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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Optimal Demand Response Incorporating Distribution LMP with PV Generation Uncertainty

The utilization of aggregated demand-side flexibility via demand response (DR) has become a promising pathway for the integration of renewable energy resources in power systems. Nowadays, there are several management strategies for DR such as the price-based transactive control strategies. However, many of such existing price-based control strategies neglect the physics and operational constraints of the underlying distribution networks when computing the price, raising concerns regarding their theoretical and practical values. This paper studies this issue and investigates optimal DR (ODR) by incorporating the distribution locational marginal price (DLMP). In particular, we discuss DR in connection with DLMPs and propose a multiperiod bilevel optimization problem to find the ODR strategy. Here, the objective is to minimize the peak load, load fluctuation, and payments of load aggregators. In addition, a robust bilevel ODR model is formulated to provide a robust ODR strategy while minimizing operating costs under the worst-case realization of uncertainties; this mitigates the impact of forecasting errors on renewable energy resources. Then, we propose an efficient solution approach by employing the Karush-Kuhn-Tucker conditions and strong duality. Simulation results are presented to illustrate the mutual impacts of the interaction between DR and DLMP and the benefits of the robust ODR strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Raccoon density estimation from camera traps for raccoon rabies management

Abstract Density estimation for unmarked animals is particularly challenging, yet density estimates are often necessary for effective wildlife management. Raccoons ( Procyon lotor ) are the primary terrestrial wildlife reservoir for Lyssavirus rabies within the United States. The raccoon rabies variant (RRVV) is actively managed at landscape scales using oral rabies vaccination (ORV) within the eastern United States. To effectively manage RRVV, it is important to know the density of raccoons to appropriately scale the density of ORV baits distributed on the landscape. We compared methods to estimate raccoon densities from camera‐trap data versus more intensive capture‐mark‐recapture (CMR) estimates across 2 land cover types (upland pine and bottomland hardwood) in the southeastern United States during 2019 and 2020. We evaluated the effect of alternative camera configurations and durations of camera trapping on density estimates and used an N‐mixture model to estimate raccoon densities, including covariates on abundance and detection. We further compared different methods of scaling camera‐based counts, with the maximum number of raccoons seen on any given image within a day best explaining density. Camera‐trap density estimates were moderately correlated with CMR estimates ( r = 0.56). However, densities from camera‐trap data were more reliable when classifying category of density as an index used to inform management (83% correct when compared to CMR estimates), although the densities in our study fell into the 2 lowest density classes only. Using more cameras reduced bias and uncertainty around density estimates; however, if ≤6 camera traps were used at a site, a line transect approach proved less biased than a grid design. Camera trapping should be conducted for at least 3 weeks for more accurate estimates of raccoon population density in our study area (<5% bias). We show that camera‐trap data can be used to assign raccoon densities to management‐relevant density index bins, but more studies are needed to ensure reliability across a greater range of environmental conditions and raccoon densities.

Davis, Amy J.↗
DOE OSTI2021

Defense Waste Processing Facility Nitric-Glycolic Flowsheet Chemical Process Cell Chemistry: Part 2

