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The Contingency Contractor Optimization Tool – Prototype (CCOT-P) database is used to store input and output data for the linear program model described in [1]. The database allows queries to retrieve this data and updating and inserting new input data.
A multi-institutional project known as D-TEC (short for “Domain- specific Technology for Exascale Computing”) set out to explore technologies to support the construction of Domain Specific Languages (DSLs) to map application programs to exascale architectures. DSLs employ automated code transformation to shift the burden of delivering portable performance from application programmers to compilers. Two chief properties contribute: DSLs permit expression at a high level of abstraction so that a programmer’s intent is clear to a compiler and DSL implementations encapsulate human domain-specific optimization knowledge so that a compiler can be smart enough to achieve good results on specific hardware. Domain specificity is what makes these properties possible in a programming language. If leveraging domain specificity is the key to keep exascale software tractable, a corollary is that many different DSLs will be needed to encompass the full range of exascale computing applications; moreover, a single application may well need to use several different DSLs in conjunction. As a result, developing a general toolkit for building domain-specific languages was a key goal for the D-TEC project. Different aspects of the D-TEC research portfolio were the focus of work at each of the partner institutions in the multi-institutional project. D-TEC research and development work at Rice University focused on on three principal topics: understanding how to automate the tuning of code for complex architectures, research and development of the Rosebud DSL engine, and compiler technology to support complex execution platforms. This report provides a summary of the research and development work on the D-TEC project at Rice University.
To help effectively plan the management and modernization of its large and diverse fleet of vehicles, the Program Executive Office Ground Combat Systems (PEO GCS) commissioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This report contains a description of the organizational fleet structure and a thorough explanation of the business rules that the CPAT formulation follows involving performance, scheduling, production, and budgets.
Historical operations at the U.S. Department of Energy’s Hanford Site included disposal of waste fluids to the subsurface in the 200 West Area on the Hanford Central Plateau. Subsequent infiltration of fluids has resulted in groundwater contamination with carbon tetrachloride, nitrate, uranium, technetium-99, and other contaminants. A pump-and-treat (P&T) system, with an extraction/injection well network and an aboveground treatment plant, was implemented as part of interim and final remedies in the 200 West area. The HYPATIA single-page web application (part of the SOCRATES suite) is being developed to provide access to and analysis of chemistry and treatment facility sensor data for this 200 West P&T system. For the web application, analytical algorithms were developed to perform summing, differencing, smoothing, outlier detection, change-point detection, mass flow rate, and injectivity calculations on the data. Candidate algorithms were identified and tested, with the best-performing algorithms then assembled for implementation in HYPATIA. Because HYPATIA is hosted on the Amazon Web Services (AWS) cloud computing platform, algorithms were implemented in a back-end AWS Lambda function that can be called by the HYPATIA front end. The Lambda function applies the requested data processing to specified data via functions written in R, Python, and JavaScript. Development, testing, and review of the data analysis algorithms was completed under an NQA-1 quality program. This new HYPATIA functionality will provide information to support site decisions regarding P&T system performance and optimization.
Research efforts to build upon the unique knowledge base of the Savannah River National Laboratory (SRNL) are funded through the Laboratory Directed Research and Development (LDRD) Program. The program yields foundational scientific research and development (R&D) essential to SRNL’s core business areas, while aligning optimally and continuously with SRNL’s Strategic Plan to provide long-term benefits to the Department of Energy (DOE), the National Nuclear Security Administration (NNSA), and other customers and stakeholders.
TSLs for H(BeH 2 ) and Be(BeH 2 ) were evaluated in the incoherent approximation using FLASSH. Minimum critical mass estimates were performed using MC21 continuous energy Monte Carlo calculations with the BeH 2 TSLs. Researchers confirmed very low estimated critical mass for BeH 2 -moderated and Be- reflected spheres. These were the lowest critical masses NNL has studied to date. Future work includes relaxation of the incoherent approximation for Be(BeH 2 ). A small increase in critical mass is expected due to increased neutron transmission at sub-Bragg cutoff energies.
This report provides an update on laser powder bed fusion of steels for nuclear applications. This report is a Milestone 3 deliverable in FY2024, under work package CT-24AN130401 to support research and qualification activities supported by the Advanced Materials and Manufacturing Technologies (AMMT) program here at Argonne National Laboratory (ANL). Continuing from FY23, the focus of FY24 for ANL includes further optimization of process parameters for three alloys: A709, G91, G92. The work package also includes fabricating test samples to conduct a thorough microstructural analysis and perform preliminary mechanical testing. For the same, we have used CALPHAD-based thermodynamic kinetic modeling to optimize the experimental activities.
