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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Roofline Analysis in the Intel® Advisor to Deliver Optimized Performance for applications on Intel® Xeon Phi™ Processor

In this session we show, in two case studies, how the roofline feature of Intel Advisor has been utilized to optimize the performance of kernels of the XGC1 and PICSAR codes in preparation for Intel Knights Landing architecture. The impact of the implemented optimizations and the benefits of using the automatic roofline feature of Intel Advisor to study performance of large applications will be presented. This demonstrates an effective optimization strategy that has enabled these science applications to achieve up to 4.6 times speed-up and prepare for future exascale architectures. # Goal/Relevance of Session The roofline model [1,2] is a powerful tool for analyzing the performance of applications with respect to the theoretical peak achievable on a given computer architecture. It allows one to graphically represent the performance of an application in terms of operational intensity, i.e. the ratio of flops performed and bytes moved from memory in order to guide optimization efforts. Given the scale and complexity of modern science applications, it can often be a tedious task for the user to perform the analysis on the level of functions or loops to identify where performance gains can be made. With new Intel tools, it is now possible to automate this task, as well as base the estimates of peak performance on measurements rather than vendor specifications. The goal of this session is to demonstrate how the roofline feature of Intel Advisor can be used to balance memory vs. computation related optimization efforts and effectively identify performance bottlenecks. A series of typical optimization techniques: cache blocking, structure refactoring, data alignment, and vectorization illustrated by the kernel cases will be addressed. # Description of the codes ## XGC1 The XGC1 code [3] is a magnetic fusion Particle-In-Cell code that uses an unstructured mesh for its Poisson solver that allows it to accurately resolve the edge plasma of a magnetic fusion device. After recent optimizations to its collision kernel [4], most of the computing time is spent in the electron push (pushe) kernel, where these optimization efforts have been focused. The kernel code scaled well with MPI+OpenMP but had almost no automatic compiler vectorization, in part due to indirect memory addresses and in part due to low trip counts of low-level loops that would be candidates for vectorization. Particle blocking and sorting have been implemented to increase trip counts of low-level loops and improve memory locality, and OpenMP directives have been added to vectorize compute-intensive loops that were identified by Advisor. The optimizations have improved the performance of the pushe kernel 2x on Haswell processors and 1.7x on KNL. The KNL node-for-node performance has been brought to within 30% of a NERSC Cori phase I Haswell node and we expect to bridge this gap by reducing the memory footprint of compute intensive routines to improve cache reuse. ## PICSAR is a Fortran/Python high-performance Particle-In-Cell library targeting at MIC architectures first designed to be coupled with the PIC code WARP for the simulation of laser-matter interaction and particle accelerators. PICSAR also contains a FORTRAN stand-alone kernel for performance studies and benchmarks. A MPI domain decomposition is used between NUMA domains and a tile decomposition (cache-blocking) handled by OpenMP has been added for shared-memory parallelism and better cache management. The so-called current deposition and field gathering steps that compose the PIC time loop constitute major hotspots that have been rewritten to enable more efficient vectorization. Particle communications between tiles and MPI domain has been merged and parallelized. All considered, these improvements provide speedups of 3.1 for order 1 and 4.6 for order 3 interpolation shape factors on KNL configured in SNC4 quadrant flat mode. Performance is similar between a node of cori phase 1 and KNL at order 1 and better on KNL by a factor 1.6 at order 3 with the considered test case (homogeneous thermal plasma).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Carbon Capture through Membranes - Leveraging Multiphysics Modeling, Dimensional Analysis and Machine Learning to Scale up and Optimize Devices and Processes for Decarbonization

