The impact of space and space-related activities on a local economy. a case study of boulder, colorado. part i- the input-output analysis
Impact of space and space-related activities on industry and general economy of Boulder, Colorado
ECONOMICS↗
SEARCH · Search NASA
Results for “input-output analysis”
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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Cascading economic losses from port disruptions under capacity constrained multimodal freight networks
This study quantifies how throughput disruptions at major seaports cascade through capacity-constrained multimodal freight networks and interregional production systems. We couple an agent-based model (ABM) multimodal freight simulation that resolves rerouting, terminal queueing, and inventory drawdown under binding modal and facility capacities with a multiregional output loss input-output (MRIIM) model that propagates realized delivery shortfalls across regions and sectors. The framework is demonstrated for the Port of Los Angeles using Freight Analysis Framework flows and Bureau of Economic Analysis input-output accounts and is evaluated over a 52-week horizon under deterministic sector targeted shocks and stochastic disruption realizations with uncertain severity and duration. Results indicate nonlinear amplification: realized national losses concentrate in manufacturing and transportation/warehousing even when exogenous port shocks are dispersed, suggesting that congestion spillback and limited short-run substitution can dominate the initial shock allocation. We further evaluate a tabular reinforcement-learning (Q-learning) intervention layer that selects among a small set of implementable system level levers (truck-to-rail and truck-to-barge shift settings) without overriding shipper routing, finding that such interventions reduce total losses for moderate disruptions but yield diminishing returns once substitute modes approach capacity. By linking operational freight behavior to system wide impacts under uncertainty, the proposed ABM-MRIIM pipeline provides a reusable workflow for port disruption stress testing, identification of structurally critical sectors/corridors, and evaluation of resilience interventions under realistic capacity limits.
42 ENGINEERING↗
Site Integration and Regulatory Considerations for an NPP Colocated with a Petroleum Refinery, Methanol Plant, and Wood Pulp Plant
This research explores the colocation of nuclear power plants (NPPs) with industrial applications. Three existing industrial sites were considered to demonstrate the siting process and illuminate technological gaps for future work. The three applications demonstrated for colocation here are a petroleum refinery, a methanol production plant, and a pulp and paper plant. This study uses a modified version of the EPRI siting criteria to explore the geological and demographic characteristics of the location of the current industrial site, as well as exploring external hazards from the industrial plant and its surrounding land use. Data was collected from public databases to estimate site characteristics. We then discuss how the site characteristics may impact the ability to colocate an NPP with an industrial application. The application site and 5 additional sites were explored for each application to give a general indication of the siting implications for an NPP in each area. The hazards for each industrial application was also explored to determine how colocation may impact reactor safety. The following gaps have been identified and should be explored in future research on colocation of NPPs with petroleum refineries, methanol plants, and pulp and paper plants: - There is a variety of industrial use, hazards, and pipelines in the surrounding area. A more thorough review of these hazards should be considered for colocation. - In general, the whole region around some applications seems to have softer soil, with implications for large site preparation costs. Further site investigations should prioritize looking into the geotechnical conditions. - Applications along coastlines are susceptible to flooding and hurricanes. The benefits of colocation should be weighed against the potential design implications. - The benefits of natural gas pipeline infrastructure in place should be explored further. If heat supply from the NPP is not required or not feasible due to the distance between the NPP and the application, there may be an opportunity to supply hydrogen to the plant through an existing pipeline. - Because there are several collocated industrial plants in the regions for the refinery and methanol plant, the benefits of sharing resources from the NPP should be explored further. This may open up additional sites for colocation. The following knowledge gaps were identified for the colocation of NPPs with these three industries, and industrial applications in general. These gaps are: - While the STAND tool contains many important characteristics for the reactor siting process, it is not calibrated for the colocation of NPPs with industrial facilities. - There are aspects of both the NPP and industrial application that need to be quantified for a siting analysis. Particularly, we need to understand the water intake requirements for NPPs and each application. - Further work may focus on