With increasingly severe wildfire conditions driven by climate change, utilities must manage the risk of wildfire ignitions from electric power lines. During "public safety power shutoff'" events, utilities de-energize power lines to reduce wildfire ignition risk, which may result in load shedding. Distributed energy resources provide flexibility that can help support the system to reduce load shedding when lines are de-energized. We investigate a coordinated transmission-distribution optimization problem that balances wildfire risk mitigation and load shedding. We model distribution systems that include battery energy storage systems which may support loads when transmission lines are de-energized. This multi-period integrated transmission-distribution optimal switching problem jointly optimizes line switching decisions, the generators' setpoints, load shedding, and the batteries' states of charge, resulting in significant computational challenges. To improve scalability, we decompose the problem over both space and time and apply a distributed optimization algorithm. Using a large-scale synthetic California test case with realistic distribution models and real wildfire risk data, we show that distributed optimization can solve large-scale multi-period switching problems that are otherwise intractable for centralized solvers. We also discuss challenges and future directions for improving the distributed algorithm's convergence performance as the number of time periods increases.
By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed in this paper for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming. The model is transformed into a readily solvable mixed integer linear programming formulation in general algebraic modeling system (GAMS) via a proposed discretized step transformation approach and finally solved by applying the CPLEX solver. By properly setting the confidence levels of the spinning reserve probability constraints, the MG operation can achieve a tradeoff between reliability and economy. The test results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab MG test system reveal that the proposal significantly exceeds the commonly used hybrid intelligent algorithm with much better and more stable optimization results and significantly reduced calculation times.
This paper examines the AC Optimal Power Flow (OPF) problem for multiphase distribution networks featuring renewable energy resources (RESs). We start by outlining a power flow model for radial multiphase systems that accommodates wye-connected and delta-connected RESs and non-controllable energy assets. We then formalize an AC OPF problem that accounts for both types of connections. Similar to various AC OPF renditions, the resultant problem is a non convex quadratically-constrained quadratic program. However, the so-called Feasible Point Pursuit-Successive Convex Approximation algorithm is leveraged to obtain a feasible and yet locally-optimal solution. The merits of the proposed solution approach are demonstrated using two unbalanced multiphase distribution feeders with both wye and delta connections.
The Fermilab accelerator complex delivers high-intensity proton beams to serve the lab’s neutrino, muon, and fixed-target programs. A normal-conducting Linac accelerates H− beam to 400 MeV and injects into the Booster rapid cycling synchrotron via charge exchange, which accelerates protons to 8 GeV. Injection from the Linac into the Booster is a critical area for high-power performance of the Fermilab proton complex. The Booster is a high-intensity proton ring with extreme space-charge forces which necessitates precise control over the beam losses through the acceleration cycle. The main challenge for the reliability of Booster performance is compensating for drifting conditions in the beam from the Linac, which can drift daily in energy by up to O(1) MeV w.r.t. design. Drifts in Linac orbit and energy must be corrected to match the Booster, while simultaneously accommodating interdependent drifts in transverse and longitudinal beam quality. Operationally, compensation for these changes is addressed by manual tuning of the Linac output energy and/or Booster acceptance, which can be inefficient and time-consuming. This works describes contextual Bayesian Optimization for injection tuning that takes into account the state of Linac beam via information from instrumentation in the injection line (Beam position monitors (BPMs), beam loss monitors (BLMs), wire scanners for transverse profiles (WSs)), as well as RF cavity setting parameters from the Linac.
Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets' planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage decision-dependent stochastic optimization model for long-term large-scale generation expansion planning, where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming model. The wind penetration, investment decisions, and the optimality of the decision-dependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.
Protection of commercial nuclear fuel cycles against adversary groups who may attempt to steal weapons-usable material is an issue of interest to the U.S. Department of Energy, Office of Nuclear Energy. The wide array of potential nuclear fuel cycle configurations and the security measures that could be deployed to protect them pose a daunting system design problem. In general, there are billions of potential design configurations. Moreover, the design choices are not independent – the effectiveness of one design feature or security measure can affect the desirability of other design choices. To explore this issue, we develop a mixed-integer optimization problem that solves a complex decision tree to allocate security resources across a commercial nuclear fuel cycle configuration. A nominal UREX+ reprocessing facility is used to demonstrate how the model allocates resources across a particular fuel cycle facility and shows how the problem can be scaled up to include other facilities and design features in the fuel cycle. In our model, the defender first chooses a set of safeguards to implement in order to mitigate the risk of a terrorist theft from various unit operations in the reprocessing facility. The attacker observes the defender’s strategy choice and executes an attack to acquire material, including the possibility of declining to attack at all. The model considers the terrorist as an intelligent adversary with a set of attack strategies known to the defender. The strategic choices made by both the defender and attacker affect the probability of a successful attack by the terrorist and in turn affect the distribution of consequences to the defender. We use a decision tree to illustrate the interactions among costs, decisions, correlated uncertainties, and outcomes, and then show how to formulate the problem as a mixed-integer program with an equivalent solution. The novel features of our formulation include the following. First, as discussed above, we use a sequential game-theoretic formulation (a Stackelberg game). This formulation assumes the attacker can observe the defender’s actions before the attack is executed. Many