The conversions of nitrite to nitrate, the destruction of glycolate, and the conversion of glycolate to formate and oxalate were modeled for the Nitric-Glycolic flowsheet using data from Chemical Process Cell (CPC) simulant runs conducted by Savannah River National Laboratory (SRNL) from 2011 to 2016. The goal of this work was to develop empirical correlation models to predict these values from measurable variables from the chemical process so that these quantities could be predicted a-priori from the sludge or simulant composition and measurable processing variables. The need for these predictions arises from the need to predict the REDuction/OXidation (REDOX) state of the glass from the Defense Waste Processing Facility (DWPF) melter. This report summarizes the work on these correlations based on the aforementioned data. Previous work on these correlations was documented in a technical report covering data from 2011-2015. This current report supersedes this previous report. Further refinement of the models as additional data are collected is recommended. The glass REDOX depends on the concentrations of nitrate and manganese (oxidants), and of glycolate, formate, oxalate, carbon, and antifoam (reductants) in the melter feed. The waste sludge contains nitrite, nitrate, manganese (Mn), and oxalate. Virtually all of the nitrite is converted to nitrate or NO+NO 2 +N 2 O gases in the CPC. The portion of the nitrite converted to nitrate increases the amount of nitrate in the sludge. The amount of glycolate in the final melter feed depends on the amount of the glycolic acid feed that is destroyed. Similarly, the amounts of formate and oxalate formed during the decomposition of glycolic acid are required. The material balance on carbon was found to not close in most cases. Generally, there was less carbon at the end of testing compared to the inputs. The most uncertain product variable was glycolate, so material balances were performed where the glycolate concentration was adjusted, usually upward, to close the balance. Correlation versus the original, as-measured, data was generally poor, but correlation against the material balance adjusted values was greatly improved. It was also shown that the correlation of the measured REDOX versus the predicted REDOX was much better when the material balance adjusted glycolate values were used. Three data series were primarily used during the regressions of the data; these series were 1) Sludge Batch 9 NG flowsheet simulant runs NG51-62 (SB9-NG); 2) Scaled Runs + Bounding Hydrogen Runs (SR+BH); and 3) Runs GN43-50 and 57 (43-50,57). The glycolate destruction was found to correlate with acid stoichiometry (AS), percent reducing acid (PRA), and for some data series, headspace to simulant volume ratio (HSV), mercury (Hg), and nitrate. Although glycolate destruction for pairs of data series (e.g., [SB9-NG] and [SR+BH]) were found to depend on HSV, the combination of all three data series was not found to have significant dependence on this variable. The best model for glycolate destruction depended on AS, nitrate, and Hg. This model predicted the product glycolate compositions of the data to within 92-106%. The conversion of glycolate to formate was high when noble metals and Hg were not present, with values up to 100%. When noble metals and Hg were present, this conversion ranged from zero to 7%, and was dependent on AS. Lower AS gave higher conversions to formate. The conversion to oxalate was found to depend on the AS and the initial concentration of nitrite. An alternative fit versus AS and the form of ruthenium (Ru) used is a possible alternative. This fit was somewhat less statistically significant. This second model predicts that more oxalate is formed when Ru-nitrosyl nitrate is used rather than Ru chloride. The conversion of glycolate to oxalate ranged from zero to 6%. The conversion of nitrite to nitrate depended primarily on AS and PRA, with HSV and Hg being significant when these variables were varied. For multiple series of data, nitrite was also needed to SRNL-STI-2017-00172 5HYLVLRQ viL distinguish between data series, and the effect of HSV became insignificant. The best model for nitrite to nitrate conversion depended on AS, PRA, nitrite, and Hg. The 95% confidence intervals on the predicted values of glycolate destruction, glycolate to oxalate conversion, and nitrite to nitrate conversion were used to determine the uncertainty in the predicted REDOX when starting with only the composition of the sludge, AS, and PRA. Using the 95% confidences on an individual value (that is the confidence in getting a particular value for one single test as opposed to what the mean would be for multiple tests), the uncertainty in the predicted REDOX was calculated. The uncertainty in the actual product composition glycolate, oxalate, formate, and nitrate concentrations translated to an uncertainty in the REDOX value of ±0.1,which is approximately the uncertainty claimed in the REDOX model itself.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Updates to Composite Analysis Base Case and Null Space Monte Carlo Sensitivity Based on New Unit Dose Factors

This environmental calculation file (ECF) presents the results of the exposure route-specific and total radiological dose assessments for the groundwater exposure pathway as a part of the updated Composite Analysis for Low-Level Waste Disposal in the Hanford Site Central Plateau (FY 2022), based on revised unit dose factors (UDFs) published in CA Special Studies: Updates to the Groundwater Pathway Radiological Dose. The reason for these radiological dose assessments is to estimate the cumulative radiological impacts from all screened sources of ionizing radiation and exposure routes that could potentially contribute to the projected dose to a hypothetical member of the public. Sources of ionizing radiation can include existing or future disposal facilities and other sources including past-practice discharge sites. Additionally, the results of the exposure route-specific and total radiological dose assessments have been revised for the groundwater pathway based on the null space Monte Carlo (NSMC) groundwater concentrations and the most recent updates to the UDFs as a part an uncertainty analysis for the updated Hanford Site CA.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Calibration and Uncertainty Estimation Using the Ensemble Kalman Filter with a Large Subsurface Flow and Transport Model - 20321

