Heat waves are increasing in severity, duration, and frequency. The Multi-Scenario Extreme Weather Simulator (MEWS) models this using historical data, climate model outputs, and heat wave multipliers. In this study, MEWS is applied for planning of a community resilience hub in Hau’ula, Hawaii. The hub will have normal operations and resilience operations modes. Both these modes were modeled using EnergyPlus. The resilience operations mode includes cutting off air conditioning for many spaces to decrease power requirements during emergencies. Results were simulated for 300 future weather files generated by MEWS for 2020, 2040, 2060, and 2080. Shared socioeconomic pathways 2–4.5, 3–7.0 and 5–8.5 were used. The resilience operations mode results show two to six times increase of hours of exceedance beyond 32.2 °C from present conditions, depending on climate scenario and future year. The resulting decrease in thermal resilience enables an average decrease of energy use intensity of 26% with little sensitivity to climate change. The decreased thermal resilience predicted in the future is undesirable, but was not severe enough to require a more energy-intensive resilience mode. Instead, planning is needed to assure vulnerable individuals are given prioritized access to air-conditioned parts of the hub if worst-case heat waves occur.
Villa, Daniel V.; Schostek, Tyler; Govertsen, Krissy; Macmillan, Madeline
Applying extreme temperature events for future conditions is not straightforward for infrastructure resilience analyses. This work introduces a stochastic model that fills this gap. The model uses at least 50 years of daily extreme temperature records, climate normals with 10%–90% confidence intervals, and shifts/offsets for increased frequency and intensity of heat wave events. Intensity and frequency are shifted based on surface temperature anomaly from 1850–1900 for 32 models from CMIP6. A case study for Worcester, Massachusetts passed 85% of cases using the two-sided Kolmogorov–Smirnov p-value test with 95% confidence for both temperature and duration. Future shifts for several climate scenarios to 2020, 2040, 2060, and 2080 had acceptable errors between the shifted model and 10- and 50-year extreme temperature event thresholds with the largest error being 2.67°C. The model is likely to be flexible enough for other patterns of extreme weather such as extreme precipitation and hurricanes.
Puerto Rico faced a double strike from hurricanes Irma and Maria in 2017. The resulting damage required a comprehensive rebuild of electric infrastructure. There are plans and pilot projects to rebuild with microgrids to increase resilience. This paper provides a techno-economic analysis technique and case study of a potential future community in Puerto Rico that combines probabilistic microgrid design analysis with tiered circuits in building energy modeling. Tiered circuits in buildings allow electric load reduction via remote disconnection of non-critiñl circuits during an emergency. When coupled to a microgrid, tiered circuitry can reduce the chances of a microgrid's storage and generation resources being depleted. The analysis technique is applied to show 1) Approximate cost savings due to a tiered circuit structure and 2) Approximate cost savings gained by simultaneously considering resilience and sustainability constraints in the microgrid optimization. The analysis technique uses a resistive capacitive thermal model with load profiles for four tiers (tier 1-3 and non-critical loads). Three analyses were conducted using: 1) open-source software called Tiered Energy in Buildings and 2) the Microgrid Design Toolkit. For a fossil fuel based microgrid 30% of the total microgrid costs of 1.18 million USD were calculated where the non-tiered case keeps all loads 99.9% available and the tiered case keeps tier 1 at 99.9%, tier 2 at 95%, tier 3 at 80% availability, with no requirement on non-critical loads. The same comparison for a sustainable microgrid showed 8% cost savings on a 5.10 million USD microgrid due to tiered circuits. The results also showed 6-7% cost savings when our analysis technique optimizes sustainability and resilience simultaneously in comparison to doing microgrid resilience analysis and renewables net present value analysis independently. Though highly specific to our case study, similar assessments using our analysis technique can elucidate value of tiered circuits and simultaneous consideration of sustainability and resilience in other locations.