We study the separation performance using membrane modules through dimensional analysis (DA). We formulate the main process equations to identify relevant dimensionless numbers inherent in the physics. In particular, we identify that the critical step in the separation process is mass transfer through the selective layer. Remarkably, the dimensionless feed flow (DFfeed) emerges as a crucial factor in describing this process. Not only does DFfeed directly appear in the governing equations, but it also holds a physical significance associated with the time scales for the mass transfer across the feed side and through the selective layer. Regarding the output performance variables, we consider the recovery, stage cut, productivity and purity. In this context, we profit from experimental data and CFD simulations to evaluate the separation performance of the modules when varying the input flowrate, the scale of the module, and the CO2 permeance. These datasets enable us to establish correlations between performance metrics and the dimensionless feed flow (DFfeed). Using simple power functions of DFfeed, we obtain R2 coefficients exceeding 0.99, indicating the accuracy of the correlations built in the present work. In the future, we wish to use DA to understand key transport mechanisms, predict and control module performance, and challenge the universality of these findings by testing various gas separations across different membrane modules beyond our case study.

Pedrozo, Hector A.↗
DOE OSTI2025

Beyond Price-Taker: Multiscale Optimization of Wind and Battery Integrated Energy Systems

Integrating renewable energy into the electric grid is challenging due to the intermittency and variability of wind and other non-dispatchable resources. Integrated energy systems (IESs) combine multiple energy technologies (e.g., fossil, nuclear, renewables, storage) to reduce costs and improve flexibility and reliability. However, standard techno-economic analysis (TEA) methods often overestimate the benefits of IESs because they fail to account for energy market adjustments. This paper systematically studies the limitations of the prevailing price-taker assumption for TEA and optimization of hybrid energy systems. As an illustrative case study, we retrofit an existing wind farm in the RTS-GMLC test system (which loosely mimics the Southwest U.S.) with battery energy storage to form an IES. We show that the standard price-taker model overestimates the electricity revenue and the net present value (NPV) of the IES up to 178% and 30.4%, respectively, compared to our more rigorous multiscale optimization. These differences arise because introducing storage creates a more flexible resource that impacts the larger wholesale electricity market. Moreover, this work highlights the impact of the IES has on the market via various strategic bidding, and underscores the importance of moving beyond price-taker for optimal storage sizing and TEA of IESs. We conclude by discussing opportunities to generalize the proposed framework to other IESs, and highlight emerging research questions regarding the complex interactions between IESs and markets.

25 ENERGY STORAGE↗
DOE OSTI2019

Extreme-Scale Distribution-Based Data Analysis

Scientists overview and identify regions of interest by transforming data into compact information descriptors that characterize simulation results and allow detailed analysis on demand. Among many existing feature descriptors, statistical information derived from data samples is a promising approach to taming the big data avalanche because data distributionscomputed from a population can compactly describe the presence and characteristics of salient data features with minimal data movement. The ability to computationally summarize and process data using distributions also provides an efficient and representative capture of information that can adjust to size and resource constraints, with the added benefit that uncertainty associated with results can be quantified and communicated. In this project, we have developed a distribution-based data analysis and visualization framework for optimizing in situ processing and analysis of extreme-scale scientific data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Optimal Control of Evolution Mixed Variational Inclusions

Optimal control problems of primal and dual evolution mixed variational inclusions, in reflexive Banach spaces, are studied. The solvability analysis of the mixed state systems is established via duality principles. The optimality analysis is performed in terms of perturbation conjugate duality methods, and proximation penalty-duality algorithms to mixed optimality conditions are further presented. Applications to nonlinear diffusion constrained problems as well as quasistatic elastoviscoplastic bilateral contact problems exemplify the theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Spacetime pq theory for AC and DC electric power systems