At routinely monitored groundwater contamination sites, periodically measured environmental conditions such as groundwater levels and contaminant concentrations are used to inform and confirm a conceptual site model (CSM) and guide the development and calibration of a numerical groundwater flow and transport model. The calibration of groundwater flow and transport models after each measurement (sampling) event can illuminate deficiencies in a CSM, identify areas where additional monitoring is warranted, and predict the behavior of the system to guide decision making. However, manual and automated (e.g. PEST) model calibration tools can be time-consuming and computationally expensive to implement after each sampling event. Perhaps as a result, such calibration tools generally utilize all available monitoring data simultaneously rather than sequentially assimilating monitoring data one sampling event at a time as the results from sampling become available. A more real-time data assimilation approach may reduce parameter uncertainty, quantify the value of additional monitoring data, and produce a usable model more quickly and with less effort. To mitigate the potential time-consuming aspects of manual and widely applied automated calibration techniques, a data assimilation algorithm called the ensemble Kalman filter (EnKF) was evaluated as a relatively efficient method of model calibration and uncertainty assessment via the sequential integration of monitoring data into a model. The EnKF was able to successfully and efficiently assimilate monitoring and modeling data to calibrate a complex flow and transport model at a real-world site with significant subsurface heterogeneity, uncertainty, and 12 years of monitoring data (over 4,000 individual measurements of groundwater levels and over 2,500 measurements of contaminant concentrations). Starting with an uncalibrated model data from annual sampling events were sequentially assimilated, and the resultant predication errors and estimated parameter uncertainties were tracked. After all monitoring data were assimilated, both flow and transport residuals at the end of the EnKF process were comparable to those produced via a concurrent PEST calibration effort but required fewer model simulations. Both uncertainty and prediction errors decreased over time. In a real-time application, the adequacy of the model could be assessed after each sampling event. The benefits of such a real-time approach to utilizing monitoring data include reduced costs (in the form of model updates or site characterization efforts), early flagging of possible errors in the CSM, and a reduced risk of overfitting and corresponding increased confidence in model predictions. This tool may be particularly useful compared to other calibration techniques (e.g. manual, PEST) when model runtimes are long, calibration parameters are many, or parameter uncertainty is large. (authors)

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NASA NTRS1992

Extended abstract: Managing disjunction for practical temporal reasoning

One of the problems that must be dealt with in either a formal or implemented temporal reasoning system is the ambiguity arising from uncertain information. Lack of precise information about when events happen leads to uncertainty regarding the effects of those events. Incomplete information and nonmonotonic inference lead to situations where there is more than one set of possible inferences, even when there is no temporal uncertainty at all. In an implemented system, this ambiguity is a computational problem as well as a semantic one. In this paper, we discuss some of the sources of this ambiguity, which we will treat as explicit disjunction, in the sense that ambiguous information can be interpreted as defining a set of possible inferences. We describe the application of three techniques for managing disjunction in an implementation of Dean's Time Map Manager. Briefly, the disjunction is either: removed by limiting the expressive power of the system, or approximated by a weaker form of representation that subsumes the disjunction. We use a combination of these methods to implement an expressive and efficient temporal reasoning engine that performs sound inference in accordance with a well-defined formal semantics.

Boddy, Mark↗
DOE OSTI2021

A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage

Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO 2 ) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation with a high-dimensional parameter space impedes fast decision-making for commercial-scale reservoir management. We propose to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast data assimilation-reservoir response forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates geologic properties and predicts reservoir performance with quantified uncertainty from pressure history and CO 2 plumes interpreted through seismic inversion. As the most computationally expensive component in such a workflow is reservoir simulation, we developed surrogate models to predict dynamic pressure and CO 2 plume extents under multi-well injection. The surrogate models employ deep convolutional neural networks, specifically, a wide residual network and a residual U-Net. The workflow is validated against a flat threedimensional reservoir model representative of a clastic shelf depositional environment. Intelligent treatments are applied to bridge between quantities in a true-3D reservoir model and those in a single-layer reservoir model. The workflow can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.

25 ENERGY STORAGE↗
DOE OSTI2023

Estimating Future Surface Water Availability Through an Integrated Climate‐Hydrology‐Management Modeling Framework at a Basin Scale Under CMIP6 Scenarios