Villa, Daniel V.; Schostek, Tyler; Bianchi, Carlo; Macmillan, Madeline; Carvallo, Juan P.
The Multi-scenario extreme weather simulator (MEWS) is a stochastic weather generation tool. The MEWS algorithm uses 50 or more years of National Oceanic and Atmospheric Association (NOAA) daily summaries [1] for maximum and minimum temperature and NOAA climate norms [2] to calculate historical heat wave and cold snap statistics. The algorithm takes these statistics and shifts them according to multiplication factors provided in the Intergovernmental Panel on Climate Change (IPCC) physical basis technical summary [3] for heat waves.
ASHRAE and IBPSA-USA Building Simulation Conference
Villa, Daniel V.; Carvallo, Juan P.; Bianchi, Carlo; Lee, Sang H.
Heat waves are increasing in severity, duration, and frequency, making historical weather patterns insufficient for assessments of building resilience. This work introduces a stochastic weather generator called the multi-scenario extreme weather simulator (MEWS) that produces credible future heat waves. MEWS calculates statistical parameters from historical weather data and then shifts them using climate projections of increasing severity and frequency. MEWS is demonstrated using the EnergyPlus medium office prototype model for climate zone 4B using five climate scenarios to 2060. The results show how changes in climate and heat waves affect electric loads, peak loads, and thermal comfort with uncertainty.
The climate crisis currently being faced by humanity is going to increase extreme weather events which are likely to make long-duration power outages for communities increase in frequency and duration. Microgrids are an important part of electrical resilience for connected communities during power outages. They also can have transactive potential to save energy on electric loads through coordinating distributed energy resources. Microgrids are expensive though. Making electric load coverage available nearly 100% of the time given known design basis threats and component failure statistics is one of the largest drivers of cost. Such high availability is non-negotiable for critical applications such as life saving equipment in a hospital but could perhaps be compromised for less critical loads.. This paper documents an analysis that used the Microgrid Design Toolkit and EnergyPlus simulation results with two energy retrofit options exercised. The results show how increasing energy efficiency and reducing availability to 90% and 80% reduced the calculated price of a photovoltaic and battery storage microgrid in a New Mexico neighborhood by 63% and 70%, respectively. A microgrid with 80% availability with 48-hour islanded run-time capability is therefore suggested as a low-cost method for accelerating microgrid infrastructure penetration into the residential sector. Such an “under-built” microgrid will significantly increase resilience even though it will not guarantee energy security for the non-critical applications in residential households. This will in turn accelerate the growth of storage potential across communities providing greater grid flexibility. The results of the study also show how increased insulation applied to the proposed residential community can be less expensive than creating a larger microgrid that carries larger electric loads. The likelihood that energy retrofits are a better investment than a larger microgrid is inversely proportional to availability. Here, availability is a metric equal to the percentage of the demand load served by the microgrid during power outages, not including the startup period.
This report provides a design study to produce 100% carbon-free electricity for Sandia NM and Kirtland Air Force Base (KAFB) using concentrating solar power (CSP). Annual electricity requirements for both Sandia and KAFB are presented, along with specific load centers that consume a significant and continuous amount of energy. CSP plant designs of 50 MW and 100 MW are then discussed to meet the needs of Sandia NM and the combined electrical needs of both Sandia NM and KAFB. Probabilistic modeling is performed to evaluate inherent uncertainties in performance and cost parameters on total construction costs and the levelized cost of electricity. Total overnight construction costs are expected to range between ~$300M - $400M for the 50 MW CSP plant and between ~$500M - $800M for the 100 MW plant. Annual operations and maintenance (O&M) costs are estimated together with potential offsets in electrical costs and CO2 emissions. Other considerations such as interconnections, land use and permitting, funding options, and potential agreements and partnerships with Public Service Company of New Mexico (PNM), Western Area Power Administration (WAPA), and other entities are also discussed.