The 50/60 Hz alternating current (AC) electric power has been the standard and most flexible energy source powering our modern societies for one and a half centuries since the war of the currents: AC versus direct current (DC). A reactive power concept that was introduced at the beginning of the AC power was very useful for circuit/system analysis, design, control, optimization, and ultimately for more efficient and stable generation, transmission, distribution, and consumption. The initial reactive power theory was based on single-phase sinusoidal AC power to capture inductive and capacitive power that yields to net-zero average power over one fundamental cycle. Soon it was expanded to non-sinusoidal AC power and finally to instantaneous three-phase AC power. However, these reactive power theories remain separate and limited to special cases and have never been consolidated and made valid to all cases. Today, more widespread adoption of power electronics and renewable energy is bringing back DC power into the electric grids. The reactive power concept has never been applied to DC power systems. There is no reactive power in DC power systems according to the existing reactive power theories. Do DC power systems really have no reactive power? Capacitors and inductors are widely used in DC just like in AC power systems. Are they not reactive power components? Why are they different from their AC counterparts? Furthermore, are batteries active or reactive power components? What about active devices like power converters (or inverters) with AC (or DC) on one side and DC (or AC) on the other? Do they generate or consume reactive power? Finally, what about AC and DC hybrid power systems? How to define reactive power in such a complex power system that has a multitude of loads, buses, and sources? Is there reactive power between any two loads, any two buses, or any two sources in a power system and what is the total reactive power in such a complex power system as a whole? As the motivation and goal of this paper to answer the above basic questions, to unify the existing AC reactive power theories and to ultimately provide theoretical and insightful guidance for system analysis, design, control, efficiency, optimization, and operation of complex power systems, a concept of spacetime (both spatial and temporal) active and reactive power (pq) theory—the spatiotemporal aspect of active and reactive power—is developed for both AC and DC power systems. The theoretical definitions and physical meanings of the spacetime reactive power will be developed, and real applications and thought experiments/cases/exercises will be explored and discussed. The developed mathematics to define the active (or real) and reactive (or imaginary) power— p and q respectively by dot (scalar) and cross (vector) products of multi-dimension spacetime vectors and time-space mapping principle/law can have some fundamental implications as well.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Multi-Objective Optimization of Uranium Target Assembly–3: A Comparison of Genetic and Traditional Methods

Commonly produced as a byproduct of uranium fission, 99 Mo is a key medical isotope that is in high demand in the United States. An international goal is to switch from medical isotope production technologies that require highly enriched uranium to medical isotope production technologies that require only low-enriched uranium. Niowave Inc. is contributing to this goal by developing an accelerator-driven subcritical assembly called the Uranium Target Assembly (UTA). This work compares the performance of Dakota’s Multi-Objective Genetic Algorithm (MOGA) against traditional sensitivity analysis in the neutronic optimization of the UTA-3 system. The design objectives are k-eigenvalue (k eff ) and natural uranium fission power, which are directly correlated with the amount of 99 Mo produced. Dakota:MOGA did not perform as well as human engineering ingenuity in optimization studies with high numbers of input parameters, such as fuel rod type selection and fuel rod placement. However, Dakota:MOGA did outperform traditional sensitivity analysis in optimization studies with fewer than 20 parameters and revealed the degree to which each parameter influences the optimal design space for k eff and natural uranium fission power (to a lesser extent). As the design model became more complex in the final stage of design, the computational resources required to calculate the design objective values in the Monte Carlo N-Particle transport code from selected input parameter combinations limited Dakota:MOGA’s performance, and, unfortunately, human intervention was required to discern the optimal design space. In conclusion, future work will attempt to reduce computational resource constraints by incorporating areduced-order neutronics model into the optimization cycle.

Accelerator-driven systems↗
DOE OSTI2026

Coupling of high-resolution mass spectrometer and photosynthesis system for comprehensive leaf volatile metabolite profiling