Abstract Climate change and increasing water demand due to population growth pose serious threats to surface water availability. The biggest challenge in addressing these threats is the gap between climate science and water management practices. Local water planning often lacks the integration of climate change information, especially with regard to its impacts on surface water storage and evaporation as well as the associated uncertainties. Using Texas as an example, state and regional water planning relies on the use of reservoir “Firm Yield” (FY)—an important metric that quantifies surface water availability. However, this existing planning methodology does not account for the impacts of climate change on future inflows and on reservoir evaporation. To bridge this knowledge gap, an integrated climate‐hydrology‐management (CHM) modeling framework was developed, which is generally applicable to river basins with geographical, hydrological, and water right settings similar to those in Texas. The framework leverages the advantages of two modeling approaches—the Distributed Hydrology Soil Vegetation Model (DHSVM) and Water Availability Modeling (WAM). Additionally, the Double Bias Correction Constructed Analogues method is utilized to downscale and incorporate Coupled Model Intercomparison Project Phase 6 GCMs. Finally, the DHSVM simulated naturalized streamflow and reservoir evaporation rate are input to WAM to simulate reservoir FY. A new term—“Ratio of Firm Yield” (RFY)—is created to compare how much FY changes under different climate scenarios. The results indicate that climate change has a significant impact on surface water availability by increasing reservoir evaporation, altering the seasonal pattern of naturalized streamflow, and reducing FY.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Bayesian characterization of uncertainties surrounding fluvial flood hazard estimates

Fluvial floods drive severe risk to riverine communities. There is strong evidence of increasing flood hazards in many regions around the world. The choice of methods and assumptions used in flood hazard estimates can impact the design of risk management strategies. In this study, we characterize the expected flood hazards conditioned on the uncertain model structures, model parameters, and prior distributions of the parameters. We construct a Bayesian framework for river stage return level estimation using a nonstationary statistical model that relies exclusively on the Indian Ocean Dipole Index. We show that ignoring uncertainties can lead to biased estimation of expected flood hazards. We find that the considered model parametric uncertainty is more influential than model structures and model priors. Our results highlight the importance of incorporating uncertainty in extreme flood stage estimates, and are of practical use for informing water infrastructure designs in a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Fertilizer management for global ammonia emission reduction

Crop production is a large source of atmospheric ammonia (NH 3 ), which poses risks to air quality, human health and ecosystems. However, estimating global NH 3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy. In this report we develop a machine learning model for generating crop-specific and spatially explicit NH 3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH 3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr -1 , lower than previous estimates that did not fully consider fertilizer management practices. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH3 emissions by about 38% (1.6 ± 0.4 Tg N yr -1 ) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH3 emissions reductions of 47% (44–56%) for rice, 27% (24–28%) for maize and 26% (20–28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH3 emissions could increase by 4.0 ± 2.7% under SSP1–2.6 and 5.5 ± 5.7% under SSP5–8.5 by 2030–2060. However, targeted fertilizer management has the potential to mitigate these increases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification

We propose an explainable machine learning (ML) model with uncertainty quantification (UQ) to improve multi-step reservoir inflow forecasting. Traditional ML methods have challenges in forecasting inflows multiple days ahead, and lack explainability and UQ. To address these limitations, we introduce an encoder–decoder long short-term memory (ED-LSTM) network for multi-step forecasting, employ the SHapley Additive exPlanation (SHAP) technique for understanding the influence of hydrometeorological factors on inflow prediction, and develop a novel UQ method for prediction trustworthiness. We apply these methods to forecast 7-day inflow in snow-dominant and rain-driven reservoirs. The results demonstrate the effectiveness of the ED-LSTM model, with high forecasting accuracy for short lead times. Our UQ method provides reliable uncertainty estimates, covering 90% of data with a 90% confidence level. The SHAP analysis reveals the importance of historical inflow and precipitation as influential factors. These findings and methods may support reservoir operators in optimizing water resources management decisions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Energy Consumption and Cost Reduction of Future Light-Duty Vehicles through Advanced Vehicle Technologies: A Modeling Simulation Study Through 2050