• Shows detailed methodology for applying building energy model fleets to institutional heat wave analysis. • Demonstrates uncertainty in heat wave analysis based on meter data. • Shows how detailed building energy models used for energy retrofit analysis can be used for heat wave analyses. • The proposed methodology is much more extensible than data-driven or low-order energy models to detailed cross analyses between energy efficiency and resilience for future institutional studies. • Cross benefits between resilience analysis and energy retrofit analyses are demonstrated. Heat waves increase electric demand from buildings which can cause power outages. Modeling can help planners quantify the risk of such events. This study shows how Building Energy Modeling (BEM), meter data, and climate projections can estimate heat wave effect on energy consumption and electric peak load. The methodology assumes that a partial representation of BEM for an entire site of buildings is sufficient to represent the entire site. Two linear regression models of the BEM results are produced: 1) Energy use as a function of heat wave heat content and 2) Peak load as a function of maximum daily temperature. The uncertainty conveyed in meter data is applied to these regressions providing slope and intercept 95% confidence intervals. The methodology was applied using 97 detailed BEM, site weather data, 242 building meters, and NEX-DCP30 down-scaled climate data for an entire institution in Albuquerque, New Mexico. A series of heat waves that vary from 2019 weather to a peak increase of 5.9 °C was derived. The results of the study provided institutional planners with information needed for a site that is presently growing very rapidly. The resulting regression models are also useful for resilience analyses involving probabilistic risk assessments.
As climate change and human migration accelerate globally, decision-makers are seeking tools that can deepen their understanding of the complex nexus between climate change and human migration. These tools can help to identify populations under pressure to migrate, and to explore proactive policy options and adaptive measures. Given the complexity of factors influencing migration, this article presents a system dynamics-based model that couples migration decision making and behavior with the interacting dynamics of economy, labor, population, violence, governance, water, food, and disease. The regional model is applied here to the test case of migration within and beyond Mali. The study explores potential systems impacts of a range of proactive policy solutions and shows that improving the effectiveness of governance and increasing foreign aid to urban areas have the highest potential of those investigated to reduce the necessity to migrate in the face of climate change.
Heat waves have catastrophic effects causing mortality, air quality loss, grid failures, infrastructure damage, and increases in electricity consumption. The literature indicates that heat waves are growing in intensity, duration, and frequency. This paper documents a heat wave study of the Sandia National Laboratories (SNL) California site. The analysis involves: 1) projection of a heat wave based on historical data and NEX-DCP30 climate projections, 2) Classification of peak electricity load points that represent the site on workdays, Fridays, and weekends 3) Regression of the peak load data to produce confidence bounds for the analysis, and 4) Calibration and projection of building energy models (BEMs) to the heat wave scenario. This approach worked well for the previous NM site analysis of meter data and 97 representative BEM's. For the CA site, the BEM calibration procedure was unsuccessful without individual BEM calibrations. Many of the 23 California BEM's required calibration at the building level rather than for the entire site. This was found to be due to many of the BEM's having significantly different electric demand profiles than their meter data whereas the NM BEM's were much more accurate. Unlike the NM site, the CA site did not distinguish Friday operations clearly and the associated K-mean cluster algorithm that worked for the NM site did not add value for the CA site. The regression analyses produced estimates of site-wide increases to daily peak loads with 95% confidence bounds that were much wider than the NM analysis. The CA site was found to have higher average peak load sensitivity of 1.07%/0C (0.59%/0F) in comparison to the NM site with 0.61%/0C (0.34%/0F). Even so, the larger sensitivity is counteracted by a milder projection for future heat waves from NEX-DCP30 downscaled climate projections. The expected heat wave maximum temperature of 45.10C (113.20F) did not even break the current record of 46.10C (115.00F) in Livermore, CA and only had total heating energy of 280C·day (510F·day) from baseline 2019 weather in comparison to NM's 380C·day (680F·day). This work emphasizes issues that can aid development of future guidelines for application of BEM and meter data to heat-wave scenarios.