Background Leaf-level biogenic volatile organic compounds (BVOCs) emissions represent a major source of organic gases in the atmosphere, influencing both climate and air quality. These emissions are strongly driven by environmental perturbations, which affect individual plant- to ecosystem-level processes. Uncovering all the BVOCs and understanding how their emissions respond to altered environmental conditions provide critical insights into vegetation-driven changes in atmospheric chemistry. We developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs. This novel system enables simultaneous, real-time monitoring of BVOC emissions and photosynthetic parameters at the leaf level, offering new opportunities to disentangle the physiological and environmental drivers of VOC release. Furthermore, we established the VOC Analysis and Processing Optimization Resource (VAPOR), an open-access software tool designed for rapid data post-processing and the analysis of the variability of hundreds of BVOCs. We assessed the performance of the tandem system under varying background conditions, using standard gas mixtures and a range of environmental factors. Results Blank emissions were substantially lower for major BVOCs (e.g., isoprene) compared to those observed in plant emissions. Despite this, the observation of background-level VOCs highlights the importance of routinely acquiring and accounting for blank measurements in analyses using the coupled instrumentation. Introduction of known VOC concentrations to the system demonstrated a linear response across different compounds with varying molecular compositions, indicating minimal gas loss regardless of chemical moieties within the coupled instrumentation. We applied the optimized system to investigate the physiological mechanisms driving BVOC emissions across different genotypes of poplar and pennycress. The high mass resolution capabilities of the PTR-ToF-MS, coupled with comprehensive VAPOR-driven data analysis, enabled the identification of several important BVOCs, including methanol and methanethiol; these BVOCs displayed substantial variation across pennycress genotypes and showed concentrations ~ 100–350% higher than the blank. Moreover, isoprene emissions varied significantly among poplar genotypes grown in different potting media. Conclusions Tandem instrumentation offers a powerful tool for profiling volatile molecular markers and elucidating their genetic and environmental underpinnings. This approach enhances our ability to predict BVOC emissions in response to genotype by environmental interactions and contributes to a deeper understanding of vegetation responses to environmental changes.

Biogenic volatile organic compounds↗
DOE OSTI2015

Subspace-based Inverse Uncertainty Quantification for Nuclear Data Assessment

Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse problem can be defined and solved to assess the sources of uncertainty, and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this work a subspace-based algorithm for inverse sensitivity/uncertainty quantification (IS/UQ) has been developed to enable analysts account for all sources of nuclear data uncertainties in support of target accuracy assessment-type analysis. An approximate analytical solution of the optimization problem is used to guide the search for the dominant uncertainty subspace. By limiting the search to a subspace, the degrees of freedom available for the optimization search are significantly reduced. A quarter PWR fuel assembly is modeled and the accuracy of the multiplication factor and the fission reaction rate are used as reactor attributes whose uncertainties are to be reduced. Numerical experiments are used to demonstrate the computational efficiency of the proposed algorithm. Our ongoing work is focusing on extending the proposed algorithm to account for various forms of feedback, e.g., thermal-hydraulics and depletion effects.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1992

Integrating aerodynamics and structures in the minimum weight design of a supersonic transport wing

An approach is presented for determining the minimum weight design of aircraft wing models which takes into consideration aerodynamics-structure coupling when calculating both zeroth order information needed for analysis and first order information needed for optimization. When performing sensitivity analysis, coupling is accounted for by using a generalized sensitivity formulation. The results presented show that the aeroelastic effects are calculated properly and noticeably reduce constraint approximation errors. However, for the particular example selected, the error introduced by ignoring aeroelastic effects are not sufficient to significantly affect the convergence of the optimization process. Trade studies are reported that consider different structural materials, internal spar layouts, and panel buckling lengths. For the formulation, model and materials used in this study, an advanced aluminum material produced the lightest design while satisfying the problem constraints. Also, shorter panel buckling lengths resulted in lower weights by permitting smaller panel thicknesses and generally, by unloading the wing skins and loading the spar caps. Finally, straight spars required slightly lower wing weights than angled spars.

