Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “operator complexity”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Time-Dependent Reliability Analysis of Nuclear Hybrid Energy Systems

This paper focuses on developing a framework for comprehensive time-dependent reliability analysis of a nuclear hybrid energy system (NHES) design. Subsystem interactions of this complex integrated system under stochastic electricity demand and load-following operation capabilities requires dynamic reliability assessment of the NHES in component, subsystem and system level. In order to capture the dynamic operational behavior of critical systems and components, a detailed thermal hydraulic model of the NHES is generated using Modelica and tested under different electricity demand histories generated using RAVEN. The physical data (e.g., valve position, flow rates, inlet outlet water temperatures) gathered from Modelica used to calculate time-dependent failure rates through fitting data into the piecewise Weibull distribution. Optimum maintenance interval is calculated, and maintenance cost estimates based on the calculated reliability metric is presented for the selected component. Component time-dependent failure rates fed into the subsystem reliability model built with non-Markovian Stochastic Petri Nets. The component and subsystem failure rates are calculated and updated in every hour to characterize the system behavior appropriately and to understand operational reliability of the NHES design in a given time.

Guler Yigitoglu, Askin↗
DOE OSTI2015

Sustainable disposition of unneeded materials and chemicals at Y-12 national security complex - 15181

The Y-12 National Security Complex (Y-12) strives to integrate sustainability into all aspects of operations. In 2006, Y-12 conducted a site inventory to identify Unneeded Materials and Chemicals (UMC). The final inventory included over 9,400 materials and equipment and over 9,000 chemicals. A cross-functional UMC Project Team (Team) was established to manage and facilitate disposition of inventory items. A Site Plan was developed to outline the strategy to methodically disposition UMC's with the commitment to put sustainability practices first. The Team works to overcome challenges and find new lives for inventory items prior to dispositioning the materials as waste. Cost-effective strategies are used to transfer UMC items to a new owner. The Team utilizes a systematic disposition evaluation process, which does not simply manage UMC's as waste but focuses on reuse with waste disposal as last resort. The dedicated efforts of the Team to divert items from disposal as waste are reflected in the results. Through Fiscal Year 2013, 35 percent of all chemicals and 55 percent of equipment and materials have been reused or recycled. Through Fiscal Year 2013, the Team has dispositioned over 9.62 million pounds of materials (11,600 items) and down-posted over 97,000 square feet of radiological areas. In Fiscal Year 2014, a UMC Phase II inventory was developed to address UMCs that have accumulated since the original inventory was finalized. Lessons learned from the original UMC scope are being applied to phase II activities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Reliability Assessment of Safety-Critical Network Communication in a Digitalized Nuclear Power Plant