A new version of the Initial Atmospheric Transport (IAT) model has been developed. This report fully characterizes the new model. IAT is designed to model heat release into the atmosphere that contains contaminated particles. The heat release forms a buoyant puff or plume. Buoyancy is quickly dissipated in 10's to 100's of seconds through drag and entrainment of ambient air. The final location of contaminant particles in the atmosphere after dissipation of abnormal heat is an important input to long term transportation codes such as the National Oceanic and Atmospheric Administration's (NOAA) Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) code. Ultimately the settling of contaminants after long term transport provides a basis for estimating health consequences. In comparison to the old version, the new model includes explicit simulation of particles, newly formulated equations of motion for a rising puff, and a new plume model. The plume model releases many instances of a single puff along a single trajectory in a way that balances energy released over a longer period than would be appropriate for a single puff. The puff model has additions for vorticity, turbulence, virtual mass, drag, and entrainment. The particle model leads to direct interfacing between IAT and the long-term transport code HYSPLIT. Previously, particle locations were given a pattern based on IAT's puff trajectory with no particle simulation. Now, particles are explicitly simulated, and 3-D spatial distributions of particles are output based on final positions. Ballistic particles are thrown outward at high velocity from explosive events and tend to hit the ground before the end of the simulation. Non-ballistic particles tend to get pulled into the rising puff s ring of vorticity with some escaping to ambient conditions as buoyant energy dissipates. A new model verification and validation effort was made for the new plume model. The Wallops Island Solid Rocket Propellant (SRP) fire plume was extracted to a 3-D transient dataset. After calibration, the model performs better than previous single puff attempts. Even so, error levels in the process used to estimate location and ambient weather conditions approach 30%. This leads to the conclusion that unplanned photogrammetry evaluations of the IAT model are useful but not sufficiently accurate to quantify model parameters. Like previous attempts, the vertical motion entrainment parameters come out much too high in comparison to values for other studies in the literature. It is suspected that a new model of shear entrainment would alleviate this problem. Unfortunately, IAT already has more calibration parameters than the data comparisons thus far can resolve with statistical certainty. This underscores the need to verify and validate the IAT model using detailed CFD studies that allow ideal statistical comparison with the IAT model for hundreds of cases. Even so, validation by comparison to carefully planned experiments (i.e. with acceptable error levels) is also necessary because the dynamics of entrainment are at a sub-grid level for CFD. Though better validation and verification are most important, many new enhancements are still envisioned for future IAT development. These include particle rain out due to condensation and agglomeration of water, direct connection to the North American Mesoscale (NAM) weather dataset, dynamic weather conditions, including ambient particle entrainment into the puff, and detrainment at the puff to ambient mixing interface. In general, including detailed aerosol dynamics is the greatest coming challenge which will require an entire new sub-model in IAT. This is very important if contaminant particles change in such a way that they increase or decrease in their potential harm to the environment.
In the summer of 2020, the National Aeronautics and Space Administration (NASA) plans to launch a spacecraft as part of the Mars 2020 mission. The rover on the proposed spacecraft will use a Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) to provide continuous electrical and thermal power for the mission. The MMRTG uses radioactive plutonium dioxide. NASA is preparing a Supplemental Environmental Impact Statement (SEIS) for the mission in accordance with the National Environmental Policy Act. This Nuclear Risk Assessment addresses the responses of the MMRTG option to potential accident and abort conditions during the launch opportunity for the Mars 2020 mission and the associated consequences. This information provides the technical basis for the radiological risks discussed in the SEIS.