Barthelemy, Jean-Francois M.↗
DOE OSTI2019

Total Protein Analysis in Algae via Bulk Amino Acid Detection: Optimization of Amino Acid Derivatization after Hydrolysis with O -Phthalaldehyde 3-Mercaptopropionic Acid (OPA-3MPA)

The analysis of protein in algal biomass is one of the most critical areas of commercial development of algae characterization for nutritional or other high value applications. A new rapid and accurate method is required that can be widely implemented and that is free from interferences from the complex algal biomass matrix. We developed a simple spectrophotometric method for primary amino acid quantification bulk measurement in an acid hydrolyzed algal biomass preparation, as an alternative to the more labor-intensive amino HPLC acid analysis or less specific nitrogen-to-protein conversion. We have validated an O -phthalaldehyde (OPA)-based derivatization method, showing accurate and linear quantification for standard reference amino acids as well as mixtures, mimicking the amino acid complexity found in algal biomass. The presence of interferences that may be derived from the complex biomass biochemical composition was tested during the method validation phase. We document the application of a novel method of OPA derivatization with 3-mercaptopropionic acid (3MPA) to determine the total amino acid content of harvested algal biomass collected from different, controlled cultivation conditions and demonstrated a within 10% accuracy against a reference measurement of amino acid content in at least 4 species and 10 algal biomass samples, across early, mid, and late-stages of cultivation.

09 BIOMASS FUELS↗
DOE OSTI2015

SU-E-T-247: Determinations of the Optimal Phase for Respiratory Gated Radiotherapy From Statistical Analysis Using a Visible Guidance System

Purpose: Respiratory gated radiation therapy (RGRT) is used to minimize the radiation dose to normal tissue in lung cancer patients. Determination of the optimal point in the respiratory phase of a patient is important in RGRT but it is not easy. The goal of the present study was to see if a visible guidance system is helpful in determining the optimal phase in respiratory gated therapy. Methods: The breathing signals of 23 lung cancer patients were recorded with a Real-time Position Management (RPM) respiratory gating system (Varian, USA). The patients underwent breathing training with our visible guidance system, after which their breathing signals were recorded during 5 min of free breathing and 5 min of guided breathing. The breathing signals recorded between 3 and 5 min before and after training were compared. We performed statistical analysis of the breathing signals to find the optimal duty cycle in guided breathing for RGRT. Results: The breathing signals aided by the visible guidance system had more regular cycles over time and smaller variations in the positions of the marker block than the free breathing signals. Of the 23 lung cancer patients, 19 showed statistically significant differences by time when the values obtained before and after breathing were compared (p < 0.05); 30% and 40% of the duty cycle, respectively, was determined to be the most effective, and the corresponding phases were 30 60% (duty cycle, 30%; p < 0.05) and 30 70% (duty cycle, 40%; p < 0.05). Conclusion: Respiratory regularity was significantly improved with the use of the RPM with our visible guiding system; therefore, it would help improve the accuracy and efficiency of RGRT.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1983

Large-scale structural optimization

Problems encountered by aerospace designers in attempting to optimize whole aircraft are discussed, along with possible solutions. Large scale optimization, as opposed to component-by-component optimization, is hindered by computational costs, software inflexibility, concentration on a single, rather than trade-off, design methodology and the incompatibility of large-scale optimization with single program, single computer methods. The software problem can be approached by placing the full analysis outside of the optimization loop. Full analysis is then performed only periodically. Problem-dependent software can be removed from the generic code using a systems programming technique, and then embody the definitions of design variables, objective function and design constraints. Trade-off algorithms can be used at the design points to obtain quantitative answers. Finally, decomposing the large-scale problem into independent subproblems allows systematic optimization of the problems by an organization of people and machines.