The U.S. Department of Energy’s (DOE’s) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) aim to develop sustainable, affordable and efficient technologies for transportation of goods and people. Translating investments in advanced transportation component technologies and powertrains to estimate vehicle-level fuel savings potential is critical for understanding DOE’s impact. In this work, we simulated technologies funded by VTO and HFTO for light duty vehicles. The simulations were performed across: Multiple powertrain configurations (i.e., conventional, power-split, extended-range electric vehicle, battery electric drive, and fuel-cell vehicles), Vehicle classes (i.e., compact car, midsize car, small sport utility vehicle [SUV], midsize SUV, and pickup trucks); and Fuels (i.e., gasoline, diesel, hydrogen, and battery electricity). These various technologies are assessed for six different timeframes: laboratory years 2015, 2020, 2025, 2030, and 2045. A delay of 5 years is assumed between laboratory year and model year (year technology is introduced into production). Finally, uncertainties are included for both technology performance and cost aspects by considering two cases: Low case, aligned with DOE technology manager estimates of expected original equipment manufacturer (OEM) improvements based on regulations, business as usual; and High case, aligned with aggressive technology advancements based on R&D targets developed through support by VTO & HFTO. These scenarios are not intended as predictions of future performances. The energy and cost impact of different technologies were estimated using Autonomie (www.autonomie.net), Argonne vehicle system simulation tool. Autonomie is a state-of-the-art vehicle system simulation tool used to assess the energy consumption, performance and cost of multiple advanced vehicle technologies across classes (from light to heavy duty), powertrains (from conventional to HEVs, FCEVs, PHEVs and BEVs), components and control strategies. Autonomie is packaged with a complete set of vehicle models for a wide range of vehicle classes, powertrain configurations and component technologies, including vehicle level and component level controls. These controls were developed and calibrated using dynamometer test data. Autonomie has been used to support a wide range of studies including analyzing various component technologies, sizing powertrains components for different vehicle requirements, comparing the benefits of powertrain configurations, optimizing both heuristic and route based vehicle energy control and predicting transportation energy use when paired with a traffic modeling tool such as POLARIS. This report documents the assumptions and estimates the vehicle-level energy consumption benefits and associated technology costs for the various types of light duty vehicles. All details of vehicle assumptions and simulation results are available in the spreadsheets accompanying this report.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Commercial integration of advanced nuclear energy with Artificial Intelligence (AI): Possible implications

The integration of advanced nuclear technologies (both fission and fusion) with artificial intelligence (AI) presents unprecedented national security challenges and opportunities. As fusion energy approaches commercial viability alongside advanced Small Modular Reactors (SMRs), their integration with AI and Artificial General Intelligence (AGI) systems could fundamentally transform the global energy and AI landscapes — two pillars of national security. This document briefly examines how AI could accelerate nuclear energy development and deployment while altering existing power structures, a lot could be done to deepen the discussions. Simultaneously, it observes how nuclear-powered AI may expedite advances toward AGI and beyond. These issues are deeply interconnected and thus need to be examined as a whole and more comprehensively than what’s being summarized here. For instance, AI-powered autonomous operation of nuclear facilities could reduce human error but introduce new cybersecurity vulnerabilities and uncertainties. Further investigation would also address how AI-enhanced nuclear technologies might complicate proliferation concerns through advanced fuel cycle management, nuclear materials production and safeguard. The strategic advantage gained by first entities achieving successful AI-nuclear integration could reshape global and national security framework. Timely analysis of these implications may be crucial for policymakers seeking to harness these technologies' benefits while effectively mitigating their potential risks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Impacts of constraints and uncertainties on projected amount of Hanford low-activity waste glasses

A systematic evaluation of Hanford low-activity waste (LAW) loading in alkali-aluminoborosilicate glasses was performed. A set of 22 waste compositions were selected to represent the range of wastes anticipated across the Hanford mission. Glass composition was formulated for each waste to simultaneously satisfy a full cadre of property and composition constraints while maximizing waste loading. Glass properties were estimated using property models recently developed for Hanford LAW Facility operation. The impacts from the property and composition constraints on the glass mass to be produced were calculated and compared. Electrical conductivity, Vapor Hydration Test and SO 3 melter tolerance, and canister-centerline cooling crystal constraints have a large effect on reducing total glass mass, while the impact from Product Consistency Test, viscosity, and isothermal crystal constraints is relatively small. Additionally, effects of model prediction and process/composition uncertainties were quantified. The results identified areas of future research that could be useful to improve the efficiency of plant operation.

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DOE OSTI2022

Multi-modelling predictions show high uncertainty of required carbon input changes to reach a 4‰ target