Many membrane distillation models have been created to simulate the heat and mass exchange process involved but most of the literature only validates models to a couple of cases with minor configuration changes. Tools are needed that allow tradeoffs between many configurations. The multiconfiguration membrane distillation model handles many configurations. This report introduces membrane distillation, provides theory, and presents the work to verify and validate the model against experimental data from Colorado School of Mines and a lower resolution model created at the National Renewable Energy Laboratory. Though more data analysis and testing are needed, an initial look at the model to experimental comparisons indicates that the model correlates to the data well but that design comparisons are likely to be incorrect across a broad range of configurations. More accurate quantification of heat and mass transfer through computational fluid mechanics is suggested.
Membrane distillation is a water purification technology which uses a porous hydrophobic membrane. Liquid water cannot penetrate the membrane at operational pressures but vapor flows through the membrane if there is a vapor pressure difference across the membrane. Many configurations for membrane distillation have been investigated over the last several decades. In this modeling effort, two successful direct contact membrane model development using steady-state control volume balances on energy and mass are presented. Verification and validation of the models is applied to the extent necessary to use the models for comparative design purposes. Significant errors between modeling and experimental membrane distillation data are argued to be due to uncertainty in membrane material property measurements. A second effort to model a vacuum membrane distillation system designed by Memsys® is still progressing. Two efforts have not yet produced output mass flow comparable to the literature. Even so, much of the framework needed to model the Memsys® system is complete.
Reducing the resource consumption and emissions of large institutions is an important step toward a sustainable future. Sandia National Laboratories' (SNL) Institutional Transformation (IX) project vision is to provide tools that enable planners to make well-informed decisions concerning sustainability, resource conservation, and emissions reduction across multiple sectors. The building sector has been the primary focus so far because it is the largest consumer of resources for SNL. The IX building module allows users to define the evolution of many buildings over time. The module has been created so that it can be generally applied to any set of DOE-2 ( http://doe2.com ) building models that have been altered to include parameters and expressions required by energy conservation measures (ECM). Once building models have been appropriately prepared, they are checked into a Microsoft Access (r) database. Each building can be represented by many models. This enables the capability to keep a continuous record of models in the past, which are replaced with different models as changes occur to the building. In addition to this, the building module has the capability to apply climate scenarios through applying different weather files to each simulation year. Once the database has been configured, a user interface in Microsoft Excel (r) is used to create scenarios with one or more ECMs. The capability to include central utility buildings (CUBs) that service more than one building with chilled water has been developed. A utility has been created that joins multiple building models into a single model. After using the utility, several manual steps are required to complete the process. Once this CUB model has been created, the individual contributions of each building are still tracked through meters. Currently, 120 building models from SNL's New Mexico and California campuses have been created. This includes all buildings at SNL greater than 10,000 sq. ft., representing 80% of the energy consumption at SNL. SNL has been able to leverage this model to estimate energy savings potential of many competing ECMs. The results helped high level decision makers to create energy reduction goals for SNL. These resources also have multiple applications for use of the models as individual buildings. In addition to the building module, a solar module built in Powersim Studio (r) allows planners to evaluate the potential photovoltaic (PV) energy generation potential for flat plate PV, concentrating solar PV, and concentration solar thermal technologies at multiple sites across SNL's New Mexico campus. Development of the IX modeling framework was a unique collaborative effort among planners and engineers in SNL's facilities division; scientists and computer modelers in SNL's research and development division; faculty from Arizona State University; and energy modelers from Bridger and Paxton Consulting Engineers Incorporated.
The World Water and Agriculture Model has been used to simulate water, hydropower, and food sector effects in Egypt, Sudan, and Ethiopia during the filling of the Grand Ethiopian Renaissance Dam reservoir. This unique capability allows tradeoffs to be made between filling policies for the Grand Ethiopian Renaissance Dam reservoir. This Nile River Basin study is presented to illustrate the capacity to use the World Water and Agriculture Model to simulate regional food security issues while keeping a global perspective. The study uses runoff data from the Intergovernmental Panel for Climate Change Coupled Model Inter-comparison Project Phase 5 and information from the literature in order to establish a reasonable set of hydrological initial conditions. Gross Domestic Product and population growth are modelled exogenously based on a composite projection of United Nations and World Bank data. The effects of the Grand Ethiopian Renaissance Dam under various percentages of water withheld are presented.