adapting the STAND site comparison methodology to comparison of sites for co-location. This will involve using the data documented in this report as a starting point and performing a comprehensive and quantitative comparison. - Without spending significant resources, it would be impossible to gather data for each site to evaluate all aspects of siting. One approach to finding data and understanding its implications to siting is looking at FSARs for existing plants. For example, most sites considered in this study have small Vs30 values, indicating soft soil. However, there are NPPs located in the vicinity of most of the sites (e.g., Waterford Steam Electric Station near New Orleans) and reviewing available site characteristics and geotechnical data for these NPPs, might provide further information for siting. - The siting analysis in this study indicates that colocation of the NPP with the industrial site could be difficult based on external hazards, cooling requirements, weather, or population. We need to determine the impact of distance between the two facilities on cost and quality of energy transport. - This study did not touch on socioeconomic impacts for NPP colocation with industrial facilities. The input-output analysis methodology could be applied to the communities referenced in this study to determine the socioeconomic impact of these projects. - Similarly, the impacts of colocation on emergency planning was not explored in this study. The impacts on emergency planning infrastructure are somewhat related to the socioeconomic impacts, and could be explored using a similar methodology. - This study also did not address physical and cybersecurity, which will be important aspects of co-location [ref] . Cybersecurity will be important, regardless of the distance, but physical security will be important if the facilities are located very closely. Physical security might also be important for the steam lines between the plants, unless they are determined to be non-safety significant. - In many site l
08 - HYDROGEN↗
Structural dynamics of the renewable energy economy: A longitudinal input-output insights for a resilient transition
As countries accelerate their energy transitions, understanding how renewable energy (RE) systems structurally integrate into national economies is essential. This study presents a longitudinal economic input-output (EIO) analysis of the renewable energy sector in South Korea from 2016 to 2022. We develop a novel EIO-based framework that disaggregates the RE sector both by energy source (thermal, hydro, nuclear and renewable) and by industrial function (manufacturing, generation, and services), allowing for a detailed assessment of production dynamics, value-added creation, and import dependency. By quantifying backward and forward linkages and induced economic effects, the analysis reveals persistent structural vulnerabilities in renewable manufacturing and increasing sectoral interdependencies. Results reveal that while the renewable energy sector's production and value-added shares have increased, critical segments remain highly import-dependent, particularly in equipment manufacturing. The analysis highlights systemic gaps in domestic supply chain resilience and offers sector-specific insights for reducing vulnerability and enhancing energy security. Although applied to South Korea as a case study, the proposed framework is designed to be transferable to other national contexts where renewable energy planning requires economic structural insights. The findings offer policy-relevant guidance for enhancing domestic energy resilience and aligning industrial strategy with long-term decarbonization goals.
Economic linkage↗
Net-2 circuit analysis program
Circuit features, variational capabilities, input-output capabilities, and transient analysis of NET-2 circuit program
COMPUTER DESIGN↗
NASAP - Network Analysis for Systems Applications Program - Present capabilities of a maintained program
Flowgraph construction, input-output formulation, and subroutines for Network Analysis for Systems Applications Program /NASAP/
NETWORK ANALYSIS↗
Distortion and Crosstalk of Linearly Filtered, Angle-Modulated Signals
Linearly filtered angle modulated signals distortion and crosstalk analysis applied to filter input-output relation, noting satellite communication FM reception
Bedrosian, E.↗
Study of spaceborne multiprocessing, phase 2 Quarterly report
Parallel system analysis, point to point communications, input-output schemes, and organizational operation of spaceborne multiple computer
Koczela, L. J.↗
useeio-infra-app
This GitHub repository hosts the source code to an RShiny web application that implements the U.S. Environmentally-Extended Input-Output (USEEIO) model in order to conduct a screening-level life cycle analysis. The USEEIO model is an open-source model developed and maintained by the Environmental Protection Agency (EPA), along with colleagues and contractors, and is available on GitHub (https://github.com/USEPA/USEEIO). The USEEIO model incorporates environmental data into pre-existing Economic Input-Output Models, which look at the interdependence of different economic industries. In this web application, the focus is on energy infrastructure applications; however, it can be used to implement USEEIO in any area for which this model is applicable.