In analog instrumentation and control (I and C) systems for nuclear power plants (NPPs), most connections are comprised of hardwired point-to-point types. Since they are based on analog technologies, they are fragile to noise and require analog-to-digital conversion time. Therefore, many NPPs have adopted communication network for the exchange of safety-critical data between programmable logic controllers (PLCs) in digital I and C systems to effectively accommodate a huge number of field controllers. However, the primary issue of digital network communication in a safety-critical system involves potential hazards, such as partial or total loss of communication between PLCs, which may result in a failure of safety critical signal generation when it is needed. Therefore, the probability that a safety-critical system in digitalized NPP becomes unsafe due to a network failure must be evaluated to quantify the risk of digital I and C system. In Korea, the Engineered Safety Feature-Component Control System (ESF-CCS) was developed as one of the digital safety-critical system in Advanced Power Reactor- 1400 (APR-1400). The function of a digital engineered safety feature (ESF) actuation system and field actuator controllers of conventional NPPs are performed by the ESF-CCS. In this system, safety-critical network, High Reliable- Safety Data Network (HR-SDN), was employed for the safety-critical data transmission from group controllers (GCs) to loop controllers (LCs). In this study, a framework for assessing the reliability of a HR-SDN network communication between GCs and LCs in the ESF-CCS in addition to a fault-tree model for ESF components considering network failure between GCs and LCs were developed to assess the plant risk effect of a newly developed digitalized ESF-CCS. Based on the case studies and cut set analysis, the risk effect of network failure on plant risk and the important risk contributors of network failure were quantitatively addressed. A framework for identifying the potential hazardous states of network communication in the ESFCCS was proposed. In addition, a fault-tree model for network communication failure was developed to estimate the risk effects of network failure between the GCs and LCs on ESF-CCS signal failure. The developed fault-tree model was then applied to several case studies. The fault-tree model of ESF-CCS signal failure for ESF components required for various mitigation actions regarding NPP DBAs was designed by considering the identified hazardous states of network failure that would result in a failure to provide input signals to the corresponding LC. As a sensitivity study, three case studies were performed based on the periodic test methods for hardware components in HR-SDN module and their test intervals in this study. The quantitative results for three case studies demonstrated that the probability of overall network communication failure, which was calculated as the sum of the failure probability associated with each failure cause, contributed up to 1.58% on the Small-LOCA CD frequency. The results also revealed the important basic events related to network failure in the case studies which included CCF of hardware and software in HR-SDN module. This study is expected to provide insight into the development of the fault-tree model for the network failure in digital I and C system and into the quantification of the risk effect of network failure for safety-critical data transmission in NPPs. As a further study, the risk effects of network failure in the ESF-CCS on the plant risk can be estimated in a more detailed manner by analyzing the failure modes and causes in a sub-component level and the fault detection coverage of the fault tolerance techniques for network failure implemented in ESF-CCS after the detailed configuration of ESF-CCS is decided. In terms of the failure of manual ESF actuation, the relationship among the human action failures must be investigated to consider multiple human error condition since ESF-CCS provides multiple manual actuation to assure the diversity of the system. Since the conventional HEP method cannot accommodate the multiple conditions in a fault tree, condition-based human reliability assessment (CBHRA) method or the dynamic human reliability modeling approach can be applied to include a complex relationship among the automated safety signal generation and the human operator's manual actuation, avoiding the optimistic estimation of HEP of the human action failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

In situ Detection of Plasma Induced Surface Interaction based on Deep Learning based Visual Diagnostics (Technical Report)

It is characteristic for many plasma devices to undergo plasma-material interaction leading to surface erosion. These processes, often not easily detectable, lead to changes in device performance and lifespan. State-of-the-art lifetime tests and wear experiments require over 1000s hours. A self-consistent model for accurately predicting the erosion's effects is not available. In situ detection of these processes is not a trivial task since the surface variations at the early stages have a micron scale. Such limitations not only restrict testing and prediction capabilities but also slow the development of new thrusters and limit mission duration. To address these challenges, an in-situ diagnostic for real-time erosion assessment has been developed, aiming to expedite lifetime testing and broaden experimental campaigns. Several works were dedicated to real-time and in situ monitoring of material erosion during plasma exposure using laser holography, microscopy, and with telemicroscopes. However, the applicability of these approaches is limited due to complexity, cost and less flexibility as they often require placing diagnostic equipment inside the vacuum chamber. In collaboration with Princeton Collaborative Research Facility (PCRF), Princeton Plasma Physics Laboratory (PPPL), a new diagnostic approach is developed, where geometry modifications to the ceramic channel walls were introduced that would result in accelerated channel erosion. We employed Long-distance microscope (LDM) imagery, combined with Deep-Learning based Shape from focus or depth from focus (DFF or SFF) approach, that provides an accessible and cost-effective solution. LDM employs focus variation techniques to continuously capture multiple images of the target object at distinct focal planes. DFF, an optical focus variation method, generates a 3D topographical surface depth map from a sequence of variably focused images. Combined with the developed diagnostic, this approach offers a controllable means to study erosion under accelerated conditions. In this work, we develop Neural Network-based DFF algorithm applicable for LDM data to quantitatively evaluate plasma induced surface modification from LDM data. Next, we develop Deep Learning-based super-resolution depth map image reconstruction technique to increase the resolution of depth maps obtained from DFF algorithm to improve the accuracy of erosion measurements. Thirdly, we develop several image processing techniques to remove noise and improve the quality of depth map image. Here we report the results of initial tests for this approach. An experimental setup designed and built in PPPL was employed that consists of a 3-cm gridded ion source that produces a neutralized argon beam with energies up to 600 eV. A hexagonal boron nitride (h-BN) ceramic target, designed based on computational predictions, was used. Tests were conducted to reconstruct the complex geometry of the target under the lighting conditions of the operated ion source.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Nonlinear modelling of polymer electrolyte membrane fuel cell stack using nonlinear cancellation technique