The Institutional Transformation (IX) building module is a software tool developed at Sandia National Laboratories to evaluate energy conservation measures (ECMs) on hundreds of DOE-2 building energy models simultaneously. In IX, ECMs can be designed through parameterizing DOE-2 building models and doing further processing via visual basic for applications subroutines. IX provides the functionality to handle multiple building models for different years, which enables incrementally changing a site of hundreds of buildings over time. It also enables evaluation of the effects of changing climate, comparisons between data and modeling results, and energy use of centralized utility buildings (CUBs). IX consists of a Microsoft Excel® user interface, Microsoft Access® database, and Microsoft Excel® CUB build utility whose functionalities are described in detail in this report. In addition to descriptions of the user interfaces, descriptions of every ECM already designed in IX is included.
The Integrated Human Futures Project provides a set of analytical and quantitative modeling and simulation tools that help explore the links among human social, economic, and ecological conditions, human resilience, conflict, and peace, and allows users to simulate tradeoffs and consequences associated with different future development and mitigation scenarios. In the current study, we integrate five distinct modeling platforms to simulate the potential risk of social unrest in Egypt resulting from the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile in Ethiopia. The five platforms simulate hydrology, agriculture, economy, human ecology, and human psychology/behavior, and show how impacts derived from development initiatives in one sector (e.g., hydrology) might ripple through to affect other sectors and how development and security concerns may be triggered across the region. This approach evaluates potential consequences, intended and unintended, associated with strategic policy actions that span the development-security nexus at the national, regional, and international levels. Model results are not intended to provide explicit predictions, but rather to provide system-level insight for policy makers into the dynamics among these interacting sectors, and to demonstrate an approach to evaluating short- and long-term policy trade-offs across different policy domains and stakeholders. The GERD project is critical to government-planned development efforts in Ethiopia but is expected to reduce downstream freshwater availability in the Nile Basin, fueling fears of negative social and economic impacts that could threaten stability and security in Egypt. We tested these hypotheses and came to the following preliminary conclusions. First, the GERD will have an important short-term impact on water availability, food production, and hydropower production in Egypt, depending on the short- term reservoir fill rate. Second, the GERD will have a very small impact on water availability in the Nile Basin over the longer term. Depending on the GERD fill rate, short-term (e.g., within its first 5 years of operation) annual losses in Egyptian food production may peak briefly at 25 percent. Long-term (e.g., 15 to 30 year) cumulative losses in Egypt's food production may be less than 3 percent regardless of the fill rate, with the GERD having essentially no impact on projected annual food production in Egypt about 25 years after opening. For the quick fill rates, the short-term losses may be sufficient to create an important decrease in overall household health among the general population, which, along with other economic stressors and different strategies employed by the government, could lead to social unrest. Third, and perhaps most importantly, our modeling suggests that the GERD's effect on Egypt's food and water resources is small when compared to the effect of projected Egyptian population and economic growth (and the concomitant increase in water consumption). The latter dominating factors are exacerbated in the modeling by natural climate variability and may be further exacerbated by climate change. Our modeling suggests that these growth dynamics combine to create long-term water scarcity in Egypt, regardless of the Ethiopian project. All else being equal, filling strategies that employ slow fill rates for the GERD (e.g., 8 to 13 years) may mitigate the risks in future scenarios for Egypt somewhat, but no policy or action regarding the GERD is likely to significantly alleviate the projected water scarcity in Egypt's Nile Basin. However, general beliefs among the Egyptian populace regarding the GERD as a major contributing factor for scarcities in Egypt could make Ethiopia a scapegoat for Egyptian grievances -- contributing to social unrest in Egypt and generating undesirable (and unnecessary) tension between these two countries. Such tension could threaten the constructive relationships between Egypt and Ethiopia that are vital to maintaining stability and security within and between their respective regional spheres of influence, Middle East and North Africa, and the Horn of Africa.