previous game-theoretic studies have assumed attacker and defender act simultaneously without knowledge of the other’s strategy choice. We believe the sequential game formulation is more applicable for knowledgeable insider adversaries, the focus of this study. Second, we build on our previous work that has developed a methodology to reflect the correlation among probability distributions represented as discrete chance nodes in a decision tree. We illustrate how this methodology can be applied to two chance nodes in a decision tree that relate the quality of the materials that might be stolen or diverted by an adversary with the yield of a weapon that might be constructed from that material. In a decision tree, the dependence of the yield on the quality of the material could be modeled with conditional probabilities of the weapon yield given the quality of the materials obtained. For three outcome discretizations, this would require the assessment of nine conditional probabilities from domain experts as part of the model building effort. In contrast, our methodology will allow this same representation of the tree based on the assessment of the correlation between these two chance nodes by the experts, which we believe is a much easier and more reliable assessment task. The advantage of this approach increases as the number of dependent chance nodes grows in a decision tree representation of this problem. Third, we extend this approach to the approximation of the dependence between discrete binary events such as success or failure represented by binary chance nodes in a decision tree or in the special case of a probabilistic risk analysis (PRA) based on an event tree. This extension is a novel application in this domain, and uses Monte Carlo simulation to determine the appropriate conditional probabilities on the binary chance nodes when the number of correlated uncertainties is more than two. Fourth, we show how these decision trees or probability trees can be formulated and solved as equivalent integer programming (IP) problems. The advantages of the IP representation include the opportunity to use the computing power associated with powerful optimization solver algorithms available on high speed computers to solve very large versions of these trees, and the ability to include several such models of different facilities into a much larger IP with connecting constraints that represent budget restrictions, number of available inspection opportunities in a given time window, or other dependencies among these facilities. Policy makers can use the resulting model to inform their decisions about how to assess terrorism risk and to safeguard the commercial nuclear fuel cycle.
Technologies for post-combustion carbon capture are essential for the reduction of greenhouse gas emissions to the atmosphere. However, they are still associated with high costs and energy consumption. Intensified processes for carbon capture have the potential to overcome these challenges due to their higher efficiency, lower capital cost, and increased operational flexibility. Here, this work investigates simultaneous optimization of process conditions and adsorbent selection for a modular Vacuum Pressure-Swing Adsorption system designed for CO 2 capture. Both surrogate-based Nonlinear Programming and Mixed-Integer Nonlinear Programming approaches are applied and compared in terms of computational efficiency and solution accuracy. Moreover, process performance results are examined by applying several data analytics techniques to gain insights into the material-process correlations. Data-driven classifiers and neural networks can accurately predict whether a material is likely to satisfy purity, recovery, and energy constraints when operated at optimal process conditions.
We detail a mixed complementarity problem (MCP) formulation of continuous state dynamic programming problems (DP-MCP). We write the solution to projection methods in value function iteration (VFI) as a joint set of optimality conditions that characterize maximization of the Bellman equation; and approximation of the value function. The MCP approach replaces the iterative component of projection based VFI with a one-shot solution to a square system of complementary conditions. We provide three numerical examples to illustrate our approach.
Cyber-physically resilient power system operation requires rapid recovery of situational awareness after disastrous scenarios such as massive cyber attacks. In this article, the concept of optimal sequential restoration of Phasor Measurement Units is introduced, aiming at rapid recovery of power system observability in post-attack scenarios. Additionally, the observability recovery problem is formulated as a Mixed Integer Linear Programming problem to maximize the cumulative observability level gained over time during the restoration process. Three alternative objective functions-maximizing the number of observable buses, the number of observable branches, and the total amount of observable power flow-are considered. The effectiveness of the proposed optimal strategy is verified by comparing it with heuristic approaches on the IEEE 57-bus system.
Tomographic volumetric additive manufacturing (T‐VAM) rapidly prints solid objects within minutes, accessing photochemistries that are traditionally challenging for layer‐based additive manufacturing methods. This includes high‐viscosity materials, air‐free chemistries, and solid‐state systems. Catalytic chemistries are appealing as a pathway to engineering advanced materials, including tough thermosets, silicone elastomers, and complex block copolymers. However, photoactivated dormant catalytic chemistries, where the catalyst irreversibly activates upon exposure to light, are incompatible with typical tomographic VAM approaches. To address this limitation, a zero‐dose optimization strategy is devised to preserve dormant catalysts in desired regions by keeping them unexposed to light. VAM printed micro‐ and millifluidic devices and instant molds are successfully produced within minutes in silicones polymerized using photoactivated dormant platinum photohydrosilylation catalysts. The printed channels are programmed to be 500 and 2500 µm for the micro‐ and millifluidic devices, and print fidelity is assessed by X‐ray computed tomography. This work demonstrates the potential of zero‐dose optimization to expand the range of chemistries accessible for VAM, enabling the rapid fabrication of complex devices.
We extend and improve recent results given by Singh and Watson on using classical bounds on the union of sets in a chance-constrained optimization problem. Specifically, we revisit the so-called Dawson and Sankoff bound that provided one of the best approximations of a chance constraint in the previous analysis. Next, we show that our work is a generalization of the previous work, and in fact the inequality employed previously is a very relaxed approximation with assumptions that do not generally hold. Computational results demonstrate on average over a 43% improvement in the bounds. As a byproduct, we provide an exact reformulation of the floor function in optimization models.