Fuel cells are promising new energy conversion devices that are friendly to the environment. A set of control systems are required in order to operate a fuel cell based power plant system optimally. For the purpose of control system design, an accurate fuel cell stack model in describing the dynamics of the real system is needed. Currently, linear model are widely used for fuel cell stack control purposes, but it has limitations in narrow operation range. While nonlinear models lead to nonlinear control implemnetation whos more complex and hard computing. In this research, nonlinear cancellation technique will be used to transform a nonlinear model into a linear form while maintaining the nonlinear characteristics. The transformation is done by replacing the input of the original model by a certain virtual input that has nonlinear relationship with the original input. Then the equality of the two models is tested by running a series of simulation. Input variation of H2, O2 and H2O as well as disturbance input I (current load) are studied by simulation. The error of comparison between the proposed model and the original nonlinear model are less than 1 %. Thus we can conclude that nonlinear cancellation technique can be used to represent fuel cell nonlinear model in a simple linear form while maintaining the nonlinear characteristics and therefore retain the wide operation range.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2016

E-36: The First Proto-Megascience Experiment at NAL

E-36, an experiment on small-angle proton-proton scattering, began testing equipment at the National Accelerator Laboratory (NAL) using a newly achieved 100 GeV proton beam on February 12, 1972, marking the beginning of NAL’s experimental program. This experiment, which drew collaborators from NAL, the Joint Institute for Nuclear Research (Dubna, USSR), the University of Rochester (Rochester, New York), and Rockefeller University (New York, New York) was significant not only as a milestone in Fermilab’s history but also as a model of cooperation between the East and West at a time when Cold War tensions still ran high. An examination of the origin, operation, and resolution of E-36 and the chain of experiments it spawned reveals the complex interplay of science and politics that drove these experiments as well as seeds of the megascience paradigm that has come to dominate high energy physics research since the 1970s.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Asymptotic and spectral analysis of the gyrokinetic-waterbag integro-differential operator in toroidal geometry

Achieving plasmas with good stability and confinement properties is a key research goal for magnetic fusion devices. The underlying equations are the Vlasov–Poisson and Vlasov–Maxwell (VPM) equations in three space variables, three velocity variables, and one time variable. Even in those somewhat academic cases where global equilibrium solutions are known, studying their stability requires the analysis of the spectral properties of the linearized operator, a daunting task. We have identified a model, for which not only equilibrium solutions can be constructed, but many of their stability properties are amenable to rigorous analysis. It uses a class of solution to the VPM equations (or to their gyrokinetic approximations) known as waterbag solutions which, in particular, are piecewise constant in phase-space. It also uses, not only the gyrokinetic approximation of fast cyclotronic motion around magnetic field lines, but also an asymptotic approximation regarding the magnetic-field-induced anisotropy: the spatial variation along the field lines is taken much slower than across them. Together, these assumptions result in a drastic reduction in the dimensionality of the linearized problem, which becomes a set of two nested one-dimensional problems: an integral equation in the poloidal variable, followed by a one-dimensional complex Schrödinger equation in the radial variable. We show here that the operator associated to the poloidal variable is meromorphic in the eigenparameter, the pulsation frequency. We also prove that, for all but a countable set of real pulsation frequencies, the operator is compact and thus behaves mostly as a finite-dimensional one. The numerical algorithms based on such ideas have been implemented in a companion paper [D. Coulette and N. Besse, “Numerical resolution of the global eigenvalue problem for gyrokinetic-waterbag model in toroidal geometry” (submitted)] and were found to be surprisingly close to those for the original gyrokinetic-Vlasov equations. The purpose of the present paper is to make these new ideas accessible to two readerships: applied mathematicians and plasma physicists.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Integration of the ITER Tokamak C-model with the Building Model