Developing nations incur a greater risk to climate change than the developed world due to poorly managed human/natural resources, unreliable infrastructure and brittle governing/economic institutions. These vulnerabilities often give rise to a climate induced “domino effect” of reduced natural resource production-leading to economic hardship, social unrest, and humanitarian crises. Integral to this cascading set of events is increased human migration, leading to the “spillover” of impacts to adjoining areas with even broader impact on global markets and security. Given the complexity of factors influencing human migration and the resultant spill-over effect, quantitative tools are needed to aid policy analysis. Toward this need, a series of migration models were developed along with a system dynamics model of the spillover effect. The migration decision models were structured according to two interacting paths, one that captured long-term “chronic” impacts related to protracted deteriorating quality of life and a second focused on short-term “acute” impacts of disaster and/or conflict. Chronic migration dynamics were modeled for two different cases; one that looked only at emigration but at a national level for the entire world; and a second that looked at both emigration and immigration but focused on a single nation. Model parameterization for each of the migration models was accomplished through regression analysis using decadal data spanning the period 1960-2010. A similar approach was taken with acute migration dynamics except regression analysis utilized annual data sets limited to a shorter time horizon (2001-2013). The system dynamics spillover model was organized around two broad modules, one simulating the decision dynamics of migration and a second module that treats the changing environmental conditions that influence the migration decision. The environmental module informs the migration decision, endogenously simulating interactions/changes in the economy, labor, population, conflict, water, and food. A regional model focused on Mali in western Africa was used as a test case to demonstrate the efficacy of the model.
Climate change, through drought, flooding, storms, heat waves, and melting Arctic ice, affects the production and flow of resource within and among geographical regions. The interactions among governments, populations, and sectors of the economy require integrated assessment based on risk, through uncertainty quantification (UQ). This project evaluated the capabilities with Sandia National Laboratories to perform such integrated analyses, as they relate to (inter)national security. The combining of the UQ results from climate models with hydrological and economic/infrastructure impact modeling appears to offer the best capability for national security risk assessments.
This paper describes techniques for determining impact deformation and the subsequent reactivity change for a space reactor impacting the ground following a potential launch accident or for large fuel bundles in a shipping container following an accident. This technique could be used to determine the margin of subcriticality for such potential accidents. Specifically, the approach couples a finite element continuum mechanics model (Pronto3D or Presto) with a neutronics code (MCNP). DAGMC, developed at the University of Wisconsin-Madison, is used to enable MCNP geometric queries to be performed using Pronto3D output. This paper summarizes what has been done historically for reactor launch analysis, describes the impact criticality analysis methodology, and presents preliminary results using representative reactor designs.
Launch safety calculations for past space reactor concepts have usually been limited to immersion of the reactor in water and/or sand, using nominal system geometries or in some cases simplified compaction scenarios. Deformation of the reactor core by impact during the accident sequence typically has not been considered because of the complexity of the calculation. Recent advances in codes and computing power have made such calculations feasible. The accuracy of such calculations depends primarily on the underlying structural analysis. Even though explicit structural dynamics is a mature field, nuclear reactors present significant challenges to obtain accurate deformation predictions. The presence of a working fluid is one of the primary contributors to challenges in these predictions. The fluid-structure interaction cannot be neglected because the fluid surrounds the nuclear fuel which is the most important region in the analysis. A detailed model of a small eighty-five pin reactor was built with the working fluid modeled as smoothed particle hydrodynamic (SPH) elements. Filling the complex volume covered by the working fluid with SPH elements required development of an algorithm which eliminates overlaps between hexahedral and SPH elements. The results with and without the working fluid were found to be considerably different with respect to reactivity predictions.