Soils store vast amounts of carbon (C) on land, and increasing soil organic carbon (SOC) stocks in already managed soils such as croplands may be one way to remove C from the atmosphere, thereby limiting subsequent warming. The main objective of this study was to estimate the amount of additional C input needed to annually increase SOC stocks by 4‰ at 16 long-term agricultural experiments in Europe, including exogenous organic matter (EOM) additions. We used an ensemble of six SOC models and ran them under two configurations: (1) with default parametrization and (2) with parameters calibrated site-by-site to fit the evolution of SOC stocks in the control treatments (without EOM). We compared model simulations and analysed the factors generating variability across models. The calibrated ensemble was able to reproduce the SOC stock evolution in the unfertilised control treatments. We found that, on average, the experimental sites needed an additional 1.5 ± 1.2 Mg C ha -1 year -1 to increase SOC stocks by 4‰ per year over 30 years, compared to the C input in the control treatments (multi-model median ± median standard deviation across sites). That is, a 119% increase compared to the control. While mean annual temperature, initial SOC stocks and initial C input had a significant effect on the variability of the predicted C input in the default configuration (i.e., the relative standard deviation of the predicted C input from the mean), only water-related variables (i.e., mean annual precipitation and potential evapotranspiration) explained the divergence between models when calibrated. Here, our work highlights the challenge of increasing SOC stocks in agriculture and accentuates the need to increasingly lean on multi-model ensembles when predicting SOC stock trends and related processes. To increase the reliability of SOC models under future climate change, we suggest model developers to better constrain the effect of water-related variables on SOC decomposition.

4 per 1000 initiative↗
NASA NTRS2004

Improving Project Management with Simulation and Completion Distribution Functions

Despite the critical importance of project completion timeliness, management practices in place today remain inadequate for addressing the persistent problem of project completion tardiness. A major culprit in late projects is uncertainty, which most, if not all, projects are inherently subject to. This uncertainty resides in the estimates for activity durations, the occurrence of unplanned and unforeseen events, and the availability of critical resources. In response to this problem, this research developed a comprehensive simulation based methodology for conducting quantitative project completion time risk analysis. It is called the Project Assessment by Simulation Technique (PAST). This new tool enables project stakeholders to visualize uncertainty or risk, i.e. the likelihood of their project completing late and the magnitude of the lateness, by providing them with a completion time distribution function of their projects. Discrete event simulation is used within PAST to determine the completion distribution function for the project of interest. The simulation is populated with both deterministic and stochastic elements. The deterministic inputs include planned project activities, precedence requirements, and resource requirements. The stochastic inputs include activity duration growth distributions, probabilities for events that can impact the project, and other dynamic constraints that may be placed upon project activities and milestones. These stochastic inputs are based upon past data from similar projects. The time for an entity to complete the simulation network, subject to both the deterministic and stochastic factors, represents the time to complete the project. Repeating the simulation hundreds or thousands of times allows one to create the project completion distribution function. The Project Assessment by Simulation Technique was demonstrated to be effective for the on-going NASA project to assemble the International Space Station. Approximately $500 million per month is being spent on this project, which is scheduled to complete by 2010. NASA project stakeholders participated in determining and managing completion distribution functions produced from PAST. The first result was that project stakeholders improved project completion risk awareness. Secondly, using PAST, mitigation options were analyzed to improve project completion performance and reduce total project cost.

Cates, Grant R.↗
DOE OSTI2020

Emerging Contaminants: Identification, Life Cycle, and Key Challenges - 20415

The term 'emerging contaminants' and its multiple variants has come to refer to previously overlooked compounds discovered in the environment which represent a potential threat to human and ecological receptors. These compounds may be discovered in the environment as new analytical techniques are developed and/or as new monitoring programs for drinking water and groundwater are implemented, while the threat to receptors may be realized through advances in understanding of toxicology and pathways to receptors. Emerging contaminants present unique and considerable challenges as demands to address them can outpace the understanding of their toxicity, their need for regulation, their occurrence, and techniques for treating the environmental media they affect. With these challenges in mind, this paper serves as a primer for understanding emerging contaminants. The paper will lay a foundation for understanding emerging contaminants through three key topics: 1. Who identifies emerging contaminants? An overview of the various stakeholder groups around the world working on identifying potential emerging contaminants and developing systems for screening potential candidates, prioritizing, and ultimately generating regulations will be provided. 2. How Chemicals Rise into Awareness as Emerging Contaminants. The process by which contaminants emerge through a cycle of awareness, characterization, management and regulation will be discussed. Current emerging contaminants will be discussed in the context of the life cycle: emerging contaminants where impacts to drinking water supplies are driving public awareness (e.g. per and polyfuoroalkyl substances (PFASs) and 1,4-dioxane), emerging contaminants impacted by changing regulatory standards or development and debate on toxicity (e.g., hexavalent chromium, 1,2,3-trichloropropane, trichloroethylene in vapor intrusion), and those with evolving understanding of impacts and usage (e.g., microplastics). 3. Key Challenges. Unique challenges associated with emerging contaminants, including visibility and public sensitivity, uncertainty and vulnerability, will be discussed. (authors)

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