysis and life-cycle assessment are critical to guiding and prioritizing bench-scale experiments and to evaluating economic and environmental performance of biofuel or biochemical production processes at scale. Traditionally, commercial process simulation tools have been used to develop detailed models for these purposes. However, developing and running such models can be costly and computationally intensive, which limits the degree to which they can be shared and reproduced in the broader research community. This study evaluates the potential of an automated machine learning approach to develop surrogate models based on conventional process simulation models. The analysis focuses on several high-value biofuels and bioproducts for which pathways of production from biomass feedstocks have been well-established. The results demonstrate that surrogate models can be an accurate and effective tool for approximating the cost, mass and energy balance outputs of more complex process simulations at a fraction of the computational expense.

09 BIOMASS FUELS↗

Cost effective management of space venture risks

The development of a model for the cost-effective management of space venture risks is discussed. The risk assessment and control program of insurance companies is examined. A simplified system development cycle which consists of a conceptual design phase, a preliminary design phase, a final design phase, a construction phase, and a system operations and maintenance phase is described. The model incorporates insurance safety risk methods and reliability engineering, and testing practices used in the development of large aerospace and defense systems.

Giuntini, Ronald E.↗

The software-cycle model for re-engineering and reuse

This paper reports on the progress of a study which will contribute to our ability to perform high-level, component-based programming by describing means to obtain useful components, methods for the configuration and integration of those components, and an underlying economic model of the costs and benefits associated with this approach to reuse. One goal of the study is to develop and demonstrate methods to recover reusable components from domain-specific software through a combination of tools, to perform the identification, extraction, and re-engineering of components, and domain experts, to direct the applications of those tools. A second goal of the study is to enable the reuse of those components by identifying techniques for configuring and recombining the re-engineered software. This component-recovery or software-cycle model addresses not only the selection and re-engineering of components, but also their recombination into new programs. Once a model of reuse activities has been developed, the quantification of the costs and benefits of various reuse options will enable the development of an adaptable economic model of reuse, which is the principal goal of the overall study. This paper reports on the conception of the software-cycle model and on several supporting techniques of software recovery, measurement, and reuse which will lead to the development of the desired economic model.

Bailey, John W.↗

Computational Simulations and the Scientific Method

As scientific simulation software becomes more complicated, the scientific-software implementor's need for component tests from new model developers becomes more crucial. The community's ability to follow the basic premise of the Scientific Method requires independently repeatable experiments, and model innovators are in the best position to create these test fixtures. Scientific software developers also need to quickly judge the value of the new model, i.e., its cost-to-benefit ratio in terms of gains provided by the new model and implementation risks such as cost, time, and quality. This paper asks two questions. The first is whether other scientific software developers would find published component tests useful, and the second is whether model innovators think publishing test fixtures is a feasible approach.

Kleb, Bil↗