Sobieszczanski-Sobieski, J.↗
DOE OSTI2025

Co-optimization of fuel properties, combustion system geometry, and injection strategy for conventional diesel fuel

Here, studies have shown that fuel properties can impact an engine’s operation in several ways, including ignition delay, sooting tendency, mixture formation, and combustion temperature. In mixing-controlled compression ignition (MCCI) engines, the fuel system design and piston bowl geometry significantly affect combustion performance and emissions. Based on current information, it is difficult to draw conclusions about fuel property effects and sensitivities. The central fuel hypothesis approach used in the US Department of Energy Co-Optima program has worked well for spark ignition fuels: identifying critical fuel property ranges is sufficient to screen fuel blends that are expected to maximize efficiency and reduce pollutant emissions. However, for MCCI-relevant fuels, the information gained from past studies is not sufficient to build such a merit function or to allow for performing a similar screening of fuel blends. It is hypothesized that a co-optimization of a fuel’s physical and chemical properties, combustion system geometry, and injection strategy could leverage synergies between the effects of the fuel properties and geometries, resulting in improved performance over state-of-the-art. A machine learning–assisted unconstrained global optimization algorithm was used to explore a design space comprising 23 independent variables. The results show that physical property effects were minimal even for large variations in fuel properties, and the only interaction effect that was observed was the effect of varied fuel density parameters on fuel/air mixture formation. Nevertheless, these interactions were not sufficient in magnitude to significantly affect optimization results. Therefore, analysis of the results suggests that fuel physical properties cannot be leveraged in a co-optimization context to increase engine efficiency.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Nuclear Materials Packaging, Transportation, and Systems Analysis Group Software Quality Assurance Plan: ANSYS Mechanical Finite Element Analysis Software Version 2023R1

ANSYS Inc. develops and markets engineering simulation software and services used in the aerospace, automotive, manufacturing, electronics, biomedical, energy, defense, and many other industries. ANSYS is dedicated to engineering simulation and is the world’s leading software provider. ANSYS was founded in 1970 and is headquartered in Canonsburg, Pennsylvania. ANSYS provides an engineering analysis tool combining structural, thermal, computational fluid dynamics, acoustic, and electromagnetic simulation capabilities. ANSYS has two main programs, which use the same solvers: (1) Mechanical APDL (ANSYS Design Parametric Language), a Fortran-based coding platform, and (2) ANSYS Workbench, which uses a graphical user interface to aid in finite element analysis implementation. This plan covers both APDL and Workbench. The ANSYS computer program is a large-scale, multipurpose finite element program that can be used to solve several classes of engineering analyses. The analysis capabilities of ANSYS include the ability to solve static and dynamic structural analyses, steady-state and transient heat transfer problems, mode-frequency and buckling eigenvalue problems, static or time-varying magnetic analyses, and various types of field and coupled-field applications. The program contains many special features that allow nonlinearities or secondary effects such as plasticity, large strain, hyperelasticity, creep, swelling, large deflections, contact, stress stiffening, temperature dependency, material anisotropy, and radiation to be included in the solution. As ANSYS has been developed, other special capabilities such as substructuring, submodeling, random vibration, kinetostatics, kinetodynamics, free convection fluid analysis, acoustics, magnetics, piezoelectrics, coupled-field analysis, and design optimization have been added to the program. These capabilities contribute further to making ANSYS a multipurpose analysis tool for varied engineering disciplines. The ANSYS program has been in commercial use for over 50 years and has been used extensively in the aerospace, automotive, construction, electronic, energy services, manufacturing, nuclear, plastics, oil, and steel industries. Additionally, many consulting firms and hundreds of universities have used ANSYS for analysis, research, and educational purposes. ANSYS is recognized worldwide as one of the most widely used and capable programs of its type. Ansys design analysis software is the first created within a quality system with ISO 9001 certification, the internationally accepted quality standard. Product development, testing, maintenance and support processes also meet the United States Nuclear Regulatory Commission's quality requirements, as they have for nearly four decades. The Quality Assurance Service Agreement is suitable for the customers working in the nuclear industry who need to meet specific federal regulations including 10CRF50 Appendix B and provisions of 10CFR21. ANSYS has retained its original International Organization for Standardization (ISO) 9001 accreditation certificate since1995-05-04, It’s current certificate is valid until 2027-05-29.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2013