The pellet injector system is designed to accommodate the requirement of continuous plasma refueling during ITER Tokamak operations. Through pellet flight tubes/injection lines, the pellets can directly reach the interior of the tokamak vacuum vessel. The pellet injector system is located at a port cell outside of the Tokamak bio-shield. The plasma radiation source in the tokamak vacuum vessel, together with the highly activated surrounding water in port cells, present major radiation challenges for the pellet injector system during ITER operation and after shutdown. These sources produce a radiation field encompassing the entire Tokamak complex. The ITER neutronics reference model C-model is an ITER radiation transport model. It represents a 40 deg. regular torus sector out to the bio-shield, which consists of 38 individual systems. The Monte Carlo N-Particle (MCNP) input of the C-model consists of more than a hundred thousand cell definitions and surface definitions, and over three hundred material compositions. The latest tokamak complex MCNP model is significantly more detailed than any previous releases. It consists of 36,863 cell definitions, 57,105 surface definitions, and a total of 21 different types of materials. The C-model and tokamak building model share the bio-shield as the boundary between them. In general, the radiation fields in the building due to the plasma source during tokamak operation are determined by generating the radiation source in the inner faces of the bio-shield. An inherent limitation of this approach is that it can only approximate the directions in which streaming paths are produced through the bio-shield plugs. To better estimate the shutdown dose rate for the pellet injector system due to the plasma source, we have decided to integrate the tokamak model (C-model) with the complex building model. Both MCNP models are provided by the ITER International Organization (IO) to the Oak Ridge National Laboratory (ORNL) Radiation Transport (RT) group.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A Machine Learning Framework to Deconstruct the Primary Drivers for Electricity Market Price Events

As the electricity grid is moving towards a 100% Renewable Energy Source Bulk Power Grid, the overall operations of the power system operations and electricity markets are changing. The electricity markets are not only dispatching resources economically but also taking into account various controllable actions like renewable curtailment, transmission congestion mitigation, and energy storage optimization to make sure the grid is operating reliably. As a result, price formations in electricity markets have become quite complex. Traditional root cause analysis and statistical approaches are rendered inapplicable to analyze and infer the main drivers behind price formation in the modern grid and markets with variable renewable energy (VRE). In this paper, we propose a machine learning analysis framework to deconstruct some primary drivers for price formation in modern electricity markets with high renewable energy and the outcomes can be utilized for various critical aspects of market design, renewable dispatch and curtailment, operations, and cyber-security applications. The framework can be applied to any ISO or market data and in this paper it is applied to open-source publicly available datasets from California Independent System Operator (CAISO) and ISO New England.

machine learning (ML), electricity markets, Renewa↗
DOE OSTI2022

Cation–Ligand Interactions Dictate Salt Partitioning and Diffusivity in Ligand-Functionalized Polymer Membranes

Membranes are an attractive alternative to current thermal separations due to their scalability and energy efficiency in desalinating water. Unfortunately, many of the conventional membrane materials available today are unable to differentiate between ionic solutes, especially alkali cations, compromising their use in ion–ion separations. Inspired by the ion-specific interactions exhibited by biological ion channels, recent research efforts have focused on synthesizing and characterizing new polymeric materials that incorporate ligands into polymer networks to bias solubility and/or diffusivity of one cationic species over another. Despite these efforts, little is known about the influence of incorporating ligands into polymer membranes on solubility and diffusivity of the complexing species. In this study, we first build a qualitative model of salt partitioning, diffusivity, and permeability in generic cation-complexing ligand-functionalized polymer membranes. Next, to validate our model and hypotheses, we perform atomistic molecular dynamics simulations of a 12-crown-4-functionalized membrane in the presence of alkali halide salts at low concentration. Generally, cation complexation enhances cation solubility but decreases diffusivity. Interestingly, the reduction in diffusivity is predicted to be larger than the enhancement in solubility for materials which operate by the mechanisms proposed in our physical picture, ultimately resulting in a reduction in the permeability of the selectively complexing ion.