AS↗
Identification of linear systems
Linear systems analysis to identify dynamic characteristics of human operator based on input-output data
Englar, T. S.↗
Research on new techniques for the analysis of manual control systems Progress report, 16 Jun. - 15 Dec. 1968
Parameter estimation for continuous input-output systems with internal sampling in human controller models
Bekey, G. A.↗
Advancements in Constitutive Model Calibration: Leveraging the Power of Full‐Field DIC Measurements and In Situ Load Path Selection for Reliable Parameter Inference
Accurate material characterization and model calibration are essential for computationally supported high-consequence engineering decisions. Historically, characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data are collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called interlaced characterization and calibration (ICC) that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference and (4) incorporates these advancements into a quasi real-time feedback loop. The ICC framework is demonstrated here on the calibration of a material model using simulated full-field data for an aluminium cruciform specimen being deformed biaxially. The cruciform is actively driven through the myopically preferred load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain (EIG). Principal component analysis (PCA) is performed on the model predictions of full-field displacements, and fast surrogate models are built to approximate the input-output relationships of the expensive finite element model. Furthermore, the tools developed and demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modelling by enabling utilization of computational simulations at earlier stages of the design cycle.
Bayesian optimal experimental design↗
On the input-output stability of time-varying nonlinear feedback systems. I - Conditions derived using concepts of loop gain, conicity, and positivity.
Input-output stability conditions of time-varying nonlinear feedback systems obtained, using concepts of loop gain, conicity and positivity
FEEDBACK CONTROL SYSTEM↗
On the input-output stability of time-varying nonlinear feedback systems. II - Conditions involving circles in the frequency plane and sector nonlinearities.
Stability theory based on functional methods, examining feedback system with linear time invariant and nonlinear elements
NONLINEAR FEEDBACK↗
Microscopic Imprints of Learned Solutions in Tunable Networks
In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is an electrical contrastive local learning network of nodes connected by edges that adjust their conductances during training. When an edge conductance changes, it upsets the current balance of every node. In response, physics adjusts the node voltages to minimize the dissipated power. Learning in these systems is therefore a coupled double-optimization process, in which the network descends both a cost landscape in the high-dimensional space of edge conductances and a physical landscape—the power dissipation—in the high-dimensional space of node voltages. Because of this coupling, the physical landscape of a trained network contains information about the learned task. Here, we derive a structure-function relation for trained tunable networks and demonstrate that all the physical information relevant to the trained input-output relation can be captured by a tuning susceptibility, an experimentally measurable quantity. We supplement our theoretical results with simulations to show that the tuning susceptibility is correlated with functional importance and that we can extract physical insight into how the system performs the task from the conductances of highly susceptible edges. Our analysis is general and can be applied directly to mechanical networks, such as networks trained for protein-inspired function such as allostery.
36 MATERIALS SCIENCE↗
A computer-controlled scintiscanning system and associated computer graphic techniques for study of regional distribution of blood flow.
Two methods whereby a digital computer may be used to regulate a scintiscanning process are discussed from the viewpoint of computer input-output software. The computer's function, in this case, is to govern the data acquisition and storage, and to display the results to the investigator in a meaningful manner, both during and subsequent to the scanning process. Several methods (such as three-dimensional maps, contour plots, and wall-reflection maps) have been developed by means of which the computer can graphically display the data on-line, for real-time monitoring purposes, during the scanning procedure and subsequently for detailed analysis of the data obtained. A computer-governed method for converting scintiscan data recorded over the dorsal or ventral surfaces of the thorax into fractions of pulmonary blood flow traversing the right and left lungs is presented.
Coulam, C. M.↗
Control theory analysis of a three-axis VTOL flight director
A control theory analysis of a VTOL flight director and the results of a fixed-based simulator evaluation of the flight-director commands are discussed. The VTOL configuration selected for this study is a helicopter-type VTOL which controls the direction of the thrust vector by means of vehicle-attitude changes and, furthermore, employs high-gain attitude stabilization. This configuration is the same as one which was simulated in actual instrument flight tests with a variable stability helicopter. Stability analyses are made for each of the flight-director commands, assuming a single input-output, multi-loop system model for each control axis. The analyses proceed from the inner-loops to the outer-loops, using an analytical pilot model selected on the basis of the innermost-loop dynamics. The time response of the analytical model of the system is primarily used to adjust system gains, while root locus plots are used to identify dominant modes and mode interactions.
Niessen, F. R.↗
Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation
We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.
97 MATHEMATICS AND COMPUTING↗