NASA Advanced Concepts Office, Earth-To-Orbit Team Design Process and Tools

The Earth to Orbit (ETO) Team of the Advanced Concepts Office (ACO) at NASA Marshal Space Flight Center (MSFC) is considered the preeminent group to go to for prephase A and phase A concept definition. The ACO team has been at the forefront of a multitude of launch vehicle studies determining the future direction of the Agency as a whole due, in part, to their rapid turnaround time in analyzing concepts and their ability to cover broad trade spaces of vehicles in that limited timeframe. Each completed vehicle concept includes a full mass breakdown of each vehicle to tertiary subsystem components, along with a vehicle trajectory analysis to determine optimized payload delivery to specified orbital parameters, flight environments, and delta v capability. Additionally, a structural analysis of the vehicle based on material properties and geometries is performed as well as an analysis to determine the flight loads based on the trajectory outputs. As mentioned, the ACO Earth to Orbit Team prides themselves on their rapid turnaround time and often need to fulfill customer requests within limited schedule or little advanced notice. Due to working in this fast paced environment, the ETO team has developed some finely honed skills and methods to maximize the delivery capability to meet their customer needs. This paper will describe the interfaces between the 3 primary disciplines used in the design process; weights and sizing, trajectory, and structural analysis, as well as the approach each discipline employs to streamline their particular piece of the design process.

Waters, Eric D.↗
DOE OSTI2013

Advanced simulation capability for environmental management - current status and future applications

The U.S. Department of Energy (US DOE) Office of Environmental Management (EM), Office of Soil and Groundwater (EM-12), is supporting development of the Advanced Simulation Capability for Environmental Management (ASCEM). ASCEM is a state-of-the-art scientific tool and approach that is currently aimed at understanding and predicting contaminant fate and transport in natural and engineered systems. ASCEM is a modular and open source high-performance computing tool. It will be used to facilitate integrated approaches to modeling and site characterization, and provide robust and standardized assessments of performance and risk for EM cleanup and closure activities. The ASCEM project continues to make significant progress in development of capabilities, with current emphasis on integration of capabilities in FY12. Capability development is occurring for both the Platform and Integrated Tool-sets and High-Performance Computing (HPC) multi-process simulator. The Platform capabilities provide the user interface and tools for end-to-end model development, starting with definition of the conceptual model, management of data for model input, model calibration and uncertainty analysis, and processing of model output, including visualization. The HPC capabilities target increased functionality of process model representations, tool-sets for interaction with Platform, and verification and model confidence testing. The integration of the Platform and HPC capabilities were tested and evaluated for EM applications in a set of demonstrations as part of Site Applications Thrust Area activities in 2012. The current maturity of the ASCEM computational and analysis capabilities has afforded the opportunity for collaborative efforts to develop decision analysis tools to support and optimize radioactive waste disposal. Recent advances in computerized decision analysis frameworks provide the perfect opportunity to bring this capability into ASCEM. This will allow radioactive waste disposal to be evaluated based on decision needs, such as disposal, closure, and maintenance. Decision models will be used in ASCEM to identify information/data needs, and model refinements that might be necessary to effectively reduce uncertainty in waste disposal decisions. Decision analysis models start with tools for framing the problem, and continue with modeling both the science side of the problem (for example, inventories, source terms, fate and transport, receptors, risk, etc.), and the cost side of the problem, which could include costs of implementation of any action that is chosen (e.g., for disposal or closure), and the values associated with those actions. The cost side of the decision problem covers economic, environmental and societal costs, which correspond to the three pillars of sustainability (economic, social, and environmental). These tools will facilitate stakeholder driven decision analysis to support optimal sustainable solutions in ASCEM. In this paper the current state of ASCEM is first described, followed by a description of the approach that will be taken to augment ASCEM for simulating fate and transport with tools that facilitate a top-down or systems level view of finding optimal, sustainable solutions to difficult environmental (including radioactive waste) problems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