36 MATERIALS SCIENCE↗
DOE OSTI2022

GOOML - Finding Optimization Opportunities for Geothermal Operations: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach. We have used this framework to develop digital twins that provide steamfield operators with an operational environment to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management for real world applications. The GOOML modeling software is built on a generic component-based systems framework that allows for both historical and forecast analysis. A GOOML model can perform historical data-assimilation using first-principal thermodynamics to create a meaningful data model. Historical production data can then be coupled with a forecast framework to train machine-learning models of steamfield components to predict future outputs. This modeling environment enables digital exploration of steamfield design configurations and operational scenarios. GOOML digital twins have been developed for steamfields in New Zealand and the United States representing differing power generation and field conditions. These digital twins have been validated by comparing hindcast predictions against historical production data. Reinforcement learning experiments were conducted to demonstrate the ability to programmatically explore the operations space using machine learning agents. Our initial results are compelling; two to five percent increases in annual energy production were demonstrated by the GOOML models with no additional infrastructure build required. GOOML offers a new approach to geothermal operations by applying state-of-the-art machine learning algorithms, comprehensive data analytics, and interaction with digital twins. Through application of these tools, operators will realize greater availability and higher net generation which will increase the cost effectiveness of geothermal energy projects.

access↗
DOE OSTI2016

Are Lock-Free Concurrent Algorithms Practically Wait-Free?

Lock-free concurrent algorithms guarantee that some concurrent operation will always make progress in a finite number of steps. Yet programmers prefer to treat concurrent code as if it were wait-free, guaranteeing that all operations always make progress. Unfortunately, designing wait-free algorithms is generally a very complex task, and the resulting algorithms are not always efficient. Although obtaining efficient wait-free algorithms has been a long-time goal for the theory community, most nonblocking commercial code is only lock-free. This article suggests a simple solution to this problem. We show that for a large class of lock-free algorithms, under scheduling conditions that approximate those found in commercial hardware architectures, lock-free algorithms behave as if they are wait-free. In other words, programmers can continue to design simple lock-free algorithms instead of complex wait-free ones, and in practice, they will get wait-free progress. Our main contribution is a new way of analyzing a general class of lock-free algorithms under a stochastic scheduler. Our analysis relates the individual performance of processes to the global performance of the system using Markov chain lifting between a complex per-process chain and a simpler system progress chain. We show that lock-free algorithms are not only wait-free with probability 1 but that in fact a general subset of lock-free algorithms can be closely bounded in terms of the average number of steps required until an operation completes. Here, to the best of our knowledge, this is the first attempt to analyze progress conditions, typically stated in relation to a worst-case adversary, in a stochastic model capturing their expected asymptotic behavior.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Tutorial on X-Ray Free-Electron Lasers

This article provides a tutorial on X-ray free-electron lasers (XFELs) which are currently being designed, built, commissioned, and operated as fourth-generation light sources to enable discovery science in materials science, biology, and chemistry. XFELs are complex devices, driven by high-energy, high-brightness electron accelerators and cost on the order of $B. Here, we provide a basic introduction to their operating physics and a description of their main accelerator components. To make their basic operating principle accessible to the electrical engineering community, we rederive the FEL dispersion relation in a manner similar to that done for traveling-wave tubes. We finish with sections describing some unique features of the X-rays generated and on the physics that lead to the main design limitations, including approaches for mitigation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Qubit Lattice Algorithms based on the Schrodinger-Dirac representation of Maxwell Equations and their Extensions

It is well known that Maxwell equations can be expressed in a unitary Schrodinger-Dirac representation for homogeneous media. However, difficulties arise when considering inhomoge- neous media. A Dyson map points to a unitary field qubit basis, but the standard qubit lattice algorithm of interleaved unitary collision-stream operators must be augmented by some sparse non-unitary potential operators that recover the derivatives on the refractive indices. The effect of the steepness of these derivatives on two dimensional scattering is examined with simulations showing quite complex wavefronts emitted due to transmissions/reflections within the dielectric objects. Maxwell equations are extended to handle dissipation using Kraus operators. Then, our theoretical algorithms are extended to these open quantum systems. A quantum circuit diagram is presented as well as estimates on the required number of quantum gates for implementation on a quantum computer.

Vahala, George↗
DOE OSTI2020

Quantifying Dynamic On-State Resistance of GaN HEMTs for Power Converter Design via a Survey of Low and High Voltage Devices

Designing and optimizing high switching frequency, ultra-efficient converters requires detailed knowledge of the behavior and parasitic parameters for both active and passive components. Recently, wide bandgap transistors have enabled simultaneous increases in both switching frequency and efficiency due to higher maximum operating junction temperature limits, lower dc on-state resistances, and reduced parasitic inductances and capacitances. Yet, the early acceptance of gallium-nitride (GaN) switches was plagued by detrimental dynamic on-state resistance effects. This complex phenomenon for GaN devices is characterized by deviations in on-state resistance from dc operating characteristics based on design choices such as the magnitude and duration of both voltage and current stress, switching mode, and junction temperature. While device manufacturers have made improvements compared to early generation devices, experimental evidence from a survey of commercial GaN transistors highlight measurable change in on-state resistance still exists due to variations in voltage stress during hard-switching operation. After sharing insights for obtaining low noise measurements, an analysis method along with two metrics are proposed to characterize dynamic on-state resistance measurements for power electronics designers. Furthermore, quantifying the performance of GaN devices with standardized metrics facilitates a fair comparison between different GaN device technologies during converter development, enables manufacturing qualification for GaN switches, and provides a benchmark to catalyze improvement for the next generation of GaN device development.

14 SOLAR ENERGY↗
DOE OSTI2023

Resilient Communication Scheme for Distributed Decision of Interconnecting Networks of Microgrids

Networking of microgrids can provide the operational flexibility needed for the increasing number of DERs deployed at the distribution level and supporting end-use demand when there is loss of the bulk power system. But, networked microgrids are vulnerable to cyber-physical attacks and faults due to the complex interconnections. As such, it is necessary To design resilient control systems to support the operations of networked microgrids in responses to cyber-physical attacks and faults. This paper introduces a resilient communication scheme for interconnecting multiple microgrids to support critical demand, in which the interconnection decision can be made distributedly by each microgrid controller even in the presence of cyberattacks to some communication links or microgrid controllers. This scheme blends a randomized peer-to-peer communication network for exchanging information among controllers and resilient consensus algorithms for achieving reliable interconnection agreement. The network of 6 microgrids divided from a modified 123-node test distribution feeder is used to demonstrate the effectiveness of the proposed resilient communication scheme.

Vu, Thanh Long↗
DOE OSTI2023

Developing energy flexibility in clusters of buildings: A critical analysis of barriers from planning to operation

This paper examines building energy flexibility at an aggregated level and addresses the main barriers and research gaps for the development of this resource across three design and development phases: market and policy, early planning and design, and operation. We review methodologies and tools and discuss barriers, challenges, and opportunities, incorporating policy, economic, technical, professional, and social perspectives. Although various legal and regulatory frameworks exist to foster the development of energy flexibility for small buildings, financing mechanisms are limited with a significant number of perceived risks undermining private investment. For the early planning and design phase, planners and designers lack appropriate tools and face interoperability challenges, which often results in insufficient consideration of demand response programs. The review of the operational phase highlighted the socio-technical challenges related to both the complexity of deployment and communication, as well as privacy and acceptability issues. Finally, the paper proposes a number of targeted research directions to address challenges and promote greater energy flexibility deployments, including capturing building demand side dynamics, improving baseline estimations and developing seamless connectivity between buildings and districts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Consensus Standardization of Slurry Simulant Development Process to Reduce Design Risk Within the DOE Complex

A critical responsibility of DOE’s Office of Environmental Management is the design, construction, and operation of equipment and facilities to process legacy radioactive waste slurries for safe, long-term disposal. The accomplishment of this mission requires the extensive use of physical and chemical simulants to represent the hazardous radioactive waste. Development and characterization of simulants can often be difficult to compare or utilize as questions exist as to the basis, techniques, or consistency associated with reported parameters. In addition, controversy has existed in the interpretation and application of the characterization data for the wastes that the simulants are intended to represent.

design↗